TITAN: Twin-Informed Topology Adaptation for LAWN-enabled D2C Communication

TL;DR

TITAN is a framework that uses digital twins and ray-traced modeling to adapt UAV relay topologies in urban LAWN environments, significantly improving coverage, sum-rate, and fairness for D2C satellite communications.

Contribution

It introduces a twin-informed, site-specific topology adaptation method using ray-traced channel modeling and Bayesian optimization for urban LAWN-enabled D2C communication.

Findings

+32.2% user coverage achieved

+64.9% system sum-rate improvement

+49.3% fairness increase

Abstract

Low-Altitude Wireless Networks (LAWN) are transforming the low-altitude airspace into a mission-driven, dynamically reconfigurable 3D network fabric for safety-critical and public-safety operations. In parallel, Direct-to-Cell (D2C) satellite access can rapidly restore connectivity after disasters, yet dense urban blockages make the satellite-to-ground link unreliable for many users. To overcome this, we leverage the LAWN aerial layer and form an adaptive low-altitude relay topology where Unmanned Aerial Vehicles (UAVs) act as D2C-assisted aerial relays for obstructed ground users. We introduce TITAN, a twin-informed topology adaptation framework that builds a high-fidelity Digital Twin (DT) of the affected urban area and performs site-specific, ray-traced air-to-ground channel modeling via Sionna RT. This informs a Bayesian optimization process that adapts the aerial topology to maximize coverage and Quality of Service (QoS) for ground users by using UAVs as optimal D2C relays. Extensive system-level simulations with Sionna show that TITAN consistently outperforms the baselines and delivers +32.2% user coverage, +64.9% system sum-rate, and +49.3% fairness over the state-of-the-art (SOTA) that employ heuristic placement or statistical channel approximations. To support further research in resilient network design, we open-source the codebase of the TITAN framework.