UAV–IRS-Assisted Covert D2D Communications Under Three Spatial Eavesdropping Scenarios

This paper investigates covert communication in spectrum-sharing Internet of Things (IoT) device-to-device (D2D) networks assisted by unmanned aerial vehicles equipped with intelligent reflecting surfaces (UAV-IRS). Existing schemes assume known or deterministic eavesdropper locations, which limits their effectiveness in the presence of spatially random adversaries in IoT environments. They also often neglect severe interference in spectrum-sharing environments and lack effective protection mechanisms for cellular links, resulting in degraded covert reliability and cellular performance. To address these challenges, we propose a UAV–IRS-assisted D2D covert communication scheme for spectrum-sharing networks, where a full-duplex base station simultaneously receives cellular signals and generates artificial noise (AN) to combat spatially random eavesdroppers. The spatial uncertainty is characterized by modeling three representative configurations between the eavesdroppers’ surveillance regions and the guard zone of the cellular link, i.e., disjoint, overlapping, and contained. Meanwhile, the average minimum detection error probability is derived as the covertness requirement. A two-layer optimization framework is then designed to jointly optimize D2D and AN transmit powers, UAV location, and IRS phase shifts, while ensuring cellular reliability. Simulation results demonstrate the effectiveness of the proposed scheme in enhancing the covert rate in IoT spectrum-sharing networks.