2 citations · 3 across the 4 of their papers we have counts for
6 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…
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…
Sense-and-Predict: Opportunistic MAC Based on Spatial Interference Correlation for Cognitive Radio Networks
Jeemin Kim, Seung-Woo Ko, Han Cha +1
Opportunity detection at secondary transmitters (TXs) is a key technique enabling cognitive radio (CR) networks. Such detection however cannot guarantee reliable communication at s…