comm.RectangularQAMModulator

(To be removed) Modulate using rectangular QAM signal constellation

comm.RectangularQAMModulator will be removed in a future release. Use qammod instead. For more information, see Compatibility Considerations.

Description

The RectangularQAMModulator object modulates using M-ary quadrature amplitude modulation with a constellation on a rectangular lattice. The output is a baseband representation of the modulated signal. This block accepts a scalar or column vector input signal.

To modulate a signal using quadrature amplitude modulation:

Define and set up your rectangular QAM modulator object. See Construction.
Call step to modulate the signal according to the properties of comm.RectangularQAMModulator. The behavior of step is specific to each object in the toolbox.

Note

Starting in R2016b, instead of using the step method to perform the operation defined by the System object™, you can call the object with arguments, as if it were a function. For example, y = step(obj,x) and y = obj(x) perform equivalent operations.

Construction

H = comm.RectangularQAMModulator creates a modulator object, H. This object modulates the input using the rectangular quadrature amplitude modulation (QAM) method.

H = comm.RectangularQAMModulator(Name,Value) creates a rectangular QAM modulator object, H, with each specified property set to the specified value. You can specify additional name-value pair arguments in any order as (Name1,Value1,...,NameN,ValueN).

H = comm.RectangularQAMModulator(M,Name,Value) creates a rectangular QAM modulator object, H. This object has the ModulationOrder property set to M, and the other specified properties set to the specified values.

Properties

`ModulationOrder`	Number of points in signal constellation Specify the number of points in the signal constellation as scalar value that is a positive integer power of two. The default is `16`.
`PhaseOffset`	Phase offset of constellation Specify the phase offset of the signal constellation, in radians, as a real scalar value. The default is `0`.
`BitInput`	Assume bit inputs Specify whether the input is bits or integers. The default is `false`. When you set this property to `true`, the `step` method input requires a column vector of bit values. The length of this vector must be an integer multiple of log2(`ModulationOrder`). This vector contains bit representations of integers between `0` and `ModulationOrder`–`1`. When you set this property to `false`, the `step` method input must be a column vector of integer symbol values between `0` and `ModulationOrder`–`1`.
`SymbolMapping`	Constellation encoding Specify how the object maps an integer or group of log2(`ModulationOrder`) input bits to the corresponding symbol as `Binary` \| `Gray` \| `Custom`. The default is `Gray`. When you set this property to `Gray`, the System object uses a Gray-coded signal constellation. When you set this property to `Binary`, the object uses a natural binary-coded constellation. When you set this property to `Custom`, the object uses the signal constellation defined in the `CustomSymbolMapping` property.
`CustomSymbolMapping`	Custom constellation encoding Specify a custom constellation symbol mapping vector. The default is `0:15`. This property is a row or column vector with a size of `ModulationOrder`. This vector has unique integer values in the range [`0`, `ModulationOrder`–`1`]. These values must be of data type `double`. The first element of this vector corresponds to the top-leftmost point of the constellation, with subsequent elements running down column-wise, from left to right. The last element corresponds to the bottom-rightmost point. This property applies when you set the `SymbolMapping` property to `Custom`.
`NormalizationMethod`	Constellation normalization method Specify the method used to normalize the signal constellation as `Minimum distance between symbols` \| `Average power` \| `Peak power`. The default is `Minimum distance between symbols`.
`MinimumDistance`	Minimum distance between symbols Specify the distance between two nearest constellation points as a positive, real, numeric scalar value. The default is `2`. This property applies when you set the `NormalizationMethod` property to `Minimum distance between symbols`.
`AveragePower`	Average power of constellation Specify the average power of the symbols in the constellation as a positive, real, numeric scalar value. The default is `1`. This property applies when you set the `NormalizationMethod` property to `Average power`.
`PeakPower`	Peak power of constellation Specify the maximum power of the symbols in the constellation as a positive real, numeric scalar value. The default is `1`. This property applies when you set the `NormalizationMethod` property to `Peak power`.
`OutputDataType`	Data type of output Specify the output data type as `double` \| `single` \| `Custom`. The default is `double`.

