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Bit Error Rate (BER)

Theoretical Results

Common Notation

The following notation is used throughout this Appendix:

Quantity or Operation Notation
Size of modulation constellation

`$M$`

Number of bits per symbol

`$k={\mathrm{log}}_{2}M$`

Energy per bit-to-noise power-spectral-density ratio

`$\frac{{E}_{b}}{{N}_{0}}$`

Energy per symbol-to-noise power-spectral-density ratio

`$\frac{{E}_{s}}{{N}_{0}}=k\frac{{E}_{b}}{{N}_{0}}$`

Bit error rate (BER)

`${P}_{b}$`

Symbol error rate (SER)

`${P}_{s}$`

Real part

`$\mathrm{Re}\left[\cdot \right]$`

Largest integer smaller than

`$⌊\cdot ⌋$`

The following mathematical functions are used:

Function Mathematical Expression
Q function

`$Q\left(x\right)=\frac{1}{\sqrt{2\pi }}\underset{x}{\overset{\infty }{\int }}\mathrm{exp}\left(-{t}^{2}/2\right)dt$`

Marcum Q function

`$Q\left(a,b\right)=\underset{b}{\overset{\infty }{\int }}t\mathrm{exp}\left(-\frac{{t}^{2}+{a}^{2}}{2}\right){I}_{0}\left(at\right)dt$`

Modified Bessel function of the first kind of order $\nu$

`${I}_{\nu }\left(z\right)=\sum _{k=0}^{\infty }\frac{{\left(z/2\right)}^{\upsilon +2k}}{k!\Gamma \left(\nu +k+1\right)}$`

where

`$\Gamma \left(x\right)=\underset{0}{\overset{\infty }{\int }}{e}^{-t}{t}^{x-1}dt$`

is the gamma function.

Confluent hypergeometric function

`${}_{1}F{}_{1}\left(a,c;x\right)=\sum _{k=0}^{\infty }\frac{{\left(a\right)}_{k}}{{\left(c\right)}_{k}}\frac{{x}^{k}}{k!}$`

where the Pochhammer symbol, ${\left(\lambda \right)}_{k}$, is defined as ${\left(\lambda \right)}_{0}=1$, ${\left(\lambda \right)}_{k}=\lambda \left(\lambda +1\right)\left(\lambda +2\right)\cdots \left(\lambda +k-1\right)$.

The following acronyms are used:

Acronym Definition
M-PSKM-ary phase-shift keying
DE-M-PSKDifferentially encoded M-ary phase-shift keying
BPSKBinary phase-shift keying
DE-BPSKDifferentially encoded binary phase-shift keying
QPSKQuaternary phase-shift keying
DE-QPSKDifferentially encoded quadrature phase-shift keying
OQPSKOffset quadrature phase-shift keying
DE-OQPSKDifferentially encoded offset quadrature phase-shift keying
M-DPSKM-ary differential phase-shift keying
M-PAMM-ary pulse amplitude modulation
M-QAMM-ary quadrature amplitude modulation
M-FSKM-ary frequency-shift keying
MSKMinimum shift keying
M-CPFSKM-ary continuous-phase frequency-shift keying

Analytical Expressions Used in berawgn

M-PSK.  From equation 8.22 in [2]

`${P}_{s}=\frac{1}{\pi }\underset{0}{\overset{\left(M-1\right)\pi /M}{\int }}\mathrm{exp}\left(-\frac{k{E}_{b}}{{N}_{0}}\frac{{\mathrm{sin}}^{2}\left[\pi /M\right]}{{\mathrm{sin}}^{2}\theta }\right)d\theta$`

The following expression is very close, but not strictly equal, to the exact BER (from [4] and equation 8.29 from [2]):

`${P}_{b}=\frac{1}{k}\left(\sum _{i=1}^{M/2}\left({w}_{i}^{\text{'}}\right){P}_{i}\right)$`

where ${w}_{i}^{\text{'}}={w}_{i}+{w}_{M-i}$, ${w}_{M/2}^{\text{'}}={w}_{M/2}$, ${w}_{i}$is the Hamming weight of bits assigned to symbol i, and

`$\begin{array}{c}{P}_{i}=\frac{1}{2\pi }\underset{0}{\overset{\pi \left(1-\left(2i-1\right)/M\right)}{\int }}\mathrm{exp}\left(-\frac{k{E}_{b}}{{N}_{0}}\frac{{\mathrm{sin}}^{2}\left[\left(2i-1\right)\pi /M\right]}{{\mathrm{sin}}^{2}\theta }\right)d\theta \\ -\frac{1}{2\pi }\underset{0}{\overset{\pi \left(1-\left(2i+1\right)/M\right)}{\int }}\mathrm{exp}\left(-\frac{k{E}_{b}}{{N}_{0}}\frac{{\mathrm{sin}}^{2}\left[\left(2i+1\right)\pi /M\right]}{{\mathrm{sin}}^{2}\theta }\right)d\theta \end{array}$`

Special case of $M=2$, e.g., BPSK (equation 5.2-57 from [1]):

`${P}_{s}={P}_{b}=Q\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)$`

Special case of $M=4$, e.g., QPSK (equations 5.2-59 and 5.2-62 from [1]):

`$\begin{array}{c}{P}_{s}=2Q\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)\left[1-\frac{1}{2}Q\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)\right]\\ {P}_{b}=Q\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)\end{array}$`

DE-M-PSK.  $M=2$, e.g., DE-BPSK (equation 8.36 from [2]):

`${P}_{s}={P}_{b}=2Q\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)-2{Q}^{2}\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)$`

$M=4$, e.g., DE-QPSK (equation 8.38 from [2]):

`${P}_{s}=4Q\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)-8{Q}^{2}\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)+8{Q}^{3}\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)-4{Q}^{4}\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)$`

From equation 5 in [3]:

`${P}_{b}=2Q\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)\left[1-Q\left(\sqrt{\frac{2{E}_{b}}{{N}_{0}}}\right)\right]$`

OQPSK.  Same BER/SER as QPSK [2].

DE-OQPSK.  Same BER/SER as DE-QPSK [3].

M-DPSK.  From equation 8.84 in [2]:

`${P}_{s}=\frac{\mathrm{sin}\left(\pi /M\right)}{2\pi }\underset{-\pi /2}{\overset{\pi /2}{\int }}\frac{\mathrm{exp}\left(-\left(k{E}_{b}/{N}_{0}\right)\left(1-\mathrm{cos}\left(\pi /M\right)\mathrm{cos}\theta \right)\right)}{1-\mathrm{cos}\left(\pi /M\right)\mathrm{cos}\theta }d\theta$`

The following expression is very close, but not strictly equal, to the exact BER [4]:

`${P}_{b}=\frac{1}{k}\left(\sum _{i=1}^{M/2}\left({w}_{i}^{\text{'}}\right){A}_{i}\right)$`

where ${w}_{i}^{\text{'}}={w}_{i}+{w}_{M-i}$, ${w}_{M/2}^{\text{'}}={w}_{M/2}$, ${w}_{i}$ is the Hamming weight of bits assigned to symbol i, and

`$\begin{array}{l}{A}_{i}=F\left(\left(2i+1\right)\frac{\pi }{M}\right)-F\left(\left(2i-1\right)\frac{\pi }{M}\right)\\ F\left(\psi \right)=-\frac{\mathrm{sin}\psi }{4\pi }\underset{-\pi /2}{\overset{\pi /2}{\int }}\frac{\mathrm{exp}\left(-k{E}_{b}/{N}_{0}\left(1-\mathrm{cos}\psi \mathrm{cos}t\right)\right)}{1-\mathrm{cos}\psi \mathrm{cos}t}dt\end{array}$`

Special case of $M=2$ (equation 8.85 from [2]):

`${P}_{b}=\frac{1}{2}\mathrm{exp}\left(-\frac{{E}_{b}}{{N}_{0}}\right)$`

M-PAM.  From equations 8.3 and 8.7 in [2], and equation 5.2-46 in [1]:

`${P}_{s}=2\left(\frac{M-1}{M}\right)Q\left(\sqrt{\frac{6}{{M}^{2}-1}\frac{k{E}_{b}}{{N}_{0}}}\right)$`

From [5]:

`$\begin{array}{c}{P}_{b}=\frac{2}{M{\mathrm{log}}_{2}M}×\\ \sum _{k=1}^{{\mathrm{log}}_{2}M}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\sum _{i=0}^{\left(1-{2}^{-k}\right)M-1}\left\{{\left(-1\right)}^{⌊\frac{i{2}^{k-1}}{M}⌋}\left({2}^{k-1}-⌊\frac{i{2}^{k-1}}{M}+\frac{1}{2}⌋\right)Q\left(\left(2i+1\right)\sqrt{\frac{6{\mathrm{log}}_{2}M}{{M}^{2}-1}\frac{{E}_{b}}{{N}_{0}}}\right)\right\}\end{array}$`

M-QAM.  For square M-QAM, $k={\mathrm{log}}_{2}M$ is even (equation 8.10 from [2], and equations 5.2-78 and 5.2-79 from [1]):

`${P}_{s}=4\frac{\sqrt{M}-1}{\sqrt{M}}Q\left(\sqrt{\frac{3}{M-1}\frac{k{E}_{b}}{{N}_{0}}}\right)-4{\left(\frac{\sqrt{M}-1}{\sqrt{M}}\right)}^{2}{Q}^{2}\left(\sqrt{\frac{3}{M-1}\frac{k{E}_{b}}{{N}_{0}}}\right)$`

From [5]:

`$\begin{array}{c}{P}_{b}=\frac{2}{\sqrt{M}{\mathrm{log}}_{2}\sqrt{M}}\\ ×\sum _{k=1}^{{\mathrm{log}}_{2}\sqrt{M}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\sum _{i=0}^{\left(1-{2}^{-k}\right)\sqrt{M}-1}\left\{{\left(-1\right)}^{⌊\frac{i{2}^{k-1}}{\sqrt{M}}⌋}\left({2}^{k-1}-⌊\frac{i{2}^{k-1}}{\sqrt{M}}+\frac{1}{2}⌋\right)Q\left(\left(2i+1\right)\sqrt{\frac{6{\mathrm{log}}_{2}M}{2\left(M-1\right)}\frac{{E}_{b}}{{N}_{0}}}\right)\right\}\end{array}$`

For rectangular (non-square) M-QAM, $k={\mathrm{log}}_{2}M$ is odd, $M=I×J$, $I={2}^{\frac{k-1}{2}}$, and $J={2}^{\frac{k+1}{2}}$:

`$\begin{array}{c}{P}_{s}=\frac{4IJ-2I-2J}{M}\\ ×Q\left(\sqrt{\frac{6{\mathrm{log}}_{2}\left(IJ\right)}{\left({I}^{2}+{J}^{2}-2\right)}\frac{{E}_{b}}{{N}_{0}}}\right)-\frac{4}{M}\left(1+IJ-I-J\right){Q}^{2}\left(\sqrt{\frac{6{\mathrm{log}}_{2}\left(IJ\right)}{\left({I}^{2}+{J}^{2}-2\right)}\frac{{E}_{b}}{{N}_{0}}}\right)\end{array}$`

From [5]:

`${P}_{b}=\frac{1}{{\mathrm{log}}_{2}\left(IJ\right)}\left(\sum _{k=1}^{{\mathrm{log}}_{2}I}{P}_{I}\left(k\right)+\sum _{l=1}^{{\mathrm{log}}_{2}J}{P}_{J}\left(l\right)\right)$`

where

`${P}_{I}\left(k\right)=\frac{2}{I}\sum _{i=0}^{\left(1-{2}^{-k}\right)I-1}\left\{{\left(-1\right)}^{⌊\frac{i{2}^{k-1}}{I}⌋}\left({2}^{k-1}-⌊\frac{i{2}^{k-1}}{I}+\frac{1}{2}⌋\right)Q\left(\left(2i+1\right)\sqrt{\frac{6{\mathrm{log}}_{2}\left(IJ\right)}{{I}^{2}+{J}^{2}-2}\frac{{E}_{b}}{{N}_{0}}}\right)\right\}$`

and

`${P}_{J}\left(k\right)=\frac{2}{J}\sum _{j=0}^{\left(1-{2}^{-l}\right)J-1}\left\{{\left(-1\right)}^{⌊\frac{j{2}^{l-1}}{J}⌋}\left({2}^{l-1}-⌊\frac{j{2}^{l-1}}{J}+\frac{1}{2}⌋\right)Q\left(\left(2j+1\right)\sqrt{\frac{6{\mathrm{log}}_{2}\left(IJ\right)}{{I}^{2}+{J}^{2}-2}\frac{{E}_{b}}{{N}_{0}}}\right)\right\}$`

Orthogonal M-FSK with Coherent Detection.  From equation 8.40 in [2] and equation 5.2-21 in [1]:

`$\begin{array}{l}{P}_{s}=1-\underset{-\infty }{\overset{\infty }{\int }}{\left[Q\left(-q-\sqrt{\frac{2k{E}_{b}}{{N}_{0}}}\right)\right]}^{M-1}\frac{1}{\sqrt{2\pi }}\mathrm{exp}\left(-\frac{{q}^{2}}{2}\right)dq\\ {P}_{b}=\frac{{2}^{k-1}}{{2}^{k}-1}{P}_{s}\end{array}$`

Nonorthogonal 2-FSK with Coherent Detection.  For $M=2$ (from equation 5.2-21 in [1] and equation 8.44 in [2]):

`${P}_{s}={P}_{b}=Q\left(\sqrt{\frac{{E}_{b}\left(1-\mathrm{Re}\left[\rho \right]\right)}{{N}_{0}}}\right)$`

$\rho$is the complex correlation coefficient:

`$\rho =\frac{1}{2{E}_{b}}\underset{0}{\overset{{T}_{b}}{\int }}{\stackrel{˜}{s}}_{1}\left(t\right){\stackrel{˜}{s}}_{2}^{*}\left(t\right)dt$`

where ${\stackrel{˜}{s}}_{1}\left(t\right)$ and ${\stackrel{˜}{s}}_{2}\left(t\right)$ are complex lowpass signals, and

`${E}_{b}=\frac{1}{2}\underset{0}{\overset{{T}_{b}}{\int }}{|{\stackrel{˜}{s}}_{1}\left(t\right)|}^{2}dt=\frac{1}{2}\underset{0}{\overset{{T}_{b}}{\int }}{|{\stackrel{˜}{s}}_{2}\left(t\right)|}^{2}dt$`

For example:

`$\begin{array}{c}\rho =\frac{1}{2{E}_{b}}\underset{0}{\overset{{T}_{b}}{\int }}\sqrt{\frac{2{E}_{b}}{{T}_{b}}}{e}^{j2\pi {f}_{1}t}\sqrt{\frac{2{E}_{b}}{{T}_{b}}}{e}^{-j2\pi {f}_{2}t}dt=\frac{1}{{T}_{b}}\underset{0}{\overset{{T}_{b}}{\int }}{e}^{j2\pi \left({f}_{1}-{f}_{2}\right)t}dt\\ =\frac{\mathrm{sin}\left(\pi \Delta f{T}_{b}\right)}{\pi \Delta f{T}_{b}}{e}^{j\pi \Delta ft}\end{array}$`

where $\Delta f={f}_{1}-{f}_{2}$.

(from equation 8.44 in [2], where $h=\Delta f{T}_{b}$)

Orthogonal M-FSK with Noncoherent Detection.  From equation 5.4-46 in [1] and equation 8.66 in [2]:

`$\begin{array}{l}{P}_{s}=\sum _{m=1}^{M-1}{\left(-1\right)}^{m+1}\left(\begin{array}{c}M-1\\ m\end{array}\right)\frac{1}{m+1}\mathrm{exp}\left[-\frac{m}{m+1}\frac{k{E}_{b}}{{N}_{0}}\right]\\ {P}_{b}=\frac{1}{2}\frac{M}{M-1}{P}_{s}\end{array}$`

Nonorthogonal 2-FSK with Noncoherent Detection.  For $M=2$ (from equation 5.4-53 in [1] and equation 8.69 in [2]):

`${P}_{s}={P}_{b}=Q\left(\sqrt{a},\sqrt{b}\right)-\frac{1}{2}\mathrm{exp}\left(-\frac{a+b}{2}\right){I}_{0}\left(\sqrt{ab}\right)$`

where

Precoded MSK with Coherent Detection.  Same BER/SER as BPSK.

Differentially Encoded MSK with Coherent Detection.  Same BER/SER as DE-BPSK.

MSK with Noncoherent Detection (Optimum Block-by-Block).  Upper bound (from equations 10.166 and 10.164 in [6]):

`$\begin{array}{c}{P}_{s}={P}_{b}\\ \le \frac{1}{2}\left[1-Q\left(\sqrt{{b}_{1}},\sqrt{{a}_{1}}\right)+Q\left(\sqrt{{a}_{1}},\sqrt{{b}_{1}}\right)\right]+\frac{1}{4}\left[1-Q\left(\sqrt{{b}_{4}},\sqrt{{a}_{4}}\right)+Q\left(\sqrt{{a}_{4}},\sqrt{{b}_{4}}\right)\right]+\frac{1}{2}{e}^{-\frac{{E}_{b}}{{N}_{0}}}\end{array}$`

where

`$\begin{array}{cc}{a}_{1}=\frac{{E}_{b}}{{N}_{0}}\left(1-\sqrt{\frac{3-4/{\pi }^{2}}{4}}\right),& {b}_{1}=\frac{{E}_{b}}{{N}_{0}}\left(1+\sqrt{\frac{3-4/{\pi }^{2}}{4}}\right)\\ {a}_{4}=\frac{{E}_{b}}{{N}_{0}}\left(1-\sqrt{1-4/{\pi }^{2}}\right),& {b}_{4}=\frac{{E}_{b}}{{N}_{0}}\left(1+\sqrt{1-4/{\pi }^{2}}\right)\end{array}$`

CPFSK Coherent Detection (Optimum Block-by-Block).  Lower bound (from equation 5.3-17 in [1]):

`${P}_{s}>{K}_{{\delta }_{\mathrm{min}}}Q\left(\sqrt{\frac{{E}_{b}}{{N}_{0}}{\delta }_{\mathrm{min}}^{2}}\right)$`

Upper bound:

`${\delta }_{\mathrm{min}}^{2}>\underset{1\le i\le M-1}{\mathrm{min}}\left\{2i\left(1-\text{sinc}\left(2ih\right)\right)\right\}$`

where h is the modulation index, and ${K}_{{\delta }_{\mathrm{min}}}$ is the number of paths having the minimum distance.

