Dual-Functional UAV-Empowered Space-Air-Ground Networks: Joint Communication and Sensing

Abstract

In this paper, we investigate a sensing-enabled integrated space-air-ground (SAG) data collection network, in which an unmanned aerial vehicle (UAV) can not only work singly to sense data from multiple targets but also collaborate with a low-earth orbit (LEO) satellite to collect communication data from multiple users. Since the coverage of the UAV is much smaller than that of the LEO satellite, we first determine the set of usable users and targets for the UAV by analyzing the signal-to-noise ratios between the UAV and the users and targets. Based on this, we pose an optimization problem designed to maximize the total amount of data collected in the network while satisfying the constraints of UAV energy consumption, memory capacity, and minimum amount of sensor data per target. Moreover, considering that the network consists of three layers and the UAV has dual functions of communication and sensing, this problem is solved by jointly optimizing the scheduling of the users’ data upload scheme, the UAV trajectory, and the allocation of communication and sensing time. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, so it is difficult to find the optimal solution. Therefore, we further design an alternating iterative optimization algorithm (AIOA) framework to find an appropriate solution. Specifically, we alternately optimize the UAV trajectory, time allocation strategy, and data upload schedule in each iteration. Finally, simulation experiments validate the effectiveness of the AIOA and its superiority over other benchmarks in terms of the amount of data collected.