Fixed-Point Properties

Methods

constellation	(To be removed) Calculate or plot ideal signal constellation
step	(To be removed) Modulate using rectangular QAM method

Common to All System Objects
`release`	Allow System object property value changes

Algorithms

This object implements the algorithm, inputs, and outputs described on the Rectangular QAM Modulator Baseband block reference page. The object properties correspond to the block parameters.

Compatibility Considerations

expand all

`comm.RectangularQAMModulator` will be removed in a future release. Use `qammod` instead.

Not recommended starting in R2018b

Constellation normalization by PeakPower and AveragePower (other than unit average power) as supported by comm.RectangularQAMModulator and comm.RectangularQAMDemodulator is not inherently provided by functions. This code shows you how to perform peak power and average power normalization using qammod and qamdemod functions.

Average Power Normalization for Hard Decision

Output shown here compares computation of average power normalization for hard decision using alternative approach. Functions used follow the output.

>> averagePowerReplacement(64)
maxConstellationErr =
     0
objModDemodOutputIsEqual =
  logical
   1
objModFcnDemodIsEqual =
  logical
   1
fcnModObjDemodIsEqual =
  logical
   1

>> averagePowerReplacement(32)
maxConstellationErr =
     0
objModDemodOutputIsEqual =
  logical
   1
objModFcnDemodIsEqual =
  logical
   1
fcnModObjDemodIsEqual =
  logical
   1

Functions shown here compares computation of average power normalization for hard decision using alternative approach.

function averagePowerReplacement(M)
% QAM alternative for "Average power" normalization method when using functions
    
    avgPow = 100;
    minD = avgPow2MinD(avgPow, M);
    
    modObj = comm.RectangularQAMModulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Average power', ...
        'AveragePower', avgPow);
    demodObj = comm.RectangularQAMDemodulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Average power', ...
        'AveragePower', avgPow);
    
    % 1) The two constellations are same
    constellationSO = modObj([0:M-1]');
    constellationFcn = qammod([0:M-1]', M);
    scaledConstellationFcn = (minD/2) .* constellationFcn;
    err = constellationSO - scaledConstellationFcn;
    maxConstellationErr = max(abs(err))
    
    x = randi([0, M-1], 100, 1);
    
    y1 = modObj(x);
    z1 = demodObj(y1);
    objModDemodOutputIsEqual = isequal(x, z1)
    
    % 2) qamdemod() demodulates modulator System object output
    y1ScaledForFcn = (2/minD) .* y1;
    z2 = qamdemod(y1ScaledForFcn, M);
    objModFcnDemodIsEqual = isequal(x, z2)
    
    % 3) Demodulator System object demodulates qammod() output
    y2 = qammod(x, M);
    y2ScaledForSO = (minD/2) .* y2;
    z3 = demodObj(y2ScaledForSO);
    fcnModObjDemodIsEqual = isequal(x, z3)
    
end

function minD = avgPow2MinD(avgPow, M)
% Average power to minimum distance    
    nBits = log2(M);
    if (mod(nBits,2)==0)
        % Square QAM
        sf = (M - 1)/6;
    else
        % Cross QAM
        if (nBits > 4)
            sf = ((31 * M / 32) - 1) / 6;
        else
            sf = ((5 * M / 4) - 1) / 6;
        end
    end
    minD = sqrt(avgPow/sf);
    
end

Peak Power Normalization for Hard Decision

Output shown here compares computation of peak power normalization for hard decision using alternative approach. Functions used follow the output.

