A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection

TL;DR

This paper introduces a neural check polytope projection method within a PDD framework to improve the efficiency of LP decoding for binary linear codes, reducing latency and maintaining decoding performance.

Contribution

It proposes a novel neural check polytope projection technique integrated into PDD decoding, enhancing speed while preserving decoding accuracy.

Findings

Neural CPP reduces decoding latency.

Proposed algorithms maintain decoding performance.

Effective in low SNR regions.

Abstract

Linear Programming (LP) is an important decoding technique for binary linear codes. However, the advantages of LP decoding, such as low error floor and strong theoretical guarantee, etc., come at the cost of high computational complexity and poor performance at the low signal-to-noise ratio (SNR) region. In this letter, we adopt the penalty dual decomposition (PDD) framework and propose a PDD algorithm to address the fundamental polytope based maximum likelihood (ML) decoding problem. Furthermore, we propose to integrate machine learning techniques into the most time-consuming part of the PDD decoding algorithm, i.e., check polytope projection (CPP). Inspired by the fact that a multi-layer perception (MLP) can theoretically approximate any nonlinear mapping function, we present a specially designed neural CPP (NCPP) algorithm to decrease the decoding latency. Simulation results demonstrate the effectiveness of the proposed algorithms.