Work

Deep Reinforcement Learning-Based Precoding for Multi-RIS-Aided Multiuser Downlink Systems With Practical Phase Shift

Year: 2024

Type: article

Abstract: This letter considers multiple reconfigurable intelligent surfaces (RISs)-aided multiuser downlink systems with the goal of optimizing the transmitter precoding and RIS phase shift matrix to maximize spectrum efficiency. A practical coupling effect is considered between the reflecting amplitude and phase shift for the reflecting elements and their corresponding reflectivities. However, with coupled optimization variables, the formulated optimization problem is non-convex. Therefore, deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) is proposed to solve the optimization problem. A practical scenario was simulated, whereby the proposed DDPG models were trained considering the random and fixed number of users. The simulation results show that the proposed DDPG, despite being comparably complex, outperforms the two optimization-based algorithms.

Source: IEEE Wireless Communications Letters

DOI: 10.1109/lwc.2024.3482720

Authors: Po-Heng Chou, Bo-Ren Zheng, Wan-Jen Huang, Walid Saad, Yu Tsao, Ronald Y. Chang

Institutions: Research Center for Information Technology Innovation, Academia Sinica, Academia Sinica, National Sun Yat-sen University, Virginia Tech

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Academia Sinica, Ministry of Science and Technology, Taiwan, National Science and Technology Council

Awards: G2132174752, Collaborative Research: SWIFT: SMALL: Enabling Seamless Coexistence between Passive and Active Networks using Reconfigurable Reflecting Surfaces, G55284852, Next-Generation Communication User-Terminal and Access-Point Key Technology Development Based on Higher C-Band Mimo Antenna System( Iv )