>> peakPowerReplacement(16)
maxConstellationErr =
     0
objModDemodOutputIsEqual =
  logical
   1
objModFcnDemodIsEqual =
  logical
   1
fcnModObjDemodIsEqual =
  logical
   1

>> peakPowerReplacement(128)
maxConstellationErr =
     0
objModDemodOutputIsEqual =
  logical
   1
objModFcnDemodIsEqual =
  logical
   1
fcnModObjDemodIsEqual =
  logical
   1

Functions shown here compares computation of peak power normalization for hard decision using alternative approach.

function peakPowerReplacement(M)
% QAM alternative for "Peak power" normalization method when using functions
    
    pkPow = 5;
    minD = pkPow2MinD(pkPow, M);
    
    modObj = comm.RectangularQAMModulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Peak power', ...
        'PeakPower', pkPow);
    demodObj = comm.RectangularQAMDemodulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Peak power', ...
        'PeakPower', pkPow);
    
    % 1) The two constellations are same
    constellationSO = modObj([0:M-1]');
    constellationFcn = qammod([0:M-1]', M);
    scaledConstellationFcn = (minD/2) .* constellationFcn;
    err = constellationSO - scaledConstellationFcn;
    maxConstellationErr = max(abs(err))
    
    x = randi([0, M-1], 100, 1);
    
    y1 = modObj(x);
    z1 = demodObj(y1);
    objModDemodOutputIsEqual = isequal(x, z1)
    
    % 2) qamdemod() demodulates modulator System object output
    y1ScaledForFcn = (2/minD) .* y1;
    z2 = qamdemod(y1ScaledForFcn, M);
    objModFcnDemodIsEqual = isequal(x, z2)
    
    % 3) Demodulator System object demodulates qammod() output
    y2 = qammod(x, M);
    y2ScaledForSO = (minD/2) .* y2;
    z3 = demodObj(y2ScaledForSO);
    fcnModObjDemodIsEqual = isequal(x, z3)
    
end

function minD = pkPow2MinD(pkPow, M)
% Peak power to minimum distance    
    nBits = log2(M);
    if (mod(nBits,2)==0)
        % Square QAM
        sf = 0.5*M - sqrt(M) + 0.5;
    else
        % Cross QAM
        mBy32 = M/32;
        if (nBits > 4)
            sf = (13 * mBy32) - (5 * sqrt(mBy32)) + 0.5;
        else
            sf = (20 * mBy32) - (6 * sqrt(mBy32)) + 0.5;
        end
    end
    minD = sqrt(pkPow/sf);
    
end

function minD = avgPow2MinD(avgPow, M)
% Average power to minimum distance    
    nBits = log2(M);
    if (mod(nBits,2)==0)
        % Square QAM
        sf = (M - 1)/6;
    else
        % Cross QAM
        if (nBits > 4)
            sf = ((31 * M / 32) - 1) / 6;
        else
            sf = ((5 * M / 4) - 1) / 6;
        end
    end
    minD = sqrt(avgPow/sf);
    
end

Average Power Normalization for Approximate LLR

Output shown here compares computation of average power normalization for approximate LLR using alternative approach. Functions used follow the output.

>> averagePowerReplacementApproxLLR(16, 10, 1) % M=16, avgPow=10, snrdB=1
maxConstellationErr =
     0
maxOutputErr =
     0
>> averagePowerReplacementApproxLLR(32, 7, 20) % M=32, avgPow=7, snrdB=20
maxConstellationErr =
     0
maxOutputErr =
   8.5265e-14
>> averagePowerReplacementApproxLLR(64, 111, -2) % M=64, avgPow=111, snrdB=-2
maxConstellationErr =
     0
maxOutputErr =
   2.2204e-15
>> averagePowerReplacementApproxLLR(1024, 87, 33) % M=1024, avgPow=87, snrdB=33
maxConstellationErr =
     0
maxOutputErr =
   1.3642e-12

Functions shown here compares computation of average power normalization for approximate LLR using alternative approach.

function averagePowerReplacementApproxLLR(M, avgPow, snrdB)
% QAM alternative for "Average power" normalization method for
% Approximate LLR output when using functions
%
% M - Modulation order. Must be supported by QAM modulator-demodulator
% avgPow - Average constellation power
% snrdB - SNR, in dB. AWGN channel adds noise to provide this SNR. 
    
