mil188qamdemod

R2026b

MIL-STD-188-110 B/C standard-specific quadrature amplitude demodulation

Syntax

Z = mil188qamdemod(Y,M)

Z = mil188qamdemod(Y,M,Name=Value)

Description

Z = mil188qamdemod(Y,M) demodulates the input signal Y, that was modulated in accordance with MIL-STD-188-110 and the modulation order M. For a description of MIL-STD-188-110 QAM demodulation, see MIL-STD-188-110 QAM Hard Demodulation and MIL-STD-188-110 QAM Soft Demodulation.

Z = mil188qamdemod(Y,M,Name=Value) specifies optional name-value arguments. For example, mil188qamdemod(Y,M,PlotConstellation=true) specifies modulation order M and plots the constellation. Specify name-value arguments after all other input arguments.

Examples

Demodulate MIL-STD-188-110B Specific 16-QAM Signal

Demodulate a 16-QAM signal that was modulated as specified in MIL-STD-188-110B. Plot the received constellation and verify that the output matches the input.

Set the modulation order and generate random data.

M = 16;
numSym = 20000;
x = randi([0 M-1],numSym,1);

Modulate the data and pass through a noisy channel.

txSig = mil188qammod(x,M);
rxSig = awgn(txSig,25,'measured');

Plot the transmitted and received signal.

plot(rxSig,'b*')
hold on; grid
plot(txSig,'r*')
xlim([-1.5 1.5]);
ylim([-1.5 1.5])
xlabel('In-Phase')
ylabel('Quadrature')
legend('Received constellation','Reference constellation')

Demodulate the received signal. Compare the demodulated data to the original data.

z = mil188qamdemod(rxSig,M);
isequal(x,z)

ans = logical
   1

Demodulate MIL-STD-188-110C Specific 64-QAM Signal

Demodulate a 64-QAM signal that was modulated as specified in MIL-STD-188-110C. Compute hard decision bit output and verify that the output matches the input.

Set the modulation order and generate random bit data.

M = 64;
numBitsPerSym = log2(M);
x = randi([0 1],1000*numBitsPerSym,1);

Modulate the data. Use name-value pairs to specify bit input data and to plot the constellation.

txSig = mil188qammod(x,M,InputType='bit',PlotConstellation=true);

Demodulate the received signal. Compare the demodulated data to the original data.

z = mil188qamdemod(txSig,M,OutputType='bit');
isequal(z,x)

ans = logical
   1

Soft Bit Demodulate MIL-STD-188-110 Specific 32-QAM Signal

Demodulate a 32-QAM signal and calculate soft bits.

Set the modulation order and generate a random bit sequence.

M = 32;
numSym = 20000;
numBitsPerSym = log2(M);
x = randi([0 1], numSym*numBitsPerSym,1);

Modulate the data. Use name-value pairs to specify bit input data and unit average power, and to plot the constellation.

txSig = mil188qammod(x,M,InputType='bit',UnitAveragePower=true, ...
    PlotConstellation=true);

Pass the transmitted data through white Gaussian noise.

rxSig = awgn(txSig,10,'measured');

View the constellation using a scatter plot.

scatterplot(rxSig) 

Demodulate the signal, computing soft bits using the approximate LLR algorithm.

z = mil188qamdemod(rxSig,M,OutputType='approxllr', ...
    NoiseVariance=10^(-1));

Input Arguments

Y — Modulated signal

scalar | vector | matrix

Modulated signal, specified as a complex scalar, vector, or matrix. When Y is a matrix, each column is treated as an independent channel.

Y must be modulated in accordance with MIL-STD-188-110 [1].

Data Types: single | double
Complex Number Support: Yes

M — Modulation order

positive integer power of two

Modulation order, specified as a positive integer power of two. The modulation order specifies the total number of points in the signal constellation.

Example: 16

Data Types: double

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: Z = mil188qamdemod(Y,M,OutputType='bit',OutputDataType='single');

OutputType — Output type

'integer' (default) | 'bit' | 'llr' | 'approxllr'

Output type, specified as 'integer', 'bit', 'llr', or 'approxllr'. For a description of returned output, see Z.

