comm.gpu.AWGNChannel

(To be removed) Add white Gaussian noise to input signal with GPU

comm.gpu.AWGNChannel will be removed in a future release. Use awgn instead. (since R2024b) For more information on updating your code, see Version History.

Description

The comm.gpu.AWGNChannel System object™ adds white Gaussian noise to an input signal using a graphics processing unit (GPU).

To add white Gaussian noise to an input signal:

1. Create the comm.gpu.AWGNChannel 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

gpuawgnchan = comm.gpu.AWGNChannel

gpuawgnchan = comm.gpu.AWGNChannel(Name=Value)

Description

gpuawgnchan = comm.gpu.AWGNChannel creates a GPU-based channel System object that adds white Gaussian noise to the input signal.

gpuawgnchan = comm.gpu.AWGNChannel(Name=Value) sets properties using one or more name-value arguments. For example, SamplesPerSymbol=4 specifies the samples per symbol value as 4.

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.

NoiseMethod — Noise level method

"Signal to noise ratio (Eb/No)" (default) | "Signal to noise ratio (Es/No)" | "Signal to noise ratio (SNR)" | "Variance"

Noise level method, specified as "Signal to noise ratio (Eb/No)", "Signal to noise ratio (Es/No)", "Signal to noise ratio (SNR)", or "Variance". For more information, see Relationship Between Eb/No, Es/No, and SNR Modes and Specifying Variance Directly or Indirectly.

EbNo — Ratio of energy per bit to noise power spectral density

10 (default) | scalar | row vector

Ratio of energy per bit to noise power spectral density (Eb/No) in decibels, specified as a scalar or 1-by-N_C vector. N_C is the number of channels.

Tunable: Yes

Dependencies

To enable this property, set NoiseMethod to "Signal to noise ratio (Eb/No)".

Data Types: double

EsNo — Ratio of energy per symbol to noise power spectral density

10 (default) | scalar | row vector

Ratio of energy per symbol to noise power spectral density (Es/No) in decibels, specified as a scalar or 1-by-N_C vector. N_C is the number of channels.

Tunable: Yes

Dependencies

To enable this property, set NoiseMethod to "Signal to noise ratio (Es/No)".

Data Types: double

SNR — Ratio of signal power to noise power

10 (default) | scalar | row vector

Ratio of signal power to noise power in decibels, specified as a scalar or 1-by-N_C vector. N_C is the number of channels.

Tunable: Yes

Dependencies

To enable this property, set NoiseMethod to "Signal to noise ratio (SNR)".

Data Types: double

BitsPerSymbol — Number of bits per symbol

1 (default) | positive integer

Number of bits per symbol, specified as a positive integer.

Dependencies

To enable this property, set NoiseMethod to "Signal to noise ratio (Eb/No)".

Data Types: double

SignalPower — Input signal power

1 (default) | positive scalar | row vector

Input signal power in watts, specified as a positive scalar or 1-by-N_C vector. N_C is the number of channels. The object assumes a nominal impedance of 1 ohms.

Tunable: Yes

Dependencies

To enable this property, set NoiseMethod to "Signal to noise ratio (Eb/No)", "Signal to noise ratio (Es/No)", or "Signal to noise ratio (SNR)".

Data Types: double

SamplesPerSymbol — Number of samples per symbol

1 (default) | positive integer | row vector

Number of samples per symbol, specified as a positive integer or 1-by-N_C vector. N_C is the number of channels.

Dependencies

To enable this property, set NoiseMethod to "Signal to noise ratio (Eb/No)" or "Signal to noise ratio (Es/No)".

Data Types: double

VarianceSource — Source of noise variance

"Property" (default) | "Input port"

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

• To specify the noise variance value using the Variance property, set VarianceSource to "Property".

• To specify the noise variance value using an input to the object, when you call it as a function, set VarianceSource to "Input port".

For more information, see Specifying Variance Directly or Indirectly.

Dependencies

To enable this property, set NoiseMethod to "Variance".

Variance — White Gaussian noise variance

1 (default) | positive scalar | row vector

White Gaussian noise variance, specified as a positive scalar or 1-by-N_C vector. N_C is the number of channels.

Tunable: Yes

Dependencies

To enable this property, set NoiseMethod to "Variance" and set VarianceSource to "Property".

Data Types: double

RandomStream — Source of random number stream

"Global stream" (default)

Source of random number stream, specified as "Global stream". When you set RandomStream to "Global stream", the object uses the MATLAB^® default random stream to generate random numbers. To generate reproducible numbers using this object, use the rng function.

For a complex input signal, the object creates the random data as follows:

noise = randn(NS,NC)+1i(randn(NS,NC))

N_S is the number of samples and N_C is the number of channels.

Dependencies

To enable this property, set NoiseMethod to "Variance".

Seed — Initial seed

67 (default) | nonnegative integer

Initial seed of the mt19937ar random number stream, specified as a nonnegative integer. For each call to the reset function, the object reinitializes the mt19937ar random number stream to the Seed value.

Dependencies

To enable this property, set RandomStream to "mt19937ar with seed".

Data Types: double

Usage

Syntax

y = gpuawgnchan(x)

y = gpuawgnchan(x,var)

Description

y = gpuawgnchan(x) adds white Gaussian noise, as specified by gpuawgnchan, to the input signal. The result is returned in y.

y = gpuawgnchan(x,var) specifies the variance of the white Gaussian noise. This syntax applies when you set NoiseMethod to "Variance" and VarianceSource to "Input port".

