Maximum-Likelihood Sequence Estimation (MLSE) equalizers provide optimal equalization of time variations in the propagation channel characteristics but are sometimes less attractive because their computational complexity is higher than Adaptive Equalizers.
In Communications Toolbox™, the
object™, and MLSE Equalizer block use the Viterbi algorithm to
equalize a linearly modulated signal through a dispersive channel. These features output the
maximum likelihood sequence estimate of the signal, using an estimate of the channel modeled
as a finite input response (FIR) filter.
To decode a received signal, the Viterbi algorithm:
Applies the FIR filter, corresponding to the channel estimate, to the symbols in the input signal.
Uses the Viterbi algorithm to compute the traceback paths and the state metric, which are the numbers assigned to the symbols at each step of the Viterbi algorithm. The metrics are based on Euclidean distance.
Outputs the maximum likelihood sequence estimate of the signal, as a sequence of complex numbers corresponding to the constellation points of the modulated signal.
An MLSE equalizer yields the best theoretically possible performance, but is computationally intensive.
For background material about MLSE equalizers, see the works listed in Selected Bibliography for Equalizers.
You can use the
mlseeq function or
object for MLSE equalization in MATLAB®. The examples in this section call the
mlseeq function. A
similar workflow applies when using the
object. For examples that use the System
object, see the
object reference page.
mlseeq function has two operation modes:
Continuous operation mode enables you to process a series of vectors using repeated
mlseeq, where the function saves its internal state
information from one call to the next. To learn more, see Equalizing in Continuous Operation
Reset operation mode enables you to specify a preamble and postamble that accompany your data. To learn more, see Using a Preamble or Postamble.
If you are not processing a series of vectors and do not need to specify a preamble or postamble, the operation modes are nearly identical. However, they differ in that continuous operation mode incurs a delay, while reset operation mode does not. The example that follows could have used either mode, except that substituting continuous operation mode produces a delay in the equalized output. To learn more about this delay, see Delays in Continuous Operation Mode.
In its simplest form, the
mlseeq function equalizes a vector of modulated data when you specify:
The estimated coefficients of the channel (modeled as an FIR filter).
The signal constellation for the modulation type.
The Viterbi algorithm traceback depth. Larger values for the traceback depth can improve the results from the equalizer but increase the computation time.
Generate a PSK modulated signal with modulation order set to four.
M = 4; msg = pskmod([1 2 2 0 3 1 3 3 2 1 0 2 3 0 1]',M);
Filter the modulated signal through a distortion channel.
chcoeffs = [.986; .845; .237; .12345+.31i]; filtmsg = filter(chcoeffs,1,msg);
Define the reference constellation, traceback length, and channel estimate for the MLSE equalizer. For simplicity the exact channel is provided as the channel estimate.
const = pskmod([0:M-1],M); tblen = 10; chanest = chcoeffs;
Equalize the received signal.
msgEq = mlseeq(filtmsg,chanest,const,tblen,'rst'); isequal(msg,msgEq)
ans = logical 1
If your data is partitioned into a series of vectors (that you process within a loop,
for example), continuous operation mode is an appropriate way to use the
mlseeq function. In continuous operation mode,
mlseeq can save its internal state information for use in a
subsequent invocation and can initialize using previously stored state information. To
choose continuous operation mode, when invoking
'cont' as an input argument.
Continuous operation mode incurs a delay, as described in Delays in Continuous Operation Mode. Also, continuous operation mode cannot accommodate a preamble or postamble.
The typical procedure for using continuous mode within a loop is to create three empty
matrix variables that eventually store the state metrics, traceback states, and traceback
inputs for the equalizer before the loop starts. Then inside the loop, invoke
mlseeq using a
sm = ; ts = ; ti = ; for ... [y,sm,ts,ti] = mlseeq(x,chcoeffs,const,tblen,'cont',nsamp,sm,ts,ti); ... end
ti as input
mlseeq to continue from where it finished in the
previous iteration. Using
ti as output arguments causes
mlseeq to update
the state information at the end of the current iteration. In the first iteration,
ti start as empty
matrices, so the first invocation of the
mlseeq function initializes
the metrics of all states to 0.
