Joint Optimization of UAV Placement and Resource Allocation in FDMA Wireless-Powered Sensor Networks

This paper investigates unmanned aerial vehicle (UAV)-assisted wireless powered sensor networks (WPSNs). In this system, sensors harvest energy radiated from a UAV and use this energy to transmit collected data back to the UAV via frequency division multiple access (FDMA). The objective is to maximize the sum-throughput, subject to constraints on transmission scheduling and bandwidth allocation, while guaranteeing a minimum throughput for each sensor. This problem is non-convex due to the presence of coupled variables. To solve it, the alternating minimization technique is used, where the problem is divided into subproblems with respect to each of the variables by fixing the others. Efficient algorithms based on the dual Lagrange method and Karush-Kuhn-Tucker (KKT) conditions are proposed for optimal transmission time scheduling and bandwidth allocation. These algorithms offer significant advantages in execution time. The UAV placement subproblem is addressed using successive convex approximation (SCA), which iteratively maximizes a lower bound. Numerical results clearly show that the proposed method outperforms the benchmarks and has a much lower execution time compared to its TDMA counterpart.