.png){kind=icon}
返回
Long-Term CSI-Based Design for RIS-Aided Multiuser MISO Systems Exploiting Deep Reinforcement Learning
.png){kind=icon}
OA
AI
DOI：10.1109/LCOMM.2021.3140155.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this letter, we study the transmission design for reconfigurable intelligent surface (RIS)-aided multiuser communication networks. Different from most of the existing contributions, we consider long-term CSI-based transmission design, where both the beamforming vectors at the base station (BS) and the phase shifts at the RIS are designed based on long-term CSI, which can significantly reduce the channel estimation overhead. Due to the lack of explicit ergodic data rate expression, we propose a novel deep deterministic policy gradient (DDPG) based algorithm to solve the optimization problem, which was trained by using the channel vectors generated in an offline manner. Simulation results demonstrate that the achievable net throughput is higher than that achieved by the conventional instantaneous-CSI based scheme when taking the channel estimation overhead into account.
Keyword:
Channel estimation
Signal to noise ratio
Interference
Precoding
Coherence time
Array signal processing
Rician channels
Reconfigurable intelligent surface (RIS)
intelligent reflecting surface (IRS)
deep reinforcement learning
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
.png){kind=icon}
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication无线通信能效的可重构智能表面
OAAI
OAAI分享收藏分享收藏
查看更多内容