Applicability of single- and two-hidden-layer neural networks in decoding linear block codes
Final Accepted Manuscript
AffiliationUniversity of Arizona, Department of Electrical and Computer Engineering
MetadataShow full item record
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.
RightsCopyright © 2021 IEEE.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
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