Windowed Decoding of Protograph-based LDPC Convolutional Codes over Erasure Channels
Aravind Iyengar, Marco Papaleo, Paul Siegel, Jack Wolf, Alessandro, Vanelli-Coralli, Giovanni Corazza

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.
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