Deep Reinforcement Learning based Access Control for Disaster Response Networks

Hang Zhou, Xiaoyan Wang, Masahiro Umehira, Xianfu Chen, Celimuge Wu, Yusheng Ji

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)


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 languageEnglish
Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020
Subtitle of host publicationProceedings
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages6
ISBN (Electronic)978-1-7281-8298-8
ISBN (Print)978-1-7281-8299-5
Publication statusPublished - Dec 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Global Communications Conference, GLOBECOM 2020: Online - Virtual, Taipei, Taiwan, Province of China
Duration: 7 Dec 202011 Dec 2020

Publication series



ConferenceIEEE Global Communications Conference, GLOBECOM 2020
Country/TerritoryTaiwan, Province of China


  • Deep reinforcement learning
  • disaster response network


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