`${P}_{b}\cong \frac{{P}_{s}}{k}$`

Analytical Expressions Used in berfading

Notation.  The following notation is used for the expressions found in `berfading`.

Value Notation
Power of the fading amplitude r$\Omega =E\left[{r}^{2}\right]$, where $E\left[\cdot \right]$ denotes statistical expectation
Number of diversity branches

`$L$`

SNR per symbol per branch

`${\overline{\gamma }}_{l}=\left({\Omega }_{l}\frac{{E}_{s}}{{N}_{0}}\right)/L=\left({\Omega }_{l}\frac{k{E}_{b}}{{N}_{0}}\right)/L$`

For identically-distributed diversity branches:

`$\overline{\gamma }=\left(\Omega \frac{k{E}_{b}}{{N}_{0}}\right)/L$`

Moment generating functions for each diversity branch

Rayleigh fading:

`${M}_{{\gamma }_{l}}\left(s\right)=\frac{1}{1-s{\overline{\gamma }}_{l}}$`

Rician fading:

`${M}_{{\gamma }_{l}}\left(s\right)=\frac{1+K}{1+K-s{\overline{\gamma }}_{l}}{e}^{\left[\frac{Ks{\overline{\gamma }}_{l}}{\left(1+K\right)-s{\overline{\gamma }}_{l}}\right]}$`

where K is the ratio of energy in the specular component to the energy in the diffuse component (linear scale).

For identically-distributed diversity branches:${M}_{{\gamma }_{l}}\left(s\right)={M}_{\gamma }\left(s\right)$ for all l.

The following acronyms are used:

Acronym Definition
MRCmaximal-ratio combining
EGCequal-gain combining

M-PSK with MRC.  From equation 9.15 in [2]:

`${P}_{s}=\frac{1}{\pi }\underset{0}{\overset{\left(M-1\right)\pi /M}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{{\mathrm{sin}}^{2}\left(\pi /M\right)}{{\mathrm{sin}}^{2}\theta }\right)d\theta$`

From [4] and [2]:

`${P}_{b}=\frac{1}{k}\left(\sum _{i=1}^{M/2}\left({w}_{i}^{\text{'}}\right){\overline{P}}_{i}\right)$`

where ${w}_{i}^{\text{'}}={w}_{i}+{w}_{M-i}$, ${w}_{M/2}^{\text{'}}={w}_{M/2}$, ${w}_{i}$ is the Hamming weight of bits assigned to symbol i, and

`$\begin{array}{l}{\overline{P}}_{i}=\frac{1}{2\pi }\underset{0}{\overset{\pi \left(1-\left(2i-1\right)/M\right)}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{1}{{\mathrm{sin}}^{2}\theta }{\mathrm{sin}}^{2}\frac{\left(2i-1\right)\pi }{M}\right)d\theta \\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}-\frac{1}{2\pi }\underset{0}{\overset{\pi \left(1-\left(2i+1\right)/M\right)}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{1}{{\mathrm{sin}}^{2}\theta }{\mathrm{sin}}^{2}\frac{\left(2i+1\right)\pi }{M}\right)d\theta \end{array}$`

For the special case of Rayleigh fading with $M=2$ (from equations C-18, C-21, and Table C-1 in [6]):

`${P}_{b}=\frac{1}{2}\left[1-\mu \sum _{i=0}^{L-1}\left(\begin{array}{c}2i\\ i\end{array}\right){\left(\frac{1-{\mu }^{2}}{4}\right)}^{i}\right]$`

where

`$\mu =\sqrt{\frac{\overline{\gamma }}{\overline{\gamma }+1}}$`

If $L=1$:

`${P}_{b}=\frac{1}{2}\left[1-\sqrt{\frac{\overline{\gamma }}{\overline{\gamma }+1}}\right]$`

DE-M-PSK with MRC.  For $M=2$ (from equations 8.37 and 9.8-9.11 in [2]):

`${P}_{s}={P}_{b}=\frac{2}{\pi }\underset{0}{\overset{\pi /2}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{1}{{\mathrm{sin}}^{2}\theta }\right)d\theta -\frac{2}{\pi }\underset{0}{\overset{\pi /4}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{1}{{\mathrm{sin}}^{2}\theta }\right)d\theta$`

M-PAM with MRC.  From equation 9.19 in [2]:

`${P}_{s}=\frac{2\left(M-1\right)}{M\pi }\underset{0}{\overset{\pi /2}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{3/\left({M}^{2}-1\right)}{{\mathrm{sin}}^{2}\theta }\right)d\theta$`

From [5] and [2]:

M-QAM with MRC.  For square M-QAM, $k={\mathrm{log}}_{2}M$ is even (equation 9.21 in [2]):

`$\begin{array}{c}{P}_{s}=\frac{4}{\pi }\left(1-\frac{1}{\sqrt{M}}\right)\underset{0}{\overset{\pi /2}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{3/\left(2\left(M-1\right)\right)}{{\mathrm{sin}}^{2}\theta }\right)d\theta \\ -\frac{4}{\pi }{\left(1-\frac{1}{\sqrt{M}}\right)}^{2}\underset{0}{\overset{\pi /4}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{3/\left(2\left(M-1\right)\right)}{{\mathrm{sin}}^{2}\theta }\right)d\theta \end{array}$`

From [5] and [2]:

For rectangular (nonsquare) M-QAM, $k={\mathrm{log}}_{2}M$ is odd, $M=I×J$, $I={2}^{\frac{k-1}{2}}$, $J={2}^{\frac{k+1}{2}}$, ${\overline{\gamma }}_{l}={\Omega }_{l}{\mathrm{log}}_{2}\left(IJ\right)\frac{{E}_{b}}{{N}_{0}}$, and

`$\begin{array}{c}{P}_{s}=\frac{4IJ-2I-2J}{M\pi }\underset{0}{\overset{\pi /2}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{3/\left({I}^{2}+{J}^{2}-2\right)}{{\mathrm{sin}}^{2}\theta }\right)d\theta \\ -\frac{4}{M\pi }\left(1+IJ-I-J\right)\underset{0}{\overset{\pi /4}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{3/\left({I}^{2}+{J}^{2}-2\right)}{{\mathrm{sin}}^{2}\theta }\right)d\theta \end{array}$`

From [5] and [2]:

`$\begin{array}{c}{P}_{b}=\frac{1}{{\mathrm{log}}_{2}\left(IJ\right)}\left(\sum _{k=1}^{{\mathrm{log}}_{2}I}{P}_{I}\left(k\right)+\sum _{l=1}^{{\mathrm{log}}_{2}J}{P}_{J}\left(l\right)\right)\\ {P}_{I}\left(k\right)=\frac{2}{I\pi }\sum _{i=0}^{\left(1-{2}^{-k}\right)I-1}\left\{{\left(-1\right)}^{⌊\frac{i{2}^{k-1}}{I}⌋}\left({2}^{k-1}-⌊\frac{i{2}^{k-1}}{I}+\frac{1}{2}⌋\right)\underset{0}{\overset{\pi /2}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{{\left(2i+1\right)}^{2}3/\left({I}^{2}+{J}^{2}-2\right)}{{\mathrm{sin}}^{2}\theta }\right)d\theta \right\}\\ {P}_{J}\left(k\right)=\frac{2}{J\pi }\sum _{j=0}^{\left(1-{2}^{-l}\right)J-1}\left\{{\left(-1\right)}^{⌊\frac{j{2}^{l-1}}{J}⌋}\left({2}^{l-1}-⌊\frac{j{2}^{l-1}}{J}+\frac{1}{2}⌋\right)\underset{0}{\overset{\pi /2}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{{\left(2j+1\right)}^{2}3/\left({I}^{2}+{J}^{2}-2\right)}{{\mathrm{sin}}^{2}\theta }\right)d\theta \right\}\end{array}$`

M-DPSK with Postdetection EGC.  From equation 8.165 in [2]:

`${P}_{s}=\frac{\mathrm{sin}\left(\pi /M\right)}{2\pi }\underset{-\pi /2}{\overset{\pi /2}{\int }}\frac{1}{\left[1-\mathrm{cos}\left(\pi /M\right)\mathrm{cos}\theta \right]}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\left[1-\mathrm{cos}\left(\pi /M\right)\mathrm{cos}\theta \right]\right)d\theta$`

From [4] and [2]:

`${P}_{b}=\frac{1}{k}\left(\sum _{i=1}^{M/2}\left({w}_{i}^{\text{'}}\right){\overline{A}}_{i}\right)$`

where ${w}_{i}^{\text{'}}={w}_{i}+{w}_{M-i}$, ${w}_{M/2}^{\text{'}}={w}_{M/2}$, ${w}_{i}$ is the Hamming weight of bits assigned to symbol i, and

`$\begin{array}{l}{\overline{A}}_{i}=\overline{F}\left(\left(2i+1\right)\frac{\pi }{M}\right)-\overline{F}\left(\left(2i-1\right)\frac{\pi }{M}\right)\\ \overline{F}\left(\psi \right)=-\frac{\mathrm{sin}\psi }{4\pi }\underset{-\pi /2}{\overset{\pi /2}{\int }}\frac{1}{\left(1-\mathrm{cos}\psi \mathrm{cos}t\right)}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\left(1-\mathrm{cos}\psi \mathrm{cos}t\right)\right)dt\end{array}$`

For the special case of Rayleigh fading with $M=2$, and $L=1$ (equation 8.173 from [2]):

`${P}_{b}=\frac{1}{2\left(1+\overline{\gamma }\right)}$`

Orthogonal 2-FSK, Coherent Detection with MRC.  From equation 9.11 in [2]:

`${P}_{s}={P}_{b}=\frac{1}{\pi }\underset{0}{\overset{\pi /2}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{1/2}{{\mathrm{sin}}^{2}\theta }\right)d\theta$`

For the special case of Rayleigh fading (equations 14.4-15 and 14.4-21 in [1]):

`${P}_{s}={P}_{b}\text{​}=\frac{1}{{2}^{L}}{\left(1-\sqrt{\frac{\overline{\gamma }}{2+\overline{\gamma }}}\right)}^{L}\sum _{k=0}^{L-1}\left(\begin{array}{c}L-1+k\\ k\end{array}\right)\frac{1}{{2}^{k}}{\left(1+\sqrt{\frac{\overline{\gamma }}{2+\overline{\gamma }}}\right)}^{k}$`

Nonorthogonal 2-FSK, Coherent Detection with MRC.  Equations 9.11 and 8.44 in [2]:

`${P}_{s}={P}_{b}=\frac{1}{\pi }\underset{0}{\overset{\pi /2}{\int }}\prod _{l=1}^{L}{M}_{{\gamma }_{l}}\left(-\frac{\left(1-\mathrm{Re}\left[\rho \right]\right)/2}{{\mathrm{sin}}^{2}\theta }\right)d\theta$`

For the special case of Rayleigh fading with $L=1$ (equation 20 in [8] and equation 8.130 in [2]):

`${P}_{s}={P}_{b}=\frac{1}{2}\left[1-\sqrt{\frac{\overline{\gamma }\left(1-\mathrm{Re}\left[\rho \right]\right)}{2+\overline{\gamma }\left(1-\mathrm{Re}\left[\rho \right]\right)}}\right]$`

Orthogonal M-FSK, Noncoherent Detection with EGC.  Rayleigh fading (equation 14.4-47 in [1]):

`$\begin{array}{l}{P}_{s}\text{​}=1-\underset{0}{\overset{\infty }{\int }}\frac{1}{{\left(1+\overline{\gamma }\right)}^{L}\left(L-1\right)!}{U}^{L-1}{e}^{-\frac{U}{1+\overline{\gamma }}}{\left(1-{e}^{-U}\sum _{k=0}^{L-1}\frac{{U}^{k}}{k!}\right)}^{M-1}dU\\ {P}_{b}=\frac{1}{2}\frac{M}{M-1}{P}_{s}\end{array}$`

Rician fading (equation 41 in [8]):

`$\begin{array}{l}{P}_{s}=\sum _{r=1}^{M-1}\frac{{\left(-1\right)}^{r+1}{e}^{-LK{\overline{\gamma }}_{r}/\left(1+{\overline{\gamma }}_{r}\right)}}{{\left(r\left(1+{\overline{\gamma }}_{r}\right)+1\right)}^{L}}\left(\begin{array}{c}M-1\\ r\end{array}\right)\sum _{n=0}^{r\left(L-1\right)}{\beta }_{nr}\frac{\Gamma \left(L+n\right)}{\Gamma \left(L\right)}{\left[\frac{1+{\overline{\gamma }}_{r}}{r+1+r{\overline{\gamma }}_{r}}\right]}^{n}{}_{1}F{}_{1}\left(L+n,L;\frac{LK{\overline{\gamma }}_{r}/\left(1+{\overline{\gamma }}_{r}\right)}{r\left(1+{\overline{\gamma }}_{r}\right)+1}\right)\\ {P}_{b}=\frac{1}{2}\frac{M}{M-1}{P}_{s}\end{array}$`

where

`$\begin{array}{c}{\overline{\gamma }}_{r}=\frac{1}{1+K}\overline{\gamma }\\ {\beta }_{nr}=\sum _{i=n-\left(L-1\right)}^{n}\frac{{\beta }_{i\left(r-1\right)}}{\left(n-i\right)!}{I}_{\left[0,\text{\hspace{0.17em}}\left(r-1\right)\left(L-1\right)\right]}\left(i\right)\\ {\beta }_{00}={\beta }_{0r}=1\\ {\beta }_{n1}=1/n!\\ {\beta }_{1r}=r\end{array}$`

and ${I}_{\left[a,b\right]}\left(i\right)=1$ if $a\le i\le b$ and 0 otherwise.

Nonorthogonal 2-FSK, Noncoherent Detection with No Diversity.  From equation 8.163 in [2]:

`${P}_{s}={P}_{b}=\frac{1}{4\pi }\underset{-\pi }{\overset{\pi }{\int }}\frac{1-{\varsigma }^{2}}{1+2\varsigma \mathrm{sin}\theta +{\varsigma }^{2}}{M}_{\gamma }\left(-\frac{1}{4}\left(1+\sqrt{1-{\rho }^{2}}\right)\left(1+2\varsigma \mathrm{sin}\theta +{\varsigma }^{2}\right)\right)d\theta$`

where

`$\varsigma =\sqrt{\frac{1-\sqrt{1-{\rho }^{2}}}{1+\sqrt{1-{\rho }^{2}}}}$`

Analytical Expressions Used in bercoding and BERTool

Common Notation for This Section

DescriptionNotation
Energy-per-information bit-to-noise power-spectral-density ratio

`${\gamma }_{b}=\frac{{E}_{b}}{{N}_{0}}$`

Message length

$K$

Code length

$N$

Code rate

`${R}_{c}=\frac{K}{N}$`

Block Coding.  Specific notation for block coding expressions: ${d}_{\mathrm{min}}$ is the minimum distance of the code.

Soft Decision

BPSK, QPSK, OQPSK, PAM-2, QAM-4, and precoded MSK (equation 8.1-52 in [1]):

`${P}_{b}\le \frac{1}{2}\left({2}^{K}-1\right)Q\left(\sqrt{2{\gamma }_{b}{R}_{c}{d}_{\mathrm{min}}}\right)$`

DE-BPSK, DE-QPSK, DE-OQPSK, and DE-MSK:

`${P}_{b}\le \frac{1}{2}\left({2}^{K}-1\right)\left[2Q\left(\sqrt{2{\gamma }_{b}{R}_{c}{d}_{\mathrm{min}}}\right)\left[1-Q\left(\sqrt{2{\gamma }_{b}{R}_{c}{d}_{\mathrm{min}}}\right)\right]\right]$`

BFSK, coherent detection (equations 8.1-50 and 8.1-58 in [1]):

`${P}_{b}\le \frac{1}{2}\left({2}^{K}-1\right)Q\left(\sqrt{{\gamma }_{b}{R}_{c}{d}_{\mathrm{min}}}\right)$`

BFSK, noncoherent square-law detection (equations 8.1-65 and 8.1-64 in [1]):

`${P}_{b}\le \frac{1}{2}\frac{{2}^{K}-1}{{2}^{2{d}_{\mathrm{min}}-1}}\mathrm{exp}\left(-\frac{1}{2}{\gamma }_{b}{R}_{c}{d}_{\mathrm{min}}\right)\sum _{i=0}^{{d}_{\mathrm{min}}-1}{\left(\frac{1}{2}{\gamma }_{b}{R}_{c}{d}_{\mathrm{min}}\right)}^{i}\frac{1}{i!}\sum _{r=0}^{{d}_{\mathrm{min}}-1-i}\left(\begin{array}{c}2{d}_{\mathrm{min}}-1\\ r\end{array}\right)$`

DPSK:

`${P}_{b}\le \frac{1}{2}\frac{{2}^{K}-1}{{2}^{2{d}_{\mathrm{min}}-1}}\mathrm{exp}\left(-{\gamma }_{b}{R}_{c}{d}_{\mathrm{min}}\right)\sum _{i=0}^{{d}_{\mathrm{min}}-1}{\left({\gamma }_{b}{R}_{c}{d}_{\mathrm{min}}\right)}^{i}\frac{1}{i!}\sum _{r=0}^{{d}_{\mathrm{min}}-1-i}\left(\begin{array}{c}2{d}_{\mathrm{min}}-1\\ r\end{array}\right)$`

Hard Decision

General linear block code (equations 4.3, 4.4 in [9], and 12.136 in [6]):

`$\begin{array}{l}{P}_{b}\le \frac{1}{N}\sum _{m=t+1}^{N}\left(m+t\right)\left(\begin{array}{c}N\\ m\end{array}\right){p}^{m}{\left(1-p\right)}^{N-m}\\ t=⌊\frac{1}{2}\left({d}_{\mathrm{min}}-1\right)⌋\end{array}$`

Hamming code (equations 4.11, 4.12 in [9], and 6.72, 6.73 in [7]):

`${P}_{b}\approx \frac{1}{N}\sum _{m=2}^{N}m\left(\begin{array}{c}N\\ m\end{array}\right){p}^{m}{\left(1-p\right)}^{N-m}=p-p{\left(1-p\right)}^{N-1}$`

(24, 12) extended Golay code (equation 4.17 in [9], and 12.139 in [6]):

`${P}_{b}\le \frac{1}{24}\sum _{m=4}^{24}{\beta }_{m}\left(\begin{array}{c}24\\ m\end{array}\right){p}^{m}{\left(1-p\right)}^{24-m}$`

where ${\beta }_{m}$ is the average number of channel symbol errors that remain in corrected N-tuple when the channel caused m symbol errors (table 4.2 in [9]).

