Performance enhancement of modified log MAP decoding algorithm for turbo codes

摘要

Turbo decoder uses any one of the decoding algorithm, Maximum A posteriori Probability (MAP), or Soft Output Viterbi Algorithm (SOVA) because it produces error correction near to Shannon's limit. The Log MAP is a Soft Input Soft Output (SISO) algorithm, which determines the log likelihood of each transmitted data bit. A simple but effective technique to improve the performance of Log MAP algorithm is to scale the extrinsic information exchanged between two decoders. Modified Log MAP (MMAP) algorithm is achieved by fixing an arbitrary value for inner decoder (S₂) and an optimized value for the outer decoder (S₁). In Enhanced Log MAP (EMAP), both S₁ and S₂ are optimized. This paper presents the performance enhancement for the modified Log MAP decoding algorithm by optimizing the scaling factors S₁, S₂ and Eb/No to achieve low bit error rate (BER). A comprehensive analysis of the selection of scaling factors according to channel conditions and decoding iterations are presented. The performance of various scaling factors is compared and optimized scaling factor is obtained. Choosing an empirical scaling factor for all Eb/No is compared with the best scaling factor selection for changing channel conditions and iterations. The use of an emphatically determined optimal scaling factor improved the performance of decoding algorithms in terms of BER. A typical BER improvement is in the order of 10^-2 for Additive White Gaussian Noise Channel (AWGN). Appropriate mathematical relationship between scaling factor and Eb/No is also obtained.