Resource Allocation in UAV-Assisted Wireless Powered Communication Networks for Urban Monitoring

Abstract

In this paper, we studied the unmanned aerial vehicle-assisted urban monitoring network, in which unmanned aerial vehicle (UAV) with wireless power transmission provides energy transmission and data collection services for the network. Considering the density of the urban monitoring network, we use the $k$ -means algorithm to cluster the monitoring nodes and reduce the complexity of the UAV services. The UAVs are serviced using a fly-hover-communication protocol. During hovering, the UAV works in the full-duplex mode, collecting data from cluster head nodes on one side and recharging nodes in coverage on the other side. We propose a multiobjective joint optimization problem that considers maximizing the amount of data collection and energy transfer and minimizing the energy consumption of the UAV during the service period. In the optimization process, there is a partial conflict between the three objectives. For this reason, the importance of the optimization objectives is considered and described by weighting parameters. A multiobjective joint deep deterministic policy gradient algorithm is proposed for the multiobjective control policy of UAVs. Numerical results show that the proposed algorithm can achieve the joint optimization of multiple objectives and is compared with other algorithms to verify the superiority of the proposed algorithm.