genqammod
General quadrature amplitude modulation (QAM)
Syntax
Description
Examples
Estimate Symbol Rate for General QAM Modulation in AWGN Channel
Transmit and receive data using a nonrectangular 16-ary constellation in the presence of Gaussian noise. Show the scatter plot of the noisy constellation and estimate the symbol error rate (SER) for two different SNRs.
Create a 16-QAM constellation based on the V.29 standard for telephone-line modems.
c = [-5 -5i 5 5i -3 -3-3i -3i 3-3i 3 3+3i 3i -3+3i -1 -1i 1 1i]; sigpower = pow2db(mean(abs(c).^2)); M = length(c);
Generate random symbols.
data = randi([0 M-1],2000,1);
Modulate the data by using the genqammod
function. General QAM modulation is necessary because the custom constellation is not rectangular.
modData = genqammod(data,c);
Pass the signal through an AWGN channel with a 20 dB SNR.
rxSig = awgn(modData,20,sigpower);
Display a scatter plot of the received signal and the reference constellation c
.
h = scatterplot(rxSig); hold on scatterplot(c,[],[],'r*',h) grid hold off
Demodulate the received signal by using the genqamdemod
function. Determine the number of symbol errors and the SER.
demodData = genqamdemod(rxSig,c); [numErrors,ser] = symerr(data,demodData)
numErrors = 4
ser = 0.0020
Repeat the transmission and demodulation process with an AWGN channel with a 10 dB SNR. Determine the SER for the reduced SNR. As expected, the performance degrades when the SNR is decreased.
rxSig = awgn(modData,10,sigpower); demodData = genqamdemod(rxSig,c); [numErrors,ser] = symerr(data,demodData)
numErrors = 457
ser = 0.2285
General QAM Modulation and Demodulation
Create the points that describe a hexagonal constellation.
inphase = [1/2 1 1 1/2 1/2 2 2 5/2]; quadr = [0 1 -1 2 -2 1 -1 0]; inphase = [inphase;-inphase]; inphase = inphase(:); quadr = [quadr;quadr]; quadr = quadr(:); const = inphase + 1i*quadr;
Plot the constellation.
h = scatterplot(const);
Generate input data symbols. Modulate the symbols using this constellation.
x = [3 8 5 10 7]; y = genqammod(x,const);
Demodulate the modulated signal, y
.
z = genqamdemod(y,const);
Plot the modulated signal in same figure.
hold on; scatterplot(y,1,0,'ro',h); legend('Constellation','Modulated signal'); hold off;
Determine the number of symbol errors between the demodulated data to the original sequence.
numErrs = symerr(x,z)
numErrs = 0
Input Arguments
X
— Message signal
scalar | vector | matrix | numeric array | dlarray
object
Message signal, specified as a scalar, vector, matrix, numeric array, or a dlarray
(Deep Learning Toolbox)
object. For more information, see Array Support. The message signal
must consist of integers in the range
[0,length
(const
) – 1]. If X
is a matrix with multiple rows, the function processes the columns independently.
Data Types: double
| single
| fi
| int8
| int16
| uint8
| uint16
const
— Signal mapping
complex vector
Signal mapping, specified as a complex vector.
Data Types: double
| single
| fi
| int8
| int16
| uint8
| uint16
Complex Number Support: Yes
Output Arguments
Y
— Complex envelope
scalar | vector | matrix | 3-D array
Complex envelope, returned as a scalar, vector, matrix, or 3-D array of numeric
values. The length of Y
is the same as the length of input
X
.
Data Types: double
| single
| fi
| int8
| int16
| uint8
| uint16
More About
Array Support
The genqammod function supports input signals represented in a
numeric array, dlarray
(Deep Learning Toolbox), or
gpuArray
(Parallel Computing Toolbox). If inputs are specified as a
combination of dlarray
and gpuArray
, the returned
matrix is a dlarray
object on the GPU.
The number of batch observations (NB) is an optional dimension that can be added to the input for all supported data types.
X
— The input data can be a 3-D array, specified as NSym-by-NChan-by-NB array.
NSym is the number of symbols. NChan is the number of channels.
For a list of Communications Toolbox™ features that support dlarray
objects, see AI for Wireless.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function supports GPU array inputs. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced before R2006aR2024a: Add deep learning array support
The genqammod
function adds support for dlarray
(Deep Learning Toolbox) object
processing for deep learning applications.
R2024a: Add GPU array support
The genqammod
function adds support for gpuArray
(Parallel Computing Toolbox) object processing to run code on a graphics processing unit
(GPU).
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