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
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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 EngineeringVol. 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
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.
Deep reinforcement learning for near-field wideband beamforming in STAR-RIS networksEnhanced Publication