Full Text:  <333>

Summary:  <125>

CLC number: TP391.4

On-line Access: 2025-01-24

Received: 2024-05-07

Revision Accepted: 2025-01-24

Crosschecked: 2024-09-30

Cited: 0

Clicked: 562

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Ji WANG

https://orcid.org/0000-0002-4536-6044

Zhao CHEN

https://orcid.org/0000-0002-8817-8270

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Frontiers of Information Technology & Electronic Engineering 

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Deep reinforcement learning for near-field wideband beamforming 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 (STAR-RIS); 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 beamforming 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",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400364"
}

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%T Deep reinforcement learning for near-field wideband beamforming 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
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%I Zhejiang University Press & Springer
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T1 - Deep reinforcement learning for near-field wideband beamforming in STAR-RIS networks
A1 - Ji WANG
A1 - Jiayi SUN
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A1 - Zhao CHEN
A1 - Yue LIU
A1 - Yuanwei LIU
J0 - Frontiers of Information Technology & Electronic Engineering
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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, and 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 softmax 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.

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
王骥1,孙嘉毅1,方炜1,陈钊2,刘玥3,刘元玮4,5
1华中师范大学物理科学与技术学院电子信息工程系,中国武汉市,430079
2清华大学北京信息科学与技术国家研究中心,中国北京市,100084
3澳门理工大学应用科学学院,中国澳门特别行政区
4伦敦玛丽女王大学电子工程与计算机科学学院,英国伦敦,E1 4NS
5香港大学电机及电子工程系,中国香港特别行政区,999077
摘要:本文研究了一种智能全向超表面辅助的多用户近场宽带通信系统,提出了一种基于深度强化学习的鲁棒算法。通过联合优化基站的主动波束成形和智能全向超表面的被动波束成形,提升用户的可达速率。为缓解宽带通信中的波束分裂问题,引入了时相联合的混合预编码结构,以实现高效的宽带波束成形。考虑到智能全向超表面相移模型的耦合性,将无源波束成形设计转化为连续与离散相移的混合控制问题,并通过混合动作映射解决高维连续动作的控制难题。此外,针对现有深度强化学习算法中的估计偏差问题,引入softmax算子有效减轻了该偏差。仿真结果表明,所提算法在克服估计过高和估计过低问题方面优于现有算法。

关键词组:深度强化学习;近场波束成形;智能全向超表面;宽带波束分裂

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

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