For example:

gpuawgnchan = comm.AWGNChannel('NoiseMethod','Variance', ...
     'VarianceSource','Input port');
var = 12;
...
y = gpuawgnchan(x,var);

Input Arguments

x — Input signal

scalar | vector | matrix

Input signal, specified as a scalar, an N_S-element vector, or an N_S-by-N_C matrix. N_S is the number of samples and N_C is the number of channels.

To decrease data transfer latency, format the input signal as a gpuArray (Parallel Computing Toolbox) object. For more information, see Array Processing with GPU-Based System Objects.

Data Types: double | single
Complex Number Support: Yes

var — Variance of additive white Gaussian noise

positive scalar | row vector

Variance of additive white Gaussian noise, specified as a positive scalar or 1-by-N_C vector. N_C is the number of channels, as determined by the number of columns in the input signal matrix.

Data Types: double | single

Output Arguments

y — Output signal

matrix

Output signal, returned with the same dimensions as x.

Data Types: double | single
Complex Number Support: Yes

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)

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

GPU AWGN Channel

Create an AWGN channel object that uses a GPU.

Specify the modulation order and generate PSK-modulated random data.

M = 8;
modData = pskmod(randi([0 M-1],1000,1),M,pi/M);

Create an AWGN channel object that uses a GPU. Pass the modulated data through the channel.

gpuChannel = comm.gpu.AWGNChannel(EbNo=15,BitsPerSymbol=log2(M));
channelOutput = gpuChannel(modData);

More About

Array Processing with GPU-Based System Objects

A GPU-based System object accepts typical MATLAB arrays or gpuArray (Parallel Computing Toolbox) objects. The output signal data type matches the input signal data type.

• When the input signal to the GPU-based System object is a gpuArray object, calculations take place entirely on the GPU, the data remains on the GPU, and the output signal is a gpuArray object. Passing gpuArray arguments minimizes data transfer latency by limiting the number of data transfers between the CPU and the GPU when your simulation runs. For more information, see Establish Arrays on a GPU (Parallel Computing Toolbox).

• When the input signal is a MATLAB array, the GPU-based System object transfers the data between the CPU and the GPU for each object call. The output signal is a MATLAB array and data transfer latency occurs.

Algorithms

Relationship Between Eb/No, Es/No, and SNR Modes

For uncoded complex input signals, comm.gpu.AWGNChannel relates E_b/N_0, E_s/N₀, and SNR according to these equations:

E_s/N₀ = N_sps × SNR

E_s/N₀ = E_b/N₀ + 10log₁₀(k) in dB

where

• E_s represents the signal energy in joules.

• E_b represents the bit energy in joules.

• N₀ represents the noise power spectral density in watts/Hz.

• N_sps represents the number of samples per symbol, SamplesPerSymbol.

• k represents the number of information bits per input symbol, BitsPerSymbol.

For real signal inputs, the comm.gpu.AWGNChannel relates E_s/N₀ and SNR according to this equation:

E_s/N₀ = 0.5 (N_sps) × SNR

Note

• All values of power assume a nominal impedance of 1 ohm.

• The equation for the real case differs from the corresponding equation for the complex case by a factor of 2. Specifically, the object uses a noise power spectral density of N₀/2 watts/Hz for real input signals, versus N₀ watts/Hz for complex signals.

For more information, see AWGN Channel Noise Level.

Specifying Variance Directly or Indirectly

To directly specify the variance of the noise generated by comm.gpu.AWGNChannel, specify VarianceSource as:

• "Property", then set NoiseMethod to "Variance" and specify the variance with the Variance property.

• "Input port", then specify the variance level for the object as an input with an input argument, var.

To specify variance indirectly, that is, to have it calculated by comm.gpu.AWGNChannel, specify VarianceSource as "Property" and the NoiseMethod as:

• "Signal to noise ratio (Eb/No)", where the object uses these properties to calculate the variance:

  • EbNo, the ratio of bit energy to noise power spectral density

  • BitsPerSymbol

  • SignalPower, the actual power of the input signal samples

  • SamplesPerSymbol

• "Signal to noise ratio (Es/No)", where the object uses these properties to calculate the variance:

  • EsNo, the ratio of signal energy to noise power spectral density

  • SignalPower, the actual power of the input signal samples

  • SamplesPerSymbol

• "Signal to noise ratio (SNR)", where the object uses these properties to calculate the variance:

  • SNR, the ratio of signal power to noise power

  • SignalPower, the actual power of the input signal samples

Changing the number of samples per symbol (SamplesPerSymbol) affects the variance of the noise added per sample, which also causes a change in the final error rate.

NoiseVariance = SignalPower × SamplesPerSymbol / 10^(EsNo/10)

Tip

Select the number of samples per symbol based on what constitutes a symbol and the oversampling applied to it. For example, a symbol could have 3 bits and be oversampled by 4. For more information, see AWGN Channel Noise Level.

References

[1] Proakis, John G. Digital Communications. 4th ed. New York: McGraw Hill, 2001.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This System object supports GPU array inputs. For more information, see Accelerate Simulation Using GPUs.

Version History

Introduced in R2012a

R2024b: To be removed

comm.gpu.AWGNChannel will be removed. Use awgn instead.