Deep Reinforcement Learning based Access Control for Disaster Response Networks
Deep Reinforcement Learning based Access Control for Disaster Response Networks
复制标题
DOI：
10.1109/globecom42002.2020.9322553
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Hang Zhou;Xiaoyan Wang;M. Umehira;Xianfu Chen;Celimuge Wu;Yusheng Ji
中科院分区：
文献类型：
--
作者：
Hang Zhou;Xiaoyan Wang;M. Umehira;Xianfu Chen;Celimuge Wu;Yusheng Ji
灾难发生后，立即重建网络并为灾民提供通信服务至关重要。在灾区部署移动可部署资源单元（MDRU），沿着部署多个接入点，以扩大MDRU的服务范围，是一个非常有前途的解决方案。在这种异构的灾难响应网络中，通过执行最优的无线接入控制来最小化来自用户终端的分组延迟是非常重要的。本文提出了一种基于深度强化学习的无线接入控制机制，可以实现智能中继选择和发射功率控制。我们通过大量的仿真来评估性能，并通过与基线方案的比较来验证所提出的机制的优越性。
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.