The ADMM penalized decoder for LDPC codes

TL;DR

This paper introduces the ADMM penalized decoder for LDPC codes, which improves low SNR performance over traditional LP and BP decoders by adding penalty terms to the optimization problem, with demonstrated empirical and theoretical benefits.

Contribution

It proposes a novel ADMM-based penalized decoding framework that enhances low SNR performance and develops a reweighted LP decoder with improved recovery thresholds.

Findings

ADMM penalized decoder outperforms BP and LP decoding at all SNRs.

Reweighted LP decoder shows significant low SNR gains.

Theoretical analysis indicates improved recovery thresholds.

Abstract

Linear programming (LP) decoding for low-density parity-check (LDPC) codes proposed by Feldman et al. is shown to have theoretical guarantees in several regimes and empirically is not observed to suffer from an error floor. However at low signal-to-noise ratios (SNRs), LP decoding is observed to have worse error performance than belief propagation (BP) decoding. In this paper, we seek to improve LP decoding at low SNRs while still achieving good high SNR performance. We first present a new decoding framework obtained by trying to solve a non-convex optimization problem using the alternating direction method of multipliers (ADMM). This non-convex problem is constructed by adding a penalty term to the LP decoding objective. The goal of the penalty term is to make "pseudocodewords", which are the non-integer vertices of the LP relaxation to which the LP decoder fails, more costly. We name this decoder class the "ADMM penalized decoder". In our simulation results, the ADMM penalized decoder with $\ell_1$ and $\ell_2$ penalties outperforms both BP and LP decoding at all SNRs. For high SNR regimes where it is infeasible to simulate, we use an instanton analysis and show that the ADMM penalized decoder has better high SNR performance than BP decoding. We also develop a reweighted LP decoder using linear approximations to the objective with an $\ell_1$ penalty. We show that this decoder has an improved theoretical recovery threshold compared to LP decoding. In addition, we show that the empirical gain of the reweighted LP decoder is significant at low SNRs.