Abstract
In 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.
Funder
Ministry of Education, Science and Technological Development of the Republic of Serbia
Publisher
Centre for Evaluation in Education and Science (CEON/CEES)
Subject
Computer Networks and Communications,Media Technology,Radiation,Signal Processing,Software
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