6 papers
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…
Downlink Channel Estimation for mmWave Systems with Impulsive Interference
Kwonyeol Park, Gyoseung Lee, Hyeongtaek Lee +2
In this paper, we investigate a channel estimation problem in a downlink millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system, which suffers from impulsive interfe…
Analysis of Beam Misalignment Effect in Inter-Satellite FSO Links
Minje Kim, Hongjae Nam, Beomsoo Ko +4
Free-space optical (FSO) communication has emerged as a promising technology for inter-satellite links (ISLs) due to its high data rate, low power consumption, and reduced interfer…
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…
Spatial-Division ISAC: A Practical Waveform Design Strategy via Null-Space Superimposition
Byunghyun Lee, Hwanjin Kim, David J. Love +1
Integrated sensing and communications (ISAC) is a key enabler of new applications, such as precision agriculture, extended reality (XR), and digital twins, for 6G wireless systems.…
Complete Power Reallocation for MU-MIMO under Per-Antenna Power Constraint
Sucheol Kim, Hyeongtaek Lee, Hwanjin Kim +2
This paper proposes a beamforming method under a per-antenna power constraint (PAPC). Although many beamformer designs with the PAPC need to solve complex optimization problems, th…