Distributed RIS-Assisted FD Systems with Discrete Phase Shifts: A Reinforcement Learning Approach

被引:8
|
作者
Faisal, Alice [1 ]
Al-Nahhal, Ibrahim [1 ]
Dobre, Octavia A. [1 ]
Ngatched, Telex M. N. [1 ]
机构
[1] Mem Univ, Fac Engn & Appl Sci, St John, NF, Canada
来源
2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022) | 2022年
基金
加拿大自然科学与工程研究理事会;
关键词
Reinforcement learning (RL); full-duplex; reconfigurable intelligent surface (RIS); discrete phase shifts; distributed RIS; INTELLIGENT REFLECTING SURFACE; OPTIMIZATION;
D O I
10.1109/GLOBECOM48099.2022.10001723
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper studies the sum-rate maximization problem of a distributed reconfigurable intelligent surface (RIS)-assisted full-duplex wireless system, where the availability of finite-resolution phase shifts at the RIS is considered. The aim is to optimize the transmit beamformers and RIS phase shifts, subject to the practical discrete phase shift and power constraints. The optimization problem is decoupled into two sub-problems; transmit beamforming and RIS phase shifts optimization. The transmit beamforming problem is mathematically addressed using approximate and closed-form solutions, while the discrete RIS phase shifts are optimized using a reinforcement learning (RL) approach. The existence and absence of a strong direct line-of-sight is investigated to show the effect of the phase shift optimization on the sum-rate. Simulation results illustrate that the proposed RL for the discrete phase shifts optimization provides a near-optimal performance with a small number of bits even for a large number of RIS elements, while improving the sum-rate compared to the random phase shift scenario and reducing the computational complexity compared to the state-of-the-art works.
引用
收藏
页码:5862 / 5867
页数:6
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