@inproceedings{86f8fea5661f479a95274c9d00068f3e,
title = "Optimization of Iterative Blind Detection Based on Expectation Maximization and Belief Propagation",
abstract = "We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and detection scheme that combines the expectation maximization (EM) algorithm and the ubiquitous belief propagation (BP) algorithm. Interweaving the iterations of both schemes signifi-cantly reduces the EM algorithm's computational burden while retaining its excellent performance. To this end, we apply simple yet effective model-based learning methods to find a suitable parameter update schedule by introducing momentum in both the EM parameter updates as well as in the BP message passing. Numerical simulations verify that the proposed method can learn efficient schedules that generalize well and even outperform coherent BP detection in high signal-to-noise scenarios.",
keywords = "6G, Factor graphs, belief propagation, expectation maximization, joint detection, model-based learning",
author = "Luca Schmid and Tomer Raviv and Nir Shlezinger and Laurent Schmalen",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024 ; Conference date: 27-10-2024 Through 30-10-2024",
year = "2024",
month = jan,
day = "1",
doi = "10.1109/IEEECONF60004.2024.10942667",
language = "English",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "572--576",
editor = "Matthews, \{Michael B.\}",
booktitle = "Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024",
address = "United States",
}