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20242026
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eess.SP2026

Digital Twin-Aided Prescreening for User Scheduling in MU-MIMO Downlink Systems

Namhyun Kim, Mahmoud Saad Abouamer, Jeonghun Park +2

In dense deployments, massive multi-user multiple-input multiple-output (MU-MIMO) base stations can acquire instantaneous channel state information (CSI) for only a limited subset…

eess.SP2026

Lightweight Vision-Aided Beam Tracking for Cross-Environment mmWave Communications

Mengyuan Ma, Ahmed Alkhateeb, Nhan Thanh Nguyen +2

Sensing-aided beam tracking is a promising approach to reduce the overhead for millimeter-wave beam management. However, real-world application remains challenging due to rapid cha…

eess.SP2026

Data-Free Knowledge Distillation for LiDAR-Aided Beam Tracking in MmWave Systems

Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb +1

We propose a data-free knowledge distillation (DF- KD) framework for LiDAR-aided mmWave beam tracking, where the objective is to predict the optimal current and future beams from a…

eess.SP2026

LWM-CDE: A Representation Space for Wireless Data Reasoning and Transferability

Sadjad Alikhani, Akshay Malhotra, Shahab Hamidi-Rad +1

Machine learning deployments in real-world wireless communication tasks face significant generalization challenges due to location and environment-specific signal structure, high d…

eess.SP2026

Wireless Digital Twin Calibration: Refining DFT-Domain Channel Information

Hao Luo, Saeed R. Khosravirad, Ahmed Alkhateeb

Wireless digital twins can be leveraged to provide site-specific synthetic channel information through precise physical modeling and signal propagation simulations. This can help r…

eess.SP2026

Knowledge Distillation for Lightweight Multimodal Sensing-Aided mmWave Beam Tracking

Mengyuan Ma, Isuri Welgamage, Ahmed Alkhateeb +3

Beam training and prediction in real-world millimeter-wave (mmWave) communications systems are challenging due to rapidly time-varying channels and strong interference from surroun…