Continuous operation mode with a traceback depth of
tblen incurs an
output delay of
tblen symbols. The first
output symbols are unrelated to the input signal, while the last
input symbols are unrelated to the output signal. For example, this command uses a
traceback depth of
3, and the first three output symbols are unrelated
to the input signal of
y = mlseeq(ones(1,10),1,[-7:2:7],3,'cont')
y = -7 -7 -7 1 1 1 1 1 1 1
It is important to keep track of delays introduced by different portions of the communications system. The Use mlseeq to Equalize in Continuous Operation Mode example illustrates how to account for the delay when computing an error rate.
This example shows the procedure for using the continuous operation mode of the
mlseeq function within a loop.
Specify runtime variables.
numsym = 200; % Number of symbols in each iteration numiter = 25; % Number of iterations M = 4; % Use 4-PSK modulation qpskMod = comm.QPSKModulator('PhaseOffset',0); chcoeffs = [1 ; 0.25]; % Channel coefficients chanest = chcoeffs; % Channel estimate
Define parameters to initialize the equalizer: the reference constellation, traceback length, number of samples per symbol, and the state variables
const = qpskMod((0:M-1)'); tblen = 10; nsamp = 1; sm = ; ts = ; ti = ;
Define variables to accumulate results from each iteration of the loop.
fullmodmsg = ; fullfiltmsg = ; fullrx = ;
Simulate the System Using a Loop
Run the simulation in a loop that generates random data, modulates that data using baseband PSK modulation, and filters that data. The
mlseeq function equalizes the filtered data. The loop also updates the variables that accumulate results from each iteration of the loop.
for jj = 1:numiter msg = randi([0 M-1],numsym,1); % Random signal vector modmsg = qpskMod(msg); % PSK-modulated signal filtmsg = filter(chcoeffs,1,modmsg); % Filtered signal % Equalize the signal. [rx,sm,ts,ti] = mlseeq(filtmsg,chanest,const, ... tblen,'cont',nsamp,sm,ts,ti); % Update vectors with cumulative results. fullmodmsg = [fullmodmsg; modmsg]; fullfiltmsg = [fullfiltmsg; filtmsg]; fullrx = [fullrx; rx]; end
Computing an Error Rate and Plotting Results
Compute the symbol error rate from all iterations of the loop. The
symerr function compares selected portions of the received and transmitted signals, not the entire signals. Because continuous operation mode incurs a delay whose length in samples is the traceback depth (
tblen) of the equalizer, it is necessary to exclude the first
tblen samples from the received signal and the last
tblen samples from the transmitted signal. Excluding samples that represent the delay of the equalizer ensures that the symbol error rate calculation compares samples from the received and transmitted signals that are meaningful and that truly correspond to each other.
Taking the delay into account, compute the total number of symbol errors.
hErrorCalc = comm.ErrorRate('ReceiveDelay',10); err = step(hErrorCalc, fullmodmsg, fullrx); numsymerrs = err(1)
numsymerrs = 0
Plot the signal before and after equalization in a scatter plot. The points in the equalized signal coincide with the points of the ideal signal constellation for 4-PSK.
h = scatterplot(fullfiltmsg); hold on; scatterplot(fullrx,1,0,'r*',h); legend('Filtered signal before equalization','Equalized signal',... 'Location','NorthOutside'); hold off;
Some systems include a sequence of known symbols at the beginning or end of a set of
data. The known sequence at the beginning or end is called a preamble
or postamble, respectively. The
can accommodate a preamble and postamble that are already incorporated into its input
signal. When you invoke the function, you specify the preamble and postamble as integer
vectors that represent the sequence of known symbols by indexing into the signal
constellation vector. For example, a preamble vector of
[1 4 4] and a
4-PSK signal constellation of
[1 j -1 -j] indicates that the modulated
signal begins with
[1 -j -j].
