29 citations · 51 across the 7 of their papers we have counts for
7 papers
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning
Han Cha, Jihong Park, Hyesung Kim +2
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent. Since the RM contains all state observations…
Demo: A Reinforcement Learning-based Flexible Duplex System for B5G with Sub-6 GHz
Soo-Min Kim, Han Cha, Seong-Lyun Kim +1
In this paper, we propose a reinforcement learning-based flexible duplex system for B5G with Sub-6 GHz. This system combines full-duplex radios and dynamic spectrum access to maxim…
Distilling On-Device Intelligence at the Network Edge
Jihong Park, Shiqiang Wang, Anis Elgabli +6
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML). As opposed to traditional ready-made public datasets, these user-generated privat…
Federated Reinforcement Distillation with Proxy Experience Memory
Han Cha, Jihong Park, Hyesung Kim +2
In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models. The experience memory, howeve…
Sense-and-Predict: Harnessing Spatial Interference Correlation for Cognitive Radio Networks
Seunghwan Kim, Han Cha, Jeemin Kim +2
Cognitive radio (CR) is a key enabler realizing future networks to achieve higher spectral efficiency by allowing spectrum sharing between different wireless networks. It is import…
Opportunism in Dynamic Spectrum Access for 5G: A Concept and Its Application to Duplexing
Jeemin Kim, Soo-Min Kim, Han Cha +4
With the envisioned massive Internet-of-Things (IoT) era, one of the challenges for 5G wireless systems will be handling the unprecedented spectrum crunch. A potential solution has…