Complexity-Adaptive Maximum-Likelihood Decoding of Modified GN-Coset Codes
Peihong Yuan, Mustafa Cemil Coşkun · 2021
A complexity-adaptive tree search algorithm is proposed for $G_{N}$-coset codes that implements maximum-likelihood (ML) decoding by using a successive decoding schedule. The average complexity is close to that of the successive cancellation (SC) decoding for practical error rates when applied to polar codes and short Reed-Muller (RM) codes, e.g., block lengths up to N = 128. By modifying the algorithm to limit the worstcase complexity, one obtains a near-ML decoder for longer RM codes and their subcodes. Unlike other bit-flip decoders, no outer code is needed to terminate decoding. The algorithm can thus be applied to modified $G_{N}$-coset code constructions with dynamic frozen bits. One advantage over sequential decoders is that there is no need to optimize a separate parameter.