Full-duplex communication substantially enhances spectral efficiency by enabling simultaneous transmission and reception on the same time-frequency resources. However, its practical deployment remains hindered by strong residual self-interference and inter-user interference, which severely degrade system performance. This work investigates a full-duplex MISO network that leverages movable-antenna base stations (MA-BS) and movable-element reconfigurable intelligent surfaces (ME-RIS) to overcome these limitations in next-generation 6G systems. Unlike conventional fixed-geometry architectures, the proposed framework jointly optimizes antenna and RIS element positions, together with RIS phase shifts, to strengthen desired links while suppressing interference. Our design objective is to maximize the system sum rate through the joint optimization of transmit and receive beamforming vectors, uplink transmit powers, RIS phase shifts, and the spatial locations of both the BS antennas and RIS elements. To solve this challenging nonconvex problem, an alternating optimization algorithm is developed, employing semidefinite relaxation for beamforming design and successive convex approximation for position optimization. Simulation results demonstrate that the proposed ME-RIS-assisted architecture with movable BS antennas offers substantial gains over conventional fixed-position full-duplex networks. These findings highlight the potential of integrating movable antennas with movable RIS elements as a key enabler for high-performance full-duplex operation in future 6G wireless systems.
Suppressing Beam Squint Effect For Near-Field Wideband Communication Through Movable Antennas
Shanghai Jiao Tong University（上海交通大学）；Dalian University of Technology（大连理工大学）；Tongji University（同济大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Movable-Antenna Relay Enhanced Multiuser Communications: Reinforcement Learning-Based Antenna Positioning and Power Allocation
School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；School of Interdisciplinary Science, Beijing Institute of Technology（北京理工大学交叉科学研究院）
Deep Learning-Empowered Movable-Antenna Position Optimization with Partial CSI
Glasgow College, University of Electronic Science and Technology of China（电子科技大学格拉斯哥学院）；National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China（电子科技大学无线通信国家重点实验室）；School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理学院）；Department of Communication Systems, KTH Royal Institute of Technology（瑞典皇家理工学院通信系统系）
Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning
University of Electronic Science and Technology of China（电子科技大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；Zhejiang University（浙江大学）；Singapore University of Technology and Design（新加坡科技设计大学）
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China（电子科技大学无线通信国家重点实验室）；State Key Laboratory of CNS/ATM and the School of Interdisciplinary Science, Beijing Institute of Technology（北京理工大学 CNS/ATM 国家重点实验室和交叉科学研究院）
