Online log-likelihood ratio scaling for robust turbo decoding

Optimal iterative log-MAP decoding of turbo codes requires accurate knowledge of the operating signal-to-noise ratio (SNR). However, the SNR information, available at practical decoders for bit-interleaved coded modulation systems, such as the third generation partnership project high-speed packet access and long-term evolution wireless cellular systems, may be inaccurate. In this study, two decoder architectures for improved turbo decoding in the presence of SNR mismatch are proposed. The SNR-mismatch aware turbo decoder selects the decoder which is estimated to have the best performance at the current mismatch, according to the test criterion. The SNR-mismatch compensated turbo decoder provides a more accurate estimation of the noise variance and concurrently scales the channel and the decoder log-likelihood ratios (LLRs) to continue decoding. Two different methods are proposed to find the optimal scaling factors online, one on the symbol level and the other on the bit level. This study shows that online LLR scaling, without prior knowledge about the noise mismatch statistics, can result in near-optimal turbo decoding regardless of the initial SNR mismatch.