Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noise
Boris Karanov, Chin-Hung Chen, Yan Wu, Alex Young, Wim van Houtum

TL;DR
This paper introduces machine learning-based data-driven symbol detection methods for ISI channels with bursty impulsive noise, reducing reliance on full channel knowledge and achieving near-optimal performance.
Contribution
It develops novel neural network and hidden Markov model approaches for trellis-based detection that learn both likelihoods and state transitions without full channel information.
Findings
Data-driven detectors outperform inaccurate CSI detection.
BCJR-HMM learns transition matrices without labeling.
Near-optimal BER achieved with learned trellis.
Abstract
We developed machine learning approaches for data-driven trellis-based soft symbol detection in coded transmission over intersymbol interference (ISI) channels in presence of bursty impulsive noise (IN), for example encountered in wireless digital broadcasting systems and vehicular communications. This enabled us to obtain optimized detectors based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm while circumventing the use of full channel state information (CSI) for computing likelihoods and trellis state transition probabilities. First, we extended the application of the neural network (NN)-aided BCJR, recently proposed for ISI channels with additive white Gaussian noise (AWGN). Although suitable for estimating likelihoods via labeling of transmission sequences, the BCJR-NN method does not provide a framework for learning the trellis state transitions. In addition to detection over…
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Taxonomy
TopicsBlind Source Separation Techniques · Power Line Communications and Noise · Cellular Automata and Applications
