4 papers
Knowledge Distillation for Collaborative Learning in Distributed Communications and Sensing
Nhan Thanh Nguyen, Mengyuan Ma, Nir Shlezinger +4
The rise of sixth generation (6G) wireless networks promises to deliver ultra-reliable, low-latency, and energy-efficient communications, sensing, and computing. However, tradition…
Hybrid Federated Learning for Noise-Robust Training
Yongjun Kim, Hyeongjun Park, Hwanjin Kim +1
Federated learning (FL) and federated distillation (FD) are distributed learning paradigms that train UE models with enhanced privacy, each offering different trade-offs between no…
Machine Learning for Future Wireless Communications: Channel Prediction Perspectives
Hwanjin Kim, Junil Choi, David J. Love
Precise channel state knowledge is crucial in future wireless communication systems, which drives the need for accurate channel prediction without additional pilot overhead. While…
Meta-Learning-Based People Counting and Localization Models Employing CSI from Commodity WiFi NICs
Jihoon Cha, Hwanjin Kim, Junil Choi
In this paper, we consider people counting and localization systems exploiting channel state information (CSI) measured from commodity WiFi network interface cards (NICs). While CS…