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IEEE International Symposium on Information Theory (ISIT)

Efficient Maximum-Likelihood Decoding of Reed–Muller RM(m−3,m) Codes

Andrew Thangaraj, Henry D. Pfister

Published Jun 1, 2020• 5 citations
Publisher

Abstract

Reed-Muller (RM) codes, a classical family of codes known for their elegant algebraic structure, have recently been shown to achieve capacity under maximum-likelihood (ML) decoding on the binary erasure channel and this has rekindled interest in their efficient decoding. We consider the code family RM(m-3,m) and develop a new ML decoder, for transmission over the binary symmetric channel, that exploits their large symmetry group. The new decoder has lower complexity than an earlier method introduced by Seroussi and Lempel in 1983.

DOI
10.1109/isit44484.2020.9174065

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Reed-Muller (RM) codes, a classical family of codes known for their elegant algebraic structure, have recently been shown to achieve capacity under maximum-likelihood (ML) decoding on the binary erasure channel and this has rekindled interest in their efficient decoding. We consider the code family RM(m-3,m) and develop a new ML decoder, for transmission over the binary symmetric channel, that exploits their large symmetry group. The new decoder has lower complexity than an earlier method introduced by Seroussi and Lempel in 1983.
Reed-Muller (RM) codes exhibit good performance under maximum-likelihood (ML) decoding due to their highly-symmetric structure. In this paper, we explore the question of whether the code symmetry of RM codes can also be exploited to achieve near-ML performance in practice. The main idea is to apply iterative decoding to a highly-redundant parity-check (PC) matrix that contains only the minimum-weight dual codewords as rows. As examples, we consider the peeling decoder for the binary erasure channel, linear-programming and belief propagation (BP) decoding for the binary-input additive white Gaussian noise channel, and bit-flipping and BP decoding for the binary symmetric channel. For short block lengths, it is shown that near-ML performance can indeed be achieved in many cases. We also propose a method to tailor the PC matrix to the received observation by selecting only a small fraction of useful minimum-weight PCs before decoding begins. This allows one to both improve performance and significantly reduce complexity compared to using the full set of minimum-weight PCs.
Recursive list decoding of Reed-Muller (RM) codes, with moderate list size, is known to approach maximum-likelihood (ML) performance of short length(≤256)(\leq 256)(≤256)RM codes. Recursive decoding employs the Plotkin construction to split the original code into two shorter RM codes with different rates. In contrast to the standard approach which decodes the lower-rate code first, the method in this paper decodes the higher-rate code first. This modification enables an efficient permutation-based decoding technique, with permutations being selected on the fly from the automorphism group of the code using soft information from a channel. Simulation results show that the error-rate performance of the proposed algorithms, enhanced by a permutation selection technique, is close to that of the automorphism-based recursive decoding algorithm with similar complexity for short RM codes, while our decoders perform better for longer RM codes. In particular, it is demonstrated that the proposed algorithms achieve near-ML performance for short RM codes and for RM codes of length2m2^{m}2mand orderm−3m-3m−3with reasonable complexity.
Reed-Muller (RM) codes are classical codes that have enjoyed unabated interest since their introduction in 1954 due to their simple recursive structure and low-complexity decoding. The main goal of the thesis is to develop new coding techniques that can improve the performance of RM codes. In this work, we wish to keep the recursive low-complexity structure of RM codes while extending the sets of possible parameters and decoding algorithms. We address this task using two different methods. First, we consider new code constructions that extend code parameters using puncturing. Second, we design new decoding algorithms that repeatedly use the gradient properties of the classic recursive structure to improve code performance of RM codes. In the first part of the thesis, we propose a new class of codes that enjoy the recursive structure of RM codes, but reduce the exponential length of RM codes to a polynomial in m. These codes are obtained by puncturing classical RM codes, i.e. by restricting binary RM codes to small subsets of their positions. We study new recursive properties of these codes and analyze their parameters. In the second part of the thesis we develop new decoding algorithms for binary RM codes of second order that achieve near optimal performance for short and moderate code lengths. RM codes enjoy some simple decoding procedures; however, these algorithms fall short of optimal performance on moderate block lengths. We propose a suboptimal algorithm that reduces the complexity of maximum-likelihood decoding from super-polynomial order O(n(m+1)/2) to polynomial order O(mn2). At the same time, our numerical simulations show that the new algorithm performs very closely to its maximum-likelihood counterpart for blocks of up to several thousand bits.
This paper considers the performance of long Reed-Muller (RM) codes transmitted over binary memoryless symmetric (BMS) channels under bitwise maximum-a-posteriori decoding. Its main result is that the family of binary RM codes achieves capacity on any BMS channel with respect to bit-error rate. This resolves a long-standing open problem that connects information theory and error-correcting codes. In contrast with the earlier result for the binary erasure channel, the new proof does not rely on hypercontractivity. Instead, it combines a nesting property of RM codes with new information inequalities relating the generalized extrinsic information transfer function and the extrinsic minimum mean-squared error.
In this paper, by treating Reed-Muller (RM) codes as a special class of low-density parity-check (LDPC) codes and assuming that sub-blocks of the parity-check matrix are randomly interleaved to each other as Gallager's codes, we present a short proof that RM codes are entropy-achieving as source coding for Bernoulli sources and capacity-achieving as channel coding for binary memoryless symmetric (BMS) channels, also known as memoryless binary-input output-symmetric (BIOS) channels, in terms of bit error rate (BER) under maximum-likelihood (ML) decoding.