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

2025-08-16

AI Summary

To address the high cost of field trials in aerial base station (ABS) deployment, this paper proposes a multi-digital-twin collaborative optimization framework. The method establishes a bidirectional interface between Sionna and NVIDIA Aerial Omniverse, integrating ray-tracing-based channel modeling, differentiable backpropagation optimization, and multi-agent coordination to jointly optimize UAV 3D positions, antenna downtilt angles, and transmit power in an end-to-end manner. An elastic coverage mechanism enables cross-platform closed-loop feedback and dynamic policy transfer. Evaluated in a large-scale dynamic scenario with 50 users and 10 ABSs, the framework significantly improves coverage efficiency and convergence speed. Moreover, it is the first work to systematically characterize how performance consistency across digital twin platforms varies with environmental complexity. This study establishes a scalable paradigm for digital-twin-driven integrated space-air-ground network deployment.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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 (UAVs) 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 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 UAVs, orientation of the antennas and transmit power to ensure efficient coverage across the swarm of the UAVs, 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.

Problem

• Optimizing UAV placement for airborne base stations efficiently
• Ensuring effective coverage across a swarm of UAVs
• Addressing performance divergence between digital twin platforms

Innovation

• Leveraging multiple digital twins for optimization
• Back-propagation algorithm for rapid UAV configuration
• Resilience mechanism for mission-critical coverage