1 code implementation • 6 Dec 2023 • 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.
1 code implementation • 18 Feb 2024 • Ju-Hyung Lee, Dong-Ho Lee, Joohan Lee, Jay Pujara
The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust, efficient, and generalizable communication frameworks.
no code implementations • 26 May 2020 • Ju-Hyung Lee, Jihong Park, Mehdi Bennis, Young-Chai Ko
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
no code implementations • 20 Oct 2020 • Ju-Hyung Lee, Jihong Park, Mehdi Bennis, Young-Chai Ko
Lastly, thanks to utilizing hybrid FSO/RF links, the proposed scheme achieves up to 62. 56x higher peak throughput and 21. 09x higher worst-case throughput than the cases utilizing either RF or FSO links, highlighting the importance of co-designing SAT-UAV associations, UAV trajectories, and hybrid FSO/RF links in beyond-5G NTNs.
no code implementations • 3 Dec 2021 • Ju-Hyung Lee, Hyowoon Seo, Jihong Park, Mehdi Bennis, Young-Chai Ko
A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems.
no code implementations • 27 Oct 2022 • Ju-Hyung Lee, Dong-Ho Lee, Eunsoo Sheen, Thomas Choi, Jay Pujara
In this work, we propose a realistic semantic network called seq2seq-SC, designed to be compatible with 5G NR and capable of working with generalized text datasets using a pre-trained language model.
no code implementations • 19 Nov 2022 • Omer Gokalp Serbetci, Ju-Hyung Lee, Daoud Burghal, Andreas F. Molisch
We also showed that if we further augment the dataset with the proposed techniques, test accuracy is improved more than three-fold.
no code implementations • 31 Oct 2023 • Ju-Hyung Lee, Chanyoung Park, Soohyun Park, Andreas F. Molisch
This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks' HO procedures.
no code implementations • 4 Feb 2024 • Chang-Yong Lim, Jihong Park, Jinho Choi, Ju-Hyung Lee, Daesub Oh, Heewook Kim
In this article, we propose a multi-agent deep reinforcement learning (MADRL) framework to train a multiple access protocol for downlink low earth orbit (LEO) satellite networks.