Near-optimal turbo decoding in presence of SNR estimation error

Optimal iterative log-MAP (LM) decoding of turbo codes requires accurate signal to noise ratio (SNR) information. In practice, there is SNR mismatch priori to decoding due to inaccurate SNR estimation. Although max-log-MAP turbo decoding avoids the detrimental effect of SNR mismatch, its performance is inferior to LM decoding at accurate SNR estimation. In this paper, we propose two architectures for improved turbo decoding in presence of SNR mismatch. The first architecture called “SNR-Mismatch Aware Turbo (SMAT) Decoder” selects the decoder with the best performance at any SNR mismatch. The second architecture called “SNR-Mismatch Compensated Turbo (SMCT) Decoder” performs accurate SNR-mismatch estimation and compensates for the mismatch while decoding. We provide symbol-based as well as bit-level LLR histogram-based approaches for SNR mismatch estimation. We show that the proposed SMCT decoder has near-optimal performance regardless of the initial SNR mismatch. We demonstrate the effectiveness of the proposed turbo decoding architectures by Monte Carlo simulations.