Research On Deployment Optimization Strategy Of Wireless Powered Communication Network

In the traditional communication network,the communication quality and working duration of the devices are restricted by the battery power condition.In order to avoid the frequent replacement of the battery and the interruption of communication affects the overall performance of the communication network,finding a sustainable and stable energy source is the key of future communication network design.With the rise of wireless power transfer technology,the Wireless Powered Communication Network(WPCN),which can provide sustainable energy for user's equipment,has received extensive attention of researchers.Unmanned Aerial Vehicle based WPCN(U-WPCN)can effectively solve the problem of energy shortage and weak signal strength in the region where user's energy and spectrum resources are scarce.With the development of UAV technology and the gradual maturity of the WPCN network theoretical research system,build a U-WPCN network with high efficiency should plan to divide the coverage area of the UAV platform,and enables UAV platform to selects the current optimal location based on the user's coordinate.Based on the above considerations,this paper studies the optimal hovering position of the UAV platform in U-WPCN system construction.The greedy algorithm is used to find the optimal hovering point of each area one by one and finally gives all UAV's hovering point in U-WPCN.Furthermore,for WPCN based on the ground,due to the dual near-far phenomenon,the energy collected by the user from the Hybrid Access Point(H-AP)is affected by the distance,while the user located far away from the hybrid access point receives less energy,which requires more power to transmit information.Therefore,it is a hot topic in the field of wireless communication to study the user cooperation mechanism in wireless powered communication network.In addition,the lack of incentive mechanism makes the current collaborative system research is based on the ideal collaboration under the premise that the relay users are willing to participate in the data forwarding process unconditionally,but in realistic scenarios,this assumption is difficult to occur between strangers.Based on the above situation,there is great practical significance to design a reasonable pricing mechanism so that the relay point is willing to consume energy to help the source user to forward the data to the hybrid access point.Based on the pricing mechanism,this dissertation mainly focuses on the key points of cooperative user relay rate optimization,user and hybrid access point location optimization,and studies the cooperation strategy of WPCN.The main contents are summarized as follows two aspects:(1)The strategy of UAV's hovering position in U-WPCN network: It separates the hybrid access point H-AP into energy points(EPs)and Information access Points(APs),where the user's current location is known,introduces rotor drone as piggyback platforms,periodically adjusting the hovering position of the drone based on the user's location within the current service area.In this paper,the optimal hovering position strategy of UAV energy platform and UAV information platform is studied in a greedy algorithm in an alternately optimized mode,and network deployment costs are reduced as much as possible on the premise of guaranteeing the lowest user communication requirements in the service area.(2)The strategy of cooperative transmission with pricing mechanism: Firstly,this paper proposes a new pricing mechanism to encourage Up-link(UP)user cooperative communication in the WPCN.This mechanism can achieve a win-win cooperation between SU and HU,and improve the reliability of the uplink wireless information transmission phase.Secondly,the probability of successful collaboration and the benefit threshold are introduced.Finally,when the SU and H-AP positions are known,by searching for collaborative users in the optimal area,a selection strategy of the optimal cooperative user is given to minimized the communication cost of the SU.