Research On Key Technologies Of UAV Location Deployment And Power Resource Allocation For Air Wireless Coverage Networks

Most of the research work in the field of wireless communication networks is focused on land mobile communication systems.Traditional cellular wireless networks are limited to a certain extent when facing communication scenarios such as forest patrols and temporary battlefields,and cannot solve traffic obstructions and electricity outage well.Therefore,it is particularly important to establish a fast and flexible wireless communication networking method to ensure smooth communication transmission.Compared with the traditional base station cellular network,UAV communication has irreplaceable advantages such as flexibility and maneuverability.It can effectively increase the wireless communication transmission capacity,thereby improving communication reliability,and completing more complex network tasks.However,when using UAV for communication,complex environment will affect communication reliability.Therefore,carrying out network layout and using network resources efficiently to improve system reliability are key issues of UAV networks.In this background,this article mainly studies a research on key technologies of UAV location deployment and power resource allocation for air wireless coverage networks.Solving the problem of power resource allocation and deployment in UAV networks and achieving the goal of improving system reliability are expected.The main contents and achievements of this article are as follows:Firstly,a joint optimization method based on the optimal altitude and horizontal position of a UAV relay network is proposed.Under the condition that the transmission distance between the ground source node and the destination node is limited,the UAV is used as a relay node and the target is to optimize the communication link loss of the UAV relay network.The genetic particle swarm hybrid algorithm with adaptive weight factor is used to calculate the optimal altitude and horizontal position of the UAV to minimize the communication loss and the scheme is simulated.Simulation results show that the algorithm can be used to calculate the UAV location and minimize the communication link loss of the UAV relay network effectively.Secondly,a communication network where an UAV is used as a relay node to assist wireless transmission from a base station to a user is proposed.A power resource allocation scheme is proposed to improve system reliability.The expression of the system outage probability of base station to user communication in the UAV relay network is derived.Then,under the condition that the average power of the system is limited,an optimization problem that minimizes the outage probability is established.The average power allocation minimizes the system's outage probability,and the closed-form solution of the scheme is obtained.In the end,the proposed optimization scheme is compared with the equal power scheme and the simulation analysis is performed.Simulation results show that the proposed optimal power allocation scheme is significantly better than the traditional equal power allocation scheme in terms of outage probability performance.Finally,an UAV location optimization method for a multi-user UAV communication network is proposed,in which an UAV is used as a base station to assist users in achieving wireless communication with the core network to form an UAV communication network.The system's outage probability of the UAV communication network is deduced.Then,the average outage probability and outage probability under user fairness of the UAV communication network is minimized as the optimization goal,and the optimal 3D space position of the UAV deployment by applying genetic particle swarm hybrid algorithm with adaptive weight factor to ensure wireless transmission reliability of UAV communication networks.The scheme is simulated and analyzed.Simulation results show that application of the algorithm can help calculate the location of the UAV and minimize the outage probability effectively.