Reed-Solomon code with $N=Q-1={2}^{q}-1$:

`${P}_{b}\approx \frac{{2}^{q-1}}{{2}^{q}-1}\frac{1}{N}\sum _{m=t+1}^{N}m\left(\begin{array}{c}N\\ m\end{array}\right){\left({P}_{s}\right)}^{m}{\left(1-{P}_{s}\right)}^{N-m}$`

for FSK (equations 4.25, 4.27 in [9], 8.1-115, 8.1-116 in [1], 8.7, 8.8 in [7], and 12.142, 12.143 in [6]), and

`${P}_{b}\approx \frac{1}{q}\frac{1}{N}\sum _{m=t+1}^{N}m\left(\begin{array}{c}N\\ m\end{array}\right){\left({P}_{s}\right)}^{m}{\left(1-{P}_{s}\right)}^{N-m}$`

otherwise.

If ${\mathrm{log}}_{2}Q/{\mathrm{log}}_{2}M=q/k=h$ where h is an integer (equation 1 in [10]):

`${P}_{s}=1-{\left(1-s\right)}^{h}$`

where s is the symbol error rate (SER) in an uncoded AWGN channel.

For example, for BPSK, $M=2$ and ${P}_{s}=1-{\left(1-s\right)}^{q}$

Otherwise, ${P}_{s}$ is given by table 1 and equation 2 in [10].

Convolutional Coding.  Specific notation for convolutional coding expressions: ${d}_{free}$ is the free distance of the code, and ${a}_{d}$ is the number of paths of distance d from the all-zero path that merge with the all-zero path for the first time.

Soft Decision

From equations 8.2-26, 8.2-24, and 8.2-25 in [1], and equations 13.28 and 13.27 in [6]:

`${P}_{b}<\sum _{d={d}_{free}}^{\infty }{a}_{d}f\left(d\right){P}_{2}\left(d\right)$`

with transfer function

`$\begin{array}{l}T\left(D,N\right)=\sum _{d={d}_{free}}^{\infty }{a}_{d}{D}^{d}{N}^{f\left(d\right)}\\ {\frac{dT\left(D,N\right)}{dN}|}_{N=1}=\sum _{d={d}_{free}}^{\infty }{a}_{d}f\left(d\right){D}^{d}\end{array}$`

where $f\left(d\right)$ is the exponent of N as a function of d.

Results for BPSK, QPSK, OQPSK, PAM-2, QAM-4, precoded MSK, DE-BPSK, DE-QPSK, DE-OQPSK, DE-MSK, DPSK, and BFSK are obtained as:

`${P}_{2}\left(d\right)={{P}_{b}|}_{\frac{{E}_{b}}{{N}_{0}}={\gamma }_{b}{R}_{c}d}$`

where ${P}_{b}$ is the BER in the corresponding uncoded AWGN channel. For example, for BPSK (equation 8.2-20 in [1]):

`${P}_{2}\left(d\right)=Q\left(\sqrt{2{\gamma }_{b}{R}_{c}d}\right)$`

Hard Decision

From equations 8.2-33, 8.2-28, and 8.2-29 in [1], and equations 13.28, 13.24, and 13.25 in [6]:

`${P}_{b}<\sum _{d={d}_{free}}^{\infty }{a}_{d}f\left(d\right){P}_{2}\left(d\right)$`

where

`${P}_{2}\left(d\right)=\sum _{k=\left(d+1\right)/2}^{d}\left(\begin{array}{c}d\\ k\end{array}\right){p}^{k}{\left(1-p\right)}^{d-k}$`

when d is odd, and

`${P}_{2}\left(d\right)=\sum _{k=d/2+1}^{d}\left(\begin{array}{c}d\\ k\end{array}\right){p}^{k}{\left(1-p\right)}^{d-k}+\frac{1}{2}\left(\begin{array}{c}d\\ d/2\end{array}\right){p}^{d/2}{\left(1-p\right)}^{d/2}$`

when d is even (p is the bit error rate (BER) in an uncoded AWGN channel).

Performance Results via Simulation

Section Overview

One way to compute the bit error rate or symbol error rate for a communication system is to simulate the transmission of data messages and compare all messages before and after transmission. The simulation of the communication system components using Communications Toolbox™ is covered in other parts of this guide. This section describes how to compare the data messages that enter and leave the simulation.

Another example of computing performance results via simulation is in Curve Fitting for Error Rate Plots in the discussion of curve fitting.

Using Simulated Data to Compute Bit and Symbol Error Rates

The `biterr` function compares two sets of data and computes the number of bit errors and the bit error rate. The `symerr` function compares two sets of data and computes the number of symbol errors and the symbol error rate. An error is a discrepancy between corresponding points in the two sets of data.

Of the two sets of data, typically one represents messages entering a transmitter and the other represents recovered messages leaving a receiver. You might also compare data entering and leaving other parts of your communication system, for example, data entering an encoder and data leaving a decoder.

If your communication system uses several bits to represent one symbol, counting bit errors is different from counting symbol errors. In either the bit- or symbol-counting case, the error rate is the number of errors divided by the total number (of bits or symbols) transmitted.

Note

To ensure an accurate error rate, you should typically simulate enough data to produce at least 100 errors.

If the error rate is very small (for example, 10-6 or smaller), the semianalytic technique might compute the result more quickly than a simulation-only approach. See Performance Results via the Semianalytic Technique for more information on how to use this technique.

Example: Computing Error Rates

The script below uses the `symerr` function to compute the symbol error rates for a noisy linear block code. After artificially adding noise to the encoded message, it compares the resulting noisy code to the original code. Then it decodes and compares the decoded message to the original one.

```m = 3; n = 2^m-1; k = n-m; % Prepare to use Hamming code. msg = randi([0 1],k*200,1); % 200 messages of k bits each code = encode(msg,n,k,'hamming'); codenoisy = rem(code+(rand(n*200,1)>.95),2); % Add noise. % Decode and correct some errors. newmsg = decode(codenoisy,n,k,'hamming'); % Compute and display symbol error rates. noisyVec = step(comm.ErrorRate,code,codenoisy); decodedVec = step(comm.ErrorRate,msg,newmsg); disp(['Error rate in the received code: ',num2str(noisyVec(1))]) disp(['Error rate after decoding: ',num2str(decodedVec(1))])```

The output is below. The error rate decreases after decoding because the Hamming decoder corrects some of the errors. Your results might vary because this example uses random numbers.

```Error rate in the received code: 0.054286 Error rate after decoding: 0.03 ```

Comparing Symbol Error Rate and Bit Error Rate

In the example above, the symbol errors and bit errors are the same because each symbol is a bit. The commands below illustrate the difference between symbol errors and bit errors in other situations.

```a = [1 2 3]'; b = [1 4 4]'; format rat % Display fractions instead of decimals. % Create ErrorRate Calculator System object serVec = step(comm.ErrorRate,a,b); srate = serVec(1) snum = serVec(2) % Convert integers to bits hIntToBit = comm.IntegerToBit(3); a_bit = step(hIntToBit, a); b_bit = step(hIntToBit, b); % Calculate BER berVec = step(comm.ErrorRate,a_bit,b_bit); brate = berVec(1) bnum = berVec(2)```

The output is below.

```snum = 2 srate = 2/3 bnum = 5 brate = 5/9 ```

`bnum` is 5 because the second entries differ in two bits and the third entries differ in three bits. `brate` is 5/9 because the total number of bits is 9. The total number of bits is, by definition, the number of entries in `a` or `b` times the maximum number of bits among all entries of `a` and `b`.

Performance Results via the Semianalytic Technique

The technique described in Performance Results via Simulation works well for a large variety of communication systems, but can be prohibitively time-consuming if the system's error rate is very small (for example, 10-6 or smaller). This section describes how to use the semianalytic technique as an alternative way to compute error rates. For certain types of systems, the semianalytic technique can produce results much more quickly than a nonanalytic method that uses only simulated data.

The semianalytic technique uses a combination of simulation and analysis to determine the error rate of a communication system. The `semianalytic` function in Communications Toolbox helps you implement the semianalytic technique by performing some of the analysis.

When to Use the Semianalytic Technique

The semianalytic technique works well for certain types of communication systems, but not for others. The semianalytic technique is applicable if a system has all of these characteristics:

• Any effects of multipath fading, quantization, and amplifier nonlinearities must precede the effects of noise in the actual channel being modeled.

• The receiver is perfectly synchronized with the carrier, and timing jitter is negligible. Because phase noise and timing jitter are slow processes, they reduce the applicability of the semianalytic technique to a communication system.

• The noiseless simulation has no errors in the received signal constellation. Distortions from sources other than noise should be mild enough to keep each signal point in its correct decision region. If this is not the case, the calculated BER is too low. For instance, if the modeled system has a phase rotation that places the received signal points outside their proper decision regions, the semianalytic technique is not suitable to predict system performance.

Furthermore, the `semianalytic` function assumes that the noise in the actual channel being modeled is Gaussian. For details on how to adapt the semianalytic technique for non-Gaussian noise, see the discussion of generalized exponential distributions in [11].

Procedure for the Semianalytic Technique

The procedure below describes how you would typically implement the semianalytic technique using the `semianalytic` function:

1. Generate a message signal containing at least ML symbols, where M is the alphabet size of the modulation and L is the length of the impulse response of the channel in symbols. A common approach is to start with an augmented binary pseudonoise (PN) sequence of total length `(log2M)ML`. An augmented PN sequence is a PN sequence with an extra zero appended, which makes the distribution of ones and zeros equal.

2. Modulate a carrier with the message signal using baseband modulation. Supported modulation types are listed on the reference page for `semianalytic`. Shape the resultant signal with rectangular pulse shaping, using the oversampling factor that you will later use to filter the modulated signal. Store the result of this step as `txsig` for later use.

3. Filter the modulated signal with a transmit filter. This filter is often a square-root raised cosine filter, but you can also use a Butterworth, Bessel, Chebyshev type 1 or 2, elliptic, or more general FIR or IIR filter. If you use a square-root raised cosine filter, use it on the nonoversampled modulated signal and specify the oversampling factor in the filtering function. If you use another filter type, you can apply it to the rectangularly pulse shaped signal.

4. Run the filtered signal through a noiseless channel. This channel can include multipath fading effects, phase shifts, amplifier nonlinearities, quantization, and additional filtering, but it must not include noise. Store the result of this step as `rxsig` for later use.

5. Invoke the `semianalytic` function using the `txsig` and `rxsig` data from earlier steps. Specify a receive filter as a pair of input arguments, unless you want to use the function's default filter. The function filters `rxsig` and then determines the error probability of each received signal point by analytically applying the Gaussian noise distribution to each point. The function averages the error probabilities over the entire received signal to determine the overall error probability. If the error probability calculated in this way is a symbol error probability, the function converts it to a bit error rate, typically by assuming Gray coding. The function returns the bit error rate (or, in the case of DQPSK modulation, an upper bound on the bit error rate).

Example: Using the Semianalytic Technique

The example below illustrates the procedure described above, using 16-QAM modulation. It also compares the error rates obtained from the semianalytic technique with the theoretical error rates obtained from published formulas and computed using the `berawgn` function. The resulting plot shows that the error rates obtained using the two methods are nearly identical. The discrepancies between the theoretical and computed error rates are largely due to the phase offset in this example's channel model.

```% Step 1. Generate message signal of length >= M^L. M = 16; % Alphabet size of modulation L = 1; % Length of impulse response of channel msg = [0:M-1 0]; % M-ary message sequence of length > M^L % Step 2. Modulate the message signal using baseband modulation. %hMod = comm.RectangularQAMModulator(M); % Use 16-QAM. %modsig = step(hMod,msg'); % Modulate data modsig = qammod(msg',M); % Modulate data Nsamp = 16; modsig = rectpulse(modsig,Nsamp); % Use rectangular pulse shaping. % Step 3. Apply a transmit filter. txsig = modsig; % No filter in this example % Step 4. Run txsig through a noiseless channel. rxsig = txsig*exp(1i*pi/180); % Static phase offset of 1 degree % Step 5. Use the semianalytic function. % Specify the receive filter as a pair of input arguments. % In this case, num and den describe an ideal integrator. num = ones(Nsamp,1)/Nsamp; den = 1; EbNo = 0:20; % Range of Eb/No values under study ber = semianalytic(txsig,rxsig,'qam',M,Nsamp,num,den,EbNo); % For comparison, calculate theoretical BER. bertheory = berawgn(EbNo,'qam',M); % Plot computed BER and theoretical BER. figure; semilogy(EbNo,ber,'k*'); hold on; semilogy(EbNo,bertheory,'ro'); title('Semianalytic BER Compared with Theoretical BER'); legend('Semianalytic BER with Phase Offset',... 'Theoretical BER Without Phase Offset','Location','SouthWest'); hold off;```

This example creates a figure like the one below.

Theoretical Performance Results

Computing Theoretical Error Statistics

While the `biterr` function discussed above can help you gather empirical error statistics, you might also compare those results to theoretical error statistics. Certain types of communication systems are associated with closed-form expressions for the bit error rate or a bound on it. The functions listed in the table below compute the closed-form expressions for some types of communication systems, where such expressions exist.

Type of Communication SystemFunction
Uncoded AWGN channel `berawgn`
Coded AWGN channel `bercoding`
Uncoded Rayleigh and Rician fading channel `berfading`
Uncoded AWGN channel with imperfect synchronization `bersync`

Each function's reference page lists one or more books containing the closed-form expressions that the function implements.

Plotting Theoretical Error Rates

The example below uses the `bercoding` function to compute upper bounds on bit error rates for convolutional coding with a soft-decision decoder. The data used for the generator and distance spectrum are from [1] and [12], respectively.

```coderate = 1/4; % Code rate % Create a structure dspec with information about distance spectrum. dspec.dfree = 10; % Minimum free distance of code dspec.weight = [1 0 4 0 12 0 32 0 80 0 192 0 448 0 1024 ... 0 2304 0 5120 0]; % Distance spectrum of code EbNo = 3:0.5:8; berbound = bercoding(EbNo,'conv','soft',coderate,dspec); semilogy(EbNo,berbound) % Plot the results. xlabel('E_b/N_0 (dB)'); ylabel('Upper Bound on BER'); title('Theoretical Bound on BER for Convolutional Coding'); grid on;```

This example produces the following plot.

