Advanced beamforming and reflection control in intelligent reflecting surfaces with integrated channel estimation
Date
2024
Authors
Monga, S.
Singh, A.R.
Saluja, N.
Prabha, C.
Malhotra, S.
Karim, A.
Hassan, M.M.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
IET Microwaves, Antennas & Propagation, 2024; 18(12):917-931
Statement of Responsibility
Conference Name
Abstract
Intelligent Reflecting Surfaces (IRS) enhance wireless communication by optimising signal reflection from the base station (BS) towards users. The passive nature of IRS components makes tuning phase shifters difficult and direct channel measurement problematic. This study presents a machine learning framework that directly maximises the beamformers at the BS and the reflective coefficients at the IRS, bypassing conventional methods that estimate channels before optimising system parameters. This is achieved by mapping incoming pilot signals and data, including user positions, with a deep neural network (DNN), guiding an optimal setup. User interactions are captured using a permutation-invariant graph neural network (GNN) architecture. Simulation results show that implicit channel estimation method requires fewer pilots than standard approaches, effectively learns to optimise sum rate or minimum-rate targets, and generalises well. Specifically, the sum rate for GDNNet (GNN + DNN) improves by (Formula presented.) over linear minimum mean square error (LMMSE) and by (Formula presented.) over perfect CSI concerning the number of users, and by (Formula presented.) over LMMSE and by (Formula presented.) over perfect CSI concerning pilot length. Offering a feasible solution with reduced computing complexity for real-world applications, the proposed GNN + DNN method outperforms conventional model-based techniques such as LMMSE and approaches the performance of perfect CSI, demonstrating its high effectiveness in various scenarios.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2024 The Authors. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Access Condition Notes: This is an open access article under the terms of the Creative Commons Attribution-Non Commercial-No Derivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.