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Deep reinforcement learning for near-field wideband beam forming in STAR-RIS networks
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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, 1998, -1(-1): .
@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",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="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 Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 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 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400364
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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