6 Papers
2 Citations
Yang Cao is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Access control & Reinforcement learning. The author has an hindex of 3, co-authored 6 publications.
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Papers
Deep Reinforcement Learning For Multi-User Access Control in Non-Terrestrial Networks
TL;DR: A UE-driven deep reinforcement learning (DRL) based scheme, in which a centralized agent deployed at the backhaul side of NT-BSs is responsible for training the parameter of a deep Q-network (DQN), and each UE independently makes its own access decisions based on the parameter from the trained DQN.
83
Federated Deep Reinforcement Learning for User Access Control in Open Radio Access Networks
Yang Cao,Shao-Yu Lien,Ying-Chang Liang,Kwang-Cheng Chen +3 more
- 14 Jun 2021
TL;DR: In this article, a federated DRL-based scheme is proposed to train the parameters of multiple DQNs in the O-RAN, so as to maximize the long-term throughput and meanwhile avoid frequent user handovers with a limited amount of signaling overheads.
58
Deep Reinforcement Learning for Channel and Power Allocation in UAV-enabled IoT Systems
Yang Cao,Lin Zhang,Ying-Chang Liang +2 more
- 01 Dec 2019
TL;DR: A DRL-based channel and power allocation framework in a UAV-enabled IoT system that is able to intelligently allocate both channels and transmit power for uplink transmissions of IoT nodes to maximize the minimum energy-efficiency among all the IoT nodes.
44
Deep Reinforcement Learning for Multi-User Access Control in UAV Networks
Yang Cao,Lin Zhang,Ying-Chang Liang +2 more
- 20 May 2019
TL;DR: A distributed deep reinforcement learning (DRL) framework for multi-user access control in UAV networks that maximizes the long-term throughput while avoiding frequent handovers and shows the superiority of the proposed DRL framework over the state of arts.
21
Patent
An unmanned aerial vehicle network multi-user access control method based on deep reinforcement learning
Liang Yingchang,Yang Cao,Zhang Lin +2 more
- 10 May 2019
TL;DR: In this paper, the authors proposed a multi-user access control scheme based on deep reinforcement learning under the condition that the global network information is unknown, which can realize higher system throughput and lower switching times compared with a traditional access control mode.
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