Applicability of single- and two-hidden-layer neural networks in decoding linear block codes
AffiliationUniversity of Arizona, Department of Electrical and Computer Engineering
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CitationBrkic, S., Ivanis, P., & Vasic, B. (2021). Applicability of single- and two-hidden-layer neural networks in decoding linear block codes. 2021 29th Telecommunications Forum, TELFOR 2021 - Proceedings.
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AbstractIn this paper, we analyze applicability of single- and two-hidden-layer feed-forward artificial neural networks, SLFNs and TLFNs, respectively, in decoding linear block codes. Based on the provable capability of SLFNs and TLFNs to approximate discrete functions, we discuss sizes of the network capable to perform maximum likelihood decoding. Furthermore, we propose a decoding scheme, which use artificial neural networks (ANNs) to lower the error-floors of low-density parity-check (LDPC) codes. By learning a small number of error patterns, uncorrectable with typical decoders of LDPC codes, ANN can lower the error-floor by an order of magnitude, with only marginal average complexity incense.
VersionFinal accepted manuscript
SponsorsScience Fund of the Republic of Serbia