6 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 Equivariance for Robust Generalization in Wireless Foundation Model
Haoyu Wang, Xigang Gao, Zhi Sun +1
Wireless foundation models (WFMs) have recently emerged as a promising paradigm for learning multiple channel state information (CSI) acquisition tasks. However, unlike natural lan…
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…
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…
Generalizable Learning for Frequency-Domain Channel Extrapolation under Distribution Shift
Haoyu Wang, Zhi Sun, Shuangfeng Han +2
Frequency-domain channel extrapolation is effective in reducing pilot overhead for massive multiple-input multiple-output (MIMO) systems. Recently, Deep learning (DL) based channel…