Resource Allocation And Trajectory Optimization For UAV-Enabled Wireless Powered Communication Networks

With the development of 5G,various new wireless communication applications have emerged,among which Internet-of-Things(IoT)is the key technology to bringing the vision of interconnected-everything to real life.How to provide convenient energy supply and high-quality communication services for large-scale IoT networks is a critical issue that needs to be solved urgently.The new wireless powered communication networks(WPCN)integrate radio frequency(RF)-based wireless power transfer(WPT)and wireless communication into a joint framework to transmit energy and receive information to/from devices in the downlink and uplink,respectively.However,due to severe RF signal propagation loss,the performance of conventional WPCN deployed on the ground is limited.Moreover,it faces the severe "doubly near-far" fairness issue,in which the devices near the access points(APs)can achieve much better performance than that of the far-apart devices.Recently,unmanned aerial vehicle(UAV)-enabled wireless communications have attracted growing interests,in which the flexible mobility of UAVs can efficiently improve the communication capacity and coverage performance.Motivatied by UAV-enabled communications,this thesis investigates a UAV-enabled WPCN with joint UAV trajectory design and resource allocation optimization to improve both energy transmission and communication performance.Specifically,the main results of this thesis are listed as follows.(1)Firstly,for a single-UAV-enabled WPCN,we study the uplink common throughput maximization problem via jointly optimizing UAV trajectory and resource allocation,subject to UAV’s maximum flight speed constraints and devices’energy causality constraints.First,consider a special case with UAV’s maximum flight speed constraints ignored.By utilizing Lagrange duality method,we obtain two sets of UAV optimal hovering locations corresponding to downlink WPT and uplink wireless information transmission(WIT),respectively,whose achievable uplink common throughput is treated as a performance upper bound.Next,consider the general case with UAV’s maximum flight speed constraints.Based on the optimal hovering locations,a successive hover-and-fly trajectory design is proposed,in which the UAV sequentially hovers and flies among these hovering locations at the maximum speed.Finally,based on alternative optimization and successive convex approximation techniques,we propose a time-quantization trajectory design jointly with resource allocation.Compared with single-location static hovering benchmark scheme,the proposed joint trajectory and resource allocation optimization designs can sufficiently improve system performance.(2)Secondly,for UAV-enabled WPCN in interference channel scenario,consider a fundamental two-UAV two-device WPCN.Under two interference suppression scenarios,we maximize the two devices’ uplink common throughput via jointly optimizing the UAVs trajectories and resource allocation,subject to UAVs’ maximum flight speed,initial/final locations,and collision avoidance constraints,as well as the devices’ energy causality constraints.With coordinate multi-point(CoMP)transmission and reception at the UAVs,two UAVs utilize cooperative energy beamforming to transfer energy towards devices and jointly detect the signal sent from the two devices,in which two UAV will hover and fly between two devices to enhance the cooperation gain.In addition,under interference coordination scenario,two UAV transfer independent energy signal and respectively decode its corresponding device’s information,in which two UAVs will keep far away from each other to avoid interference.Note that two devices will change their WIT protocol depending on the interference level.The proposed joint UAV trajectory and resource allocation optimization designs for both scenarios can efficiently improve system performance,and CoMP outperforms interference coordination.It provides basis theory and technical reference for multi-UAV-enabled WPCN(3)Finally,for multi-UAV-enabled WPCN scenario,assume that uplink WIT is implemented under TDMA principle,thus we only need to optimize the UAVs trajectories to maximize the devices’ minimum received energy in the downlink WPT,subject to UAVs’ maximum flight speed and collision avoidance constraints,via considering two heuristic designs,namely UAV swarming and devices clustering.Under UAV swarming scenario,multiple UAVs form into a fixed-formation swarm and fly following the same speed and orientation,in which the UAVs utilize cooperative energy beamforming technique to transfer energy towards different devices based on a TDMA principle.Moreover,under devices clustering scenario,the devices are divided into multiple clusters and each cluster is served by one UAV.There exists a performance tradeoff between the two designs above.Since the UAV swarm generally needs to take more time to visit the energy transfer locations towards different devices,when duration is short,devices clustering design outperforms UAV swarming;as the duration becomes larger,however,the performance of UAV swarming design surpasses that of devices clustering design,and the performance gain becomes more significant.This thesis innovatively studies a novel UAV-enabled WPCN.Under different scenarios,the corresponding joint UAV trajectory and resource allocation optimization design is proposed and its superiority is validated.This thesis lays the foundation for the UAV-enabled WPCN,establishes theoretic performance optimization limits,and provides significant guidelines.