Bernoulli Binary Generator

Generate Bernoulli-distributed random binary numbers

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Libraries:
Communications Toolbox / Comm Sources / Random Data Sources

Description

The Bernoulli Binary Generator block generates random binary numbers using a Bernoulli distribution. Use this block to generate random data bits to simulate digital communication systems and obtain performance metrics such as bit error rate. The Bernoulli distribution with parameter p produces zero with probability p and one with probability 1-p. The Bernoulli distribution has mean value 1-p and variance p(1-p). The Probability of zero parameter specifies p and can be any real number in range [0, 1].

The output signal can be a column or row vector, two-dimensional matrix, or scalar. The number of rows in the output signal corresponds to the number of samples in one frame and is set by the Samples per frame parameter. The number of columns in the output signal corresponds to the number of channels and is set by the number of elements in the Probability of zero parameter. For more details, see Sources and Sinks in Communications Toolbox™ User's Guide

Examples

Generate Bernoulli Binary Numbers

Use the Open model button to open the Bernoulli generator model. The model generates binary data, applies BPSK modulation, and displays the output.

Open Model

Ports

Output

Out — Output data signal

scalar | vector | matrix

Output data signal, returned as a scalar, vector, or matrix.

Data Types: double

Parameters

Probability of zero — Probability of generating zero at output

0.5 (default) | integer in the range [0, 1] | vector of integers in the range [0, 1]

Probability of zero must be in the range of [0, 1]. The number of elements in the Probability of zero parameter corresponds to the number of independent channels output from the block. The Bernoulli distribution with parameter p produces zero with probability p and one with probability 1-p.

Source of initial seed — Source of initial seed for random number generator

Auto (default) | Parameter

Select Parameter to use the Initial seed parameter to specify the initial seed for the random number generator.

Note

When the Source of initial seed parameter is set to Auto and the Simulate using parameter is set to Code generation, the random number generator uses an initial seed of zero. In this case, the block generates the same random numbers each time it is started. To ensure that the model uses different initial seeds, set Simulate using parameter to Interpreted execution. If you run Interpreted execution in Rapid accelerator mode, then the model behaves the same as Code generation mode.

Dependencies

Select Auto for the block to use the global random number stream as the initial seed. For more information, see Managing the Global Stream Using RandStream and Random Number Generators.

Initial seed — Initial seed for random number generator

nonnegative scalar

If you set the Initial seed parameter to a constant value, then the resulting sequence is repeatable.

Dependencies

To enable this parameter, set the Source of initial seed to Parameter.

Sample time — Sample time of output signal

1 (default) | -1 | positive scalar

Positive scalars specify the time in seconds between each sample of the output signal. If you set the Sample time to -1, the output signal inherits the sample time from downstream. For information on the relationship between the Sample time and Samples per frame parameters, see Sample Timing.

Samples per frame — Samples per frame of output signal

1 (default) | positive scalar

Samples per frame in one channel of the output signal, specified as a positive integer. For information on the relationship between Sample time and Samples per frame, see Sample Timing.

Output data type — Data type of output signal

double (default) | boolean

Select the data type for the output signal.

Simulate using — Type of simulation to run

Code generation (default) | Interpreted execution

Type of simulation to run, specified as Code generation or Interpreted execution.

• Code generation — Simulate the model by using generated C code. The first time you run a simulation, Simulink^® generates C code for the block. The model reuses the C code for subsequent simulations unless the model changes. This option requires additional startup time, but the speed of the subsequent simulations is faster than with the Interpreted execution option.

• Interpreted execution — Simulate the model by using the MATLAB^® interpreter. This option shortens startup time, but the speed of subsequent simulations is slower than with the Code generation option. In this mode, you can debug the source code of the block.

Block Characteristics

Data Types	Boolean | double | integer | single
Multidimensional Signals	no
Variable-Size Signals	no

More About

Sample Timing

The time between output updates is equal to the product of the Samples per frame and Sample time parameter values. For example, if Sample time and Samples per frame each equal 1, the block outputs a sample every second. If you increase Samples per frame to 10, then the block outputs a 10-by-1 vector every 10 seconds. This ensures that the equivalent output rate is not dependent on the Samples per frame parameter.

Extended Capabilities

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

Version History

Introduced before R2006a

R2020a: Bernoulli Binary Generator block update supported in Upgrade Advisor

Starting in R2020a, Bernoulli Binary Generator block allows you to use the Upgrade Advisor. You can update to the block version announced in R2015b or keep the block version available before R2015b.

• Use the Upgrade Advisor to update existing models that include the Bernoulli Binary Generator block.

• Behavior of the random number generator is changed. The statistics are improved. For more information, see Source blocks output frames of contiguous time samples but do not use the frame attribute in the R2015b Release Notes.