Different perspective and approach to implement adaptive normalised belief propagation-based decoding for low-density parity check codes

Abstract

In this study, the authors propose an improved version of the min-sum algorithm for low-density parity check code decoding, which the authors call 'adaptive normalised BP-based' algorithm. Their decoder provides a compromise solution between belief propagation and the min-sum algorithms by adding an exponent offset to each variable node's intrinsic information in the check node update equation. The extrinsic information from the min-sum decoder is then adjusted by applying a negative power of the two scale factor, which can be easily implemented by right shifting the min-sum extrinsic information. The difference between their approach and other adaptive normalised min-sum decoders is that the authors select the normalisation scale factor using a clear analytical approach based on the underlying principles. The simulation results show that the proposed decoder outperforms the min-sum decoder and performs very close to the BP decoder with lower complexity.

New Min-sum LDPC Decoding Algorithm Using SNR-Considered Adaptive Scaling Factors

“…Thus, a block error rate (BLER) performance is an important indicator for evaluating the performance of the LDPC decoding algorithm. Figures 8,9,and 10 show the comparison results of the BLER performances for the SP, MS, NMS, TDNMS, and the proposed SANMS algorithms. In Figs.…”

“…A modified NMS algorithm in [8] applied two different SFs to the first minimum value and the second minimum value. An adaptive NMS algorithm in [9] adjusted the SF α depending on the distributions of the received LLR value. Another adaptive NMS algorithm in [10] applied different SFs depending on the state of the check node.…”