A memory reduced decoding scheme for double binary convolutional turbo code based on forward recalculation

Abstract

In the implementation of iterative decoder for double binary convolutional turbo code (DB CTC), memory accessing accounts for a large part of the overall power consumption. In this paper, an iterative decoding scheme with small memory size is proposed. The new method is based on an improved maximum a posterior probability (MAP) algorithm, and stores part of the backward metrics in the state metrics cache (SMC). While at the corresponding time that the not stored metrics are used, they can be recalculated by a Compare-Select-Recalculate Processing (CSRP) unit in the forward direction. Since the memory size for SMC is 25% decreased as compared with conventional scheme, less memory accessing is needed. Moreover, complexity analysis and numerical simulation are presented to demonstrate the effectiveness of our proposed scheme.