Digital Twin-Empowered Post-Disaster Movable Base Station Deployment Optimization

In post-disaster scenarios where infrastructure is extensively damaged, rapid deployment of reliable communication networks is critical for effective rescue operations. Digital Twin (DT) technology offers an innovative solution for disaster management and recovery by creating high-fidelity virtual replicas of affected environments. In this article, we propose a DT-empowered framework for post-disaster communication recovery. First, Unmanned Aerial Vehicles (UAVs) capture high-resolution images of the affected terrain and buildings along predetermined flight paths. These images are then processed to construct a DT model, which is visualized on the Omniverse platform. The environmental data extracted from the DT is subsequently integrated with a signal attenuation model to accurately predict communication coverage across the disaster area. Finally, we employ the Soft Actor-Critic (SAC) algorithm, enhanced by an optimized exploration strategy, to iteratively refine Movable Base Station (MBS) placements and achieve a deployment scheme that maximizes network coverage in complex environments. Experimental results demonstrate that our approach achieves up to 93% signal coverage, highlighting its potential to significantly improve the speed and reliability of emergency communication deployments in post-disaster environments.