Neural Normalized Min-Sum Message-Passing vs. Viterbi Decoding for the CCSDS Line Product Code

TL;DR

This paper compares neural normalized Min-Sum message passing decoding with Viterbi decoding for the CCSDS Line Product Code, showing that the neural approach nearly matches maximum-likelihood performance with lower complexity.

Contribution

It introduces a neural normalized Min-Sum message passing decoder for the CCSDS LPC and demonstrates its near-ML performance with reduced complexity.

Findings

Neural N-NMS decoder closely approaches ML decoding performance.

The neural decoder achieves lower complexity than traditional ML decoding.

The bipartite graph formulation enables effective message passing algorithms.

Abstract

The Consultative Committee for Space Data Systems (CCSDS) 141.11-O-1 Line Product Code (LPC) provides a rare opportunity to compare maximum-likelihood decoding and message passing. The LPC considered in this paper is intended to serve as the inner code in conjunction with a (255,239) Reed Solomon (RS) code whose symbols are bytes of data. This paper represents the 141.11-O-1 LPC as a bipartite graph and uses that graph to formulate both maximum likelihood (ML) and message passing algorithms. ML decoding must, of course, have the best frame error rate (FER) performance. However, a fixed point implementation of a Neural-Normalized MinSum (N-NMS) message passing decoder closely approaches ML performance with a significantly lower complexity.