Adaptive quantization in min-sum based irregular LDPC decoder

In this paper, we present adaptive quantization schemes in the normalized min-sum decoding algorithm considering scaling effects to improve the performance of irregular low-density parity-check (LDPC) decoder for WirelessMAN (IEEE 802.16e) applications. We discuss the finite precision effects on the performance of irregular LDPC codes and develop optimal finite word lengths of variables over an SNR. For floating point simulation, it is known that in the normalized min-sum or offset min-sum algorithms the performance of a min-sum based decoder is not sensitive to scaling in the log-likelihood ratio (LLR) values. However, when considering the finite precision for hardware implementation, the scaling affects the dynamic range of the LLR values. The proposed adaptive quantization approach provides the optimal performance in selecting suitable input LLR values to the decoder as far as the tradeoffs between error performance and hardware complexity are concerned.