TransfoREM: Transformer aided 3D Radio Environment Mapping

Providing reliable cellular connectivity to Unmanned Aerial Vehicles (UAV) is a key challenge, as existing terrestrial networks are deployed mainly for ground-level coverage. The cellular network coverage may be available for a limited range from the antenna side lobes, with poor connectivity further exacerbated by UAV flight dynamics. In this work, we propose TransfoREM, a 3D Radio Environment Map (REM) generation method that combines deterministic channel models and real-world data to map terrestrial network coverage at higher altitudes. At the core of our solution is a transformer model that translates radio propagation mapping into a sequence prediction task to construct REMs. Our results demonstrate that TransfoREM offers improved interpolation capability on real-world data compared against conventional Kriging and other machine learning (ML) techniques. Furthermore, TransfoREM is designed for holistic integration into cellular networks at the base station (BS) level, where it can build REMs, which can then be leveraged for enhanced resource allocation, interference management, and spatial spectrum utilization.

Collection: Datasets from AFAR Challenge

This paper presents a comprehensive real-world and Digital Twin (DT) dataset collected as part of the Find A Rover (AFAR) Challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) testbed and hosted at the Lake Wheeler Field in Raleigh, North Carolina. The AFAR Challenge was a competition involving five finalist university teams, focused on promoting innovation in UAV-assisted radio frequency (RF) source localization. Participating teams were tasked with designing UAV flight trajectories and localization algorithms to detect the position of a hidden unmanned ground vehicle (UGV), also referred to as a rover, emitting wireless probe signals generated by GNU Radio. The competition was structured to evaluate solutions in a DT environment first, followed by deployment and testing in AERPAW's outdoor wireless testbed. For each team, the UGV was placed at three different positions, resulting in a total of 30 datasets, 15 collected in a DT simulation environment and 15 in a physical outdoor testbed. Each dataset contains time-synchronized measurements of received signal strength (RSS), received signal quality (RSQ), GPS coordinates, UAV velocity, and UAV orientation (roll, pitch, and yaw). Data is organized into structured folders by team, environment (DT and real-world), and UGV location. The dataset supports research in UAV-assisted RF source localization, air-to-ground (A2G) wireless propagation modeling, trajectory optimization, signal prediction, autonomous navigation, and DT validation. With approximately 300k time-synchronized samples collected from real-world experiments, the dataset provides a substantial foundation for training and evaluating deep learning (DL) models. Overall, the AFAR dataset serves as a valuable resource for advancing robust, real-world solutions in UAV-enabled wireless communications and sensing systems.

Digital Twins for Supporting AI Research with Autonomous Vehicle Networks

Digital twins (DTs), which are virtual environments that simulate, predict, and optimize the performance of their physical counterparts, are envisioned to be essential technologies for advancing next-generation wireless networks. While DTs have been studied extensively for wireless networks, their use in conjunction with autonomous vehicles with programmable mobility remains relatively under-explored. In this paper, we study DTs used as a development environment to design, deploy, and test artificial intelligence (AI) techniques that use real-time observations, e.g. radio key performance indicators, for vehicle trajectory and network optimization decisions in an autonomous vehicle networks (AVN). We first compare and contrast the use of simulation, digital twin (software in the loop (SITL)), sandbox (hardware-in-the-loop (HITL)), and physical testbed environments for their suitability in developing and testing AI algorithms for AVNs. We then review various representative use cases of DTs for AVN scenarios. Finally, we provide an example from the NSF AERPAW platform where a DT is used to develop and test AI-aided solutions for autonomous unmanned aerial vehicles for localizing a signal source based solely on link quality measurements. Our results in the physical testbed show that SITL DTs, when supplemented with data from real-world (RW) measurements and simulations, can serve as an ideal environment for developing and testing innovative AI solutions for AVNs.