    minD = avgPow2MinD(avgPow, M);
    
    modObj = comm.RectangularQAMModulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Average power', ...
        'AveragePower', avgPow);
    
    % 1) The two constellations are same
    constellationSO = modObj((0:M-1)');
    constellationFcn = qammod((0:M-1)', M);
    scaledConstellationFcn = (minD/2) .* constellationFcn;
    err = constellationSO - scaledConstellationFcn;
    maxConstellationErr = max(abs(err))
    
    x = randi([0, M-1], 100, 1);
    
    y1 = modObj(x);
    
    % Add noise
    % Reset global rng stream for repeatable noise samples
    reset(RandStream.getGlobalStream);
    y1Rec = awgn(y1, snrdB, 'measured', 'db');
    
    % noise variance
    nv = mean(abs(y1).^2) / 10^(snrdB/10);
    
    demodObj = comm.RectangularQAMDemodulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Average power', ...
        'AveragePower', avgPow, ...
        'BitOutput', true, ...
        'DecisionMethod', 'Approximate log-likelihood ratio', ...
        'Variance', nv);
    
    z1 = demodObj(y1Rec);
    
    % 2) Functions' output is same as System objects' output, with right
    % scaling
    y2 = qammod(x, M);
    % Scale function's output so that it matches System object output. 
    y2Scaled = (minD/2) .* y2;
    
    % Add noise
    % Reset global rng stream for repeatable noise samples
    reset(RandStream.getGlobalStream);
    y2Rec = awgn(y2Scaled, snrdB, 'measured', 'db');

    % noise variance
    nv1 = mean(abs(y2Scaled).^2) / 10^(snrdB/10);
    
    % Scale the received signal for the constellation used by function
    y2RecScaled = (2/minD) .* y2Rec;
    % Scale the noise variance appropriately
    z2 = qamdemod(y2RecScaled, M, 'OutputType', 'approxllr', ...
        'NoiseVariance', nv1 * (2/minD)^2);
    
    maxOutputErr = max(abs(z1-z2))     
end

function minD = avgPow2MinD(avgPow, M)
% Average power to minimum distance    
    nBits = log2(M);
    if (mod(nBits,2)==0)
        % Square QAM
        sf = (M - 1)/6;
    else
        % Cross QAM
        if (nBits > 4)
            sf = ((31 * M / 32) - 1) / 6;
        else
            sf = ((5 * M / 4) - 1) / 6;
        end
    end
    minD = sqrt(avgPow/sf);
end

Peak Power Normalization for Approximate LLR

Output shown here compares computation of peak power normalization for approximate LLR using alternative approach. Functions used follow the output.

>> peakPowerReplacementApproxLLR(4, 2.5, 7) % M=4, pkPow=2.5, snrdB=7
maxConstellationErr =
     0
maxOutputErr =
   7.1054e-15
>> peakPowerReplacementApproxLLR(16, 19, 0) % M=16, pkPow=19, snrdB=0
maxConstellationErr =
     0
maxOutputErr =
   4.4409e-15
>> peakPowerReplacementApproxLLR(128, 12, 4.4) % M=128, pkPow=12, snrdB=4.4
maxConstellationErr =
     0
maxOutputErr =
   2.6645e-15
>> peakPowerReplacementApproxLLR(256, 221, 16) % M=256, pkPow=221, snrdB=16
maxConstellationErr =
     0
maxOutputErr =
   2.8422e-14

Functions shown here compares computation of peak power normalization for approximate LLR using alternative approach.

function peakPowerReplacementApproxLLR(M, pkPow, snrdB)
% QAM alternative for "Peak power" normalization method for 
% Approximate LLR output when using functions
%
% M - Modulation order. Must be supported by QAM modulator-demodulator
% avgPow - Average constellation power
% snrdB - SNR, in dB. AWGN channel adds noise to provide this SNR.
    
