TY - GEN
T1 - Deep Reinforcement Learning based Access Control for Disaster Response Networks
AU - Zhou, Hang
AU - Wang, Xiaoyan
AU - Umehira, Masahiro
AU - Chen, Xianfu
AU - Wu, Celimuge
AU - Ji, Yusheng
N1 - Funding Information:
This research was supported in part by the JSPS Grant-in-Aid for Scientific Research (C) 20K11764, ROIS NII Open Collaborative Research 2020-20FA01, and The Telecommunications Advancement Foundation.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Deep reinforcement learning
KW - disaster response network
UR - http://www.scopus.com/inward/record.url?scp=85100431880&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9322553
DO - 10.1109/GLOBECOM42002.2020.9322553
M3 - Conference article in proceedings
AN - SCOPUS:85100431880
SN - 978-1-7281-8299-5
T3 - Globecom
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020
PB - IEEE Institute of Electrical and Electronic Engineers
T2 - IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -