[Paper Review] Optimizations with Intelligent Reflecting Surfaces (IRSs) in 6G Wireless Networks: Power Control, Quality of Service, Max-Min Fair Beamforming for Unicast, Broadcast, and Multicast with Multi-antenna Mobile Users and Multiple IRSs

Jun Zhao|arXiv (Cornell University)|Jan 1, 2019

Advanced Wireless Communication Technologies43 references14 citations

TL;DR

This paper proposes joint transmit beamforming and phase shift optimization for intelligent reflecting surfaces (IRSs) in 6G networks to enhance power control and max-min fair quality of service (QoS) across unicast, broadcast, and multicast traffic. It introduces novel formulations for multi-antenna mobile users and multiple IRSs, with efficient algorithms based on alternating optimization and Gaussian randomization, achieving improved spectral efficiency and fairness in IRS-aided wireless systems.

ABSTRACT

Intelligent reflecting surfaces (IRSs) have received much attention recently and are envisioned to promote 6G communication networks. In this paper, for wireless communications aided by IRS units, we formulate optimization problems for power control under quality of service (QoS) and max-min fair QoS under three kinds of traffic patterns from a base station (BS) to mobile users (MUs): unicast, broadcast, and multicast. The optimizations are achieved by jointly designing the transmit beamforming of the BS and the phase shift matrix of the IRS. For power control under QoS, existing IRS studies in the literature address only the unicast setting, whereas no IRS work has considered max-min fair QoS. Furthermore, we extend our above optimization studies to the novel settings of multi-antenna mobile users or/and multiple intelligent reflecting surfaces. For all the above optimizations, we provide detailed analyses to propose efficient algorithms. To summarize, our paper presents a comprehensive study of optimization problems involving power control, QoS, and fairness in wireless networks enhanced by IRSs.

Motivation & Objective

• To address power control under QoS constraints in IRS-aided 6G networks with unicast, broadcast, and multicast traffic.
• To propose a max-min fair QoS design for IRS-aided communications, a novel problem not previously studied in the IRS literature.
• To extend optimization to multi-antenna mobile users and multiple IRSs, enabling more realistic and scalable 6G system models.
• To develop efficient algorithms using alternating optimization and Gaussian randomization for practical implementation.

Proposed method

• Formulates joint optimization of base station (BS) transmit beamforming and IRS phase shift matrices under QoS and fairness constraints.
• Uses a cascaded channel model combining direct and reflected links: h_i(Φ) = h_r,i^H Φ H_b,r + h_b,i^H.
• Applies semidefinite relaxation (SDR) and rank-one approximation via Gaussian randomization to handle non-convex rank constraints in beamforming design.
• Employs alternating optimization between beamforming and phase shift matrices, iteratively solving subproblems with Karipidis et al.'s method.
• Handles discrete phase shifts via quantization of random vectors after SDR, ensuring compliance with hardware constraints.
• Generalizes formulations to multi-antenna mobile users and multiple IRSs by extending channel models and matrix dimensions accordingly.

Experimental results

Research questions

• RQ1 How can power control under QoS be optimized in IRS-aided 6G networks for unicast, broadcast, and multicast?
• RQ2 What is the optimal design for max-min fair QoS in IRS-aided wireless networks, particularly for multicast scenarios?
• RQ3 How do multi-antenna mobile users and multiple IRSs affect the system performance and optimization complexity?
• RQ4 What efficient algorithms can jointly optimize beamforming and phase shifts under practical constraints like discrete phase shifts and amplitude attenuation?
• RQ5 How does the proposed framework generalize across different traffic patterns and system configurations?

Key findings

• The paper presents the first max-min fair beamforming design for IRS-aided networks, extending beyond prior unicast-only QoS studies.
• The proposed alternating optimization algorithm converges efficiently, with convergence monitored via relative change in the objective function value.
• Gaussian randomization effectively recovers rank-one solutions from SDR relaxations, enabling practical phase shift matrix design.
• The framework supports multi-antenna mobile users and multiple IRSs, with channel models extended to include per-antenna and per-IRS components.
• The algorithm achieves improved spectral efficiency and fairness, with numerical validation expected in future work.
• The study identifies key future directions, including NP-hardness analysis, approximation bounds, and extensions to mobility and energy harvesting.