Spectral-Energy Efficient Resource Allocation in RIS-Aided FD-MIMO Systems

• Published in: IEEE transactions on wireless communications Vol. 23; no. 5; pp. 5125 - 5141
• Main Authors: Kurma, Sravani, Katwe, Mayur, Singh, Keshav, Duong, Trung Q., Li, Chih-Peng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 6G mobile communication; Algorithms; alternate optimization (AO); Communication; Covariance; Covariance matrices; Deep reinforcement learning (DRL); Efficiency; full-duplex (FD); Interference; Machine learning; Markov processes; Maximization; MIMO communication; multi-user multiple-input multiple-output (MIMO); Optimization; re-configurable intelligent surface (RIS); Resource allocation; Resource management; Run time (computers); spectral and energy efficiency trade-off; spectral efficiency (SE); Wireless communication
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2023.3324641

Re-configurable intelligent surface (RIS)-aided communication has been envisaged as a frontier scheme to enable ultra-high spectral efficiency (SE) and energy efficiency (EE) for next-generation communication. This paper investigates an unconventional framework of RIS-aided full-duplex (FD) multi-user multiple-input multiple-output (MIMO) communication and analyzes its resource efficiency (RE), a preferable performance metric for realizing trade-off between SE and EE maximization. In particular, we focus on the RE maximization problem via a joint optimization of transmit covariance, optimal receive covariance, and phase-shift matrices for each RIS subject to the given constraint on the power budget. To solve the formulated non-convex problem, we propose two optimization approaches: a) policy gradient-based deep-reinforcement learning (DRL) algorithm based on a Markov decision process formulation for a stochastic-time varying channel and b) alternate optimization (AO) algorithm based on general approximations and majorization-minimization (MM) for static channel conditions. Simulation results validate the out-performance of the considered RIS-aided FD-MIMO system compared to the counterpart system with half-duplex (HD) mode and without RIS case. The proposed DRL algorithm achieves comparable RE performance with reduced computational complexity and running time compared to the traditional AO-based algorithm.