Comparing Theoretical and Empirical Error Rates

The example below uses the `berawgn` function to compute symbol error rates for pulse amplitude modulation (PAM) with a series of Eb/N0 values. For comparison, the code simulates 8-PAM with an AWGN channel and computes empirical symbol error rates. The code also plots the theoretical and empirical symbol error rates on the same set of axes.

```% 1. Compute theoretical error rate using BERAWGN. rng('default') % Set random number seed for repeatability % M = 8; EbNo = 0:13; [ber, ser] = berawgn(EbNo,'pam',M); % Plot theoretical results. figure; semilogy(EbNo,ser,'r'); xlabel('E_b/N_0 (dB)'); ylabel('Symbol Error Rate'); grid on; drawnow; % 2. Compute empirical error rate by simulating. % Set up. n = 10000; % Number of symbols to process k = log2(M); % Number of bits per symbol % Convert from EbNo to SNR. % Note: Because No = 2*noiseVariance^2, we must add 3 dB % to get SNR. For details, see Proakis' book listed in % "Selected Bibliography for Performance Evaluation." snr = EbNo+3+10*log10(k); % Preallocate variables to save time. ynoisy = zeros(n,length(snr)); z = zeros(n,length(snr)); berVec = zeros(3,length(EbNo)); % PAM modulation and demodulation system objects %h = comm.PAMModulator(M); %h2 = comm.PAMDemodulator(M); % AWGNChannel System object hChan = comm.AWGNChannel('NoiseMethod', 'Signal to noise ratio (SNR)'); % ErrorRate calculator System object to compare decoded symbols to the % original transmitted symbols. hErrorCalc = comm.ErrorRate; % Main steps in the simulation x = randi([0 M-1],n,1); % Create message signal. %y = step(h,x); % Modulate. y = pammod(x,M); % Modulate. hChan.SignalPower = (real(y)' * real(y))/ length(real(y)); % Loop over different SNR values. for jj = 1:length(snr) reset(hErrorCalc) hChan.SNR = snr(jj); % Assign Channel SNR ynoisy(:,jj) = step(hChan,real(y)); % Add AWGN % z(:,jj) = step(h2,complex(ynoisy(:,jj))); % Demodulate. z(:,jj) = pamdemod(complex(ynoisy(:,jj)),M); % Demodulate. % Compute symbol error rate from simulation. berVec(:,jj) = step(hErrorCalc, x, z(:,jj)); end % 3. Plot empirical results, in same figure. hold on; semilogy(EbNo,berVec(1,:),'b.'); legend('Theoretical SER','Empirical SER'); title('Comparing Theoretical and Empirical Error Rates'); hold off; ```

This example produces a plot like the one in the following figure. Your plot might vary because the simulation uses random numbers.

Error Rate Plots

Section Overview

Error rate plots provide a visual way to examine the performance of a communication system, and they are often included in publications. This section mentions some of the tools you can use to create error rate plots, modify them to suit your needs, and do curve fitting on error rate data. It also provides an example of curve fitting. For more detailed discussions about the more general plotting capabilities in MATLAB®, see the MATLAB documentation set.

Creating Error Rate Plots Using `semilogy`

In many error rate plots, the horizontal axis indicates Eb/N0 values in dB and the vertical axis indicates the error rate using a logarithmic (base 10) scale. To see an example of such a plot, as well as the code that creates it, see Comparing Theoretical and Empirical Error Rates. The part of that example that creates the plot uses the `semilogy` function to produce a logarithmic scale on the vertical axis and a linear scale on the horizontal axis.

Other examples that illustrate the use of `semilogy` are in these sections:

Curve Fitting for Error Rate Plots

Curve fitting is useful when you have a small or imperfect data set but want to plot a smooth curve for presentation purposes. The `berfit` function in Communications Toolbox offers curve-fitting capabilities that are well suited to the situation when the empirical data describes error rates at different Eb/N0 values. This function enables you to

• Customize various relevant aspects of the curve-fitting process, such as the type of closed-form function (from a list of preset choices) used to generate the fit.

• Plot empirical data along with a curve that `berfit` fits to the data.

• Interpolate points on the fitted curve between Eb/N0 values in your empirical data set to make the plot smoother looking.

• Collect relevant information about the fit, such as the numerical values of points along the fitted curve and the coefficients of the fit expression.

Note

The `berfit` function is intended for curve fitting or interpolation, not extrapolation. Extrapolating BER data beyond an order of magnitude below the smallest empirical BER value is inherently unreliable.

For a full list of inputs and outputs for `berfit`, see its reference page.

Example: Curve Fitting for an Error Rate Plot

This example simulates a simple DBPSK (differential binary phase shift keying) communication system and plots error rate data for a series of Eb/N0 values. It uses the `berfit` function to fit a curve to the somewhat rough set of empirical error rates. Because the example is long, this discussion presents it in multiple steps:

Setting Up Parameters for the Simulation.  The first step in the example sets up the parameters to be used during the simulation. Parameters include the range of Eb/N0 values to consider and the minimum number of errors that must occur before the simulation computes an error rate for that Eb/N0 value.

Note

For most applications, you should base an error rate computation on a larger number of errors than is used here (for instance, you might change `numerrmin` to `100` in the code below). However, this example uses a small number of errors merely to illustrate how curve fitting can smooth out a rough data set.

```% Set up initial parameters. siglen = 100000; % Number of bits in each trial M = 2; % DBPSK is binary. % DBPSK modulation and demodulation System objects hMod = comm.DBPSKModulator; hDemod = comm.DBPSKDemodulator; % AWGNChannel System object hChan = comm.AWGNChannel('NoiseMethod', 'Signal to noise ratio (SNR)'); % ErrorRate calculator System object to compare decoded symbols to the % original transmitted symbols. hErrorCalc = comm.ErrorRate; EbNomin = 0; EbNomax = 9; % EbNo range, in dB numerrmin = 5; % Compute BER only after 5 errors occur. EbNovec = EbNomin:1:EbNomax; % Vector of EbNo values numEbNos = length(EbNovec); % Number of EbNo values % Preallocate space for certain data. ber = zeros(1,numEbNos); % final BER values berVec = zeros(3,numEbNos); % Updated BER values intv = cell(1,numEbNos); % Cell array of confidence intervals```

Simulating the System Using a Loop.  The next step in the example is to use a `for` loop to vary the Eb/N0 value (denoted by `EbNo` in the code) and simulate the communication system for each value. The inner `while` loop ensures that the simulation continues to use a given `EbNo` value until at least the predefined minimum number of errors has occurred. When the system is very noisy, this requires only one pass through the `while` loop, but in other cases, this requires multiple passes.

The communication system simulation uses these toolbox functions:

• `randi` to generate a random message sequence

• `dpskmod` to perform DBPSK modulation

• `awgn` to model a channel with additive white Gaussian noise

• `dpskdemod` to perform DBPSK demodulation

• `biterr` to compute the number of errors for a given pass through the `while` loop

• `berconfint` to compute the final error rate and confidence interval for a given value of `EbNo`

As the example progresses through the `for` loop, it collects data for later use in curve fitting and plotting:

• `ber`, a vector containing the bit error rates for the series of `EbNo` values.

• `intv`, a cell array containing the confidence intervals for the series of `EbNo` values. Each entry in `intv` is a two-element vector that gives the endpoints of the interval.

```% Loop over the vector of EbNo values. berVec = zeros(3,numEbNos); % Reset for jj = 1:numEbNos EbNo = EbNovec(jj); snr = EbNo; % Because of binary modulation reset(hErrorCalc) hChan.SNR = snr; % Assign Channel SNR % Simulate until numerrmin errors occur. while (berVec(2,jj) < numerrmin) msg = randi([0,M-1], siglen, 1); % Generate message sequence. txsig = step(hMod, msg); % Modulate. hChan.SignalPower = (txsig'*txsig)/length(txsig); % Calculate and % assign signal power rxsig = step(hChan,txsig); % Add noise. decodmsg = step(hDemod, rxsig); % Demodulate. if (berVec(2,jj)==0) % The first symbol of a differentially encoded transmission % is discarded. berVec(:,jj) = step(hErrorCalc, msg(2:end),decodmsg(2:end)); else berVec(:,jj) = step(hErrorCalc, msg, decodmsg); end end % Error rate and 98% confidence interval for this EbNo value [ber(jj), intv1] = berconfint(berVec(2,jj),berVec(3,jj)-1,.98); intv{jj} = intv1; % Store in cell array for later use. disp(['EbNo = ' num2str(EbNo) ' dB, ' num2str(berVec(2,jj)) ... ' errors, BER = ' num2str(ber(jj))]) end```

This part of the example displays output in the Command Window as it progresses through the `for` loop. Your exact output might be different, because this example uses random numbers.

```EbNo = 0 dB, 189 errors, BER = 0.18919 EbNo = 1 dB, 139 errors, BER = 0.13914 EbNo = 2 dB, 105 errors, BER = 0.10511 EbNo = 3 dB, 66 errors, BER = 0.066066 EbNo = 4 dB, 40 errors, BER = 0.04004 EbNo = 5 dB, 18 errors, BER = 0.018018 EbNo = 6 dB, 6 errors, BER = 0.006006 EbNo = 7 dB, 11 errors, BER = 0.0055028 EbNo = 8 dB, 5 errors, BER = 0.00071439 EbNo = 9 dB, 5 errors, BER = 0.00022728 EbNo = 10 dB, 5 errors, BER = 1.006e-005 ```

Plotting the Empirical Results and the Fitted Curve

The final part of this example fits a curve to the BER data collected from the simulation loop. It also plots error bars using the output from the `berconfint` function.

```% Use BERFIT to plot the best fitted curve, % interpolating to get a smooth plot. fitEbNo = EbNomin:0.25:EbNomax; % Interpolation values berfit(EbNovec,ber,fitEbNo,[],'exp'); % Also plot confidence intervals. hold on; for jj=1:numEbNos semilogy([EbNovec(jj) EbNovec(jj)],intv{jj},'g-+'); end hold off;```

BERTool

The command `bertool` launches the Bit Error Rate Analysis Tool (BERTool) application.

The application enables you to analyze the bit error rate (BER) performance of communications systems. BERTool computes the BER as a function of signal-to-noise ratio. It analyzes performance either with Monte-Carlo simulations of MATLAB functions and Simulink® models or with theoretical closed-form expressions for selected types of communication systems.

Using BERTool you can:

• Generate BER data for a communication system using

• Closed-form expressions for theoretical BER performance of selected types of communication systems.

• The semianalytic technique.

• Simulations contained in MATLAB simulation functions or Simulink models. After you create a function or model that simulates the system, BERTool iterates over your choice of Eb/N0 values and collects the results.

• Plot one or more BER data sets on a single set of axes. For example, you can graphically compare simulation data with theoretical results or simulation data from a series of similar models of a communication system.

• Fit a curve to a set of simulation data.

• Send BER data to the MATLAB workspace or to a file for any further processing you might want to perform.

Note

BERTool is designed for analyzing bit error rates only, not symbol error rates, word error rates, or other types of error rates. If, for example, your simulation computes a symbol error rate (SER), convert the SER to a BER before using the simulation with BERTool.

The following sections describe the Bit Error Rate Analysis Tool (BERTool) and provide examples showing how to use its GUI.

Start BERTool

To open BERTool, type

```bertool ```

The BERTool Environment

Components of BERTool

• A data viewer at the top. It is initially empty.

After you instruct BERTool to generate one or more BER data sets, they appear in the data viewer. An example that shows how data sets look in the data viewer is in Example: Using a MATLAB Simulation with BERTool.

• A set of tabs on the bottom. Labeled Theoretical, Semianalytic, and Monte Carlo, the tabs correspond to the different methods by which BERTool can generate BER data.

Note

When using BERTool to compare theoretical results and Monte Carlo results, the Simulink model provided must model exactly the system defined by the parameters on the Theoretical tab.

To learn more about each of the methods, see

• A separate BER Figure window, which displays some or all of the BER data sets that are listed in the data viewer. BERTool opens the BER Figure window after it has at least one data set to display, so you do not see the BER Figure window when you first open BERTool. For an example of how the BER Figure window looks, see Example: Using the Theoretical Tab in BERTool.

Interaction Among BERTool Components.  The components of BERTool act as one integrated tool. These behaviors reflect their integration:

• If you select a data set in the data viewer, BERTool reconfigures the tabs to reflect the parameters associated with that data set and also highlights the corresponding data in the BER Figure window. This is useful if the data viewer displays multiple data sets and you want to recall the meaning and origin of each data set.

• If you click data plotted in the BER Figure window, BERTool reconfigures the tabs to reflect the parameters associated with that data and also highlights the corresponding data set in the data viewer.

Note

You cannot click on a data point while BERTool is generating Monte Carlo simulation results. You must wait until the tool generates all data points before clicking for more information.

• If you configure the Semianalytic or Theoretical tab in a way that is already reflected in an existing data set, BERTool highlights that data set in the data viewer. This prevents BERTool from duplicating its computations and its entries in the data viewer, while still showing you the results that you requested.

• If you close the BER Figure window, then you can reopen it by choosing BER Figure from the Window menu in BERTool.

• If you select options in the data viewer that affect the BER plot, the BER Figure window reflects your selections immediately. Such options relate to data set names, confidence intervals, curve fitting, and the presence or absence of specific data sets in the BER plot.

Note

If you want to observe the integration yourself but do not yet have any data sets in BERTool, then first try the procedure in Example: Using the Theoretical Tab in BERTool.

Note

If you save the BER Figure window using the window's File menu, the resulting file contains the contents of the window but not the BERTool data that led to the plot. To save an entire BERTool session, see Saving a BERTool Session.

Computing Theoretical BERs

Section Overview.  You can use BERTool to generate and analyze theoretical BER data. Theoretical data is useful for comparison with your simulation results. However, closed-form BER expressions exist only for certain kinds of communication systems.

To access the capabilities of BERTool related to theoretical BER data, use the following procedure:

1. Open BERTool, and go to the Theoretical tab.

2. Set the parameters to reflect the system whose performance you want to analyze. Some parameters are visible and active only when other parameters have specific values. See Available Sets of Theoretical BER Data for details.

3. Click .

For an example that shows how to generate and analyze theoretical BER data via BERTool, see Example: Using the Theoretical Tab in BERTool.

Also, Available Sets of Theoretical BER Data indicates which combinations of parameters are available on the Theoretical tab and which underlying functions perform computations.

Example: Using the Theoretical Tab in BERTool.  This example illustrates how to use BERTool to generate and plot theoretical BER data. In particular, the example compares the performance of a communication system that uses an AWGN channel and QAM modulation of different orders.

Running the Theoretical Example

1. Open BERTool, and go to the Theoretical tab.

2. Set the parameters as shown in the following figure.

3. Click .

BERTool creates an entry in the data viewer and plots the data in the BER Figure window. Even though the parameters request that Eb/N0 go up to 18, BERTool plots only those BER values that are at least 10-8. The following figures illustrate this step.

4. Change the Modulation order parameter to `16`, and click .

BERTool creates another entry in the data viewer and plots the new data in the same BER Figure window (not pictured).

5. Change the Modulation order parameter to `64`, and click .

BERTool creates another entry in the data viewer and plots the new data in the same BER Figure window, as shown in the following figures.

6. To recall which value of Modulation order corresponds to a given curve, click the curve. BERTool responds by adjusting the parameters in the Theoretical tab to reflect the values that correspond to that curve.

7. To remove the last curve from the plot (but not from the data viewer), clear the check box in the last entry of the data viewer in the Plot column. To restore the curve to the plot, select the check box again.

Available Sets of Theoretical BER Data.  BERTool can generate a large set of theoretical bit-error rates, but not all combinations of parameters are currently supported. The Theoretical tab adjusts itself to your choices, so that the combination of parameters is always valid. You can set the Modulation order parameter by selecting a choice from the menu or by typing a value in the field. The Normalized timing error must be between 0 and 0.5.

BERTool assumes that Gray coding is used for all modulations.

For QAM, when ${\mathrm{log}}_{2}M$ is odd (M being the modulation order), a rectangular constellation is assumed.

Combinations of Parameters for AWGN Channel Systems

The following table lists the available sets of theoretical BER data for systems that use an AWGN channel.

ModulationModulation OrderOther Choices
PSK 2, 4Differential or nondifferential encoding.
8, 16, 32, 64, or a higher power of 2
OQPSK4Differential or nondifferential encoding.
DPSK2, 4, 8, 16, 32, 64, or a higher power of 2
PAM 2, 4, 8, 16, 32, 64, or a higher power of 2
QAM 4, 8, 16, 32, 64, 128, 256, 512, 1024, or a higher power of 2
FSK2Orthogonal or nonorthogonal; `Coherent` or `Noncoherent` demodulation.
4, 8, 16, 32, or a higher power of 2Orthogonal; `Coherent` demodulation.
4, 8, 16, 32, or 64Orthogonal; `Noncoherent` demodulation.
MSK2`Coherent` conventional or precoded MSK; `Noncoherent` precoded MSK.
CPFSK2, 4, 8, 16, or a higher power of 2`Modulation index` > 0.

BER results are also available for the following:

• block and convolutional coding with hard-decision decoding for all modulations except CPFSK

• block coding with soft-decision decoding for all binary modulations (including 4-PSK and 4-QAM) except CPFSK, noncoherent non-orthogonal FSK, and noncoherent MSK

• convolutional coding with soft-decision decoding for all binary modulations (including 4-PSK and 4-QAM) except CPFSK

• uncoded nondifferentially-encoded 2-PSK with synchronization errors

For more information about specific combinations of parameters, including bibliographic references that contain closed-form expressions, see the reference pages for the following functions:

Combinations of Parameters for Rayleigh and Rician Channel Systems

The following table lists the available sets of theoretical BER data for systems that use a Rayleigh or Rician channel.

When diversity is used, the SNR on each diversity branch is derived from the SNR at the input of the channel (`EbNo`) divided by the diversity order.

ModulationModulation OrderOther Choices
PSK2

Differential or nondifferential encoding

```Diversity order``` ≧1

In the case of nondifferential encoding, diversity order being 1, and Rician fading, a value for RMS phase noise (in radians) can be specified.

4, 8, 16, 32, 64, or a higher power of 2`Diversity order` ≧1
OQPSK4`Diversity order` ≧1
DPSK2, 4, 8, 16, 32, 64, or a higher power of 2`Diversity order` ≧1
PAM2, 4, 8, 16, 32, 64, or a higher power of 2`Diversity order` ≧1
QAM4, 8, 16, 32, 64, 128, 256, 512, 1024, or a higher power of 2`Diversity order` ≧1
FSK2

Correlation coefficient $\in \left[-1,1\right]$.

`Coherent` or `Noncoherent` demodulation

```Diversity order``` ≧1

In the case of a nonzero correlation coefficient and noncoherent demodulation, the diversity order is 1 only.

4, 8, 16, 32, or a higher power of 2`Noncoherent` demodulation only. `Diversity order` ≧1

For more information about specific combinations of parameters, including bibliographic references that contain closed-form expressions, see the reference page for the `berfading` function.

Using the Semianalytic Technique to Compute BERs

Section Overview.  You can use BERTool to generate and analyze BER data via the semianalytic technique. The semianalytic technique is discussed in Performance Results via the Semianalytic Technique, and When to Use the Semianalytic Technique is particularly relevant as background material.

To access the semianalytic capabilities of BERTool, open the Semianalytic tab.

For further details about how BERTool applies the semianalytic technique, see the reference page for the `semianalytic` function, which BERTool uses to perform computations.

