Low-complexity ML decoding for convolutional tail-biting codes

Abstract

Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting codes [1]. The first phase applies the Viterbi algorithm to obtain the trellis information, and then the second phase employs the algorithm A* to find the ML solution. In this work, we improve the complexity of the algorithm A* by using a new evaluation function. Simulations showed that the improved A* algorithm has over 5 times less average decoding complexity in the second phase when E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ≥4 dB.