Deep Reinforcement Learning based Access Control for Disaster Response Networks
Hang Zhou, Xiaoyan Wang, Masahiro Umehira, Xianfu Chen, Celimuge Wu, Yusheng Ji · 2020
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.