Search

Radio dynamic zones (RDZs) are geographical areas within which dedicated spectrum resources are monitored and controlled to enable the development and testing of new spectrum technologies. Real-time spectrum awareness within an RDZ is critical for preventing interference with nearby incumbent users of the spectrum. In this article, we consider a 3-D RDZ scenario and propose to use unmanned aerial vehicles (UAVs) equipped with spectrum sensors to create and maintain a 3-D radio map of received signal power from different sources within the RDZ. In particular, we introduce a 3-D Kriging interpolation technique that uses realistic 3-D correlation models of the signal power extracted from extensive measurements carried out at the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) platform. Using ${C}$ -band signal measurements by a UAV at altitudes between 30 and 110 m, we first develop realistic propagation models on air-to-ground path loss, shadowing, spatial correlation, and semi-variogram, while taking into account the knowledge of antenna radiation patterns and ground reflection. Subsequently, we generate a 3-D radio map of a signal source within the RDZ using the Kriging interpolation and evaluate its sensitivity to the number of measurements used and their spatial distribution. Our results show that the proposed 3-D Kriging interpolation technique provides significantly better radio maps when compared with an approach that assumes perfect knowledge of path loss. Specifically, the root-mean-square error (RMSE) of the signal power prediction achieved by our proposed 3-D Kriging method is notably lower compared to that of the perfect path loss-based prediction, especially when the height difference between measured and the target locations is less than 20 m.

Unmanned Aircraft Systems (UASs) have been receiving significant interest and support from academia, industry, and regulatory bodies over the past decade due to their various use cases. To safely integrate UAS operations into the national airspace, particularly overpopulated regions, the risk posed to ground users, buildings, and vehicles due to unmanned aerial vehicle (UAV) flight should be minimized. This risk can be represented by a numerical metric, which we refer to in this article as the “ground risk.” Many UAS applications also depend on the presence of a reliable wireless communication link between the UAV and a control station for the transmission of UAV position, surveillance video, UAV payload commands, and other mission-related data. Such wireless communication requirements also need to be considered in the design of UAS operations. In this article, we consider both these aspects and study the design of nonintersecting trajectories for UAS operations to minimize ground risk, subject to constraints on the wireless signal strength and geometry of the trajectory, specified in terms of: 1) an enclosing cylinder within which the trajectory must lie and 2) an integrated angular change along the UAV's trajectory. The performance of a computationally expensive optimal algorithm is compared with that of a computationally faster heuristic approach within the dense urban environment of Manhattan, NY, USA. Performance evaluation using ray-tracing simulations shows that the heuristic approach performs close to the optimal algorithm at a reduced computation cost. This research can be utilized to make UAS operations safe and reliable and accelerate their adoption.

Unmanned aerial vehicles (UAVs) have recently been gaining considerable attention due to their vast range of potential applications. To facilitate UAV use cases involving beyond visual line of sight (BVLOS), cellular networks have emerged as ground connectivity points, enabling remote control and payload communication for UAV links. However, the availability of limited datasets obstructs the study of cellular technology coverage for UAV flights at different altitudes and the development of machine learning (ML) techniques for improving UAV communication and navigation. In this article, we introduce raw LTE in-phase and quadrature (I/Q) sample data sets obtained from physical field experiments of the NSF AERPAW experimentation platform. A UAV equipped with a software-defined radio (SDR) was flown at altitudes ranging from 30 m to 110 m, collecting raw I/Q samples from an SDR-based LTE base station operating at 3.51 GHz. We have implemented a standardized metadata format that can be used to replicate the results obtained from the collected datasets. The post-processing of raw I/Q samples is described and representative results are provided. In the end, we give examples of potential uses of the provided dataset, post-processing sample code, and I/Q collection sample experiment code by other ML, wireless, and UAV researchers.