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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)
    • DOI:10.1631/FITEE.2400364    

      CLC: TP391.4
    • Received:07 May 2024

      Revised:30 September 2024

      Accepted:2024-09-30

      Published:2024-12

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  • 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.

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