Error Structure Aware Parallel BP-RNN Decoders for Short LDPC Codes - Archive ouverte HAL
Conference Papers Year : 2021

Error Structure Aware Parallel BP-RNN Decoders for Short LDPC Codes

Abstract

This article deals with the decoding of short block length Low Density Parity Check (LDPC) codes. It has already been demonstrated that Belief Propagation (BP) can be adjusted to the short coding length, thanks to its modeling by a Recurrent Neural Network (BP-RNN). To strengthen this adaptation, we introduce a new training method for the BP-RNN. Its aim is to specialize the BP-RNN on error events sharing the same structural properties. This approach is then associated with a new decoder composed of several parallel specialized BP-RNN decoders, each trained on correcting a different type of error events. Our results show that the proposed specialized BP-RNNs working in parallel effectively enhance the decoding capacity for short block length LDPC codes.
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Dates and versions

hal-03845041 , version 1 (09-11-2022)

Identifiers

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Joachim Rosseel, Valérian Mannoni, Valentin Savin, Inbar Fijalkow. Error Structure Aware Parallel BP-RNN Decoders for Short LDPC Codes. 2021 11th International Symposium on Topics in Coding (ISTC), Aug 2021, Montreal, Canada. ⟨10.1109/ISTC49272.2021.9594200⟩. ⟨hal-03845041⟩
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