Threshold Selection for Iterative Decoding of $(v,w)$-regular Binary Codes

TL;DR

This paper analyzes threshold selection for iterative bit flipping decoders of sparse $(v,w)$-regular codes, proposing a new model for syndrome weight distribution and improving decoding failure rate estimation.

Contribution

It introduces a closed-form model for syndrome weight distribution and concrete criteria for threshold determination in parallel hard decision bit flipping decoders.

Findings

New model accurately predicts syndrome weight distribution after first iteration.

Proposed thresholds improve decoding failure rate estimation.

Enhanced DFR estimation over existing methods.

Abstract

Iterative bit flipping decoders are an efficient and effective decoder choice for decoding codes which admit a sparse parity-check matrix. Among these, sparse $(v,w)$-regular codes, which include LDPC and MDPC codes are of particular interest both for efficient data correction and the design of cryptographic primitives. In attaining the decoding the choice of the bit flipping thresholds, which can be determined either statically, or during the decoder execution by using information coming from the initial syndrome value and its updates. In this work, we analyze a two-iterations parallel hard decision bit flipping decoders and propose concrete criteria for threshold determination, backed by a closed form model. In doing so, we introduce a new tightly fitting model for the distribution of the Hamming weight of the syndrome after the first decoder iteration and substantial improvements on the DFR estimation with respect to existing approaches.