Research On Trajectory And Resource Optimization Of UAV Assisted Communication Network

The Fifth Generation Mobile Communication system(5G)has three major application scenarios:ultra-high mobile bandwidth in terms of communication speed,massive machine communication in terms of low power consumption,and high reliability in terms of ultra-low latency.Mobile bandwidth,ultra-reliable and low-latency,and massive machine communication provide broader choices for UAV communication technology,and also place higher requirements on wireless communication technology and network infrastructure.UAV communication can not only realize high-speed data transmission,but also can be combined with artificial intelligence technology based on the communication characteristics of massive machines to further promote the interconnection of everything.Ground communication infrastructure is damaged or difficult to be temporarily deployed in disaster scenarios or hot spots.Therefore,ground communication infrastructure alone cannot meet the communication requirements of all services and cannot guarantee the quality of communication services.UAV assisted communication with communication equipment carried by UAVs brings new possibilities to alleviate these difficulties,which can quickly establish communication services and effectively ensure the quality of communication services.However,UAVs assist communication networks also face severe challenges:communication interference,deployment strategy,flight trajectory optimization and wireless resource allocation.In addition,the limited battery capacity of UAV is also a major difficulty restricting the performance of UAV auxiliary communication network.Based on the challenges and difficulties encountered in UAV assisted communication network,this paper jointly optimizes the trajectory and resource allocation of UAV under the consideration of energy consumption,so as to effectively improve the quality of communication service and prolong the working time of UAV.The main content of this paper is firstly to analyze the application scenarios of UAV assisted communication network and the system model,including the channel model and the energy consumption model.Then,for UAV trajectory and resource optimization,this paper uses Deep Deterministic Policy Gradient(DDPG)algorithm in Deep Reinforcement Learning(DRL)to solve the problem.Compared with Q-learning and Deep Q Network(DQN),DDPG can not only deal with the space problem of complex state in time-varying environment,but also can deal with the continuous action problem.Therefore,this paper proposes a joint optimization algorithm of Trajectory Design and Power Allocation based on DDPG(TDPA-DDPG)to solve the problems of energy efficiency and communication quality of service.Simulation results show that compared with random algorithm,Q-learning algorithm and DQN algorithm,the proposed algorithm has significantly improved energy efficiency and communication quality of service.