Random Integer Generator

Generate integers randomly distributed in specified range

Library:
Communications Toolbox / Comm Sources / Random Data Sources

Description

The Random Integer Generator block generates uniformly distributed random integers in the range [0, M-1], where M is specified by the Set size parameter. Use this block to generate random binary-valued or integer-valued data.

To ensure that the model uses different initial seeds, set the Simulate using parameter to Interpreted execution, and run the simulation in Normal or Accelerator mode. For more information, see Limitations.

Limitations

In Rapid Accelerator simulation mode, when you set Simulate using to Interpreted execution and Source of initial seed to Auto, the block generates the same numbers every time the simulation runs. This behavior is equivalent to setting Source of initial seed to Parameter and setting Initial seed to 0.
In all simulation modes (Normal, Accelerator, and Rapid Accelerator), when you set Simulate using to Code generation and Source of initial seed to Auto, the block generates the same numbers every time the simulation runs. This behavior is equivalent to setting Source of initial seed to Parameter and Initial seed to 0.

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

Ports

Output

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`Out` — Random integer output
scalar | vector | matrix

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

The number of rows in the output data equals the value of the Samples per frame parameter and corresponds to the number of samples in one frame. The number of columns in the output data equals the number of elements in the Set size parameter and corresponds to the number of channels.

Parameters

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`Set size` — Set size
`8` (default) | positive integer | row vector of positive integers

Set size,M, specified as a positive integer or row vector of positive integers. The block generates integers in the range [0, (M – 1)]. The number of elements in Set size corresponds to the number of independent channels output from the block.

If Set size is a scalar, then all output random variables are independent and identically distributed (i.i.d.).
If Set size is a vector, then the length of the vector determines the number of output channels. The channels can have differing output ranges.

`Source of initial seed` — Source of initial seed
`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
Parameter –– the block sets the random number generator seed to Initial seed

`Initial seed` — Initial seed value
`0` (default) | nonnegative integer

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

Dependencies

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

`Sample time` — Sample time
`1` (default) | -1 | positive scalar

Output sample time, specified as -1 or a positive scalar that represents the time between each sample of the output signal. If Sample time is set to -1, the sample time is inherited from downstream. For information on the relationship between Sample time and Samples per frame, see Sample Timing.

`Samples per frame` — Samples per frame
`1` (default) | positive integer

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` — Output data type
`double` (default) | `single` | `uint8` | `uint16` | `uint32` | `boolean`

Output data type, specified as double, single, uint8, uint16, uint32, or boolean. If this parameter is set to boolean, you must set the Set size parameter to 2.

`Simulate using` — Type of simulation to run
`Interpreted execution` (default) | `Code generation`

Type of simulation to run, specified as:

Code generation –– Simulate the model using generated C code. The first time you run a simulation, Simulink^® generates C code for the block. The C code is reused for subsequent simulations as long as the model does not change. This option requires additional startup time.
Interpreted execution –– Simulate the model using the MATLAB^® interpreter. This option shortens startup time. In Interpreted execution mode, you can debug the source code of the block.

Model Examples

Plot Noisy QPSK Constellation

Open the QPSK model. The model generates QPSK data, applies white noise, and displays the resulting constellation diagram.

Random Integer Generator Initial Seed Source

Demonstrate that the Random Integer Generator sequences output when the initial seed source is Auto are different and that when the initial seed source is Parameter the sequences are repeatable when the seed is not changed.

Viterbi Decoding of Binary Symmetric Channel Impaired Data

The cm_ex_viterbi_decode_binary_seq model generates a binary sequence using the Random Integer Generator block. The sequence is encoded with the Convolutional Encoder block and then impaired with the Binary Symmetric Channel block. The Viterbi Decoder block decodes the data sequence and the bit error rate is computed.

Block Characteristics

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

More About

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Sample Timing

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

Compatibility Considerations

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Random Integer Generator block update supported in Upgrade Advisor

Behavior changed in R2020a

Starting in R2020a, Random Integer 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 Random Integer 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.

Documentation

Random Integer Generator

Description

Limitations