    minD = pkPow2MinD(pkPow, M);
    
    modObj = comm.RectangularQAMModulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Peak power', ...
        'PeakPower', pkPow);
    
    % 1) The two constellations are same
    constellationSO = modObj((0:M-1)');
    constellationFcn = qammod((0:M-1)', M);
    scaledConstellationFcn = (minD/2) .* constellationFcn;
    err = constellationSO - scaledConstellationFcn;
    maxConstellationErr = max(abs(err))
    
    x = randi([0, M-1], 100, 1);
    
    y1 = modObj(x);
    
    % Add noise
    % Reset global rng stream for repeatable noise samples
    reset(RandStream.getGlobalStream);
    y1Rec = awgn(y1, snrdB, 'measured', 'db');

    % noise variance
    nv = mean(abs(y1).^2) / 10^(snrdB/10);
    
    demodObj = comm.RectangularQAMDemodulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Peak power', ...
        'PeakPower', pkPow, ...
        'BitOutput', true, ...
        'DecisionMethod', 'Approximate log-likelihood ratio', ...
        'Variance', nv);
    
    z1 = demodObj(y1Rec);

    % 2) Functions' output is same as System objects' output, with right
    % scaling
    y2 = qammod(x, M);
    % Scale function's output so that it matches System object output. 
    y2Scaled = (minD/2) .* y2;

    % Add noise
    % Reset global rng stream for repeatable noise samples
    reset(RandStream.getGlobalStream);
    y2Rec = awgn(y2Scaled, snrdB, 'measured', 'db');

    % noise variance
    nv1 = mean(abs(y2Scaled).^2) / 10^(snrdB/10);
    
    % Scale the received signal for the constellation used by function
    y2RecScaled = (2/minD) .* y2Rec;
    % Scale the noise variance appropriately
    z2 = qamdemod(y2RecScaled, M, 'OutputType', 'approxllr', ...
        'NoiseVariance', nv1 * (2/minD)^2);
    
    maxOutputErr = max(abs(z1-z2))
    
end

function minD = pkPow2MinD(pkPow, M)
% Peak power to minimum distance    
    nBits = log2(M);
    if (mod(nBits,2)==0)
        % Square QAM
        sf = 0.5*M - sqrt(M) + 0.5;
    else
        % Cross QAM
        mBy32 = M/32;
        if (nBits > 4)
            sf = (13 * mBy32) - (5 * sqrt(mBy32)) + 0.5;
        else
            sf = (20 * mBy32) - (6 * sqrt(mBy32)) + 0.5;
        end
    end
    minD = sqrt(pkPow/sf);
    
end

Average Power Normalization for LLR

Output shown here compares computation of average power normalization for LLR using alternative approach. Functions used follow the output.

>> averagePowerReplacementLLR(16, 20, 1) % M=16, avgPow=20, snrdB=1
maxConstellationErr =
     0
maxOutputErr =
   8.8818e-16
>> averagePowerReplacementLLR(32, 5.5, 15) % M=32, avgPow=5.5, snrdB=15
maxConstellationErr =
     0
maxOutputErr =
   7.1054e-15
>> averagePowerReplacementLLR(64, 100, -2) % M=64, avgPow=100, snrdB=-2
maxConstellationErr =
     0
maxOutputErr =
   8.8818e-16
>> averagePowerReplacementLLR(256, 117, 8) % M=256, avgPow=117, snrdB=8
maxConstellationErr =
     0
maxOutputErr =
   3.5527e-15

Functions shown here compares computation of average power normalization for LLR using alternative approach.

function averagePowerReplacementLLR(M, avgPow, snrdB)
% QAM alternative for "Average power" normalization method for
% LLR output when using functions
%
% M - Modulation order. Must be supported by QAM modulator-demodulator
% avgPow - Average constellation power
% snrdB - SNR, in dB. AWGN channel adds noise to provide this SNR. 
    
