Poisson Integer Generator

Generate Poisson-distributed random integers

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Libraries:
Communications Toolbox / Comm Sources / Random Data Sources

Description

The Poisson Integer Generator block generates random integers using a Poisson distribution. The probability of generating a nonnegative integer k is $${\lambda }^k\exp (-\lambda )/(k!)$$, where λ is a positive number known as the Poisson parameter.

When you use this block to generate noise in a binary transmission channel, typical values for the Poisson parameter(Lambda) parameter are much less than 1.

Examples

Generate Poisson Integers

Generate Poisson integers (k) and plot the distribution for various values.

The doc_poisson_gen_src model generates integers with Poisson distributions for values indicated on the histogram plot. The model configures the Poisson Integer Generator blocks to different values and common values for the sample time and samples per frame. To configure the blocks, the model assigns workspace variables spf and Ts, initializing them by using the InitFcn callback. For more information, see Model Callbacks (Simulink).

Run the model and plot the Poisson distribution output for each value.

Limitations

• To use this block in a For Each Subsystem (Simulink) you must set Source of initial seed to Auto and the model to Normal or Accelerator simulation mode. This ensures that each run will generate independent noise samples.

Ports

Output

Out — Random integer output

scalar | vector | matrix

Random integer output, returned as a scalar, vector, or matrix. This port is unnamed on the block. Set the data type by using the Output data type parameter.

• The number of rows in the output data corresponds to the number of samples in one frame and equals the value of the Samples per frame parameter.

• The number of columns in the output signal corresponds to the number of channels and equals the number of elements in the Poisson parameter(Lambda) parameter.

Parameters

To edit block parameters interactively, use the Property Inspector. From the Simulink^® Toolstrip, on the Simulation tab, in the Prepare gallery, select Property Inspector.

Poisson parameter(Lambda) — Poisson parameter λ

0.1 (default) | positive scalar | vector of positive scalars

The Poisson parameter λ, specified as a positive scalar or row vector of positive scalar values.

• If you set this parameter to a scalar value, then every element in the output vector shares the same Poisson parameter.

• If you set this parameter to a row vector, then the number of elements correspond to the number of independent channels output from the block.

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

Auto (default) | Parameter

Source of the initial seed for the random number generator, specified as either:

• Auto –– The block uses the global random number stream. If you use the parsim (Simulink) function, see Tips.

• Parameter –– The block sets the random number generator seed to 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 integer

Initial seed value for the random number generator, specified as a nonnegative integer. If this parameter is a constant, then the resulting sequence is repeatable.

Tunable: Yes

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) | uint8 | uint16 | uint32 | boolean

Output data type, specified as double, uint8, uint16, uint32, or boolean.

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.

For more information, see Interpreted Execution vs. Code Generation (Simulink).

Block Characteristics

Data Types	double | integer
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 one sample every second. If you increase Samples per frame to 10, then the block outputs a 10-by-1 vector every 10 seconds. This timing ensures that the equivalent output rate is not dependent on the Samples per frame parameter.

Tips

Set Source of initial seed to Parameter and make sure that all blocks have different Initial seed values when running simulations in parallel using the parsim (Simulink) function, such as for Monte Carlo simulation runs.

Otherwise, the different workers may generate the same random numbers (leading to misleading results) for these model configurations:

• If the model is in rapid accelerator mode.

• If this block has Simulate using set to Code generation and Source of initial seed set to Auto.

For more information, see Choosing a Simulation Mode (Simulink).

Extended Capabilities

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

Version History

Introduced before R2006a

R2024a: Random number stream processing updates

Updates to the random number stream processing for the Poisson Integer Generator block remove some limitations. Before this update, certain operating modes resulted in the block generating the same numbers every time the simulation ran. This change removes the limitations noted for those operating modes.

R2024a: Seed value must be integer

The Poisson Integer Generator block requires a nonnegative integer value for the Seed parameter.

R2020a: Existing models automatically update this block to current version

Starting in R2020a, Simulink no longer allows you to use the Poisson Integer Generator block version available before R2015b.

• Existing models automatically update to load the Poisson Integer Generator block version announced in R2015b. For more information on block forwarding, see Maintain Compatibility of Library Blocks Using Forwarding Tables (Simulink).

• 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 frame attribute.