Throughput Maximization for UAV-Assisted Data Collection with Hybrid NOMA

Owing to the excellent mobility character, unmanned aerial vehicles (UAVs) show great potentials as a means of data collector in a wireless sensor network (WSN). However, limitations in communication resources require UAVs to collect data from large-scale WSNs with high spectral efficiency. In this work, we investigate a UAV-assisted data collection strategy for a WSN enabled by hybrid NOMA. Specifically, the WSN is organized into clusters and each cluster is further subdivided into multiple groups. The sensor nodes in each group transmit data to the UAV using NOMA scheme. To ensure fairness between sensor nodes, we aim to maximize the minimum throughput by jointly optimizing the sensor node pairing, decoding order, transmit power and the UAV waypoints, subject to transmit power and UAV trajectory length constraints. To address the formulated mixed-integer nonlinear programming problem, we first propose an optimization-based algorithm by applying block coordinate descent method where a closed-form solution of the transmit power is derived, and then design a low-complexity heuristic algorithm. In addition, we present an initialization scheme for the proposed algorithms and an implementation strategy for our system. Extensive simulations demonstrate that our proposed UAV-assisted data collection scheme with hybrid NOMA can enhance system performance efficiently.