comm.QPSKDemodulator

(To be removed) Demodulate using QPSK method

comm.QPSKDemodulator will be removed in a future release. Use pskdemod instead. For information on updating your code, see Version History.

Description

The comm.QPSKDemodulator object demodulates a signal that was modulated using the quadrature phase shift keying (QPSK) method. The input is a baseband representation of the modulated signal.

To demodulate a signal that was modulated using the QPSK method:

1. Create the comm.QPSKDemodulator object and set its properties.

2. Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

Creation

Syntax

qpskdemod = comm.QPSKDemodulator

qpskdemod = comm.QPSKDemodulator(Name=Value)

qpskdemod = comm.QPSKDemodulator(phase=Name,Value)

Description

qpskdemod = comm.QPSKDemodulator creates a System object™ to demodulate input QPSK signals.

qpskdemod = comm.QPSKDemodulator(Name=Value) sets properties using one or more name-value arguments. For example, DecisionMethod="Hard decision" specifies demodulation using the hard-decision method.

qpskdemod = comm.QPSKDemodulator(phase=Name,Value) sets the PhaseOffset property to phase, and optional name-value arguments. Specify phase in radians.

Properties

Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the release function unlocks them.

If a property is tunable, you can change its value at any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

PhaseOffset — Phase of zeroth point in constellation

pi/4 (default) | scalar

Phase of the zeroth point in the constellation in radians, specified as a scalar.

Example: PhaseOffset=0 aligns the QPSK signal constellation points on the axes {(1,0), (0,j), (-1,0), (0,-j)}.

Data Types: double

BitOutput — Option to output data as bits

0 or false (default) | 1 or true

Option to output data as bits, specified as a logical 0 (false) or 1 (true).

• Set this property to false to output symbols as integer values in the range [0, 3] with length equal to the input data vector length.

• Set this property to true to output a column vector of bit values with length equal to twice the input data vector length.

Data Types: logical

SymbolMapping — Symbol encoding

'Gray' (default) | 'Binary'

Symbol encoding mapping of constellation bits, specified as 'Gray' or 'Binary'.

Setting	Constellation Mapping for Integers	Constellation Mapping for Bits	Comment
Gray			Map symbols using Gray-coded ordering.
Binary			Map symbols using natural binary-coded ordering. The signal constellation maps to the complex value ej(PhaseOffset + (2πm/4)), where m is an integer in the range [0, 3].

DecisionMethod — Demodulation decision method

'Hard decision' (default) | 'Log-likelihood ratio' | 'Approximate log-likelihood ratio'

Demodulation decision method, specified as 'Hard decision', 'Log-likelihood ratio', or 'Approximate log-likelihood ratio'. When you set the BitOutput property to false, the object always performs hard-decision demodulation.

Dependencies

To enable this property, set the BitOutput property to true.

VarianceSource — Source of noise variance

'Property' (default) | 'Input port'

Source of noise variance, specified as 'Property' or 'Input port'.

Dependencies

To enable this property, set the BitOutput property to true and the DecisionMethod property to 'Log-likelihood ratio' or 'Approximate log-likelihood ratio'.

Variance — Noise variance

1 (default) | positive scalar

Noise variance, specified as a positive scalar.

Tunable: Yes

Tips

The exact LLR algorithm computes exponentials using finite precision arithmetic. For computations involving very large positive or negative magnitudes, the exact LLR algorithm yields:

• Inf or -Inf if the noise variance is a very large value

• NaN if the noise variance and signal power are both very small values

The approximate LLR algorithm does not compute exponentials. You can avoid Inf, -Inf, and NaN results by using the approximate LLR algorithm.

Dependencies

To enable this property, set the BitOutput property to true, the DecisionMethod property to 'Log-likelihood ratio' or 'Approximate log-likelihood ratio', and the VarianceSource property to 'Property'.

Data Types: double

OutputDataType — Data type of output

'Full precision' (default) | 'Smallest unsigned integer' | 'double' | ...

Data type of the output, specified as 'Full precision', 'Smallest unsigned integer', 'double', 'single', 'int8', 'uint8', 'int16', 'uint16', 'int32', or 'uint32','logical'.

• When the input data type is single or double precision and you set the BitOutput property to true, the DecisionMethod property to 'Hard decision', and the OutputDataType property to 'Full precision', the output has the same data type as that of the input.

• When the input data is of a fixed-point type, the output data type behaves as if you had set the OutputDataType property to 'Smallest unsigned integer'.

• When you set BitOutput to true and the DecisionMethod property to 'Hard Decision', then 'logical' data type is a valid option.

• When you set the BitOutput property to true and the DecisionMethod property to 'Log-likelihood ratio' or 'Approximate log-likelihood ratio', the output data type is the same as that of the input and the input data type must be single or double precision.

Dependencies

To enable this property, set the BitOutput property to false or set the BitOutput property to true and the DecisionMethod property to 'Hard decision'.

Fixed-Point Properties

DerotateFactorDataType — Data type of derotate factor

'Same word length as input' (default) | 'Custom'

Data type of the derotate factor, specified as 'Same word length as input' or 'Custom'. The object uses the derotate factor in the computations only when the input signal is a fixed-point type and the PhaseOffset property has a value that is not an even multiple of π/4.

Dependencies

To enable this property, set the BitOutput property to false or set the BitOutput property to true and the DecisionMethod property to 'Hard decision'.

CustomDerotateFactorDataType — Fixed-point data type of derotate factor

numerictype([],16) (default) | unscaled numerictype object

Fixed-point data type of the derotate factor, specified as an unscaled numerictype (Fixed-Point Designer) object with a Signedness of Auto.

Dependencies

To enable this property, set the DerotateFactorDataType property to 'Custom'.

Data Types: numerictype object

Usage

Syntax

y = qpskdemod(x)

y = qpskdemod(x,var)

Description

y = qpskdemod(x) applies QPSK demodulation to the input signal and returns the demodulated signal.

y = qpskdemod(x,var) uses soft decision demodulation and noise variance var. This syntax applies when you set the BitOutput property to true, the DecisionMethod property to 'Approximate log-likelihood ratio' or 'Log-likelihood ratio', and the VarianceSource property to 'Input port'.

Input Arguments

x — QPSK-modulated signal

scalar | column vector

QPSK-modulated signal, specified as a scalar or column vector.

Dependencies

The object accepts inputs with a signed integer data type or signed fixed point (sfi (Fixed-Point Designer)) objects when you set the BitOutput property to false or you set the DecisionMethod property to 'Hard decision' and the BitOutput property to true.

Data Types: double | single | int | fi

var — Noise variance

scalar

Noise variance, specified as a scalar.

Dependencies

To enable this argument, set the VarianceSource property to 'Input port', the BitOutput property to true, and the DecisionMethod property to 'Approximate log-likelihood ratio' or 'Log-likelihood ratio'.

Data Types: single | double

Output Arguments

y — Output signal

scalar | column vector

Output signal, returned as a scalar or column vector. To specify whether the object outputs values as integers or bits, use the BitOutput property. To specify the output data type, use the OutputDataType property.

Object Functions

To use an object function, specify the System object as the first input argument. For example, to release system resources of a System object named obj, use this syntax:

release(obj)

Specific to comm.QPSKDemodulator

constellation

Calculate or plot ideal signal constellation

Common to All System Objects

step	Run System object algorithm
release	Release resources and allow changes to System object property values and input characteristics
reset	Reset internal states of System object

Examples

BER Estimate of QPSK Signal

Estimate the bit error rate of a QPSK modulated signal filtered through an AWGN channel.

Create a QPSK modulator and demodulator pair that operate on bits.

qpskModulator = comm.QPSKModulator('BitInput',true);
qpskDemodulator = comm.QPSKDemodulator('BitOutput',true);

Create an AWGN channel object and an error rate counter.

channel = comm.AWGNChannel('EbNo',4,'BitsPerSymbol',2);
errorRate = comm.ErrorRate;

Generate random binary data and apply QPSK modulation.

data = randi([0 1],1000,1);
txSig = qpskModulator(data);

Pass the signal through the AWGN channel and demodulate it.

rxSig = channel(txSig);
rxData = qpskDemodulator(rxSig);

Calculate the error statistics. Display the BER.

errorStats = errorRate(data,rxData);

errorStats(1)

ans = 0.0100

Algorithms

Hard-Decision QPSK Demodulation

The signal preprocessing required for QPSK demodulation depends on the configuration.

This figure shows the hard-decision QPSK demodulation signal diagram for the trivial phase offset (odd multiple of π/4) configuration.

This figure shows the hard-decision QPSK demodulation floating-point signal diagram for the nontrivial phase offset configuration.

This figure shows the hard-decision QPSK demodulation fixed-point signal diagram for the nontrivial phase offset configuration.

Soft-Decision QPSK Demodulation

For soft demodulation, two soft-decision log-likelihood ratio (LLR) algorithms are available: exact LLR and approximate LLR. The exact LLR algorithm is more accurate but has slower execution speed than the approximate LLR algorithm. For further description of these algorithms, see the Hard- vs. Soft-Decision Demodulation topic.

Note

The exact LLR algorithm computes exponentials using finite precision arithmetic. For computations involving very large positive or negative magnitudes, the exact LLR algorithm yields:

• Inf or -Inf if the noise variance is a very large value

• NaN if the noise variance and signal power are both very small values

The approximate LLR algorithm does not compute exponentials. You can avoid Inf, -Inf, and NaN results by using the approximate LLR algorithm.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

See System Objects in MATLAB Code Generation (MATLAB Coder).

HDL Code Generation

Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL Coder™.

The software supports double and single data types for simulation, but not for HDL code generation.

To generate HDL code from predefined System objects, see HDL Code Generation from Viterbi Decoder System Object (HDL Coder).

Version History

Introduced in R2012a

R2023a: To be removed

comm.QPSKDemodulator will be removed in a future release. Use the pskdemod function with the modulation order set to 4 to QPSK demodulate the input signal.