Optimization of Iterative Blind Detection Based on Expectation Maximization and Belief Propagation

Luca Schmid, Tomer Raviv, Nir Shlezinger, Laurent Schmalen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationConference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers
Pages572-576
Number of pages5
ISBN (Electronic)9798350354058
DOIs
StatePublished - 1 Jan 2024
Event58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024 - Hybrid, Pacific Grove, United States
Duration: 27 Oct 202430 Oct 2024

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Conference

Conference58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024
Country/TerritoryUnited States
CityHybrid, Pacific Grove
Period27/10/2430/10/24

Keywords

  • 6G
  • Factor graphs
  • belief propagation
  • expectation maximization
  • joint detection
  • model-based learning

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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