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

Ji WANG; Jiayi SUN; Wei FANG; Zhao CHEN; Yue LIU; Yuanwei LIU

doi:10.1631/FITEE.2400364

Your Location：

Home >

Browse articles >

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

Special Issue on Near-Field Communications: Theories and Applications | Updated：2025-02-25

- Deep reinforcement learning for near-field wideband beamforming in STAR-RIS networks
  Enhanced Publication
- 基于深度强化学习的智能全向超表面辅助近场宽带通信系统波束赋形研究
- “In the field of near-field wideband communication, a robust deep reinforcement learning algorithm is proposed to enhance users' achievable rate by jointly optimizing active and passive beamforming. Expert introduced a delay-phase hybrid precoding structure to facilitate wideband beamforming and mitigate the biased estimation issue. Simulation results show that the proposed algorithm outperforms existing ones.”
- Frontiers of Information Technology & Electronic Engineering Vol. 25, Issue 12, Pages: 1651-1663(2024)
- Affiliations：
  
  1.Department of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China
  2.Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
  3.Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR, China
  4.School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
  5.Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong 999077
- Author bio：
  
  E-mail: jiwang@ccnu.edu.cn
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2400364
  CLC： TP391.4
- Received：07 May 2024，
  
  Revised：2024-09-30，
  
  Accepted：30 September 2024，
  
  Published：2024-12
- Accepted：
Scan QR Code
Ji WANG, Jiayi SUN, Wei FANG, et al. Deep reinforcement learning for near-field wideband beamforming in STAR-RIS networks[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(12): 1651-1663.
DOI：

Ji WANG, Jiayi SUN, Wei FANG, et al. Deep reinforcement learning for near-field wideband beamforming in STAR-RIS networks[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(12): 1651-1663. DOI： 10.1631/FITEE.2400364.

Views

334

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Federated deep reinforcement learning based computation offloading in a low Earth orbit satellite edge computing system

Significance extraction based on data augmentation for reinforcement learning

Incentive-based task offloading for digital twins in 6G native artificial intelligence networks: a learning approach

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments

Deep reinforcement learning: a survey

Related Author

Qing GUO

Xinyu WANG

Jian WU

Min JIA

Yang YANG

Dequan LI

Yuxi HAN

Qinqin TANG

Related Institution

School of Electronics and Information Engineering, Harbin Institute of Technology

Faculty of Artificial Intelligence, Anhui University of Science and Technology

China Mobile Research Institute

ZGC Institute of Ubiquitous-X Innovation and Applications

China Mobile Communications Group Corporation

Intro

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰

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

DOI：10.1631/FITEE.2400364