A low-memory intensive decoding architecture for double-binary convolutional turbo code
Memory accesses take a large part of the power consumption in the iterative decoding of double-binary convolutional turbo code (DB-CTC). To deal with this, a low-memory intensive decoding architecture is proposed for DB-CTC in this paper. The new scheme is based on an improved maximum a posteriori probability algorithm, where instead of storing all of the state metrics, only a part of these state metrics is stored in the state metrics cache (SMC), and the memory size of the SMC is thus reduced by 25%. Owing to a compare-select{recalculate processing (CSRP) module in the proposed decoding architecture, the unstored state metrics are recalculated by simple operations, while maintaining near optimal decoding performance.
state metrics cache branch metrics computational complexity map algorithm