Traceback-Based Optimizations for Maximum a Posteriori Decoding Algorithms

Abstract

Maximum A Posteriori (MAP) decoding is a crucial enabler of turbo coding and other powerful feedbackbased algorithms. To allow pervasive use of these techniques in resources constrained systems, it is important to limit their implementation complexity, without sacrificing the superior performance they are known for. We show that introducing traceback information into the MAP algorithm, thereby leveraging components that are also part of Soft-Output Viterbi Algorithms (SOVA), offers two unique possibilities to simplify the computational requirements. Our proposed enhancements are effective at each individual decoding iteration and therefore provide gains on top of existing techniques such as early termination and memory optimizations. Based on these enhancements, we will present three new architectural variants for the decoder. Each one of these may be preferable depending on the decoder memory hardware requirements and number of trellis states. Computational complexity is reduced significantly, without incurring significant performance penalty.