FAID Diversity via Neural Networks

Xin Xiao, Nithin Raveendran, Bane Vasic, Shu Lin, Ravi Tandon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Decoder diversity is a powerful error correction framework in which a collection of decoders collaboratively correct a set of error patterns otherwise uncorrectable by any individual decoder. In this paper, we propose a new approach to design the decoder diversity of finite alphabet iterative decoders (FAIDs) for Low-Density Parity Check (LDPC) codes over the binary symmetric channel (BSC), for the purpose of lowering the error floor while guaranteeing the waterfall performance. The proposed decoder diversity is achieved by training a recurrent quantized neural network (RQNN) to learn/design FAIDs. We demonstrated for the first time that a machine-learned decoder can surpass in performance a man-made decoder of the same complexity. As RQNNs can model a broad class of FAIDs, they are capable of learning an arbitrary FAID. To provide sufficient knowledge of the error floor to the RQNN, the training sets are constructed by sampling from the set of most problematic error patterns - trapping sets. In contrast to the existing methods that use the cross-entropy function as the loss function, we introduce a frame-error-rate (FER) based loss function to train the RQNN with the objective of correcting specific error patterns rather than reducing the bit error rate (BER). The examples and simulation results show that the RQNN-aided decoder diversity increases the error correction capability of LDPC codes and lowers the error floor.

Original languageEnglish (US)
Title of host publication2021 11th International Symposium on Topics in Coding, ISTC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665409438
DOIs
StatePublished - 2021
Event11th International Symposium on Topics in Coding, ISTC 2021 - Montreal, Canada
Duration: Aug 30 2021Sep 3 2021

Publication series

Name2021 11th International Symposium on Topics in Coding, ISTC 2021

Conference

Conference11th International Symposium on Topics in Coding, ISTC 2021
Country/TerritoryCanada
CityMontreal
Period8/30/219/3/21

Keywords

  • Decoder diversity
  • Error floor
  • LDPC codes
  • Quantized neural network

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Software

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