Decode binary low-density parity-check data with GPU
GPU LDPCDecoder object decodes a binary
low-density parity-check code using a graphics processing unit (GPU).
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 that you create using the gpuArray class as an input. GPU-based System objects support input signals with double- or single-precision data types. The output signal inherits its datatype from the input signal.
If the input signal is a MATLAB array, then the output signal is also a MATLAB array. In this case, the System object handles data transfer between the CPU and GPU.
If the input signal is a gpuArray, then the output signal is also a gpuArray. In this case, the data remains on the GPU. Therefore, 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).
This object performs LDPC decoding using the belief-passing or message-passing algorithm, implemented as the log-domain sum-product algorithm. For more information, see Algorithm. To decode a binary low-density parity-check code:
Starting in R2016b, instead of using the
to perform the operation defined by the System
object, you can
call the object with arguments, as if it were a function. For example,
= step(obj,x) and
y = obj(x) perform
h = comm.gpu.LDPCDecoder creates a GPU-based
LDPC binary low-density parity-check decoder object,
This object performs LDPC decoding based on the specified parity-check
matrix. The object does not assume any patterns in the parity-check
h = comm.gpu.LDPCDecoder( creates
a GPU-based LDPC decoder object,
h, with each
specified property set to the specified value. You can specify additional
name-value pair arguments in any order as (
h = comm.gpu.LDPCDecoder(PARITY) creates
a GPU-based LDPC decoder object,
h, with the
Specify the parity-check matrix as a binary valued sparse matrix with dimension (N-by-K) by N, where N > K > 0. The last N−K columns in the parity check matrix must be an invertible matrix in GF(2). This property accepts numeric or logical data types. The upper bound for the value of N is (231)-1. The default is the parity-check matrix of the half-rate LDPC code from the DVB-S.2 standard, which is the result of dvbs2ldpc(1/2).
Select output value format
Specify the output value format as one of
Specify the decision method used for decoding as one of
Maximum number of decoding iterations
Specify the maximum number of iterations the object uses as
an integer valued numeric scalar. The default is
Condition for iteration termination
Specify the condition to stop the decoding iterations as one
Output number of iterations performed
Set this property to true to output the actual number of iterations the object performed. The default is false.
Output final parity checks
Set this property to true to output the final parity checks the object calculated. The default is false.
|step||Decode input signal using LDPC decoding scheme|
This object performs LDPC decoding using the belief-passing or message-passing algorithm, implemented as the log-domain sum-product algorithm. For more information, see the Decoding Algorithm section on the LDPC Decoder block reference page.
Transmit an LDPC-encoded, QPSK-modulated bit stream through an AWGN channel, then demodulate, decode, and count errors.
hEnc = comm.LDPCEncoder; hMod = comm.PSKModulator(4, 'BitInput',true); hChan = comm.AWGNChannel(... 'NoiseMethod','Signal to noise ratio (SNR)','SNR',1); hDemod = comm.PSKDemodulator(4, 'BitOutput',true,... 'DecisionMethod','Approximate log-likelihood ratio', ... 'Variance', 1/10^(hChan.SNR/10)); hDec = comm.gpu.LDPCDecoder; hError = comm.ErrorRate; for counter = 1:10 data = logical(randi([0 1], 32400, 1)); encodedData = step(hEnc, data); modSignal = step(hMod, encodedData); receivedSignal = step(hChan, modSignal); demodSignal = step(hDemod, receivedSignal); receivedBits = step(hDec, demodSignal); errorStats = step(hError, data, receivedBits); end fprintf('Error rate = %1.2f\nNumber of errors = %d\n', ... errorStats(1), errorStats(2))