A Dome packing method for UAV positioning using 3D Beamforming in WPCN for water distribution network

Abstract

In the practical implementation of wireless powered communication network (WPCN) project, the energy consumption is an important factor to evaluate the performance and efficiency of the communication. In this project, a UAV enabled WPCN acts as a hybrid access point to handle multiple ground terminals (GT's) in an uneven plane. The ground terminals will harvest radio frequency (RF) energy from the RF signals directed by the UAV and these terminal uses this harvested energy to send information to the uplink. The objective of the paper is to find an optimal position of UAV using a proposed dome packing method to navigate the UAV, thus UAV will be able to charge the GTs within the cluster using 3D beamforming method where the beam will be focusing to a particular terminal rather than broadcasting the signals everywhere and thereby minimize the total energy consumption and mission completion time.

Energy Efficient Communication Design in UAV Enabled WPCN Using Dome Packing Method in Water Distribution System

“…The paper is based on extending the work [35] which focuses on finding an optimal position using a dome packing method where the UAV acts as an energy transmitter and a data receiver. Whereas, this paper proposes two optimization models, in which a dome packing technique is used to combine and pack the sensors based on the beamforming range and a dynamic optimization model to determine the ideal positions of the UAV to charge the sensors on the ground.…”

“…The GN's are represented as sensors and UAV at an initial height of h and the distance between the UAV and the GN is represented by d in Equation ( 1) and θ is the angle between the UAV and the GN. The dome packing Algorithm 1 [35] is as follows: In the dome packing method, M domes are arranged inside the simulated area with a maximum packing density and radius r without overlapping each other. For implementation, K means algorithm is used to implement three-dimensional (3D) clustering and after calculating the beaming range [35], an initial optimal height (h uav ) ranges are calculated for each of the clusters since the positions of the GN's and the distance from the cluster centre towards the GN's are known.…”

“…The dome packing Algorithm 1 [35] is as follows: In the dome packing method, M domes are arranged inside the simulated area with a maximum packing density and radius r without overlapping each other. For implementation, K means algorithm is used to implement three-dimensional (3D) clustering and after calculating the beaming range [35], an initial optimal height (h uav ) ranges are calculated for each of the clusters since the positions of the GN's and the distance from the cluster centre towards the GN's are known. The optimal height (h uav ) is calculated as in Section 3.2 using extremum seeking control.…”