Example: Using the Semianalytic Tab in BERTool.  This example illustrates how BERTool applies the semianalytic technique, using 16-QAM modulation. This example is a variation on the example in Example: Using the Semianalytic Technique, but it is tailored to use BERTool instead of using the `semianalytic` function directly.

Running the Semianalytic Example

1. To set up the transmitted and received signals, run steps 1 through 4 from the code example in Example: Using the Semianalytic Technique. The code is repeated below.

```% Step 1. Generate message signal of length >= M^L. M = 16; % Alphabet size of modulation L = 1; % Length of impulse response of channel msg = [0:M-1 0]; % M-ary message sequence of length > M^L % Step 2. Modulate the message signal using baseband modulation. %hMod = comm.RectangularQAMModulator(M); % Use 16-QAM. %modsig = step(hMod,msg'); % Modulate data modsig = qammod(msg',M); % Modulate data Nsamp = 16; modsig = rectpulse(modsig,Nsamp); % Use rectangular pulse shaping. % Step 3. Apply a transmit filter. txsig = modsig; % No filter in this example % Step 4. Run txsig through a noiseless channel. rxsig = txsig*exp(1i*pi/180); % Static phase offset of 1 degree```
2. Open BERTool and go to the Semianalytic tab.

3. Set parameters as shown in the following figure.

4. Click .

Visible Results of the Semianalytic Example

After you click , BERTool creates a listing for the resulting data in the data viewer.

BERTool plots the data in the BER Figure window.

Procedure for Using the Semianalytic Tab in BERTool.  The procedure below describes how you typically implement the semianalytic technique using BERTool:

1. Generate a message signal containing at least ML symbols, where M is the alphabet size of the modulation and L is the length of the impulse response of the channel in symbols. A common approach is to start with an augmented binary pseudonoise (PN) sequence of total length `(log2M)ML`. An augmented PN sequence is a PN sequence with an extra zero appended, which makes the distribution of ones and zeros equal.

2. Modulate a carrier with the message signal using baseband modulation. Supported modulation types are listed on the reference page for `semianalytic`. Shape the resultant signal with rectangular pulse shaping, using the oversampling factor that you will later use to filter the modulated signal. Store the result of this step as `txsig` for later use.

3. Filter the modulated signal with a transmit filter. This filter is often a square-root raised cosine filter, but you can also use a Butterworth, Bessel, Chebyshev type 1 or 2, elliptic, or more general FIR or IIR filter. If you use a square-root raised cosine filter, use it on the nonoversampled modulated signal and specify the oversampling factor in the filtering function. If you use another filter type, you can apply it to the rectangularly pulse shaped signal.

4. Run the filtered signal through a noiseless channel. This channel can include multipath fading effects, phase shifts, amplifier nonlinearities, quantization, and additional filtering, but it must not include noise. Store the result of this step as `rxsig` for later use.

5. On the Semianalytic tab of BERTool, enter parameters as in the table below.

Parameter NameMeaning
Eb/No rangeA vector that lists the values of Eb/N0 for which you want to collect BER data. The value in this field can be a MATLAB expression or the name of a variable in the MATLAB workspace.
Modulation typeThese parameters describe the modulation scheme you used earlier in this procedure.
Modulation order
Differential encodingThis check box, which is visible and active for MSK and PSK modulation, enables you to choose between differential and nondifferential encoding.
Samples per symbolThe number of samples per symbol in the transmitted signal. This value is also the sampling rate of the transmitted and received signals, in Hz.
Transmitted signalThe `txsig` signal that you generated earlier in this procedure
Received signalThe `rxsig` signal that you generated earlier in this procedure
NumeratorCoefficients of the receiver filter that BERTool applies to the received signal
Denominator

Note

Consistency among the values in the GUI is important. For example, if the signal referenced in the Transmitted signal field was generated using DPSK and you set Modulation type to `MSK`, the results might not be meaningful.

6. Click .

Semianalytic Computations and Results

After you click , BERTool performs these tasks:

• Filters `rxsig` and then determines the error probability of each received signal point by analytically applying the Gaussian noise distribution to each point. BERTool averages the error probabilities over the entire received signal to determine the overall error probability. If the error probability calculated in this way is a symbol error probability, BERTool converts it to a bit error rate, typically by assuming Gray coding. (If the modulation type is DQPSK or cross QAM, the result is an upper bound on the bit error rate rather than the bit error rate itself.)

• Enters the resulting BER data in the data viewer of the BERTool window.

• Plots the resulting BER data in the BER Figure window.

Run MATLAB Simulations

Section Overview.  You can use BERTool in conjunction with your own MATLAB simulation functions to generate and analyze BER data. The MATLAB function simulates the communication system whose performance you want to study. BERTool invokes the simulation for Eb/N0 values that you specify, collects the BER data from the simulation, and creates a plot. BERTool also enables you to easily change the Eb/N0 range and stopping criteria for the simulation.

To learn how to make your own simulation functions compatible with BERTool, see Use Simulation Functions with BERTool.

Example: Using a MATLAB Simulation with BERTool.  This example illustrates how BERTool can run a MATLAB simulation function. The function is `viterbisim`, one of the demonstration files included with Communications Toolbox software.

To run this example, follow these steps:

1. Open BERTool and go to the Monte Carlo tab. (The default parameters depend on whether you have Communications Toolbox software installed. Also note that the BER variable name field applies only to Simulink models.)

2. Set parameters as shown in the following figure.

3. Click .

BERTool runs the simulation function once for each specified value of Eb/N0 and gathers BER data. (While BERTool is busy with this task, it cannot process certain other tasks, including plotting data from the other tabs of the GUI.)

Then BERTool creates a listing in the data viewer.

BERTool plots the data in the BER Figure window.

4. To change the range of Eb/N0 while reducing the number of bits processed in each case, type `[5 5.2 5.3]` in the Eb/No range field, type `1e5` in the Number of bits field, and click .

BERTool runs the simulation function again for each new value of Eb/N0 and gathers new BER data. Then BERTool creates another listing in the data viewer.

BERTool plots the data in the BER Figure window, adjusting the horizontal axis to accommodate the new data.

The two points corresponding to 5 dB from the two data sets are different because the smaller value of Number of bits in the second simulation caused the simulation to end before observing many errors. To learn more about the criteria that BERTool uses for ending simulations, see Varying the Stopping Criteria.

For another example that uses BERTool to run a MATLAB simulation function, see Example: Prepare a Simulation Function for Use with BERTool.

Varying the Stopping Criteria.  When you create a MATLAB simulation function for use with BERTool, you must control the flow so that the simulation ends when it either detects a target number of errors or processes a maximum number of bits, whichever occurs first. To learn more about this requirement, see Requirements for Functions; for an example, see Example: Prepare a Simulation Function for Use with BERTool.

After creating your function, set the target number of errors and the maximum number of bits in the Monte Carlo tab of BERTool.

Typically, a Number of errors value of at least `100` produces an accurate error rate. The Number of bits value prevents the simulation from running too long, especially at large values of Eb/N0. However, if the Number of bits value is so small that the simulation collects very few errors, the error rate might not be accurate. You can use confidence intervals to gauge the accuracy of the error rates that your simulation produces; the larger the confidence interval, the less accurate the computed error rate.

As an example, follow the procedure described in Example: Using a MATLAB Simulation with BERTool and set Confidence Level to `95` for each of the two data sets. The confidence intervals for the second data set are larger than those for the first data set. This is because the second data set uses a small value for Number of bits relative to the communication system properties and the values in Eb/No range, resulting in BER values based on only a small number of observed errors.

Note

You can also use the button in BERTool to stop a series of simulations prematurely, as long as your function is set up to detect and react to the button press.

Plotting Confidence Intervals.  After you run a simulation with BERTool, the resulting data set in the data viewer has an active menu in the Confidence Level column. The default value is `off`, so that the simulation data in the BER Figure window does not show confidence intervals.

To show confidence intervals in the BER Figure window, set Confidence Level to a numerical value: `90%`, `95%`, or `99%`.

The plot in the BER Figure window responds immediately to your choice. A sample plot is below.

For an example that plots confidence intervals for a Simulink simulation, see Example: Using a Simulink Model with BERTool.

To find confidence intervals for levels not listed in the Confidence Level menu, use the `berconfint` function.

Fitting BER Points to a Curve.  After you run a simulation with BERTool, the BER Figure window plots individual BER data points. To fit a curve to a data set that contains at least four points, select the box in the Fit column of the data viewer.

The plot in the BER Figure window responds immediately to your choice. A sample plot is below.

For an example that performs curve fitting for data from a Simulink simulation and generates the plot shown above, see Example: Using a Simulink Model with BERTool. For an example that performs curve fitting for data from a MATLAB simulation function, see Example: Prepare a Simulation Function for Use with BERTool.

For greater flexibility in the process of fitting a curve to BER data, use the `berfit` function.

Use Simulation Functions with BERTool

Requirements for Functions.  When you create a MATLAB function for use with BERTool, ensure the function interacts properly with the GUI. This section describes the inputs, outputs, and basic operation of a BERTool-compatible function.

Input Arguments

BERTool evaluates your entries in fields of the GUI and passes data to the function as these input arguments, in sequence:

• One value from the Eb/No range vector each time BERTool invokes the simulation function

• The Number of errors value

• The Number of bits value

Output Arguments

Your simulation function must compute and return these output arguments, in sequence:

• Bit error rate of the simulation

• Number of bits processed when computing the BER

BERTool uses these output arguments when reporting and plotting results.

Simulation Operation

Your simulation function must perform these tasks:

• Simulate the communication system for the Eb/N0 value specified in the first input argument.

• Stop simulating when the number of errors or the number of processed bits equals or exceeds the corresponding threshold specified in the second or third input argument, respectively.

• Detect whether you click in BERTool and abort the simulation in that case.

Template for a Simulation Function.  Use the following template when adapting your code to work with BERTool. You can open it in an editor by entering ```edit bertooltemplate``` in the MATLAB Command Window. Understanding the Template explains the template's key sections, while Using the Template indicates how to use the template with your own simulation code. Alternatively, you can develop your simulation function without using the template, but be sure it satisfies the requirements described in Requirements for Functions.

Note

The template is not yet ready for use with BERTool. You must insert your own simulation code in the places marked ```INSERT YOUR CODE HERE```. For a complete example based on this template, see Example: Prepare a Simulation Function for Use with BERTool.

```function [ber, numBits] = bertooltemplate(EbNo, maxNumErrs, maxNumBits) % Import Java class for BERTool. import com.mathworks.toolbox.comm.BERTool; % Initialize variables related to exit criteria. berVec = zeros(3,1); % Updated BER values % --- Set up parameters. --- % --- INSERT YOUR CODE HERE. % Simulate until number of errors exceeds maxNumErrs % or number of bits processed exceeds maxNumBits. while((berVec(2) < maxNumErrs) && (berVec(3) < maxNumBits)) % Check if the user clicked the Stop button of BERTool. if (BERTool.getSimulationStop) break; end % --- Proceed with simulation. % --- Be sure to update totErr and numBits. % --- INSERT YOUR CODE HERE. end % End of loop % Assign values to the output variables. ber = berVec(1); numBits = berVec(3);```
Understanding the Template.

From studying the code in the function template, observe how the function either satisfies the requirements listed in Requirements for Functions or indicates where your own insertions of code should do so. In particular,

• The function has appropriate input and output arguments.

• The function includes a placeholder for code that simulates a system for the given Eb/N0 value.

• The function uses a loop structure to stop simulating when the number of errors exceeds `maxNumErrs` or the number of bits exceeds `maxNumBits`, whichever occurs first.

Note

Although the `while` statement of the loop describes the exit criteria, your own code inserted into the section marked ```Proceed with simulation``` must compute the number of errors and the number of bits. If you do not perform these computations in your own code, clicking is the only way to terminate the loop.

• In each iteration of the loop, the function detects when the user clicks in BERTool.

Using the Template.

Here is a procedure for using the template with your own simulation code:

1. Determine the setup tasks you must perform. For example, you might want to initialize variables containing the modulation alphabet size, filter coefficients, a convolutional coding trellis, or the states of a convolutional interleaver. Place the code for these setup tasks in the template section marked `Set up parameters`.

2. Determine the core simulation tasks, assuming that all setup work has already been performed. For example, these tasks might include error-control coding, modulation/demodulation, and channel modeling. Place the code for these core simulation tasks in the template section marked ```Proceed with simulation```.

3. Also in the template section marked ```Proceed with simulation```, include code that updates the values of `totErr` and `numBits`. The quantity `totErr` represents the number of errors observed so far. The quantity `numBits` represents the number of bits processed so far. The computations to update these variables depend on how your core simulation tasks work.

Note

Updating the numbers of errors and bits is important for ensuring that the loop terminates. However, if you accidentally create an infinite loop early in your development work using the function template, click in BERTool to abort the simulation.

4. Omit any setup code that initializes `EbNo`, `maxNumErrs`, or `maxNumBits`, because BERTool passes these quantities to the function as input arguments after evaluating the data entered in the GUI.

5. Adjust your code or the template's code as necessary to use consistent variable names and meanings. For example, if your original code uses a variable called `ebn0` and the template's function declaration (first line) uses the variable name `EbNo`, you must change one of the names so they match. As another example, if your original code uses SNR instead of Eb/N0, you must convert quantities appropriately.

Example: Prepare a Simulation Function for Use with BERTool.  This section adapts the function template given in Template for a Simulation Function.

Preparing the Function

To prepare the function for use with BERTool, follow these steps:

1. Copy the template from Template for a Simulation Function into a new MATLAB file in the MATLAB Editor. Save it in a folder on your MATLAB path using the file name `bertool_simfcn`.

2. From the original example, the following lines are setup tasks. They are modified from the original example to rely on the input arguments that BERTool provides to the function, instead of defining variables such as `EbNovec` and `numerrmin` directly.

```% Set up initial parameters. siglen = 1000; % Number of bits in each trial M = 2; % DBPSK is binary. % DBPSK modulation and demodulation System objects hMod = comm.DBPSKModulator; hDemod = comm.DBPSKDemodulator; % AWGNChannel System object hChan = comm.AWGNChannel('NoiseMethod', 'Signal to noise ratio (SNR)'); % ErrorRate calculator System object to compare decoded symbols to the % original transmitted symbols. hErrorCalc = comm.ErrorRate; snr = EbNo; % Because of binary modulation hChan.SNR = snr; %Assign Channel SNR```

Place these lines of code in the template section marked `Set up parameters`.

3. From the original example, the following lines are the core simulation tasks, after all setup work has been performed.

```msg = randi([0,M-1], siglen, 1); % Generate message sequence. txsig = step(hMod, msg); % Modulate. hChan.SignalPower = (txsig'*txsig)/length(txsig); % Calculate and % assign signal power rxsig = step(hChan,txsig); % Add noise. decodmsg = step(hDemod, rxsig); % Demodulate. berVec = step(hErrorCalc, msg, decodmsg); % Calculate BER```

Place the code for these core simulation tasks in the template section marked `Proceed with simulation`.

The `bertool_simfcn` function is now compatible with BERTool. Note that unlike the original example, the function here does not initialize `EbNovec`, define `EbNo` as a scalar, or use `numerrmin` as the target number of errors; this is because BERTool provides input arguments for similar quantities. The `bertool_simfcn` function also excludes code related to plotting, curve fitting, and confidence intervals in the original example because BERTool enables you to do similar tasks interactively without writing code.

Using the Prepared Function

To use `bertool_simfcn` in conjunction with BERTool, continue the example by following these steps:

1. Open BERTool and go to the Monte Carlo tab.

2. Set parameters on the Monte Carlo tab as shown in the following figure.

3. Click .

BERTool spends some time computing results and then plots them. They do not appear to fall along a smooth curve because the simulation required only five errors for each value in `EbNo`.

4. To fit a curve to the series of points in the BER Figure window, select the box next to Fit in the data viewer.

BERTool plots the curve, as shown in the following figure.

Run Simulink Simulations

Section Overview.  You can use BERTool in conjunction with Simulink models to generate and analyze BER data. The Simulink model simulates the communication system whose performance you want to study, while BERTool manages a series of simulations using the model and collects the BER data.

Note

To use Simulink models within BERTool, you must have a Simulink license. Communications Toolbox software is highly recommended. The rest of this section assumes you have a license for both Simulink and Communications Toolbox applications.

To access the capabilities of BERTool related to Simulink models, open the Monte Carlo tab.

For further details about confidence intervals and curve fitting for simulation data, see Plotting Confidence Intervals and Fitting BER Points to a Curve, respectively.

Example: Using a Simulink Model with BERTool.  This example illustrates how BERTool can manage a series of simulations of a Simulink model, and how you can vary the plot. The model is `commgraycode`, one of the demonstration models included with Communications Toolbox software. The example assumes that you have Communications Toolbox software installed.

To run this example, follow these steps:

1. Open BERTool and go to the Monte Carlo tab. The model's file name, `commgraycode.mdl`, appears as the Simulation MATLAB file or Simulink model parameter. (If `viterbisim.m` appears there, select to indicate that Communications Toolbox software is installed.)

2. Click .

BERTool loads the model into memory (which in turn initializes several variables in the MATLAB workspace), runs the simulation once for each value of Eb/N0, and gathers BER data. BERTool creates a listing in the data viewer.

BERTool plots the data in the BER Figure window.

3. To fit a curve to the series of points in the BER Figure window, select the box next to Fit in the data viewer.

BERTool plots the curve, as below.

4. To indicate the 99% confidence interval around each point in the simulation data, set Confidence Level to `99%` in the data viewer.

BERTool displays error bars to represent the confidence intervals, as below.

Another example that uses BERTool to manage a series of Simulink simulations is in Example: Prepare a Model for Use with BERTool.

