A Low-Complexity Dual Trellis Decoding Algorithm for High-Rate Convolutional Codes

Abstract

Decoding using the dual trellis is considered as a potential technique to increase the throughput of soft-input soft-output decoders for high coding rate convolutional codes. However, the dual Log-MAP algorithm suffers from a high decoding complexity. More specifically, the source of complexity comes from the soft-output unit, which has to handle a high number of extrinsic values in parallel. In this paper, we present a new low-complexity sub-optimal decoding algorithm using the dual trellis, namely the dual Max-Log-MAP algorithm, suited for high coding rate convolutional codes. A complexity analysis and simulation results are provided to compare the dual Max-Log-MAP and the dual Log-MAP algorithms. Despite a minor loss of about 0.2 dB in performance, the dual Max-Log-MAP algorithm significantly reduces the decoder complexity and makes it a first-choice algorithm for high-throughput high-rate decoding of convolutional and turbo codes.