A low-memory intensive decoding architecture for double-binary convolutional turbo code

Authors : Ming ZHAN, Liang ZHOU, Jun WU

Pages : 202-213

Doi:10.3906/elk-1203-86

View : 18 | Download : 11

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Memory accesses take a large part of the power consumption in the iterative decoding of double-binary convolutional turbo code insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(SMC);, and the memory size of the SMC is thus reduced by 25%. Owing to a compare-select--recalculate processing insert ignore into journalissuearticles values(CSRP); module in the proposed decoding architecture, the unstored state metrics are recalculated by simple operations, while maintaining near optimal decoding performance.
Keywords : Branch metrics, computational complexity, MAP algorithm, state metrics cache