Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2018 | 2018 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT) | Journal |
Volume | Citations | PageRank |
abs/1804.10319 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elia Santi | 1 | 1 | 0.73 |
| Christian Häger | 2 | 1 | 3.06 |
| Henry D. Pfister | 3 | 227 | 25.28 |