Clustering-Based Downlink Scheduling of IRS-Assisted Communications With Reconfiguration Constraints

• Published in: IEEE transactions on wireless communications Vol. 23; no. 12; pp. 18487 - 18501
• Main Authors: Rech, Alberto, Pagin, Matteo, Badia, Leonardo, Tomasin, Stefano, Giordani, Marco, Gambini, Jonathan, Zorzi, Michele
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Base stations; Beamforming; Cellular communication; Clustering; Clustering algorithms; Configuration management; Constraints; Costs; Downlink; Heuristic scheduling; Intelligent reflecting surfaces (IRS); Iterative algorithms; millimeter wave (mmWave) communication; Millimeter wave communication; multiple access; NOMA; Optimization; Phase shifters; Reconfigurable intelligent surfaces; Reconfiguration; scheduling; System effectiveness; Terahertz communications; Time Division Multiple Access; Wireless communication
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2024.3469559

Intelligent reflecting surfaces (IRSs) are being widely investigated as a potential low-cost and energy-efficient alternative to active relays for improving coverage in next-generation cellular networks. However, technical constraints in the configuration of IRSs should be taken into account in the design of scheduling solutions and the assessment of their performance. To this end, we examine an IRS-assisted time division multiple access (TDMA) cellular network where the reconfiguration of the IRS incurs a communication cost; thus, we aim at limiting the number of reconfigurations over time. Along these lines, we propose a clustering-based heuristic scheduling scheme that maximizes the cell sum capacity, subject to a fixed number of reconfigurations within a TDMA frame. First, the best configuration of each user equipment (UE), in terms of joint beamforming and optimal IRS configuration, is determined using an iterative algorithm. Then, we propose different clustering techniques to divide the UEs into subsets sharing the same suboptimal IRS configuration, derived through distance- and capacity-based algorithms. Finally, UEs within the same cluster are scheduled accordingly. We provide extensive numerical results for different propagation scenarios, IRS sizes, and phase shifters quantization constraints, showing the effectiveness of our approach in supporting multi-user IRS systems with practical constraints.