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Noisy deletion, markov codes and deep decoding
Published in Institute of Electrical and Electronics Engineers Inc.
Motivated by the classical synchronization problem and emerging applications in bioinformatics, we study noisy deletion channels in a regime of practical interest: short code length, low decoding complexity and low SNR. Our work is inspired by an important insight from information theory and Markov chains: appropriately parametrized Markov codewords can correct deletions and errors (due to noise) simultaneously. We extend this idea to practice by developing a low complexity decoder for short Markov codes, which displays competitive performance in simulations at low SNRs. Our decoder design combines the sequence prediction capability of recurrent neural networks with the assured performance of maximum a posteriori (MAP) decoders like the BCJR decoder. © 2020 IEEE.
About the journal
JournalData powered by Typeset26th National Conference on Communications, NCC 2020
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo