Local-Optimality Guarantees for Optimal Decoding Based on Paths

TL;DR

This paper introduces a unified framework for analyzing local-optimality in graph-based codes, linking it to ML and LP decoding guarantees, and demonstrating its effectiveness on Tanner codes with polynomial error bounds.

Contribution

It provides a new proof technique based on path structures in Tanner graphs, establishing local-optimality guarantees for decoding.

Findings

Local-optimality implies ML and LP optimality.

Iterative message-passing decoding finds the unique locally-optimal codeword.

Polynomial bounds on word error probability for Tanner codes.

Abstract

This paper presents a unified analysis framework that captures recent advances in the study of local-optimality characterizations for codes on graphs. These local-optimality characterizations are based on combinatorial structures embedded in the Tanner graph of the code. Local-optimality implies both unique maximum-likelihood (ML) optimality and unique linear-programming (LP) decoding optimality. Also, an iterative message-passing decoding algorithm is guaranteed to find the unique locally-optimal codeword, if one exists. We demonstrate this proof technique by considering a definition of local-optimality that is based on the simplest combinatorial structures in Tanner graphs, namely, paths of length $h$. We apply the technique of local-optimality to a family of Tanner codes. Inverse polynomial bounds in the code length are proved on the word error probability of LP-decoding for this family of Tanner codes.