Designing Finite Alphabet Iterative Decoders of LDPC Codes Via Recurrent Quantized Neural Networks
| Autor: | Xin Xiao, Shu Lin, Bane Vasic, Ravi Tandon |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Channel code
Artificial neural network Computer science Quantization (signal processing) 020208 electrical & electronic engineering 020206 networking & telecommunications Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Belief propagation Binary symmetric channel 0202 electrical engineering electronic engineering information engineering Bit error rate Electrical and Electronic Engineering Low-density parity-check code Algorithm Decoding methods Computer Science::Information Theory Parity bit Communication channel |
| Zdroj: | IEEE Transactions on Communications. 68:3963-3974 |
| ISSN: | 1558-0857 0090-6778 |
| Popis: | In this paper, we propose a new approach to design finite alphabet iterative decoders (FAIDs) for Low-Density Parity Check (LDPC) codes over binary symmetric channel (BSC) via recurrent quantized neural networks (RQNN). We focus on the linear FAID class and use RQNNs to optimize the message update look-up tables by jointly training their message levels and RQNN parameters. Existing neural networks for channel coding work well over Additive White Gaussian Noise Channel (AWGNC) but are inefficient over BSC due to the finite channel values of BSC fed into neural networks. We propose the bit error rate (BER) as the loss function to train the RQNNs over BSC. The low precision activations in the RQNN and quantization in the BER cause a critical issue that their gradients vanish almost everywhere, making it difficult to use classical backward propagation. We leverage straight-through estimators as surrogate gradients to tackle this issue and provide a joint training scheme. We show that the framework is flexible for various code lengths and column weights. Specifically, in high column weight case, it automatically designs low precision linear FAIDs with superior performance, lower complexity, and faster convergence than the floating-point belief propagation algorithms in waterfall region. |
| Databáze: | OpenAIRE |
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