University of Oulu

G. C. Alexandropoulos, S. Samarakoon, M. Bennis and M. Debbah, "Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces," 2020 IEEE Globecom Workshops (GC Wkshps, 2020, pp. 1-6, doi: 10.1109/GCWkshps50303.2020.9367575

Phase configuration learning in wireless networks with multiple reconfigurable intelligent surfaces

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Author: Alexandropoulos, George C.1; Samarakoon, Sumudu2; Bennis, Mehdi2;
Organizations: 1Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece
2Centre for Wireless Communications, University of Oulu, Finland
3Mathematical and Algorithmic Sciences Lab, Paris Research Center, Huawei Technologies France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: IEEE, 2020
Publish Date: 2022-03-21


Reconfigurable Intelligent Surfaces (RISs) are recently gaining remarkable attention as a low-cost, hardware-efficient, and highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation. Their envisioned dense deployment over various obstacles of the, otherwise passive, wireless communication environment has been considered as a revolutionary means to transform them into network entities with reconfigurable properties, providing increased environmental intelligence for diverse communication objectives. One of the major challenges with RIS-empowered wireless communications is the low-overhead dynamic configuration of multiple RISs, which according to the current hardware designs have very limited computing and storage capabilities. In this paper, we consider a typical communication pair between two nodes that is assisted by a plurality of RISs, and devise low-complexity supervised learning approaches for the RISs’ phase configurations. By assuming common tunable phases in groups of each RIS’s unit elements, we present multi-layer perceptron Neural Network (NN) architectures that can be trained either with positioning values or the instantaneous channel coefficients. We investigate centralized and individual training of the RISs, as well as their federation, and assess their computational requirements. Our simulation results, including comparisons with the optimal phase configuration scheme, showcase the benefits of adopting individual NNs at RISs for the link budget performance boosting.

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ISBN: 978-1-7281-7307-8
ISBN Print: 978-1-7281-7308-5
Article number: 367575
Host publication: 2020 IEEE Globecom Workshops, GC Wkshps 2020
Conference: IEEE Globecom Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
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