Better Together: Leveraging Multiple Digital Twins for Deployment Optimization of Airborne Base Stations

Abstract--Airborne Base Stations (ABSs) allow for flexible geographical allocation of network resources with dynamically changing load as well as rapid deployment of alternate connectivity solutions during natural disasters. Since the radio infrastructure is carried by unmanned aerial vehicles (UA Vs) with limited flight time, it is important to establish the best location for the ABS without exhaustive field trials. This paper proposes a digital twin (DT)-guided approach to achieve this goal through the following key contributions: (i) Implementation of an interactive software bridge between two open-source DTs such that the same scene is evaluated with high fidelity across NVIDIA's Sionna and Aerial Omniverse Digital Twin (AODT), highlighting the unique features of each of these platforms for this allocation problem, (ii) Design of a back-propagation-based algorithm in Sionna for rapidly converging on the physical location of the UA Vs, orientation of the antennas and transmit power to ensure efficient coverage across the swarm of the UA Vs, and (iii) numerical evaluation in AODT for large network scenarios (50 UEs, 10 ABS) that identifies the environmental conditions in which there is agreement or divergence of performance results between these twins. Finally, (iv) we propose a resilience mechanism to provide consistent coverage to mission-critical devices and demonstrate a use case for bi-directional flow of information between the two DTs. Unmanned Aerial V ehicle (UA V)-mounted Base Stations, or Airborne Base Stations (ABSs), have gained significant attention as a complement to ground-based cellular networks [1]. As UA Vs become more accessible, their ability to navigate 3-dimensional (3D) space provides flexibility in adapting to dynamic network demands [2], [3], enabling line-of-sight links to mission-critical units [4] and enhancing user tracking [5]. However, ABS-enabled connectivity introduces challenges such as collision avoidance, coordinated coverage, and optimal placement, considering limited flight times of 20 to 100 minutes [6]. These challenges are highly dependent on the RF propagation environment, making prior channel knowledge essential for effective network planning. Motivation for Digital Twins: Optimal placement of Base Stations (BSs) is traditionally handled by telecom operators relying on domain knowledge and best practices. Digital Twins (DTs) and, specifically, Digital Twins for Networking (DTNs) [7], have emerged as strategic tools for network simulation, performance analysis, and "what-if" scenarios.