comm.gpu.BlockInterleaver

Create block interleaved sequence with GPU

Description

The GPU BlockInterleaver object permutes the symbols in the input signal using a graphics processing unit (GPU).

Note

To use this object, you must install a Parallel Computing Toolbox™ license and have access to an appropriate GPU. For more about GPUs, see GPU Computing (Parallel Computing Toolbox).

A GPU-based System object™ accepts typical MATLAB^® arrays or objects created using the gpuArray class. A GPU-based System object supports input signals with double- or single-precision data types. The output signal inherits its data type from the input signal.

• If the input signal is a MATLAB array, the System object handles data transfer between the CPU and the GPU. The output signal is a MATLAB array.

• If the input signal is a gpuArray, the data remains on the GPU. The output signal is a gpuArray. When the object is given a gpuArray, calculations take place entirely on the GPU, and no data transfer occurs. Passing gpuArray arguments provides increased performance by reducing simulation time. For more information, see Establish Arrays on a GPU (Parallel Computing Toolbox).

To interleave the input signal:

1. Define and set up your block interleaver object. See Construction.

2. Call step to reorder the input symbols according to the properties of comm.gpu.BlockInterleaver. The behavior of step is specific to each object in the toolbox.

Note

Starting in R2016b, instead of using the step method to perform the operation defined by the System object, you can call the object with arguments, as if it were a function. For example, y = step(obj,x) and y = obj(x) perform equivalent operations.

Construction

H = comm.gpu.BlockInterleaver creates a GPU-based block interleaver System object, H. This object permutes the symbols in the input signal based on a permutation vector.

H = comm.gpu.BlockInterleaver(Name,Value) creates a GPU-based block interleaver object, H, with the specified property Name set to the specified Value.

H = comm.gpu.BlockInterleaver(PERMVEC) creates a GPU-based block deinterleaver object, H, with the PermutationVector property set to PERMVEC.

Properties

PermutationVector

Permutation vector Specify the mapping used to permute the input symbols as a column vector of integers. The default is [5;4;3;2;1]. The mapping is a vector where the number of elements is equal to the length, N, of the input to the step method. Each element must be an integer between 1 and N, with no repeated values.

Methods

step	Permute input symbols using a permutation vector

Common to All System Objects
release	Allow System object property value changes

Algorithm

The GPU Block Interleaver System object uses the same algorithm as the comm.BlockInterleaver System object. See Algorithms on the comm.BlockInterleaver help page for details.

Examples

Block Interleaving and Deinterleaving with GPU

Create interleaver and deinterleaver objects.

interleaver = comm.gpu.BlockInterleaver([3 4 1 2]');
deinterleaver = comm.gpu.BlockDeinterleaver([3 4 1 2]');

Pass random data through the interleaver and deinterleaver.

data = randi(7,4,1);
intData = interleaver(data);
deIntData = deinterleaver(intData);

Display the original sequence, interleaved sequence, and restored sequence.

[data intData deIntData]

ans =

     6     1     6
     7     7     7
     1     6     1
     7     7     7

Confirm the original and deinterleaved data are identical.

isequal(data,deIntData)

ans =

     1

Generate a random vector of unique integers as a permutation vector.

permVec = randperm(7)';

Specify permVec as the permutation vector for the interleaver and deinterleaver objects.

interleaver = comm.gpu.BlockInterleaver(permVec);
deinterleaver = comm.gpu.BlockDeinterleaver(permVec);

Pass random data through the interleaver and deinterleaver.

data = randi(10,7,1);
intData = interleaver(data);
deIntData = deinterleaver(intData);

Confirm the original and deinterleaved data are identical.

isequal(data,deIntData)

ans =

     1

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This System object runs on a GPU, and also supports GPU array inputs. For more information, see Simulation Acceleration Using GPUs.