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.

翻译：低空无线网络（LAWN）正将低空空域转变为任务驱动、动态可重构的三维网络架构，以支撑安全关键型与公共安全业务。与此同时，直连卫星（D2C）接入技术可在灾后快速恢复通信，但密集的城市遮挡导致星地链路对多数用户不可靠。为克服此问题，我们利用LAWN空中层构建自适应低空中继拓扑，使无人机作为D2C辅助空中中继为受遮挡地面用户提供服务。本文提出TITAN——一种双孪生拓扑自适应框架，该框架构建受灾城区的高保真数字孪生体，并通过Sionna RT实现场地特定的射线追踪空对地信道建模。基于此，贝叶斯优化过程动态调整空中拓扑，通过将无人机作为最优D2C中继来最大化地面用户的覆盖范围和服务质量。基于Sionna的大规模系统级仿真表明，TITAN在采用启发式部署或统计信道近似的现有最优方法基础上，持续实现+32.2%的用户覆盖率提升、+64.9%的系统总速率提升及+49.3%的公平性提升。为促进弹性网络设计的进一步研究，我们开源了TITAN框架的代码库。