Research On UAV Deployment In UAV Aided Communication

With the increasing demand of users for data communication services,UAV base station,as a mobile and flexible communication service facility,has been widely used in current wireless communication services,especially in communication equipment failure and natural disaster emergency communication services.The deployment of UAV base station is the primary task of UAV auxiliary communication.The deployment performance of UAV is directly related to user experience and effective utilization of resources.On the basis of comprehensive analysis of related fields at domestic and foreign,this paper studies three core issues of user division,base station allocation and UAV space deployment,and gives corresponding optimization strategies.Firstly,a hybrid clustering algorithm,DC-ISO,was proposed to divide user clusters according to the known user location distribution in the region.The hybrid clustering algorithm DC-ISO takes the service performance of UAV base station,such as the maximum number of service users,the minimum number of service users and the service radius,as the constraint conditions of user area division.Through the strategy of merging,splitting,balancing and dynamic minimum cluster elements in the process of clustering algorithm,the mutual relation between the elements in the cluster and the elements between the clusters was coordinated,and the partition of user clusters was optimized.Hybrid clustering algorithm DC-ISO can solve the problem of regional restriction and cluster element balance in user cluster partition.Secondly,a fuzzy comprehensive evaluation allocation strategy based on user demand is proposed for the assignment of UAV base station under the condition of user request difference and UAV base station performance difference.In this paper,the relationship between user cluster demand and UAV base station performance is comprehensively considered,and the relationship between user cluster and UAV base station to be selected is comprehensively evaluated from five aspects including computing power,bandwidth,energy consumption,cost and stability.The proposed algorithm evaluates the performance of UAV based on the personalized needs of users,and gives the assignment scheme according to the needs.The algorithm takes the overall customer satisfaction maximization as the objective function,establishes the UAV base station allocation optimization problem model,and realizes the optimal allocation of UAV base station.Thirdly,aiming at the spatial location optimization of rotor UAV base station in UAV assisted communication,a deployment strategy of static UAV base station is proposed to maximize the overall download rate of users.In this paper,the influence of environmental factors and user distribution ignored in the existing methods on the deployment of UAV base station is emphasized.At the same time,the spatial deployment of rotorcraft UAV is analyzed with four environments as the background.The optimization model of the deployment problem is established to maximize the total download rate of users.By defining the objective function as fitness function,the algorithm gives a solution method based on particle swarm search algorithm,and obtains the optimal static deployment of uav base station.Finally,aiming at the flight trajectory optimization problem of fixed-wing UAV base station,a dynamic UAV base station trajectory optimization strategy was proposed to maximize the user cluster download rate and energy consumption ratio.By analyzing the data service communication model of UAV base station and the flight energy consumption model of UAV base station,the optimization objective function was established to maximize the download rate and minimize the energy consumption.The linear state space approximation and continuous convex optimization algorithm are used to solve the nonlinear fractional programming problem,and the non-convex fractional optimization is transformed into linear convex optimization problem for solving,and the optimal energy efficiency ratio flight trajectory of the fixed-wing UAV base station is obtained.