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Abstract
After a disaster occurred, it is extremely important to reconstruct the network and provide the communication services to the victims immediately. Deploying MDRU (Movable and Deployable Resource Unit) in the disaster area, along with multiple access points to extend the service area of MDRU is a very promising solution. In this kind of heterogeneous disaster response networks, it is of great importance to minimize the packet delay from user terminals by performing optimal radio access control. In this paper, we propose a deep reinforcement learning based radio access control mechanism, which enables the smart relay selection and transmitting power control. We evaluate the performance by extensive simulations, and validate the superiority of the proposed mechanism by comparing with baseline schemes.
Original language | English |
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Title of host publication | 2020 IEEE Global Communications Conference, GLOBECOM 2020 |
Subtitle of host publication | Proceedings |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-8298-8 |
ISBN (Print) | 978-1-7281-8299-5 |
DOIs | |
Publication status | Published - Dec 2020 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE Global Communications Conference, GLOBECOM 2020: Online - Virtual, Taipei, Taiwan, Province of China Duration: 7 Dec 2020 → 11 Dec 2020 |
Publication series
Series | Globecom |
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ISSN | 1930-529X |
Conference
Conference | IEEE Global Communications Conference, GLOBECOM 2020 |
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Country/Territory | Taiwan, Province of China |
City | Taipei |
Period | 7/12/20 → 11/12/20 |
Keywords
- Deep reinforcement learning
- disaster response network
Fingerprint
Dive into the research topics of 'Deep Reinforcement Learning based Access Control for Disaster Response Networks'. Together they form a unique fingerprint.Projects
- 1 Finished
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MISSION: Mission-Critical Internet of Things Applications over Fog Networks
Chen, X., Forsell, M., Chen, T. & Räty, T.
1/01/19 → 31/12/21
Project: Academy of Finland project