    minD = avgPow2MinD(avgPow, M);
    
    modObj = comm.RectangularQAMModulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Average power', ...
        'AveragePower', avgPow);
    
    % 1) The two constellations are same
    constellationSO = modObj((0:M-1)');
    constellationFcn = qammod((0:M-1)', M);
    scaledConstellationFcn = (minD/2) .* constellationFcn;
    err = constellationSO - scaledConstellationFcn;
    maxConstellationErr = max(abs(err))
    
    x = randi([0, M-1], 100, 1);
    
    y1 = modObj(x);
    
    % Add noise
    % Reset global rng stream for repeatable noise samples
    reset(RandStream.getGlobalStream);
    y1Rec = awgn(y1, snrdB, 'measured', 'db');
    
    % noise variance
    nv = mean(abs(y1).^2) / 10^(snrdB/10);
    
    demodObj = comm.RectangularQAMDemodulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Average power', ...
        'AveragePower', avgPow, ...
        'BitOutput', true, ...
        'DecisionMethod', 'Log-likelihood ratio', ...
        'Variance', nv);
    
    z1 = demodObj(y1Rec);
    
    % 2) Get the same output as System object using functions
    y2 = qammod(x, M);
    % Scale function's output so that it matches System object output. 
    y2Scaled = (minD/2) .* y2;
    
    % Add noise
    % Reset global rng stream for repeatable noise samples
    reset(RandStream.getGlobalStream);
    y2Rec = awgn(y2Scaled, snrdB, 'measured', 'db');

    % noise variance
    nv1 = mean(abs(y2Scaled).^2) / 10^(snrdB/10);
    
    % Create inputs required by utility function to compute LLR
    nBits = log2(M);
    bitwiseMapping = de2bi((0:M-1)', nBits, 'left-msb');    
    % c0 contains indices of mapping which has 0 at various bit positions
    [c0, ~] = find(bitwiseMapping==0);
    c0 = reshape(int32(c0), M/2, nBits);    
    % c1 contains indices of mapping which has 1 at various bit positions
    [c1, ~] = find(bitwiseMapping==1);
    c1 = reshape(int32(c1), M/2, nBits);
    
    z2 = comm.internal.utilities.computeLLRsim(y2Rec, M, nBits, ...
        scaledConstellationFcn, c0, c1, nv1);       
    
    maxOutputErr = max(abs(z1-z2))
       
end

function minD = avgPow2MinD(avgPow, M)
% Average power to minimum distance    
    nBits = log2(M);
    if (mod(nBits,2)==0)
        % Square QAM
        sf = (M - 1)/6;
    else
        % Cross QAM
        if (nBits > 4)
            sf = ((31 * M / 32) - 1) / 6;
        else
            sf = ((5 * M / 4) - 1) / 6;
        end
    end
    minD = sqrt(avgPow/sf);
end

Peak Power Normalization for LLR

Output shown here compares computation of peak power normalization for LLR using alternative approach. Functions used follow the output.

>> peakPowerReplacementLLR(8, 21, 0) % M=8, pkPow=21, snrdB=0
maxConstellationErr =
     0
maxOutputErr =
   8.8818e-16
>> peakPowerReplacementLLR(64, 7, 22) % M=64, pkPow=7, snrdB=22
maxConstellationErr =
     0
maxOutputErr =
   7.1054e-15
>> peakPowerReplacementLLR(512, 1000, -5) % M=512, pkPow=1000, snrdB=-5
maxConstellationErr =
     0
maxOutputErr =
   2.6645e-15
>> peakPowerReplacementLLR(1024, 1, 6) % M=1024, pkPow=1, snrdB=6
maxConstellationErr =
     0
maxOutputErr =
   3.5527e-15

Functions shown here compares computation of peak power normalization for LLR using alternative approach.

