A novel decoding scheme based on recalculation for double binary convolutional turbo code

To decrease the storage complexity of a double binary convolutional turbo code (DB-CTC) decoder, a novel decoding scheme is proposed in this paper. Different from the conventional decoding scheme, only a part of the state metrics is stored in the last-in first-out (LIFO) state metrics cache (SMC). Based on an improved maximum a posteriori probability (MAP) algorithm, we present a method to recalculate the unstored state metrics at the corresponding decoding time slot, and discuss in detail the procedures of the recalculation are discussed. Because of the compare-select-recalculate processing operations, compared to the classical decoding scheme, the proposed decoding scheme reduces the storage complexity of SMC and the amount of memory accesses by approximately 40% while limiting involved computational cost. Moreover, simulation results show that the proposed scheme achieves good decoding performance, which is close to that of the well-known Log-MAP algorithm.