JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

Deep reinforcement learning for near-field wideband beam forming in STAR-RIS networks

Author(s): Ji WANG, Jiayi SUN, Wei FANG, Zhao CHEN, Yue LIU, Yuanwei LIU
Affiliation(s): Department of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China; more
Corresponding email(s): jiwang@ccnu.edu.cn, zhao_chen@tsinghua.edu.cn
Key Words: Deep reinforcement learning; Near-field beamforming; Simultaneously transmitting and reflecting reconfigurable intelligent surface; Wideband beam split

Share this article to： More <<< Previous Paper \|Next Paper >>>

Ji WANG, Jiayi SUN, Wei FANG, Zhao CHEN, Yue LIU, Yuanwei LIU. Deep reinforcement learning for near-field wideband beam forming in STAR-RIS networks[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400364

@article{title="Deep reinforcement learning for near-field wideband beam forming in STAR-RIS networks",
author="Ji WANG, Jiayi SUN, Wei FANG, Zhao CHEN, Yue LIU, Yuanwei LIU",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400364"
}

%0 Journal Article
%T Deep reinforcement learning for near-field wideband beam forming in STAR-RIS networks
%A Ji WANG
%A Jiayi SUN
%A Wei FANG
%A Zhao CHEN
%A Yue LIU
%A Yuanwei LIU
%J Frontiers of Information Technology & Electronic Engineering
%P
%@ 2095-9184
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2400364"

TY - JOUR
T1 - Deep reinforcement learning for near-field wideband beam forming in STAR-RIS networks
A1 - Ji WANG
A1 - Jiayi SUN
A1 - Wei FANG
A1 - Zhao CHEN
A1 - Yue LIU
A1 - Yuanwei LIU
J0 - Frontiers of Information Technology & Electronic Engineering
SP -
EP -
%@ 2095-9184
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/10.1631/FITEE.2400364"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-user near-field wideband communication system is investigated, in which a robust deep reinforcement learning (DRL)-based algorithm is proposed to enhance the users’ achievable rate by jointly optimizing the active beamforming at the base station (BS) and passive beamforming at the STAR-RIS. To mitigate the beam split issue, the delay-phase hybrid precoding structure is introduced to facilitate wideband beamforming. Considering the coupled nature of the STAR-RIS phase-shift model, the passive beamforming design is formulated as a problem of hybrid continuous and discrete phase-shift control, the proposed algorithm controls the high-dimensional continuous action through hybrid action mapping. Additionally, to address the issue of biased estimation encountered by existing DRL algorithms, a soft-max operator is introduced into the algorithm to mitigate this bias. Simulation results illustrate that the proposed algorithm outperforms existing algorithms and overcomes the issues of overestimation and underestimation.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

Open peer comments: Debate/Discuss/Question/Opinion

<1>

- Go to

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference