On Low-Complexity Maximum-Likelihood Decoding of Convolutional Codes

On Optimality Test in Low Complexity Maximum Likelihood Decoding of Convolutional Codes

This paper considers the average complexity of maximum likelihood (ML) decoding of convolutional codes. ML decoding can be modeled as finding the most probable path taken through a Markov graph. Integrated with the Viterbi algorithm (VA), complexity reduction methods such as the sphere decoder often use the sum log likelihood (SLL) of a Markov path as a bound to disprove (or test) the optimalit...

Convolutional Codes II. Maximum-Likelihood Decoding

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

Maximum Likelihood Decoding of Convolutional Codes for Non-Gaussian Channels

The Viterbi algorithm plays a fundamental role in the design of receivers for digital communication systems corrupted by Gaussian noise. This algorithm arises as the maximum likelihood sequence detector of the transmitted data symbols in several applications, including equalization for channels subject to intersymbol interference, multiuser communications, and the detection of convolutionally e...

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...