Windowed Decoding of Protograph-based LDPC Convolutional Codes over Erasure Channels

TL;DR

This paper investigates a windowed belief-propagation decoding scheme for LDPC convolutional codes, demonstrating near-optimal performance over memoryless erasure channels and analyzing limitations over channels with memory.

Contribution

It introduces and analyzes a windowed decoding approach for LDPC convolutional codes, highlighting its advantages and performance characteristics over different erasure channels.

Findings

Close to theoretical limits on memoryless erasure channels

Performance limitations on channels with memory

Advantages of windowed decoding scheme

Abstract

We consider a windowed decoding scheme for LDPC convolutional codes that is based on the belief-propagation (BP) algorithm. We discuss the advantages of this decoding scheme and identify certain characteristics of LDPC convolutional code ensembles that exhibit good performance with the windowed decoder. We will consider the performance of these ensembles and codes over erasure channels with and without memory. We show that the structure of LDPC convolutional code ensembles is suitable to obtain performance close to the theoretical limits over the memoryless erasure channel, both for the BP decoder and windowed decoding. However, the same structure imposes limitations on the performance over erasure channels with memory.