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主講人：
主講人：
沈立翔 教授 (國立中央大學 通訊工程學系)
沈立翔 教授 (國立中央大學 通訊工程學系)
Li-Hsiang Shen received Ph.D. degree from the Institute of Communication Engineering, National Chiao Tung University (NCTU), Hsinchu, Taiwan, in 2020. Since February 2024, he has been an Assistant Professor with the Department of Communication Engineering, National Central University (NCU), Taoyuan, Taiwan. From 2018 to 2019, he was a Visiting Scholar with the Next Generation Wireless Research Group of the Department of Electrical and Computer Engineering (ECE), University of Southampton, U.K. From 2021 to 2023, he was a Postdoc with ECE, National Yang Ming Chiao Tung University (NYCU), Hsinchu, Taiwan. In 2023, he was a Visiting Scholar with California PATH, Berkeley DeepDrive, University of California, Berkeley (UCB), USA. His research interests include wireless broadband (5G/6G), low Earth orbit (LEO), reconfigurable intelligent surface (RIS), wireless local area networks (WLANs), wireless sensing, and machine and deep learning. He was the recipient of Ph. D Scholarship from NCTU and from Industry-Academic Elite Program. He was rewarded the first prize of Broadcom Foundation Asia Pacific Workshop in 2019. In 2021, he was rewarded IEEE Best PhD Thesis Award, NYCU Outstanding Ph.D. Research, and Phi Tau Phi Scholastic Honor Society of Taiwan. In 2022, he was rewarded National Science and Technology Council (NSTC) FutureTech Award, NSTC Postdoctoral Research Abroad Program, and NSTC Postdoctoral Research Award.
演講主題：
演講主題：
Advanced Simultaneously Transmitting and Reflecting (STAR) Surfaces in Full-Duplex Transmissions
Advanced Simultaneously Transmitting and Reflecting (STAR) Surfaces in Full-Duplex Transmissions
The full-duplex design of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is conceived in support of both uplink (UL) and downlink (DL) users. Furthermore, the dual STAR-RISs (D-STAR) concept is conceived as a promising architecture for 360-degree full-plane coverage, including UL/DL users located between the base station (BS) and the D-STAR as well as beyond. The primary STAR-RIS (STAR-P) tackles the P-region inter-user interference, self-interference (SI) from the BS and from the reflective as well as refractive UL users imposed on the DL receiver. By contrast, the secondary STAR-RIS (STAR-S) aims for mitigating the S-region interferences. The non-linear and non-convex rate-maximization problem formulated is solved by alternating optimization amongst the decomposed convex sub-problems of the BS beamformer, and the D-STAR configurations. Extending from D-STAR, a double-sided STAR-RIS (DS-STAR) becomes a promising solution, with only one metasurface enabling signals impinging from both sides of the surface. From implemenetaion perspective, we have established an intelligent deployment of RIS (i-Dris) prototype. The RISs are deployed on the auto-guided-vehicle (AGV) with configured incident/reflection angles. While, both the BS and receiver are associated with an edge server monitoring downlink throughput. Federated multi-agent reinforcement learning scheme is proposed with each AGV-RIS consisting of the deployment factors. Experimental results presented that i-Dris can reach up to 980 Mbps throughput under a bandwidth of 100 MHz, with comparably low complexity and rapid deployment.
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