Poisson Integer Generator

Generate Poisson-distributed random integers

Library

Random Data Sources sublibrary of Comm Sources

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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.

You can use the Poisson Integer Generator to generate noise in a binary transmission channel. In this case, the Poisson parameter Lambda should be less than 1, usually much less.

Attributes of Output Signal

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

Parameters

Lambda

The Poisson parameter λ. Specify λ as a scalar or row vector whose elements are real numbers. If Lambda is a scalar, then every element in the output vector shares the same Poisson parameter. If Lambda is a row vector, then the number of elements correspond to the number of independent channels output from the block.

Source of initial seed

The source of the initial seed for the random number generator. Specify the source as either Auto or Parameter. When set to Auto, the block uses the global random number stream.

Note

When Source of initial seed is Auto in Code generation mode, the random number generator uses an initial seed of zero. Therefore, the block generates the same random numbers each time it is started. Use Interpreted execution to ensure that the model uses different initial seeds. If Interpreted execution is run in Rapid accelerator mode, then it behaves the same as Code generation mode.

Initial seed

The initial seed value for the random number generator. Specify the seed as a nonnegative integer scalar. Initial seed is available when the Source of initial seed parameter is set to Parameter.

Sample time

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

Output data type

The output type of the block can be specified as a boolean, uint8, uint16, uint32, single, or double. The default is double.

Simulate using

Select the simulation mode.

Code generation

On the first model run, simulate and generate code. If the structure of the block does not change, subsequent model runs do not regenerate the code.

If the simulation mode is Code generation, System objects corresponding to the blocks accept a maximum of nine inputs.

Interpreted execution

Simulate model without generating code. This option results in faster start times but can slow subsequent simulation performance.

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: 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 the frame attribute in the R2015b Release Notes.