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The l1 penalized decoder and its reweighted LP

Linear programming (LP) decoding for low density parity check (LDPC) codes proposed by Feldman et al. has attracted considerable attention in recent years. Despite having theoretical guarantees in some regimes, at low SNRs LP decoding is observed to have worse error performance than belief propagation (BP) decoding. In this paper, we present a novel decoding algorithm 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 an ℓ1 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 the “ℓ1 penalized decoder”. We also develop a reweighted LP decoding algorithm base on this ℓ1 penalized objective. We show that this reweighted LP has an improved theoretical recovery threshold compared to the original LP. In addition, simulations show that, in comparison to LP decoding, these two decoders both achieve significantly lower error rates and are not observed to have an “error floor”. In particular, the ℓ1 penalized decoder meets or outperforms the BP decoder at all SNRs.