11 papers
Unified Generalization for Frequency-Domain Channel Extrapolation Across Near-Field and Far-Field Scenarios
Haoyu Wang, Zhi Sun, Shuangfeng Han +2
As antenna arrays grow, near-field effects become non-negligible in large-scale MIMO, making accurate low-overhead channel acquisition crucial in both far-field and near-field regi…
Physics-Informed Path-Parametric Learning for Efficient and Lightweight CSI Feedback
Chunyu Ling, Jiajia Guo, Yiming Cui +4
Channel State Information (CSI) feedback is vital for high spectral efficiency in wireless systems, yet high-dimensional CSI introduce significant feedback overhead. Recent deep le…
Generalizable Learning for Massive MIMO CSI Feedback in Unseen Environments
Haoyu Wang, Zhi Sun, Shuangfeng Han +2
Deep learning is promising to enhance the accuracy and reduce the overhead of channel state information (CSI) feedback, which can boost the capacity of frequency division duplex (F…
Path Evolution Model for Endogenous Channel Digital Twin towards 6G Wireless Networks
Haoyu Wang, Zhi Sun, Shuangfeng Han +3
Massive Multiple Input Multiple Output (MIMO) is critical for boosting 6G wireless network capacity. Nevertheless, high dimensional Channel State Information (CSI) acquisition beco…
TREE:Token-Responsive Energy Efficiency Framework For Green AI-Integrated 6G Networks
Tao Yu, Kaixuan Huang, Tengsheng Wang +7
As wireless networks evolve toward AI-integrated intelligence, conventional energy-efficiency metrics fail to capture the value of AI tasks. In this paper, we propose a novel EE me…
Enhancing Environment Generalizability for Deep Learning-Based CSI Feedback
Haoyu Wang, Shuangfeng Han, Xiaoyun Wang +1
Accurate and low-overhead channel state information (CSI) feedback is essential to boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIM…