Generalized Simplified Variable-Scaled Min Sum LDPC decoder for irregular LDPC Codes

TL;DR

This paper introduces a generalized, optimized variable-scaled min-sum decoding algorithm for irregular LDPC codes, improving performance and reducing complexity compared to existing methods.

Contribution

It develops a new scaling strategy using offline optimization techniques, enhancing min-sum decoding for irregular LDPC codes with superior performance.

Findings

Outperforms other Min-Sum algorithms in WER and latency

Achieves performance close to LLR-SPA with lower complexity

Optimized parameters significantly improve decoding performance

Abstract

In this paper, we propose a novel low complexity scaling strategy of min-sum decoding algorithm for irregular LDPC codes. In the proposed method, we generalize our previously proposed simplified Variable Scaled Min-Sum (SVS-min-sum) by replacing the sub-optimal starting value and heuristic update for the scaling factor sequence by optimized values. Density evolution and Nelder-Mead optimization are used offline, prior to the decoding, to obtain the optimal starting point and per iteration updating step size for the scaling factor sequence of the proposed scaling strategy. The optimization of these parameters proves to be of noticeable positive impact on the decoding performance. We used different DVB-T2 LDPC codes in our simulation. Simulation results show the superior performance (in both WER and latency) of the proposed algorithm to other Min-Sum based algorithms. In addition to that, generalized SVS-min-sum algorithm has very close performance to LLR-SPA with much lower complexity.