function peakPowerReplacementLLR(M, pkPow, snrdB)
% QAM alternative for "Peak power" normalization method for
% LLR output when using functions
%
% M - Modulation order. Must be supported by QAM modulator-demodulator
% avgPow - Average constellation power
% snrdB - SNR, in dB. AWGN channel adds noise to provide this SNR.
    
    minD = pkPow2MinD(pkPow, M);
    
    modObj = comm.RectangularQAMModulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Peak power', ...
        'PeakPower', pkPow);
    
    % 1) The two constellations are same
    constellationSO = modObj((0:M-1)');
    constellationFcn = qammod((0:M-1)', M);
    scaledConstellationFcn = (minD/2) .* constellationFcn;
    err = constellationSO - scaledConstellationFcn;
    maxConstellationErr = max(abs(err))
    
    x = randi([0, M-1], 100, 1);
    
    y1 = modObj(x);
    
    % Add noise
    % Reset global rng stream for repeatable noise samples
    reset(RandStream.getGlobalStream);
    y1Rec = awgn(y1, snrdB, 'measured', 'db');

    % noise variance
    nv = mean(abs(y1).^2) / 10^(snrdB/10);
    
    demodObj = comm.RectangularQAMDemodulator('ModulationOrder', M, ...
        'NormalizationMethod', 'Peak power', ...
        'PeakPower', pkPow, ...
        'BitOutput', true, ...
        'DecisionMethod', 'Log-likelihood ratio', ...
        'Variance', nv);
    
    z1 = demodObj(y1Rec);

    % 2) Get the same output as System object using functions
    y2 = qammod(x, M);
    % Scale function's output so that it matches System object output. 
    y2Scaled = (minD/2) .* y2;

    % Add noise
    % Reset global rng stream for repeatable noise samples
    reset(RandStream.getGlobalStream);
    y2Rec = awgn(y2Scaled, snrdB, 'measured', 'db');

    % noise variance
    nv1 = mean(abs(y2Scaled).^2) / 10^(snrdB/10);
    
    % Create inputs required by utility function to compute LLR
    nBits = log2(M);
    bitwiseMapping = de2bi((0:M-1)', nBits, 'left-msb');    
    % c0 contains indices of mapping which has 0 at various bit positions
    [c0, ~] = find(bitwiseMapping==0);
    c0 = reshape(int32(c0), M/2, nBits);    
    % c1 contains indices of mapping which has 1 at various bit positions
    [c1, ~] = find(bitwiseMapping==1);
    c1 = reshape(int32(c1), M/2, nBits);
    
    z2 = comm.internal.utilities.computeLLRsim(y2Rec, M, nBits, ...
        scaledConstellationFcn, c0, c1, nv1);       
    
    maxOutputErr = max(abs(z1-z2))
end

function minD = pkPow2MinD(pkPow, M)
% Peak power to minimum distance    
    nBits = log2(M);
    if (mod(nBits,2)==0)
        % Square QAM
        sf = 0.5*M - sqrt(M) + 0.5;
    else
        % Cross QAM
        mBy32 = M/32;
        if (nBits > 4)
            sf = (13 * mBy32) - (5 * sqrt(mBy32)) + 0.5;
        else
            sf = (20 * mBy32) - (6 * sqrt(mBy32)) + 0.5;
        end
    end
    minD = sqrt(pkPow/sf);
end

Documentation

comm.RectangularQAMModulator

Description

Construction

Properties

Methods

Algorithms

Compatibility Considerations

`comm.RectangularQAMModulator` will be removed in a future release. Use `qammod` instead.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

See Also

Functions

Objects

Communications Toolbox Documentation

Support

Documentation

comm.RectangularQAMModulator

Description

Construction

Properties

Methods

Algorithms

Compatibility Considerations

comm.RectangularQAMModulator will be removed in a future release. Use qammod instead.

Extended Capabilities

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

See Also

Functions

Objects

Communications Toolbox Documentation

Support

`comm.RectangularQAMModulator` will be removed in a future release. Use `qammod` instead.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.