5 papers
Generalizable and Robust Beam Prediction for 6G Networks: An Deep-Learning Framework with Positioning Feature Fusion
Yanliang Jin, Yunfan Li, Jiang Jun +5
Beamforming (BF) is essential for enhancing system capacity in fifth generation (5G) and beyond wireless networks, yet exhaustive beam training in ultra-massive multiple-input mult…
Context-Aware Deep Learning for Robust Channel Extrapolation in Fluid Antenna Systems
Yanliang Jin, Runze Yu, Yuan Gao +4
Fluid antenna systems (FAS) offer remarkable spatial flexibility but face significant challenges in acquiring high-resolution channel state information (CSI), leading to considerab…
F-CKM: Learning Channel Knowledge Map with Radio Frequency Radiance Field Rendering
Kequan Zhou, Guangyi Zhang, Hanlei Li +3
In 6G mobile communications, acquiring accurate and timely channel state information (CSI) becomes increasingly challenging due to the growing antenna array size and bandwidth. To…
AI-Driven Channel State Information (CSI) Extrapolation for 6G: Current Situations, Challenges and Future Research
Yuan Gao, Zichen Lu, Xinyi Wu +7
CSI extrapolation is an effective method for acquiring channel state information (CSI), essential for optimizing performance of sixth-generation (6G) communication systems. Traditi…
SSNet: Flexible and robust channel extrapolation for fluid antenna systems enabled by an self-supervised learning framework
Yuan Gao, Yiming Liu, Runze Yu +5
Fluid antenna systems (FAS) signify a pivotal advancement in 6G communication by enhancing spectral efficiency and robustness. However, obtaining accurate channel state information…