Jointly Learned Symbol Detection and Signal Reflection in RIS-Aided Multi-user MIMO Systems

Liuhang Wang, Nir Shlezinger, George C. Alexandropoulos, Haiyang Zhang, Baoyun Wang, Yonina C. Eldar

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

6 Scopus citations
74 Downloads (Pure)

Abstract

Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology for future wireless communications, enabling programmable radio propagation environments. However, the passive reflecting feature of RISs induces notable challenges on channel estimation, making coherent symbol detection a challenging task. In this paper, we consider the uplink of RIS-aided multi-user Multiple-Input Multiple-Output (MIMO) systems and propose a Machine Learning (ML) approach to jointly design the multi-antenna receiver and configure the RIS reflection coefficients, which does not require explicit full knowledge of the channel input-output relationship. Our approach devises a ML-based receiver, while the configurations of the RIS reflection patterns affecting the underlying propagation channel are treated as hyperparameters. Based on this system design formulation, we propose a Bayesian ML framework for optimizing the RIS hyperparameters, according to which the transmitted pilots are directly used to jointly tune the RIS and the multi-antenna receiver. Our simulation results demonstrate the capability of the proposed approach to provide reliable communications in non-linear channel conditions corrupted by Gaussian noise.

Original languageEnglish
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers
Pages715-721
Number of pages7
ISBN (Electronic)9781665458283
DOIs
StatePublished - 4 Mar 2022
Event55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
Duration: 31 Oct 20213 Nov 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2021-October
ISSN (Print)1058-6393

Conference

Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Country/TerritoryUnited States
CityVirtual, Pacific Grove
Period31/10/213/11/21

Keywords

  • Bayesian machine learning
  • multi-user MIMO
  • Reconfigurable intelligent surfaces
  • reflection configuration

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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