43 citations · 52 across the 6 of their papers we have counts for
6 papers
Deep Reinforcement Learning for Adaptive Power Allocation in ISAC Systems with Mobile Target
Zhilin Fu, Sangmin Kim, Sangwon Hwang +4
In this paper, we study the power allocation for an integrated sensing and communication (ISAC) system which tracks a mobile target. We first model the problem as a Markov decision…
Cooperative Multi-Agent Deep Reinforcement Learning Methods for UAV-aided Mobile Edge Computing Networks
Mintae Kim, Hoon Lee, Sangwon Hwang +2
This paper presents a cooperative multi-agent deep reinforcement learning (MADRL) approach for unmmaned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks. An UAV with…
A Codebook Design for FD-MIMO Systems with Multi-Panel Array
Zhilin Fu, Sangwon Hwang, Jihwan Moon +2
In this work, we study codebook designs for full-dimension multiple-input multiple-output (FD-MIMO) systems with a multi-panel array (MPA). We propose novel codebooks which allow p…
Decentralized Computation Offloading With Cooperative UAVs: Multi-Agent Deep Reinforcement Learning Perspective
Sangwon Hwang, Hoon Lee, Juseong Park +1
Limited computing resources of internet-of-things (IoT) nodes incur prohibitive latency in processing input data. This triggers new research opportunities toward task offloading sy…
Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks
Sangwon Hwang, Hanjin Kim, Hoon Lee +1
This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple h…
Deep Learning-based Limited Feedback Designs for MIMO Systems
Jeonghyeon Jang, Hoon Lee, Sangwon Hwang +2
We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedu…