AI-Enabled Digital Twins for Underground UAV Communication: A Survey, Taxonomy, and Research Roadmap

Abstract

Underground environments, including tunnels, mines, and collapsed structures, impose severe constraints on unmanned aerial vehicle (UAV) communication because of signal attenuation, nonline-of-sight (NLoS) propagation, intermittent connectivity, and global positioning system (GPS)-denied operation. Conventional communication and networking models, which are often designed for open-air or infrastructure-supported environments, are difficult to adapt to these dynamic, partially observable, and data-sparse conditions. Digital twin (DT) technology offers a promising direction by enabling virtual representations of physical UAV systems and underground communication environments for monitoring, simulation, prediction, and decision support. When combined with artificial intelligence (AI), DTs can support predictive channel modeling, synchronization under incomplete feedback, adaptive communication-mode selection, and distributed learning across UAV networks. However, existing surveys have generally examined UAV communication, DT technologies, underground systems, or AI-enabled networking as separate research areas, leaving the intersection of AI-enabled DTs and underground UAV communication insufficiently systematized. To address this gap, this article presents a structured survey of AI-enabled DT approaches for underground UAV communication. It organizes the literature according to DT objectives, communication modalities, synchronization mechanisms, AI integration levels, and mission-specific quality-of-service requirements. This article further develops a conceptual architecture that links the physical underground UAV environment, cyber twin layer, intelligence layer, and application-control layer. In addition, it analyzes the roles of predictive AI, DT-assisted synchronization, explainable AI (XAI), and federated learning in improving communication reliability, operational transparency, and swarm-level adaptability under subterranean constraints. Finally, this article identifies open challenges related to channel modeling, dataset scarcity, synchronization fidelity, energy-efficient intelligence, interoperability, security, and real-world validation, and presents a research roadmap for developing robust, trustworthy, and adaptive DT-enabled underground UAV communication systems.