Research On Resource Allocation Of UAV Communication Networks For Data Collection

With the rapid development of unmanned aerial vehicle(UAV)technology and the progress in the field of wireless communication,UAV communication networks have gradually become a research hotspot in both academia and industry area,attracting a lot of attention.There are many important application scenarios for UAV communication networks.Among them,the UAV communication network for data collection is an important aspect.In data collection applications,by optimizing the 3D trajectory/hovering location of the UAV,as well as the allocation of communication resources,the 3D mobility characteristics of the UAV can be fully utilized to obtain better performance for data collection in wireless networks.However,there are still many problems to be solved in the UAV communication networks for data collection,such as the optimization of the UAV 3D trajectory under the complex air-to-ground channel models,the interference avoidance of UAVs,and the joint coverage of multi-UAV data collection,etc.To cope with these problems,this thesis uses mathematical tools such as convex optimization,bilevel programming,and submodular function maximization to study the resource allocation problem of UAV communication networks for data collection.The main research scenarios are cooperative multi-UAV coverage for data collection,and single UAV-assisted sensor network data collection.Firstly,for the cooperative multi-UAV coverage scenario of hovering UAVs for data collection.This thesis proposes a cooperative multi-UAV coverage scheme considering the limited backhaul constraint:multiple UAVs conduct the coverage task for data collection in the same mission area,and then compress and forward the collected data back to the ground central processing station.In this thesis,the UAV-enabled cloud radio access network(CRAN)architecture is adopted for efficient data collection.By solving a joint UAV 3D hover location optimization,coverage area division and backhaul resource allocation problem,the average transmit power of ground user equipments is minimized.In order to solve the considered joint optimization problem,this thesis first adapts the bilevel programming tool to equivalently transform the modeled jointoptimization problem into a bi-level programming problem.Besides,an optimization framework based on submodular function maximization named the overlap-Lloyd algorithm is proposed.Simulation results show that the proposed overlap-Lloyd algorithm has good convergence properties,and the proposed cooperative multi-UAV coverage scheme has more than 20%performance gain compared with the traditional cell division-based schemes.Secondly,for single UAV-assisted wireless sensor network data collection,this thesis proposes a joint UAV trajectory planning and resource allocation scheme based on distributed source coding(DSC).By introducing distributed source coding,the data correlation of terrestrial sensor networks can be fully utilized to reduce sensor node energy consumption.The goal of this thesis is to minimize the maximum energy consumption of sensor nodes by jointly optimizing the three-dimensional flight trajectory of UAVs,ground sensor scheduling and distributed source coding rate.In order to solve the considered complex and non-convex joint optimization problem,this thesis proposes a joint UAV three-dimensional trajectory optimization and communication resource allocation algorithm scheme based on bilevel programming.Simulation results show that compared with the traditional data collection schemes for UAV-assisted wireless sensor networks,the proposed scheme significantly reduces the energy consumption of sensor nodes,especially when the number of sensor nodes is large.In addition,compared with the existing UAV trajectory optimization and communication resource allocation algorithms based on alternating optimization and successive convex approximation,the proposed algorithm has advantages in both performance and complexity.