comm.gpu.ConvolutionalDeinterleaver

Restore ordering of symbols using shift registers with GPU

Description

The GPU ConvolutionalDeinterleaver object recovers a signal that was interleaved using the GPU-based convolutional interleaver object. The parameters in the two blocks should have the same values.

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 recover convolutionally interleaved binary data:

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

2. Call step to convolutionally deinterleave according to the properties of comm.gpu.ConvolutionalDeinterleaver. 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.ConvolutionalDeinterleaver creates a GPU-based convolutional deinterleaver System object, H. This object restores the original ordering of a sequence that was interleaved using a convolutional interleaver.

H = comm.gpu.ConvolutionalDeinterleaver(Name,Value) creates a GPU-based convolutional deinterleaver 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.ConvolutionalDeinterleaver(M,B,IC) creates a convolutional deinterleaver 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 (except the first shift register, which has zero delay) as a numeric scalar or vector. The default is 0. 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 NumRegistersproperty, then the i-th shift register stores the i-th element of the specified vector. The value of the first element of this property is unimportant, since the first shift register has zero delay.

Methods

reset	Reset states of the convolutional deinterleaver 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 Deinterleaver 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.