If your system uses a preamble without a postamble, use a postamble vector of
 when invoking
mlseeq. Similarly, if your system
uses a postamble without a preamble, use a preamble vector of
Recover a message that includes a preamble, equalize the signal, and check the symbol error rate.
Specify the modulation order, equalizer traceback depth, number of samples per symbol, preamble, and message length.
M = 4; tblen = 16; nsamp = 1; preamble = [3;1]; msgLen = 500;
Generate the reference constellation.
const = pskmod(0:3,4);
Generate a message with random data and prepend the preamble to the message. Modulate the random data.
msgData = randi([0 M-1],msgLen,1); msgData = [preamble; msgData]; msgSym = pskmod(msgData,M);
Filter the data through a distortion channel and add Gaussian noise to the signal.
chcoeffs = [0.623; 0.489+0.234i; 0.398i; 0.21]; chanest = chcoeffs; msgFilt = filter(chcoeffs,1,msgSym); msgRx = awgn(msgFilt,9,'measured');
Equalize the received signal. To configure the equalizer, provide the channel estimate, reference constellation, equalizer traceback depth, operating mode, number of samples per symbol, and preamble. The same preamble symbols appear at the beginning of the message vector and in the syntax for
mlseeq. Because the system uses no postamble, an empty vector iis specified as the last input argument in this
Check the symbol error rate of the equalized signal. Run-to-run esults vary due to use of random numbers.
eqSym = mlseeq(msgRx,chanest,const,tblen,'rst',nsamp,preamble,); [nsymerrs,ser] = symerr(msgSym,eqSym)
nsymerrs = 8
ser = 0.0159
The MLSE Equalizer block uses the Viterbi algorithm to equalize a linearly modulated signal through a dispersive channel. The block outputs the maximum likelihood sequence estimate (MLSE) of the signal, using your estimate of the channel modeled as a finite input response (FIR) filter. When using the MLSE Equalizer block, you specify the channel estimate and the signal constellation of the input signal. You can also specify an expected input signal preamble and postamble as input parameters to the MLSE Equalizer block.
Use a Maximum Likelihood Sequence Estimation (MLSE) equalizer to equalize the effects of a multipath Rayleigh fading channel. The MLSE equalizer inputs data that has passed through a time varying dispersive channel and an estimate of the channel. The channel estimate contains dynamically evolving channel coefficients of a two-path Rayleigh fading channel.
Structure of the model
The transmitter generates QPSK random signal data.
Channel impairments include multipath fading and AWGN.
The receiver applies MLSE equalization and QPSK demodulation.
The model also uses scopes and a BER calculation to show the system behavior.
Explore Example Model
Experimenting with the model
Bernoulli Binary Generator block sample time of 5e-6 seconds, corresponds to a bit rate of 200 kbps, and a QPSK symbol rate of 100 ksym/sec.
Multipath Rayleigh Fading Channel block settings are:
Maximum Doppler shift is set to 30 Hz.
Discrete path delay is [0 1e-5], which corresponds to two consecutive sample times of the input QPSK symbol data. This reflects the simplest delay vector for a two-path channel.
Average path gain is [0 -10].
Average path gains are normalized to 0 dB, so that the average power input to the
AWGN block is 1 W.
MLSE Equalizer block has the Traceback depth set to 10. Vary this to study its effect on Bit Error rate (BER).
The QPSK demodulator accepts an N-by-1 input frame and generates a 2N-by-1 output frame. This, along with the traceback depth of 10 results in a delay of 20 bits. The model performs frame-based processing with 100 samples per frame. Thus, there is a delay of 100 bits inherent in the model. The combined receive delay of 120 is set in the Receive delay parameter of the Error Rate Calculation block, aligning the samples.
The computed BER is displayed. Constellation plots show the constellation before and after equalization.
BER = 0.033656