Varying the Stopping Criteria.  When you create a Simulink model for use with BERTool, you must set it up so that the simulation ends when it either detects a target number of errors or processes a maximum number of bits, whichever occurs first. To learn more about this requirement, see Requirements for Models; for an example, see Example: Prepare a Model for Use with BERTool.

After creating your Simulink model, set the target number of errors and the maximum number of bits in the Monte Carlo tab of BERTool.

Typically, a Number of errors value of at least `100` produces an accurate error rate. The Number of bits value prevents the simulation from running too long, especially at large values of Eb/N0. However, if the Number of bits value is so small that the simulation collects very few errors, the error rate might not be accurate. You can use confidence intervals to gauge the accuracy of the error rates that your simulation produces; the larger the confidence interval, the less accurate the computed error rate.

You can also click in BERTool to stop a series of simulations prematurely.

Use Simulink Models with BERTool

Requirements for Models.  A Simulink model must satisfy these requirements before you can use it with BERTool, where the case-sensitive variable names must be exactly as shown below:

• The channel block must use the variable `EbNo` rather than a hard-coded value for Eb/N0.

• The simulation must stop when the error count reaches the value of the variable `maxNumErrs` or when the number of processed bits reaches the value of the variable `maxNumBits`, whichever occurs first.

You can configure the Error Rate Calculation block in Communications Toolbox software to stop the simulation based on such criteria.

• The simulation must send the final error rate data to the MATLAB workspace as a variable whose name you enter in the BER variable name field in BERTool. The variable must be a three-element vector that lists the BER, the number of bit errors, and the number of processed bits.

This three-element vector format is supported by the Error Rate Calculation block.

Tips for Preparing Models.  Here are some tips for preparing a Simulink model for use with BERTool:

• To avoid using an undefined variable name in the dialog box for a Simulink block in the steps that follow, set up variables in the MATLAB workspace using a command such as the one below.

```EbNo = 0; maxNumErrs = 100; maxNumBits = 1e8; ```

You might also want to put the same command in the model's preload function callback, to initialize the variables if you reopen the model in a future MATLAB session.

When you use BERTool, it provides the actual values based on what you enter in the GUI, so the initial values above are somewhat arbitrary.

• To model the channel, use the AWGN Channel block in Communications Toolbox software with these parameters:

• Mode = ```Signal to noise ratio (Eb/No)```

• Eb/No = `EbNo`

• To compute the error rate, use the Error Rate Calculation block in Communications Toolbox software with these parameters:

• Check Stop simulation.

• Target number of errors = `maxNumErrs`

• Maximum number of symbols = `maxNumBits`

• To send data from the Error Rate Calculation block to the MATLAB workspace, set Output data to `Port`, attach a To Workspace block, and set the latter block's Limit data points to last parameter to `1`. The Variable name parameter in the To Workspace block must match the value you enter in the BER variable name field of BERTool.

Tip

More than one To Workspace block is available. Be sure to select To Workspace from the DSP System Toolbox™ / Sinks sublibrary.

• If your model computes a symbol error rate instead of a bit error rate, use the Integer to Bit Converter block in Communications Toolbox software to convert symbols to bits.

• Frame-based simulations often run faster than sample-based simulations for the same number of bits processed. The number of errors or number of processed bits might exceed the values you enter in BERTool, because the simulation always processes a fixed amount of data in each frame.

• If you have an existing model that uses the AWGN Channel block using a Mode parameter other than ```Signal to noise ratio (Eb/No)```, you can adapt the block to use the Eb/No mode instead. To learn about how the block's different modes are related to each other, press the AWGN Channel block's button to view the online reference page.

• If your model uses a preload function or other callback to initialize variables in the MATLAB workspace upon loading, make sure before you use the button in BERTool that one of these conditions is met:

• The model is not currently in memory. In this case, BERTool loads the model into memory and runs the callback functions.

• The model is in memory (whether in a window or not), and the variables are intact.

If you clear or overwrite the model's variables and want to restore their values before using the button in BERTool, you can use the `bdclose` function in the MATLAB Command Window to clear the model from memory. This causes BERTool to reload the model after you click . Similarly, if you refresh your workspace by issuing a ```clear all``` or `clear variables` command, you should also clear the model from memory by using `bdclose all`.

Example: Prepare a Model for Use with BERTool.  This example uses a Simulink model that is set up for use with BERTool. The example also illustrates how to compare the BER performance of a Simulink simulation with theoretical BER results. The example assumes that you have Communications Toolbox software installed.

To prepare the model for use with BERTool, follow these steps, using the exact case-sensitive variable names as shown:

1. Open the model by entering the following command in the MATLAB Command Window.

```doc_bpsk ```

2. To initialize parameters in the MATLAB workspace and avoid using undefined variables as block parameters, enter the following command in the MATLAB Command Window.

```EbNo = 0; maxNumErrs = 100; maxNumBits = 1e8; ```
3. To ensure that BERTool uses the correct amount of noise each time it runs the simulation, open the dialog box for the AWGN Channel block by double-clicking the block. Verify that Es/No is set to `EbNo` and click . In this particular model, Es/N0 is equivalent to Eb/N0 because the modulation type is BPSK.

4. To ensure that BERTool uses the correct stopping criteria for each iteration,

• Open the dialog box for the Error Rate Calculation block. Verify that Target number of errors is set to `maxNumErrs`, and that Maximum number of symbols is set to `maxNumBits`. Click .

• The simulation stop time must be set to `Inf`.

5. To enable BERTool to access the BER results that the Error Rate Calculation block computes, the To Workspace block, `BER`, is connected to the output of the Error Rate Calculation block.

Tip

More than one To Workspace block is available. Be sure to select To Workspace from the DSP System Toolbox / Sinks sublibrary.

To use the `doc_bpsk` model with BERTool, follow these steps:

1. Open BERTool and go to the Monte Carlo tab.

2. Set parameters on the Monte Carlo tab as shown in the following figure.

3. Click .

BERTool spends some time computing results and then plots them.

4. To compare these simulation results with theoretical results, go to the Theoretical tab in BERTool and set parameters as shown below.

5. Click .

BERTool plots the theoretical curve in the BER Figure window along with the earlier simulation results.

Manage BER Data

Exporting Data Sets or BERTool Sessions.  BERTool enables you to export individual data sets to the MATLAB workspace or to MAT-files. One option for exporting is convenient for processing the data outside BERTool. For example, to create a highly customized plot using data from BERTool, export the BERTool data set to the MATLAB workspace and use any of the plotting commands in MATLAB. Another option for exporting enables you to reimport the data into BERTool later.

BERTool also enables you to save an entire session, which is useful if your session contains multiple data sets that you want to return to in a later session.

This section describes these capabilities:

Exporting Data Sets

To export an individual data set, follow these steps:

1. In the data viewer, select the data set you want to export.

2. Choose File > Export Data.

3. Set Export to to indicate the format and destination of the data.

1. If you want to reimport the data into BERTool later, you must choose either `Workspace structure` or `MAT-file structure` to create a structure in the MATLAB workspace or a MAT-file, respectively.

A new field called Structure name appears. Set it to the name that you want BERTool to use for the structure it creates.

If you selected ```Workspace structure``` and you want BERTool to use your chosen variable name, even if a variable by that name already exists in the workspace, select Overwrite variables.

2. If you do not need to reimport the data into BERTool later, a convenient way to access the data outside BERTool is to have BERTool create a pair of arrays in the MATLAB workspace. One array contains Eb/N0 values, while the other array contains BER values. To choose this option, set Export to to `Workspace arrays`.

Then type two variable names in the fields under Variable names.

If you want BERTool to use your chosen variable names even if variables by those names already exist in the workspace, select Overwrite variables.

4. Click . If you selected `MAT-file structure`, BERTool prompts you for the path to the MAT-file that you want to create.

To reimport a structure later, see Importing Data Sets.

Examining an Exported Structure

This section briefly describes the contents of the structure that BERTool exports to the workspace or to a MAT-file. The structure's fields are indicated in the table below. The fields that are most relevant for you when you want to manipulate exported data are `paramsEvaled` and `data`.

Name of FieldSignificance
`params`The parameter values in the BERTool GUI, some of which might be invisible and hence irrelevant for computations.
`paramsEvaled`The parameter values that BERTool uses when computing the data set.
`data`The Eb/N0, BER, and number of bits processed.
`dataView`Information about the appearance in the data viewer. Used by BERTool for data reimport.
`cellEditabilities`Indicates whether the data viewer has an active Confidence Level or Fit entry. Used by BERTool for data reimport.

Parameter Fields

The `params` and `paramsEvaled` fields are similar to each other, except that `params` describes the exact state of the GUI whereas `paramsEvaled` indicates the values that are actually used for computations. As an example of the difference, for a theoretical system with an AWGN channel, `params` records but `paramsEvaled` omits a diversity order parameter. The diversity order is not used in the computations because it is relevant only for systems with Rayleigh channels. As another example, if you type `[0:3]+1` in the GUI as the range of Eb/N0 values, `params` indicates `[0:3]+1` while `paramsEvaled` indicates ```1 2 3 4```.

The length and exact contents of `paramsEvaled` depend on the data set because only relevant information appears. If the meaning of the contents of `paramsEvaled` is not clear upon inspection, one way to learn more is to reimport the data set into BERTool and inspect the parameter values that appear in the GUI. To reimport the structure, follow the instructions in Importing Data Sets or BERTool Sessions.

Data Field

If your exported workspace variable is called `ber0`, the field `ber0.data` is a cell array that contains the numerical results in these vectors:

• `ber0.data{1}` lists the Eb/N0 values.

• `ber0.data{2}` lists the BER values corresponding to each of the Eb/N0 values.

• `ber0.data{3}` indicates, for simulation or semianalytic results, how many bits BERTool processed when computing each of the corresponding BER values.

Saving a BERTool Session.

To save an entire BERTool session, follow these steps:

1. Choose File > Save Session.

2. When BERTool prompts you, enter the path to the file that you want to create.

BERTool creates a text file that records all data sets currently in the data viewer, along with the GUI parameters associated with the data sets.

Note

If your BERTool session requires particular workspace variables (such as `txsig` or `rxsig` for the Semianalytic tab), save those separately in a MAT-file using the `save` command in MATLAB.

Importing Data Sets or BERTool Sessions.  BERTool enables you to reimport individual data sets that you previously exported to a structure, or to reload entire sessions that you previously saved. This section describes these capabilities:

To learn more about exporting data sets or saving sessions from BERTool, see Exporting Data Sets or BERTool Sessions.

Importing Data Sets.

To import an individual data set that you previously exported from BERTool to a structure, follow these steps:

1. Choose File > Import Data.

2. Set Import from to either `Workspace structure` or `MAT-file structure`. If you select `Workspace structure`, type the name of the workspace variable in the Structure name field.

3. Click . If you select `MAT-file`, BERTool prompts you to select the file that contains the structure you want to import.

After you dismiss the Data Import dialog box (and the file selection dialog box, in the case of a MAT-file), the data viewer shows the newly imported data set and the BER Figure window plots it.

Opening a Previous BERTool Session

To replace the data sets in the data viewer with data sets from a previous BERTool session, follow these steps:

1. Choose File > Open Session.

Note

If BERTool already contains data sets, it asks you whether you want to save the current session. If you answer no and continue with the loading process, BERTool discards the current session upon opening the new session from the file.

2. When BERTool prompts you, enter the path to the file you want to open. It must be a file that you previously created using the Save Session option in BERTool.

After BERTool reads the session file, the data viewer shows the data sets from the file.

If your BERTool session requires particular workspace variables (such as `txsig` or `rxsig` for the Semianalytic tab) that you saved separately in a MAT-file, you can retrieve them using the `load` command in MATLAB.

Managing Data in the Data Viewer.  The data viewer gives you flexibility to rename and delete data sets, and to reorder columns in the data viewer.

• To rename a data set in the data viewer, double-click its name in the BER Data Set column and type a new name.

• To delete a data set from the data viewer, select it and choose Edit > Delete.

Note

If the data set originated from the Semianalytic or Theoretical tab, BERTool deletes the data without asking for confirmation. You cannot undo this operation.

• To move a column in the data viewer, drag the column's heading to the left or right with the mouse. For example, the image below shows the mouse dragging the BER column to the left of its default position. When you release the mouse button, the columns snap into place.

Error Rate Test Console

The Error Rate Test Console is an object capable of running simulations for communications systems to measure error rate performance.

The Error Rate Test Console is compatible with communications systems created with a specific API defined by the testconsole.SystemBasicAPI class. Within this class definition you define the functionality of a communications system.

You attach a system to the Error Rate Test Console to run simulations and obtain error rate data.

You obtain error rate results at different locations in the system under test, by defining unique test points. Each test point contains a pair of probes that the system uses to log data to the test console. The information you register with the test console specifies how each pair of test probes compares data. For example, in a frame based system, the Error Rate Test Console can compare transmitted and received header bits or transmitted and received data bits. Similarly, it can compare CRC error counts to obtain frame error rates at different points in the system. You can also configure the Error Rate Test Console to compare data in multiple pairs of probes, obtaining multiple error rate results.

You can run simulations with as many test parameters as desired, parse the results, and obtain parametric or surface plots by specifying which parameters act as independent variables.

There are two main tasks associated with using the Error Rate Test Console: Creating a System and Attaching a System to the Error Rate Test Console.

When you run a system that is not attached to an Error Rate Test Console, the system is running in debug mode. Debug mode is useful when evaluating or debugging the code for the system you are designing.

To see a full-scale example on creating a system and running simulations, see Bit Error Rate Simulations For Various Eb/No and Modulation Order Values.

The following sections describe the Error Rate Test Console and its functionality:

Creating a System

You attach a system to the Error Rate Test Console to run simulations and obtain error rate data. When you attach the system under test, you also register specific information to the test console in order to define the system's test inputs, test parameters, and test probes.

Creating a communications system for use with the Error Rate Test Console, involves the following steps.

• Writing a system class, extending the testconsole.SystemBasicAPI class.

• Writing a registration method

• Registration is test related

• Defines items such as test parameters, test probes, and test inputs

• Writing a setup method

• Writing a reset method

• Writing a run method

Methods allows the system to communicate with the test console.

To see the system file, navigate to the following location:

matlab\toolbox\comm\comm\+commtest

Then, enter the following syntax at the MATLAB command line:

`edit MPSKSYSTEM.m`

Writing A Register Method.  Using the `register` method, you register test inputs, test parameters, and test probes to the Error Rate Test Console. You register these items to the Error Rate Test Console using the `registerTestInput`, `registerTestParameter`, and `registerTestProbe` methods.

• Write a `register` method for every communication system you create.

• If you do not implement a `register` method for a system, you can still attach the system to the Error Rate Test Console. While the test console runs the specified number of iterations on the system, you cannot control simulation parameters or retrieve results from the simulation.

Registering Test Inputs

In order to run simulations, the system under test requests test inputs from the Error Rate Test Console. These test inputs provide data, driving simulations for the system under test.

A system under test cannot request a specific input type until you attach it to the Error Rate Test Console. Additionally, the specific input type must be registered to the test console.

Inside the `register` method, you call the `registerTestInput(sys,inputName)` method to register test inputs.

• `sys` represents the handle to a user-defined system object.

• `inputName` represents the name of the input that the system registers. This name must coincide with the name of an available test input in the Error Rate Test Console or an error occurs.

• 'NumTransmissions' - calling the `getInput` method returns the frame length. The system itself is responsible for generating a data frame using a data source.

• 'RandomIntegerSource' - calling the `getInput` method returns a vector of symbols with a length the Error Rate Test Console `FrameLength` property specifies. If the system registers this source type, then it must also register a test parameter named 'M' that corresponds to the modulation order.

Registering Test Parameters

Test parameters are the system parameters for which the Error Rate Test Console obtains simulation results. You specify the sweep range of these parameters using the Error Rate Test Console and obtain simulation results for different system conditions.

The system under test registers a system parameter to the Error Rate Test Console, creating a test parameter. You register a test parameter to the Error Rate Test Console using the `registerTestParameter(sys,name,default,validRange)` method.

• `sys` represents the handle to the user-defined system object

• `name` represents the parameter name that the system registers to the Error Rate Test Console

• `default` specifies the default value of the test parameter – it can be a numeric value or a character vector

• `validRange` specifies a range of input values for the test parameter — it can be a 1x2 vector of numeric values with upper and lower ranges or a cell array of chars (an Enum).

A parameter of type char becomes useful when defining system conditions. For example, in a communications system, a Channel parameter may be defined so that it takes values such as ‘Rayleigh’, ‘Rician’, or ‘AWGN’. Depending on the Channel char value, the system may filter transmitted data through a different channel. This allows the simulation of the system over different channel scenarios.

If the system registers a test parameter named ‘X’ then the system must also contain a readable property named ‘X’. If not, the registration process issues an error. This process ensures that calling the `getTestParameter` method in debug mode returns the value held by the corresponding property.

Registering Test Probes

Test probes log the simulation data the Error Rate Test Console uses for computing test metrics, such as: number of errors, number of transmissions, and error rate. To log data into a probe, your communications system must register the probe to the Error Rate Test Console.

You register a test probe to the Error Rate Test Console using the `registerTestProbe(sys,name,description)` method.

• `sys` represents the handle to the user-defined system object

• `name` represents the name of the test probe

• `description` contains information about the test probes; useful for indicating what the probe is used for. The description input is optional.

You can define an arbitrary number of probes to log test data at several points within the system.

Writing a Setup Method.  The Error Rate Test Console calls the `setup` method at the beginning of simulations for each new sweep point. A sweep point is one of several sets of simulation parameters for which the system will be simulated. Using the `getTestParameter` method of the system under test, the `setup` method requests the current simulation sweep values from the Error Rate Test Console and sets the various system components accordingly. The `setup` method sets the system to the conditions the current test parameter sweep values generate.

Writing a `setup` method for each communication system you create is not necessary. The `setup` method is optional.

Writing a Reset Method.  Use the `reset` method to reset states of various system components, such as: objects, data buffers, or system flags. The Error Rate Test Console calls the `reset` method of the system:

• at the beginning of simulations for a new sweep point. (This condition occurs when you set the `ResetMode` of the Error Rate Test Console to “Reset at new simulation point'.)

• at each simulation iteration. (This condition occurs when you set the `ResetMode` of the Error Rate Test Console to 'Reset at every iteration’.)

Writing a `reset` method for each communication system you create is not mandatory. The `reset` method is optional.

Writing a Run Method.  Write a `run` method for each communication system you create. The `run` method includes the core functionality of the system under test. At each simulation iteration, the Error Rate Test Console calls the `run` method of the system under test.

When designing a communication system, ensure at run time that your system sets components to the current simulation test parameter sweep values. Depending on your unique design, at run time, the communication system:

• requests test inputs from the test console using the `getInput` method

• logs test data to its test probes using the `setTestProbeData` method

• logs user-data to the test console using the `setUserData` method

• Although it is recommended you do this at setup time, the system can also request the current simulation sweep values using the `getTestParameter` method.

Methods Allowing You to Communicate with the Error Rate Test Console at Simulation Run Time

Getting Test Inputs From the Error Rate Test Console.  At simulation time, the communications system you design can request input data to the Error Rate Test Console. To request a particular type of input data, the system under test must register the specific input type to the Error Rate Test Console. The system under test calls getInput(obj,inputName) method to request test inputs to the test console.

• obj represents the handle of the Error Rate Test Console

• inputName represents the input that the system under test gets from the Error Rate Test Console

For an Error Rate Test Console, 'NumTransmissions' or 'RandomDiscreetSource' are acceptable selections for `inputName`.

The system under test provides the following inputs:

• 'NumTransmissions' - calling the `getInput` method returns the frame length. The system itself is responsible for generating a data frame using a data source.

• 'RandomIntegerSource' - calling the `getInput` method returns a vector of symbols with a length the Error Rate Test Console `FrameLength` property specifies. If the system registers this source type, then it must also register a test parameter named 'M' that corresponds to the modulation order.

Getting the Current Simulation Sweep Value of a Registered Test Parameter.  For each simulation iteration, the system under test may require the current simulation sweep values from the registered test parameters. To obtain these values from the Error Rate Test console, the system under test calls the `getTestParameter(sys,name)` method.

Logging Test Data to a Registered Test Probe.  At simulation time, the system under test may log data to a registered test probe using the `setTestProbeData(sys,name,data)` method.

• `sys` represents the handle to the system

• `name` represents the name of a registered test probe

• `data` represents the data the probe logs to the Error Rate Test Console.

Logging User-Defined Data To The Test Console.  At simulation time, the system under test may log user-data to the Error Rate Test Console by calling the `setUserData` method. This user-data passes directly to the specific user-defined metric calculator functions. Log user-data to the Error Rate Test Console as follows:

`setUserData(sys,data)`

• `sys` represents the handle to the system

• `data` represents the data the probe logs to the Error Rate Test Console.

Debug Mode

When you run a system that is not attached to an Error Rate Test Console, the system is running in debug mode. Debug mode is useful when evaluating or debugging the code for the system you are designing.

A system that extends the testconsole.SystemBasicAPI class can run by itself, without the need to attach it to a test console. This scenario is referred to as debug mode. Debug mode is useful when evaluating or debugging the code for the system you are designing. For example, if you define break points when designing your system, you can run the system in debug mode and confirm that the system runs without errors or warnings.

Implementing A Default Input Generator Function For Debug Mode.  If your system registers a test input and calls the `getInput` method at simulation run time then for it to run in debug mode, the system must implement a default input generator function. This method should return an input congruent to the test console.

`input = generateDefaultInput(obj)`

Run Simulations Using the Error Rate Test Console

Running simulations with the Error Rate Test Console involves the following tasks:

• Creating a test console

• Attaching a system

• Defining simulation conditions

• Specifying stop criterion

• Specifying iteration mode

• Specifying reset mode

• Specifying sweep values

• Registering test points

• Running simulations

• Getting results and plotting

Creating a Test Console.  You create a test console in one of the following ways:

• `h = commtest.ErrorRate` returns an error rate test console, h. The error rate test console runs simulations of a system under test to obtain error rates.

• `h = commtest.ErrorRate(sys)` returns an error rate test console, h, with an attached system under test, sys.

• `h = commtest.ErrorRate(sys,'PropertyName',PropertyValue,...)` returns an error rate test console, h, with an attached system under test, sys. Each specified property, 'PropertyName', is set to the specified value, PropertyValue.

• `h = commtest.ErrorRate('PropertyName',PropertyValue,...)` returns an error rate test console, h, with each specified property 'PropertyName', set to the specified value, PropertyValue.

Attaching a System to the Error Rate Test Console.  You attach a system to the Error Rate Test Console to run simulations and obtain error rate data. There are two ways to attach a system to the Error Rate Test Console.

• To attach a system to the Error Rate Test Console, type the following at the MATLAB command line:

`attachSystem(testConsole, mySystem)`

• To attach a system at construction time of an Error Rate Test Console, see Creating a Test Console.

• `mySystem` is the name of the system under test

If system under test A is currently attached to the Error Rate Test Console H1, and you call `attachSystem(H2,A)`, then A detaches from H1 and attaches to Error Rate Test Console H2. This causes system A to display a warning message, stating that it has detached from H1 and attached to H2.

Defining Simulation Conditions

Stop Criterion

The Error Rate Test Console controls the simulation stop criterion using the `SimulationLimitOption` property. You define the criterion to stop a simulation when reaching either a specific number of transmissions or a specific number of errors.

• Setting `SimulationLimitOption` property to 'Number of transmissions’ stops the simulation for each sweep parameter point when the Error Rate Test Console counts the number of transmissions specified in `MaxNumTransmissions`

• Setting `SimulationLimitOption` property to 'Number of errors' stops the simulation for a sweep parameter point when the Error Rate Test Console counts the number of errors specified in `MinNumErrors`. The `ErrorCountTestPoint` property should be set to the name of the registered test point containing the error count being compared to the `MinNumErrors` property to control the simulation length.

• Setting `SimulationLimitOption` property to 'Number of errors or transmissions’ stops the simulation for each sweep parameter point when the Error Rate Test Console completes the number of transmissions specified in `MaxNumTransmissions` or when obtaining the number of errors specified in `MinNumErrors`, whichever happens first.

Iteration Mode

The iteration mode defines the way that the Error Rate Test Console combines test parameter sweep values to perform simulations. The `IterationMode` property of the test console controls this behavior.

• Setting `IterationMode` to 'Combinatorial' performs simulations for all possible combinations of registered test parameter sweep values.

• Setting `IterationMode` to 'Indexed' performs simulations for all indexed sweep value sets. The ith sweep value set consists of the ith element from every sweep value vector for each registered test parameter. All sweep value vectors must be of equal length, with the exception of those that are unit length.

Specifying and Obtaining Sweep Values

The Error Rate Test Console performs simulations for a set of sweep points, which consist of combinations of sweep values specified for each registered test parameter. The way the test console forms sweep points depends on the `IterationMode` settings. The iteration mode defines the way in which sweep values for different test parameters combine to produce simulation results.

Using the `setTestParameterSweepValues` method, you specify sweep values for each test parameter that the system under test registers to the Error Rate Test Console.

`setTestParameterSweepValues(obj,name,value)`

where

• `obj` represents handle to the Error Rate Test Console.

• `name` represents the name of the registered test parameter (this name must correspond to a test parameter registered by the system under test or an error occurs)

• `value` represents the sweep values you specify for the test parameter named ‘name’. Depending on the application, sweep values may be a vector with numeric values or a cell array of characters. The test console issues an error if you attempt to set sweep values that are out of the specified valid range for a test parameter (valid ranges are defined by the system when attaching to a test console).

You obtain the list of test parameters registered by the system under test using the `info` method of the Error Rate Test Console.

You obtain the sweep values for a specific registered test parameter using the `getTestParameterSweepValues` method of the Error Rate Test Console. You obtain the valid ranges of a specific registered test parameter using the `getTestParameterValidRanges` method of the Error Rate Test Console.

If you do not specify sweep values for a particular test parameter, the Error Rate Test Console. always uses the parameter’s default value to run simulations. (Default values for test parameters are defined by the system when attaching to a test console at registration time.)

Reset Mode

You control the reset criteria for the system under test using the `SystemResetMode` property of the Error Rate Test Console.

• Setting `SystemResetMode` to 'Reset at new simulation point' resets the system under test resets at the beginning of iterations for a new simulation sweep point.

• Setting `SystemResetMode` to 'Reset at every iteration' resets the system under test at every simulation.

Registering a Test Point.  You obtain error rate results at different points in the system under test, by defining unique test points. Each test point groups a pair of probes that the system under test uses to log data and the Error Rate Test Console uses to obtain data. In order to create a test point for a pair of probes, the probes must be registered to the Error Rate Test Console.

The Error Rate Test Console calculates error rates by comparing the data available in a pair of probes.

Test points hold error and transmission counts for each sweep point simulation.

The info method displays which test points are registered to the test console.

`registerTestPoint(h, name, actprobe, expprobe)` registers a new test point with name, name, to the error rate test console, `h`.

The test point must contain a pair of registered test probes `actprobe` and `expprobe` whose data will be compared to obtain error rate values. `actprobe` contains actual data, and `expprobe` contains expected data. Error rates will be calculated using a default error rate calculator function that simply performs one-to-one comparisons of the data vectors available in the probes.

`registerTestPoint(h, name, actprobe, expprobe, fcnhandle)` adds a function handle, `fcnhandle`, that points to a user-defined error calculator function that will be used instead of the default function to compare the data in probes `actprobe` and `exprobe`, to obtain error rate results.

Writing a user-defined error calculator function

A user-defined error calculator function must comply with the following syntax:

`[ecnt tcnt] = functionName(act, exp, udata)` where `ecnt` output corresponds to the error count, and `tcnt` output is the number of transmissions used to obtain the error count. Inputs `act` and `exp` correspond to actual and expected data. The error rate test console will set these inputs to the data available in the pair of test point probes `actprobe` and `expprobe` previously mentioned. `udata` is a user data input that the system under test may pass to the test console at run time using the `setUserData` method. `udata` may contain data necessary to compute errors such as delays, data buffers, and so on. The error rate test console will pass the same user data logged by the system under test to the error calculator functions of all the registered test points. You call the info method to see the names of the registered test points and the error rate calculator functions associated with them, and to see the names of the registered test probes.

Getting Test Information.  Returns a report of the current test console settings.

info(h) displays:

• Test console name

• System under test name

• Available test inputs

• Registered test inputs

• Registered test parameters

• Registered test probes

• Registered test points

• Metric calculator functions

• Test metrics

Running a Simulation.  You run simulations by calling the `run` method of the Error Rate Test Console.

`run(testConsole)` runs a specified number of iterations of an attached system under test for a specified set of parameter values. If a Parallel Computing Toolbox™ license is available and a parpool is open, then you can distribute the iterations among the available number of workers.

Getting Results and Plotting Data.  Call the `getResults` method of the error rate test console to obtain test results.

`r = getResults(testConsole)`returns the simulation results, r, for the test console, testConsole. r is an object of type `testconsole.Results` and contains the simulation data for all the registered test points.

You call the `getData` method of results object r to get simulation results data. You call the plot and `semilogy` method of the results object r to plot results data. See `testconsole.Results` for more information.

Parsing and Plotting Results for Multiple Parameter Simulations.  The DPSKModulationTester.mat file contains an Error Rate Test Console with a DPSK modulation system. This system defines three test parameters:

• The bit energy to noise power spectral density ratio, `EbNo` (in decibels)

• The modulation order, `M`

• The maximum Doppler shift, `MaxDopplerShift` (in hertz)

These parameters have the following sweep values:

• `EbNo` = [-2:4] dB

• `M` = [2 4 8 16]

• `MaxDopplerShift` = [0 0.001 0.09] Hz

Because simulations generally take a long time to run, a simulation was run offline. DPSKModulationTester.mat file contains a saved Error Rate Test Console with the saved results. The simulations were run to obtain at least 2500 errors and 5e6 frame transmissions per simulation point.

Load the simulation results by entering the following at the MATLAB command line:

`load DPSKModulationTester.mat`

To parse and plot results for multiple parameter simulations, perform the following steps:

1. Using the `getSweepParameterValues` method, display the sweep parameter values used in the simulation for each test parameter. For example, you display the sweep values for `MaxDopplerShift` by entering:

``getTestParameterSweepValues(testConsole,'MaxDopplerShift') ``

MATLAB returns the following result:

