The application of successive relaxation (SR) to the fixed-point problem associated with the iterative decoding of low-density parity-check (LDPC) codes is studied. We consider finite-length codes decoded by belief propagation (BP) and a well-known approximation of it, referred to as min-sum (MS), over a binary input additive white Gaussian noise (BIAWGN) channel. For both algorithms, we show that the application of SR in different domains results in different error correcting performance. In particular, the performances of SR in log-likelihood ratio (LLR) and LR domains for BP and MS are compared, and it is shown that SR-MS-LLR has the best performance. For the tested codes, SR-MS-LLR outperforms standard BP by up to about 0.5dB, offering an attractive solution in terms of performance/complexity tradeoff. We also investigate the effects of quantization on SR-MS-LLR and demonstrate that at least 6-7 bits of quantization is required to capture close to floating-point performance.
24th Biennial Symposium on Communications, BSC 2008
Department of Systems and Computer Engineering

Xiao, H. (Hua), Tolouei, S. (Sina), & Banihashemi, A. (2008). Successive relaxation for decoding of LDPC codes. In 24th Biennial Symposium on Communications, BSC 2008 (pp. 107–110). doi:10.1109/BSC.2008.4563216