An Efficient ML Decoder for Tail-Biting Codes Based on Circular Trap Detection

Abstract

Tail-biting codes are efficient coding techniques to eliminate the rate loss in conventional known-tail convolutional codes at a cost of increased complexity in decoders. In addition, tail-biting trellis representation of block codes makes the trellis-based maximum likelihood (ML) decoder desirable for implementation. Circular Viterbi algorithm (CVA) is introduced to decode the tail-biting codes for its decoding efficiency. However, its decoding process suffers from circular traps, which degrade the decoding efficiency. In this paper, we propose an efficient checking rule for the detection of circular traps. Based on this rule, a novel maximum likelihood (ML) decoding algorithm for tail-biting codes is presented. On tail-biting trellis, computational complexity and memory consumption of this decoder are significantly reduced comparing to other available ML decoders, such as the two-phase ML decoder. To further reduce the decoding complexity, we propose a new near-optimal decoding algorithm based on a simplified trap detection strategy. The performance of the above algorithms is validated with simulation.