Energy Efficiency and Spectral Efficiency Tradeoff in RIS-Aided Multiuser MIMO Uplink Transmission

• Published in: IEEE transactions on signal processing Vol. 69; pp. 1407 - 1421
• Main Authors: You, Li, Xiong, Jiayuan, Ng, Derrick Wing Kwan, Yuen, Chau, Wang, Wenjin, Gao, Xiqi
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Beamforming; discrete phase shifts; Energy consumption; Energy conversion efficiency; Energy efficiency; intelligent reflecting surface (IRS); Iterative methods; Numerical methods; Optimization; partial channel state information (CSI); Phase shifters; Precoding; Radio waves; Reconfigurable intelligent surface (RIS); Reconfigurable intelligent surfaces; Resource management; Spectral efficiency; Tradeoffs; Wave propagation; Wireless communications
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2020.3047474

The emergence of reconfigurable intelligent surfaces (RISs) enables us to establish programmable radio wave propagation that caters for wireless communications, via employing low-cost passive reflecting units. This work studies the non-trivial tradeoff between energy efficiency (EE) and spectral efficiency (SE) in multiuser multiple-input multiple-output (MIMO) uplink communications aided by a RIS equipped with discrete phase shifters. For reducing the required signaling overhead and energy consumption, our transmission strategy design is based on the partial channel state information (CSI), including the statistical CSI between the RIS and user terminals (UTs) and the instantaneous CSI between the RIS and the base station. To investigate the EE-SE tradeoff, we develop a framework for the joint optimization of UTs' transmit precoding and RIS reflective beamforming to maximize a performance metric called resource efficiency (RE). For the design of UT's precoding, it is simplified into that of UTs' transmit powers with the aid of the closed-form solutions of UTs' optimal transmit directions. To avoid the high complexity in computing the nested integrals involved in the expectations, we derive an asymptotic deterministic objective expression. For the design of the RIS phases, an iterative mean-square error minimization approach is proposed via capitalizing on the homotopy, accelerated projected gradient, and majorization-minimization methods. Numerical results illustrate the effectiveness and rapid convergence rate of our proposed optimization framework.