A Geometry-Based RIS-Assisted Multi-User Channel Model with Deep Reinforcement Learning

被引：0

作者：

Yuan, Yuan ^{[1
]}

He, Ruisi ^{[1
]}

Ai, Bo ^{[1
]}

Wu, Tong ^{[2
]}

Chen, Ruifeng ^{[3
]}

Zhang, Zhengyu ^{[1
]}

Jin, Yunwei ^{[1
]}

Zhong, Zhangdui ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China

[2] Natl Inst Metrol, Ctr Adv Metering Infrastruct, Beijing 100029, Peoples R China

[3] China Acad Railway Sci Corp Ltd, Inst Comp Technol, Beijing 100081, Peoples R China

来源：

2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING | 2024年

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

Reconfigurable intelligent surface (RIS); Multi-user; channel model; deep reinforcement learning;

D O I：

10.1109/VTC2024-SPRING62846.2024.10683242

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a 3D geometry-based stochastic channel model (GBSM) is proposed for RIS-assisted multi-user communications. The proposed GBSM is divided into two sub-channels, that is, BS-RIS and RIS-Rx links, and propagation distances and angles of multipath components are derived to describe multi-user channels. In addition, optimization objective for multi-user channel is proposed, and deep reinforcement learning is introduced to solve high-dimensional RIS phase problem. Based on the proposed model and solved RIS phase, channel capacity and root mean square delay spread are derived. The simulation results show that RIS optimization parameters and channel parameters have major impact on channel characteristics. The conclusions can provide a reference for designing and developing of RIS-assisted multi-user systems.

引用

页数：5

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