Enhancing the Downlink Rate Fairness of Low-Resolution Active RIS-Aided Signaling by Closed-Form Expression-Based Iterative Optimization

作     者：Chen, Yufeng Tuan, Hoang Duong Fang, Yong Yu, Hongwen Poor, H. Vincent Hanzo, Lajos 

作者机构：Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England 

出 版 物：《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 (IEEE Trans. Veh. Technol.)

年 卷 期：2024年第73卷第6期

页      面：8013-8029页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0823[工学-交通运输工程] 

基　　金：Australian Research Council#x0027 s Discovery Projects EPSRC [EP/W016605/1, EP/X01228X/1, EP/D056691/1, EP/N004558/1, EP/P003990/1] Funding Source: UKRI 

主　　题：Optimization Closed-form solutions Minimax techniques Reconfigurable intelligent surfaces Quantization (signal) Iterative methods Energy efficiency Active power control active reconfigurable intelligent surface (aRIS) large-scale computation low-resolution quantization max-min rate optimization mixed discrete continuous optimization transmit beamforming 

摘      要：This paper proposes a joint design strategy for enhancing individual user rates in a multi-user system by optimizing both the programmable reflecting elements (PREs) of an active reconfigurable intelligent surface (aRIS) and the transmit beamforming at a base station. Given that the aRIS s PREs are bound by discrete constraints due to low-resolution quantization, this design approach relies on large-scale mixed discrete-continuous problems, which are addressed through a new universal penalised optimization reformulations. Initially, we develop iterations based on convex quadratic solvers (CQ) to tackle the problem of maximizing the users minimum rate (MR). Given that the computational complexity of these CQs is cubic, leading to high costs in large-scale computations, we introduce a pair of surrogate objectives. These objectives are designed in a way that their constrained optimization can be efficiently managed through iterations of closed-form expressions with scalable complexity, rendering them practical for large-scale computations. This pair of surrogate objectives comprises the maximization of the geometric mean of users rates (GM-rate maximization) and the soft-maximization of users MR (soft max-min rate optimization). Remarkably, they not only enhance MR but also contribute to the improvement of the sum-rate (SR). Building upon the GM-rate optimization, we further propose addressing the energy efficiency problem, which achieves a high ratio of SR to power consumption and MR to power dissipation through closed-form expressions. Comprehensive simulations are conducted to validate the efficacy of the proposed solutions.