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Decoding Short LDPC Codes via BP-RNN Diversity and Reliability-Based Post-Processing
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DOI：10.1109/TCOMM.2022.3218821.png)
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摘要
En 中文
This paper investigates decoder diversity architectures for short low-density parity-check (LDPC) codes, based on recurrent neural network (RNN) models of the belief-propagation (BP) algorithm. We propose a new approach to achieve decoder diversity in the waterfall region, by specializing BP-RNN decoders to specific classes of errors, with absorbing set support. We further combine our approach with an ordered statistics decoding (OSD) post-processing step, which effectively leverages the bit-error rate optimization deriving from the use of the binary cross-entropy loss function. We show that a single specialized BP-RNN decoder combines better than BP with the OSD post-processing step. Moreover, combining OSD post-processing with the diversity brought by the use of multiple BP-RNN decoders, provides an efficient way to bridge the gap to maximum likelihood decoding.
Keyword:
Iterative decoding
Maximum likelihood decoding
Codes
Artificial neural networks
Neurons
Training
Diversity reception
LDPC
short LDPC
neural network aided decoding
belief propagation
ordered statistics decoding post-processing
期刊
IF:
8.3
论文数:
1.2W
被引数:
3.6W
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