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On-line Access: 2024-11-05

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Frontiers of Information Technology & Electronic Engineering 

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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


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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

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author="Ji WANG, Jiayi SUN, Wei FANG, Zhao CHEN, Yue LIU, Yuanwei LIU",
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year="in press",
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doi="https://doi.org/10.1631/FITEE.2400364"
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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.

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