```ans = 0 0.0010 0.0900```
2. Get the results object that parses and plots simulation results by entering the following at the command line:

``DPSKResults = getResults(testConsole)``

MATLAB returns the following result:

```DPSKResults = TestConsoleName: 'commtest.ErrorRate' SystemUnderTestName: 'commexample.DPSKModulation' IterationMode: 'Combinatorial' TestPoint: 'BitErrors' Metric: 'ErrorRate' TestParameter1: 'EbNo' TestParameter2: 'None'```
3. Use the `setParsingValues` method to enable the plotting of error rate results versus Eb/No for a modulation order of 4 and maximum Doppler shift of 0.001 Hz. To do so, enter the following:.

``setParsingValues(DPSKResults,'M',4,'MaxDopplerShift',0.001)``
4. Use the` getParsingValues` method to verify the current parsing values settings:

``getParsingValues(DPSKResults)``

MATLAB returns the following:

```ans = EbNo: -2 M: 4 MaxDopplerShift: 1.0000e-003```

If not specified, the parsing value for a test parameter defaults to its first sweep value. In this example, the first sweep value for EbNo equals -2 dB. However, in this example, `TestParameter1` is set to EbNo; therefore, the Error Rate Test Console plots results for all EbNo sweep values, not just for the value listed by the `getParsingValues` method.

5. Obtain a log-scale plot of bit error rate versus Eb/No for a modulation order of 4 and a maximum Doppler shift of 0.001 Hz:

``semilogy(DPSKResults)``

MATLAB generates the following figure.

6. Set the `TestParameter2` property of the results object to 'MaxDopplerShift'. This setting enables the plotting of multiple error rate curves versus Eb/No for each sweep value of the maximum Doppler shift.

`DPSKResults.TestParameter2 = 'MaxDopplerShift';`

7. Obtain log-scale plots of bit error rate versus Eb/No for a modulation order of 2 at each of the maximum Doppler shift sweep values.

``````setParsingValues(DPSKResults,'M',2) semilogy(DPSKResults) ``````

MATLAB generates the following figure.

8. Obtain the same type of curves as in the previous step, but now for a modulation order of 16.

``````setParsingValues(DPSKResults,'M',16) semilogy(DPSKResults) ``````

MATLAB generates the following figure.

9. Obtain error rate plots versus the modulation order for each Eb/No sweep value by setting `TestParameter1` equal to M and `TestParameter2` equal to EbNo. You can plot the results for the case when the maximum Doppler shift is 0 Hz by using the `setParsingValues` method:

``````DPSKResults.TestParameter1 = 'M'; DPSKResults.TestParameter2 = 'EbNo'; setParsingValues(DPSKResults, 'MaxDopplerShift',0) semilogy(DPSKResults) ``````

MATLAB generates the following figure.

10. Obtain a data matrix with the bit error rate values previously plotted by entering the following:

``BERMatrix = getData(DPSKResults)``

MATLAB returns the following result:

```BERMatrix = Columns 1 through 7 0.2660 0.2467 0.2258 0.2049 0.1837 0.1628 0.1418 0.3076 0.2889 0.2702 0.2504 0.2296 0.2082 0.1871 0.3510 0.3384 0.3258 0.3120 0.2983 0.2837 0.2685 0.3715 0.3631 0.3535 0.3442 0.3350 0.3246 0.3147 Columns 8 through 13 0.1217 0.1022 0.0844 0.0677 0.0534 0.0406 0.1658 0.1451 0.1254 0.1065 0.0890 0.0728 0.2531 0.2369 0.2204 0.2042 0.1874 0.1704 0.3044 0.2945 0.2839 0.2735 0.2626 0.2512```

The rows of the matrix correspond to the values of the test parameter defined by the `TestParameter1` property, M. The columns correspond to the values of the test parameter defined by the `TestParameter2` property, EbNo.

11. Plot the results as a 3-D data plot by entering the following:

``surf(DPSKResults) ``

MATLAB generates the following plot:

In this case, the parameter defined by the `TestParameter1` property, M, controls the x-axis and the parameter defined by the `TestParameter2` property, EbNo, controls the y-axis.

Bit Error Rate Simulations For Various Eb/No and Modulation Order Values

Tasks for running bit error rate simulations for various En/No and modulation order values.

Load the Error Rate Test Console.  The Error Rate Test Console is a simulation tool for obtaining error rate results. The MATLAB software includes a data file for use with the Error Rate Test Console. You will use the data file while performing the steps of this tutorial. The data file contains an Error Rate Test Console object with an attached Gray coded modulation system. This example Error Rate Test Console is configured to run bit error rate simulations for various EbNo and modulation order, or M, values.

