Graph-Based Decoding in the Presence of ISI
Information Theory
2016-11-18 v1 math.IT
Abstract
We propose an approximation of maximum-likelihood detection in ISI channels based on linear programming or message passing. We convert the detection problem into a binary decoding problem, which can be easily combined with LDPC decoding. We show that, for a certain class of channels and in the absence of coding, the proposed technique provides the exact ML solution without an exponential complexity in the size of channel memory, while for some other channels, this method has a non-diminishing probability of failure as SNR increases. Some analysis is provided for the error events of the proposed technique under linear programming.
Cite
@article{arxiv.0707.1241,
title = {Graph-Based Decoding in the Presence of ISI},
author = {Mohammad H. Taghavi and Paul H. Siegel},
journal= {arXiv preprint arXiv:0707.1241},
year = {2016}
}
Comments
25 pages, 8 figures, Submitted to IEEE Transactions on Information Theory