Near Maximum Likelihood Decoding with Deep Learning

Eliya Nachmani, yaron bachar, Elad Marciano, David Burshtein, Yair Beery

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


A novel and efficient neural decoder algorithm
is proposed. The proposed decoder is based on the neural
Belief Propagation algorithm and the Automorphism Group. By
combining neural belief propagation with permutations from
the Automorphism Group we achieve near maximum likelihood
performance for High Density Parity Check codes. Moreover, the
proposed decoder significantly improves the decoding complexity,
compared to our earlier work on the topic. We also investigate the
training process and show how it can be accelerated. Simulations
of the hessian and the condition number show why the learning
process is accelerated. We demonstrate the decoding algorithm
for various linear block codes of length up to 63 bits.
Original languageAmerican English
Title of host publicationInternational Zurich Seminar on Information and Communication (IZS 2018)
Place of PublicationZurich, Switzerland
StatePublished - 21 Feb 2018


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