57 citations · 100 across the 8 of their papers we have counts for
12 papers
Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding
Jeonghyeon Jang, Hoon Lee, Il-Min Kim +1
In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have be…
Deep Learning Based Resource Assignment for Wireless Networks
Minseok Kim, Hoon Lee, Hongju Lee +1
This paper studies a deep learning approach for binary assignment problems in wireless networks, which identifies binary variables for permutation matrices. This poses challenges i…
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…
Joint Design of Fronthauling and Hybrid Beamforming for Downlink C-RAN Systems
Jaein Kim, Seok-Hwan Park, Osvaldo Simeone +2
Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This work studies the optimization of the analog and digit…
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
Jihwan Moon, Osvaldo Simeone, Seok-Hwan Park +1
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at deliverin…