Random Integer Generator

R2026b

Generate integers randomly distributed in specified range

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  • Random Integer Generator block

Libraries:
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.

For information on achieving independent results for individual simulations run in parallel and on improving execution speed, see Tips.

Examples

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This example uses:

Open Model

Demonstrate that sequences output from the Random Integer Generator block differ when you set the seed source to Auto. Show the output sequences repeat when you set the seed source to Parameter and the seed value is not changed. Show the output sequence changes when you set the seed source to Parameter and you change the seed value.

Run the slex_rng_seed_source model twice and compare the output sequence.

Seed source = Auto
     6     1
     7     7
     1     7
     7     3
     5     6
     0     1
     2     3
     4     7
     7     6
     7     7

Change the Seed source to Parameter. Run the slex_rng_seed_source model twice with the seed value set to 0. Compare the results with previous results.

Seed source = Parameter, Seed = 0
     6     1     6     6
     7     7     7     7
     1     7     1     1
     7     3     7     7
     5     6     5     5
     0     1     0     0
     2     3     2     2
     4     7     4     4
     7     6     7     7
     7     7     7     7

Run after changing the seed value to 1993. Compare the results with previous results.

Seed = 1993
     6     1     6     6     0
     7     7     7     7     1
     1     7     1     1     7
     7     3     7     7     2
     5     6     5     5     1
     0     1     0     0     1
     2     3     2     2     4
     4     7     4     4     5
     7     6     7     7     4
     7     7     7     7     1

This example uses:

Open Model

Apply QPSK modulation to a signal of random data. Pass the modulated signal through an additive white Gaussian noise (AWGN) channel. Plot the signal constellation.

The doc_qpsk_mod model generates QPSK data, applies the AWGN, and displays the resulting constellation diagram.

Run the model with the Eb/N0 of the AWGN Channel block set to 15 dB. The constellation diagram shows the QPSK symbol samples with AWGN.

Change the Eb/N0 from 15 dB to 10 dB. The noise level increases as shown by the greater distance between the samples in the constellation diagram.

This example uses:

Open Model

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.

The InitFcn callback is used to initialize workspace parameters for samples per frame, BSC error probability, and the Viterbi decoder traceback depth. The signal delay between the transmitted and received signal is equal to the traceback depth. The signal delay is needed for the error rate calculation.

To produce a binary bit stream, the Random Integer Generator block specifies a set size of 2, and output type of boolean.

The computed error rate approximates the Error probability specified in the Binary Symmetric Channel block.

Computed error rate = 0.095023

Limitations

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

Ports

Output

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

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 the 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 Seed.

For more information, see Managing the Global Stream Using RandStream and Specify Random Number Seed and Generator.

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

Tunable: Yes

Dependencies

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

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, 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, 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.

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

  • Interpreted execution — Simulate the model by using the MATLAB® interpreter. This option requires less 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.

  • 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.

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

Block Characteristics

Data Types

Boolean | double | integer | single

Multidimensional Signals

no

Variable-Size Signals

no

More About

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Tips

  • Independent random numbers

    • To achieve independent and identically distributed (IID) streams when running simulations in parallel using the parsim (Simulink) function, such as for Monte Carlo simulation runs, set Source of seed to Parameter and make sure that all blocks have different Seed values.

      If workers use the same seed for this block, they can generate the same random stream of numbers, which can lead to misleading results. This can occur when you configure the model to run in rapid accelerator mode, and in this block set the Simulate using parameter to Code generation and the Source of seed parameter to Auto.

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

  • Execution speed

    To optimize execution speed:

    • When samples per frame (SPF) = 1 for long simulation runs (>>100k steps), set Simulate using to Code generation and set Source of seed to Auto. This combination provides the fastest per-step execution.

    • When SPF >> 1, set Simulate using to Interpreted execution and set Source of seed to Auto. This combination provides the fastest total simulation time and there is no compiler dependency.

Extended Capabilities

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

Version History

Introduced before R2006a

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See Also

Functions

Topics