OutputDataType — Data type of output

'double' (default) | ...

Data type of the output, specified as one of the data types listed in this table. Acceptable values for OutputDataType depend on the OutputType value.

OutputType Value	Acceptable OutputDataType Values
'integer'	'double', 'single', 'int8', 'int16', 'int32', 'uint8', 'uint16', or 'uint32'
'bit'	'double', 'single', 'int8', 'int16', 'int32', 'uint8', 'uint16', 'uint32', or 'logical'

The default value is the data type of input Y.

Dependencies

To enable this argument, set OutputType to either 'integer' or 'bit'. Otherwise, the output data type matches the data type of input Y.

UnitAveragePower — Unit average power flag

false or 0 (default) | true or 1

Unit average power flag, specified as logical 0 (false) or 1 (true).

• When UnitAveragePower is true, the function scales the constellation to an average power of 1 watt referenced to 1 ohm.

• When UnitAveragePower is false, the function scales the constellation based on specifications in the relevant standard, as described in [1].

Data Types: logical

NoiseVariance — Noise variance

1 (default) | positive scalar | vector of positive values

Noise variance, specified as one of these options:

• Positive scalar — The function uses the same noise variance value on all input elements.

• Vector of positive values — For all the elements of the input along the corresponding last dimension, the function uses the noise variance specified by each element of the vector. The vector length must be equal to the number of columns in the input signal.

Dependencies

This name-value pair argument applies only when OutputType is set to 'llr' or 'approxllr'.

Data Types: double

PlotConstellation — Option to plot constellation

false or 0 (default) | true or 1

Option to plot constellation, specified as logical 0 (false) or 1 (true). To plot the constellation, set PlotConstellation to true.

Data Types: logical

Output Arguments

Z — Demodulated signal

scalar | vector | matrix

Demodulated signal, returned as a scalar, vector, or matrix. The value and dimension of this output vary depending on the specified OutputType value, as shown in this table.

OutputType Value	Return Value of mil188qamdemod	Dimensions of Z
'integer'	Demodulated integer values from 0 to (M – 1)	Z has the same dimensions as input Y.
'bit'	Demodulated bits	The number of rows in Z is log2(M) times the number of rows in Y. Each demodulated symbol is mapped to a group of log2(M) elements in a column, where the first element represents the MSB and the last element represents the LSB.
'llr'	Log-likelihood ratio value for each bit
'approxllr'	Approximate log-likelihood ratio value for each bit

Algorithms

MIL-STD-188-110 QAM Hard Demodulation

The hard demodulation algorithm uses optimum decision region-based demodulation. Since all the constellation points are equally probable, maximum a posteriori probability (MAP) detection reduces to a maximum likelihood (ML) detection. The ML detection rule is equivalent to choosing the closest constellation point to the received symbol. The decision region for each constellation point is designed by drawing perpendicular bisectors between adjacent points. A received symbol is mapped to the proper constellation point based on which decision region it lies in.

Since all MIL-STD constellations are quadrant-based symmetric, for each symbol the optimum decision region-based demodulation:

• Maps the received symbol into the first quadrant

• Chooses the decision region for the symbol

• Maps the constellation point back to its original quadrant using the sign of real and imaginary parts of the received symbol

MIL-STD-188-110 QAM Soft 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.

References

[1] MIL-STD-188-110B & C: "Interoperability and Performance Standards for Data Modems." Department of Defense Interface Standard, USA.

Extended Capabilities

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

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Version History

Introduced in R2018a

R2026b: Update exact LLR algorithm to use LogSumExp for improved numerical stability

Starting in R2026b, the exact LLR algorithm uses LogSumExp for improved numerical stability, returning finite values for extreme noise variance or signal power conditions. Previously, outputs could be Inf, –Inf, or NaN in these cases. For more information, see Exact LLR Algorithm.