Low Complexity Scaling Factor Aided Decoder for Double Binary Convolutional Turbo Codes

Abstract

Double binary convolutional turbo code (DB-CTC) has been utilized in communication systems since its outstanding performance, but it has high decoding complexity and latency. In this paper, we design efficient DB-CTC decoding scheme to improve the decoding performance and complexity in hardware implementation. We firstly analyze the influence of scaling factor for extrinsic information of DB-CTC decoders. Then, the optimized scaling factor is searched and applied for various types of decoders to reduce the decoding complexity and latency. Moreover, we simulate the performance of the proposed scaling factor aided (SF) decoders under integral operations, which is able to show the performance of decoders on hardware implementations more precisely. Simulation results validate the effectiveness of scaling factor for different code lengths and code rates. In addition, the scaling factor aided parallel DB-CTC decoder outperforms the max-log-MAP decoder, and approaches the performance of the optimal log-MAP decoder with much lower decoding complexity and latency.