On the error correction of iterative bounded distance decoding of generalized LDPC codes

TL;DR

This paper analyzes the error correction capabilities of iterative bounded distance decoding for generalized LDPC codes, relaxing previous conditions and providing probabilistic guarantees for both worst-case and random errors.

Contribution

It introduces a relaxed analysis framework for GLDPC codes with small degrees, establishing conditions for successful decoding and extending results to non-binary codes.

Findings

Conditions for correcting a positive fraction of worst-case errors

Probability of failure vanishes with large blocklength

Lower bounds on correctable random errors

Abstract

Consider an ensemble of regular generalized LDPC (GLDPC) codes and assume that the same component code is associated with each parity check node. To decode a GLDPC code from the ensemble, we use the bit flipping bounded distance decoding algorithm, which is an extension of the bit flipping algorithm for LDPC codes. Previous work has shown conditions, under which, for a typical code in the ensemble with blocklength sufficiently large, a positive constant fraction of worst case errors can be corrected. In this work we first show that these requirements can be relaxed for ensembles with small left degrees. While previous work on GLDPC codes has considered expander graph arguments, our analysis formulates a necessary condition that the Tanner graph needs to satisfy for a failure event and then shows that the probability of this event vanishes for a sufficiently large blocklength. We then extend the analysis to random error correction and derive a lower bound on the fraction of random errors that can be corrected asymptotically. We discuss the extension of our results to non-binary GLDPC codes and present numerical examples.