1 research outputs found
Maximum-likelihood Soft-decision Decoding for Binary Linear Block Codes Based on Their Supercodes
Based on the notion of supercodes, we propose a two-phase maximum-likelihood
soft-decision decoding (tpMLSD) algorithm for binary linear block codes in this
work. The first phase applies the Viterbi algorithm backwardly to a trellis
derived from the parity-check matrix of the supercode of the linear block code.
Using the information retained from the first phase, the second phase employs
the priority-first search algorithm to the trellis corresponding to the linear
block code itself, which guarantees finding the ML decision. Simulations on
Reed-Muller codes show that the proposed two-phase scheme is an order of
magnitude more efficient in average decoding complexity than the recursive
maximum-likelihood decoding (RMLD) [1] when the signal-to-noise ratio per
information bit is 4.5 dB.Comment: 5 pages, 1 tabl