comm.gpu.ConvolutionalInterleaver

Permute input symbols using shift registers with GPU

Description

The GPU ConvolutionalInterleaver object permutes the symbols in the input signal using a graphics processing unit (GPU). Internally, this class uses a set of shift registers.

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 convolutionally interleave binary data:

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

2. Call step to convolutionally interleave according to the properties of comm.gpu.ConvolutionalInterleaver. 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.ConvolutionalInterleaver creates a GPU-based convolutional interleaver System object, H. This object permutes the symbols in the input signal using a set of shift registers.

H = comm.gpu.ConvolutionalInterleaver(Name,Value) creates a GPU-based convolutional interleaver System object, H, with the specified property Name set to the specified Value. You can specify additional name-value pair arguments in any order as (Name1,Value1,...,NameN,ValueN).

H = comm.gpu.ConvolutionalInterleaver(M,B,IC) creates a GPU-based convolutional interleaver System object H, with the NumRegisters property set to M, the RegisterLengthStep property set to B, and the InitialConditions property set to IC. M, B, and IC are value-only arguments. To specify a value-only argument, you must also specify all preceding value-only arguments.

Properties

NumRegisters	Number of internal shift registers Specify the number of internal shift registers as a scalar, positive integer. The default is 6.
RegisterLengthStep	Number of additional symbols that fit in each successive shift register Specify the number of additional symbols that fit in each successive shift register as a positive, scalar integer. The default is 2. The first register holds zero symbols.
InitialConditions	Initial conditions of shift registers Specify the values that are initially stored in each shift register as a numeric scalar or vector. You do not need to specify a value for the first shift register, which has zero delay. The default is 0. The value of the first element of this property is unimportant because the first shift register has zero delay. If you set this property to a scalar, then all shift registers, except the first one, store the same specified value. If you set it to a column vector with length equal to the value of the NumRegisters property, then the i-th shift register stores the i-th element of the specified vector.

Methods

reset	Reset states of the convolutional interleaver object
step	Permute input symbols using shift registers

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

Examples

Convolutional Interleaving and Deinterleaving with GPU

Create convolutional interleaver and deinterleaver objects.

interleaver = comm.gpu.ConvolutionalInterleaver('NumRegisters',2, ...
    'RegisterLengthStep',3);
deinterleaver = comm.gpu.ConvolutionalDeinterleaver('NumRegisters',2, ...
    'RegisterLengthStep',3);

Generate data, and pass the data through the convolutional interleaver. Pass the interleaved data through the convolutional deinterleaver.

data = (0:20)';
intrlvData = interleaver(data);
deintrlvData = deinterleaver(intrlvData);

Display the original sequence, interleaved sequence and restored sequence.

[data intrlvData deintrlvData]

ans =

     0     0     0
     1     0     0
     2     2     0
     3     0     0
     4     4     0
     5     0     0
     6     6     0
     7     1     1
     8     8     2
     9     3     3
    10    10     4
    11     5     5
    12    12     6
    13     7     7
    14    14     8
    15     9     9
    16    16    10
    17    11    11
    18    18    12
    19    13    13
    20    20    14

The delay through the interleaver and deinterleaver pair is equal to the product of the NumRegisters and RegisterLengthStep properties. After accounting for this delay, confirm that the original and deinterleaved data are identical.

intrlvDelay = interleaver.NumRegisters * interleaver.RegisterLengthStep
numSymErrors = symerr(data(1:end-intrlvDelay),deintrlvData(1+intrlvDelay:end))

intrlvDelay =

     6


numSymErrors =

     0

Algorithms

This object implements the algorithm, inputs, and outputs described on the Convolutional Interleaver block reference page. The object properties correspond to the block parameters.

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.