1. Load the file containing the Error Rate Test Console and attached Gray coded modulation system. At the MATLAB command line, enter:

``load GrayCodedModTester_EbNo_M``

2. Examine the test console by displaying its properties. At the MATLAB command line, enter:

``testConsole``

MATLAB returns the following output:

```testConsole = Description: 'Error Rate Test Console' SystemUnderTestName: 'commexample.GrayCodedMod_EbNo_M' IterationMode: 'Combinatorial' SystemResetMode: 'Reset at new simulation point' SimulationLimitOption: 'Number of errors or transmissions' TransmissionCountTestPoint: 'DemodBitErrors' MaxNumTransmissions: 100000000 ErrorCountTestPoint: 'DemodBitErrors' MinNumErrors: 100 ```

Notice that SystemUnderTest is a Gray coded modulation system. Because the SimulationLimitOption is 'Number of error or transmission', the simulation runs until reaching 100 errors or 1e8 bits.

Run the Simulation and Obtain Results.  In this example, you use `tic` and `toc` to compare simulation run time.

1. Run the simulation, using the `tic` and `toc` commands to measure simulation time. At the MATLAB command line, enter:

``tic; run(testConsole); toc``

MATLAB returns output similar to the following:

```Running simulations... Elapsed time is 174.671632 seconds. ```
2. Obtain the results of the simulation using the `getResults` method by typing the following at the MATLAB command line:

``grayResults = getResults(testConsole)``

MATLAB returns the following output:

```grayResults = TestConsoleName: 'commtest.ErrorRate' SystemUnderTestName: 'commexample.GrayCodedMod_EbNo_M' IterationMode: 'Combinatorial' TestPoint: 'DemodBitErrors' Metric: 'ErrorRate' TestParameter1: 'EbNo' TestParameter2: 'None' ```

In the next section, you use the results object to obtain error values and plot error rate curves.

Generate an Error Rate Results Figure Window.  The `semilogy` method generates a figure containing error rate curves for the demodulator bit error test point (DemodBitErrors) of the Gray coded modulation system. The next figure shows an Error Rate and Eb over No curve for the demodulator bit errors test point. This test point collects bit errors by comparing the bits the system transmits with the bits it receives. The x-axis displays the `TestParameter1` property of `grayResults`, which contains EbNo values.

1. Generate the figure by entering the following at the MATLAB command line:

``semilogy(grayResults)``

This script generates the following figure.

2. Set the `TestParameter2` property to `M`. At the MATLAB command line, enter:

``grayResults.TestParameter2 = 'M'``

Previously, the simulation ran for multiple modulation order (M) values. The x-axis displays the `TestParameter1` property of `grayResults`, which contains EbNo values. Although the simulation ran for multiple M values, this run contains data for M=2.

3. Plot multiple error rate curves by entering the following at the MATLAB command line.

``semilogy(grayResults)``

This script generates the following figure.

Run Parallel Simulations Using Parallel Computing Toolbox Software.  If you have a Parallel Computing Toolbox user license and you create a parpool, the test console runs the simulation in parallel. This approach reduces the processing time.

Note

If you do not have a Parallel Computing Toolbox user license you are unable to perform this section of the tutorial.

1. If you have a Parallel Computing Toolbox license, run the following command to start your default parpool:

``mypool = parpool()``

If you have a multicore computer, then the default parpool uses the cores as workers.

2. Using the workers, run the simulation. At the MATLAB command line, enter:

``````tic; run(testConsole); toc ``````

MATLAB returns output similar to the following:

```4 workers available for parallel computing. Simulations ..., will be distributed among these workers. Running simulations... Elapsed time is 87.449652 seconds. ```

Notice that the simulation runs more than three times as fast than in the previous section.

Create a System File and Attach It to the Test Console.  In the previous sections, you used an existing Gray coded modulator system file to generate data. In this section, you create a system file and then attach it to the Error Rate Test Console.

This example outlines the tasks necessary for converting legacy code to a system file you can attach to the Error Rate Test Console. Use commdoc_gray as the starting point for your system file. The files you use in this section of the tutorial reside in the following folder:

matlab\help\toolbox\comm\examples

1. Copy the system basic API template, SystemBasicTemplate.m, as MyGrayCodedModulation.m.

2. Rename the references to the system name in the file. First, rename the system definition by changing the class name to MyGrayCodedModulation. Replace the following lines, lines 1 and 2, of the file:

```classdef SystemBasicTemplate < testconsole.SystemBasicAPI %SystemBasicTemplate Template for creating a system ```

with these lines:

```classdef MyGrayCodedModulation < testconsole.SystemBasicAPI %MyGrayCodedModulation Gray coded modulation system ```
3. Rename the constructor by replacing:

```function obj = SystemBasicTemplate %SystemBasicTemplate Construct a system```
with
```function obj = MyGrayCodedModulation %MyGrayCodedModulation Construct a Gray coded modulation system ```

4. Enter a description for your system. Update the `obj.Description` parameter with the following information:

```obj.Description = 'Gray coded modulation'; ```

Because you are not using the `reset` and `setup` methods for this system, leave these methods empty.

5. Copy lines 12–44 from commdoc_gray.m to the body of the `run` method.

6. Copy Lines 54–57 from commdoc_gray.m to the body of the `run` method.

7. Change EbNo to a test parameter. This change allows the system to obtain EbNo values from the Error Rate Test Console. As a test parameter, EbNo becomes a variable, which allows simulations to run for different values. Locate the following line of syntax in the file:

```EbNo = 10; % In dB ```
Replace it with:
`EbNo = getTestParameter(obj,'EbNo');`

8. Add modulation order, M, as a new test parameter for the simulation. Locate the following syntax:

` M = 16; % Size of signal constellation`
Replace it with:
```M = getTestParameter(obj,'M'); ```

9. Register the test parameters to the test console.

• Declare EbNo as a test parameter by placing the following line of code in the body of the `register` method:

`registerTestParameter(obj,'EbNo',0,[-50 50]);`
The parameter defaults to 0 dB and can take values between -50 dB and 50 dB.

• Declare M as a test parameter by placing the following line of code in the body of the `register` method:

```registerTestParameter(obj,'M',16,[2 1024]); ```

The parameter defaults to 16 QAM Modulation and can take values from 2 through 1024.

10. Add EbNo and M to the test parameters list in the MyGrayCodedModulationFile file.

``` % Test Parameters properties EbNo = 0; M = 16; end```
This adds EbNo and M to the possible test parameters list. EbNo defaults to a value of 0 dB. M defaults to a value of 16.

11. Define test probe locations in the `run` method. In this example, you are calculating end-to-end error rate. This calculation requires transmitted bits and received bits. Add one probe for obtaining transmitted bits and one probe for received bits.

• Locate the random binary data stream creation code by searching for the following lines:

```% Create a binary data stream as a column vector. x = randi([0 1],n,1); % Random binary data stream ```

• Add a probe, TxBits, after the random binary data stream creation:

``` % Create a binary data stream as a column vector. x = randi([0 1],n,1); % Random binary data stream setTestProbeData(obj,'TxBits',x); ```
This code sends the random binary data stream, x, to the probe TxBits.

• Locate the demodulation code by searching for the following lines:

```% Demodulate signal using 16-QAM. z = demodulate(hDemod,yRx); ```

• Add a probe, RxBits, after the demodulation code.

```% Demodulate signal using 16-QAM. z = demodulate(hDemod,yRx); setTestProbeData(obj,'RxBits',z); ```

This code sends the binary received data stream, z, to the probe RxBits.

12. Register the test probes to the Error Rate Test Console, making it possible to obtain data from the system. Add these probes to the function `register(obj)` by adding two lines to the `register` method:

```function register(obj) % REGISTER Register the system with a test console % REGISTER(H) registers test parameters and test probes of the % system, H, with a test console. registerTestParameter(obj,'EbNo',0,[-50 50]); registerTestParameter(obj,'M',16,[2 1024]); registerTestProbe(obj,'TxBits') registerTestProbe(obj,'RxBits') end ```
13. Save the file. The file is ready for use with the system.

14. Create a Gray coded modulation system. At the MATLAB command line, enter:

`mySystem = MyGrayCodedModulation`

MATLAB returns the following output:

```mySystem = Description: 'Gray coded modulation' EbNo: 0 M: 16 ```

15. Create an Error Rate Test Console by entering the following at the MATLAB command line:

`testConsole = commtest.ErrorRate`

The MATLAB software returns the following output:

```testConsole = Description: 'Error Rate Test Console' SystemUnderTestName: 'commtest.MPSKSystem' FrameLength: 500 IterationMode: 'Combinatorial' SystemResetMode: 'Reset at new simulation point' SimulationLimitOption: 'Number of transmissions' TransmissionCountTestPoint: 'Not set' MaxNumTransmissions: 1000 ```
16. Attach the system file MyGrayCodedModulation to the error rate test console by entering the following at the MATLAB command line:

``attachSystem(testConsole, mySystem)``

Configure the Error Rate Test Console and Run a Simulation.  Configure the Error Rate Test Console to obtain error rate metrics from the attached system. The Error Rate Test Console defines metrics as number of errors, number of transmissions, and error rate.

1. At the MATLAB command line, enter:

`registerTestPoint(testConsole, 'DemodBitErrors', 'TxBits', 'RxBits');`

This line defines the test point, DemodBitErrors, and compares bits from the TxBits probe to the bits from the RxBits probe. The Error Rate Test Console calculated metrics for this test point.

2. Configure the Error Rate Test Console to run simulations for EbNo values. Start at 2 dB and end at 10 dB, with a step size of 2 dB and M values of 2, 4, 8, and 16. At the MATLAB command line, enter:

```setTestParameterSweepValues(testConsole, 'EbNo', 2:2:10) setTestParameterSweepValues(testConsole, 'M', [2 4 8 16]) ```

3. Set the simulation limit to the number of transmissions.

`testConsole.SimulationLimitOption = 'Number of transmissions'`

4. Set the maximum number of transmissions to 1000.

```testConsole.MaxNumTransmissions = 1000 ```
5. Configure the Error Rate Test Console so it uses the demodulator bit error test point for determining the number of transmitted bits.

```testConsole.TransmissionCountTestPoint = 'DemodBitErrors' ```
6. Run the simulation. At the MATLAB command line, enter:

``run(testConsole)``

7. Obtain the results of the simulation. At the MATLAB command line, enter:

``grayResults = getResults(testConsole)``
8. To obtain more accurate results, run the simulations for a given minimum number of errors. In this example, you also limit the number of simulation bits so that the simulations do not run indefinitely. At the MATLAB command line, enter:

``````testConsole.SimulationLimitOption = 'Number of errors or transmissions'; testConsole.MinNumErrors = 100; testConsole.ErrorCountTestPoint = 'DemodBitErrors'; testConsole.MaxNumTransmissions = 1e8; testConsole``````
9. Run the simulation by entering the following at the MATLAB command line.

``run(testConsole);``
10. Generate the new results in a Figure window by entering the following at the MATLAB command line.

``````grayResults = getResults(testConsole); grayResults.TestParameter2 = 'M' semilogy(grayResults)``````

This script generates the following figure.

Optimize System Performance Using Parameterized Simulations.  In the previous example, the system only utilizes the `run` method. Every time the object calls the `run` method, which is every 3e4 bits for this simulation, the object sets the M and SNR values. This time interval includes: obtaining numbers from the test console, calculating intermediate values, and setting other variables.

In contrast, the system basic API provides a `setup` method where the Error Rate Test Console configures the system once for each simulation point. This change relieves the `run` method from getting and setting simulation parameters, thus reducing simulation time.

The `run` method of a system also creates a new modulator (hMod) and a new demodulator (hDemod). Creating a modulator or a demodulator is much more time consuming than just modifying a property of these objects. Create a modulator and a demodulator object once when the system is constructed. Then, modify its properties in the setup method of the system to speed up the simulations.

1. Save the file MyGrayCodedModulation as MyGrayCodedModulationOptimized.

2. In the MyGrayCodedModulationOptimized file, replace the constructor name and the class definition name.

• Locate the following lines of code:

```classdef MyGrayCodedModulation < testconsole.SystemBasicAPI %MyGrayCodedModulation Gray coded modulation system```

• Replace them with:

```classdef MyGrayCodedModulationOptimized < testconsole.SystemBasicAPI %MyGrayCodedModulationOptimized Gray coded modulation system```
3. In the MyGrayCodedModulationOptimized ﬁle, replace the constructor name.

• Locate the following lines of code:

```function obj = MyGrayCodedModulation %MyGrayCodedModulation Construct a Gray coded modulation system```

• Replace them with:

```function obj = MyGrayCodedModulationOptimized %MyGrayCodedModulationOptimized Construct a Gray %coded modulation system ```

4. Move the oversampling rate definition from the `run` method to the `setup` method.

`nSamp = 1; % Oversampling rate`

5. Move code related to setting M to the `setup` method. Cut the following lines from the `run` method and paste to the `setup` method.

``` M = getTestParameter(obj,'M'); k = log2(M); % Number of bits per symbol ```

6. In the `setup` method, replace M with the object property M.

```obj.M = getTestParameter(obj,'M'); k = log2(obj.M); % Number of bits per symbol ```
This change provides access to the M value from the `run` method.

7. Move code related to setting EbNo to the `setup` method. Cut the following lines from the `run` method and paste to the `setup` method.

```EbNo = getTestParameter(obj,'EbNo'); SNR = EbNo + 10*log10(k) - 10*log10(nSamp); ```

8. In the `setup` method, replace EbNo with the object property EbNo. This change provides access to the EbNo value from the run method.

```obj.EbNo = getTestParameter(obj,'EbNo'); SNR = obj.EbNo + 10*log10(k) - 10*log10(nSamp); ```

9. Create a new internal variable called SNR to store the calculated SNR value. Define the SNR property as a private property; it is not a test parameter. With this change, the system calculates SNR in the `setup` method and accesses it from the `run` method. Add the following lines of code the system file, after the Test Parameters block.

```%================================================================= % Internal variables properties (Access = private) SNR end ```

10. In the `setup` method, replace SNR with object property SNR.

` obj.SNR = obj.EbNo + 10*log10(k) - 10*log10(nSamp);`

11. In the `run` method, replace M with obj.M and SNR with obj.SNR.

```hMod = comm.RectangularQAMModulator(obj.M); % Create a 16-QAM modulator yNoisy = awgn(yTx,obj.SNR,'measured'); ```
Notice that the `run` method creates the QAM modulator and demodulator.

12. Move the QAM modulator and demodulator creation out of the `run` method. Move following lines from the `run` method to the constructor (i.e the method named MyGrayCodedModulationOptimized)

```%% Create Modulator and Demodulator hMod = comm.RectangularQAMModulator(obj.M); % Create a 16-QAM modulator hMod.BitInput = true; % Accept bits as inputs hMod.SymbolMapping = 'Gray'; % Gray coded symbol mapping hDemod = comm.RectangularQAMDemodulator(obj.M); % Create a 16-QAM demodulator hDemod.BitOutput = true; % Output bits hDemod.SymbolMapping = 'Gray'; % Gray coded symbol mapping ```

Create private properties called Modulator and Demodulator to store the modulator and demodulator objects.

```% Internal variables properties (Access = private) SNR Modulator Demodulator end ```

13. In the constructor method, replace `hMod` and `hDemod` with the object property `obj.Modulator` and `obj.Demodulator` respectively.

```% Create a 16-QAM modulator obj.Modulator = comm.RectangularQAMModulator(obj.M, ... 'BitInput',true,'SymbolMapping','Gray'); % Create a 16-QAM demodulator obj.Demodulator = comm.RectangularQAMDemodulator(obj.M, ... 'BitOutput',true,'SymbolMapping','Gray');```

In the `run` method, replace `hMod` and `hDemod` with object properties `obj.Modulator` and `obj.Demodulator`.

```y = modulate(obj.Modulator,x); z = demodulate(obj.Demodulator,yRx); ```

14. Locate the setup region of the ﬁle.

```function setup(obj) % SETUP Initialize the system % SETUP(H) gets current test parameter value(s) from the test % console and initializes system, H, accordingly. ```

15. Set the M value of the modulator and demodulator by adding the following lines of code to the setup.

```obj.Modulator.M = obj.M; obj.Demodulator.M = obj.M; ```

16. Save the ﬁle.

17. Create an optimized system. At the MATLAB command line, enter:

``myOptimSystem = MyGrayCodedModulationOptimized``
18. Create an Error Rate Test Console and attach the system to the test console. At the MATLAB command line, type:

``testConsole = commtest.ErrorRate(myOptimSystem)``
19. At the MATLAB command line, type:

``registerTestPoint(testConsole, 'DemodBitErrors', 'TxBits', 'RxBits');``

This line defines the test point, DemodBitErrors, and compares bits from the TxBits probe to the bits from the RxBits probe. The Error Rate Test Console calculated metrics for this test point.

20. Configure the Error Rate Test Console to run simulations for EbNo values. Start at 2 dB and end at 10 dB, with a step size of 2 dB and M values of 2, 4, 8, and 16. At the MATLAB command line, type:

```setTestParameterSweepValues(testConsole, 'EbNo', 2:2:10) setTestParameterSweepValues(testConsole, 'M', [2 4 8 16]) ```

21. Configure the Error Rate Test Console so it uses the demodulator bit error test point for determining the number of transmitted bits.

```testConsole.TransmissionCountTestPoint = 'DemodBitErrors' ```
22. To obtain more accurate results, run the simulations for a given minimum number of errors. In this example, you also limit the number of simulation bits so that the simulations do not run indefinitely. At the MATLAB command line, type:

``````testConsole.SimulationLimitOption = 'Number of errors or transmissions'; testConsole.MinNumErrors = 100; testConsole.ErrorCountTestPoint = 'DemodBitErrors'; testConsole.MaxNumTransmissions = 1e8; testConsole``````
23. Run the simulation. At the MATLAB command line, type:

``tic; run(testConsole); toc``

MATLAB returns the following information:

```Running simulations... Elapsed time is 191.748359 seconds. ```

Notice that these optimization changes reduce the simulation run time about 10%.

24. Generate the new results in a Figure window. At the MATLAB command line, type:

```grayResults = getResults(testConsole); grayResults.TestParameter2 = 'M' semilogy(grayResults) ```
This script generates the following figure.

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