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In a digital communication system, information is sent from one place toanother over a noisy communication channel. It may be possible to detect andcorrect errors that occur during the transmission if one encodes the originalinformation by adding redundant bits. Low-density parity-check (LDPC)convolutional codes, a member of the LDPC code family, encode the originalinformation to improve error correction capability. In practice these codes areused to decode very long information sequences, where the information arrivesin subsequent packets over time, such as video streams. We consider the problemof decoding the received information with minimum error from an optimizationpoint of view and investigate integer programming-based exact and heuristicdecoding algorithms for its solution. In particular, we consider relax-and-fixheuristics that decode information in small windows. Computational resultsindicate that our approaches identify near-optimal solutions significantlyfaster than a commercial solver in high channel error rates. Our proposedalgorithms can find higher quality solutions compared with commonly usediterative decoding heuristics.

Optimization-Based Decoding Algorithms for LDPC Convolutional Codes in Communication Systems