Critical point for maximum likelihood decoding of linear block codes

Hybrid Maximum Likelihood Decoding for Linear Block Codes

In this paper we propose a hybrid maximum likelihood decoding (MLD) for linear block codes. For the reliable data transmission over noisy channels, convolutional and block codes are widely used in most digital communication systems. Much more efficient algorithms have been found for using channel measurement information in the decoding of convolutional codes than in the decoding of block codes....

Adaptive Maximum-Likelihood Decoding Algorithms for Linear Block Codes

This correspondence represents two new soft decision decoding algorithms that promise to reduce complexity and at the same time achieve the maximum likelihood decoding (MLD) performance. The first method is an Adaptive Two-Stage Maximum Likelihood Decoder [1] that first estimates a minimum sufficient set and performs decoding within the smaller set to reduce complexity and at the same time achi...

Reduced Complexity Maximum Likelihood Decoding of Linear Block Codes

This paper proposes a reduced complexity Maximum-Likelihood (ML) decoding Algorithm for Linear Block Codes based on the Kaneko decoder and incorporating ruling out conditions for useless iteration steps. The proposed decoding scheme is evaluated over the Additive White Gaussian Noise (AGWN) channel using Binary Phase Shift Key (BPSK) signalling by simulation. Simulations results show that for n...

Space-time block codes with low maximum-likelihood decoding complexity

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 pr...