1 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins
Ju-Hyung Lee, Andreas F. Molisch
This paper introduces AutoBS, a reinforcement learning (RL)-based framework for optimal base station (BS) deployment in 6G radio access networks (RAN). AutoBS leverages the Proxima…
Generative vs. Predictive Models in Massive MIMO Channel Prediction
Ju-Hyung Lee, Joohan Lee, Andreas F. Molisch
Massive MIMO (mMIMO) systems are essential for 5G/6G networks to meet high throughput and reliability demands, with machine learning (ML)-based techniques, particularly autoencoder…
Integrating Pre-Trained Language Model with Physical Layer Communications
Ju-Hyung Lee, Dong-Ho Lee, Joohan Lee +1
The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust,…
A Scalable and Generalizable Pathloss Map Prediction
Ju-Hyung Lee, Andreas F. Molisch
Large-scale channel prediction, i.e., estimation of the pathloss from geographical/morphological/building maps, is an essential component of wireless network planning. Ray tracing…
Handover Protocol Learning for LEO Satellite Networks: Access Delay and Collision Minimization
Ju-Hyung Lee, Chanyoung Park, Soohyun Park +1
This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagatio…