Aerial IRS with Robotic Anchoring: Novel Adaptive Coverage Enhancement in 6G Networks

It is widely accepted that the integration of intelligent reflecting surfaces (IRSs) with unmanned aerial vehicles (UAVs), or drones, can significantly enhance wireless network coverage and end-user Quality of Service (QoS). However, drones’ hovering/flying time are limited by their board battery. In this paper, we propose the concept of robotic aerial IRSs (RA-IRSs), which are essentially drones that, in addition to incorporating IRS, embed an anchoring mechanism enabling them to grasp energy-efficiently onto tall urban landforms such as lampposts. By doing so, RAIRSs can substantially reduce their total energy consumption and offer service for multiple hours, or even days, a feat not possible with traditional UAV-mounted IRS (U-IRS). Utilizing this property, we demonstrate how RA-IRS can enhance network performance by dynamically changing their anchoring location to align with the spatio-temporal traffic demand. Our proposed methodology, developed through Integer Linear Programming (ILP) formulations, offers significant Signal-to-Noise Ratio (SNR) gains in highly heterogeneous traffic regions compared to fixed IRSs, effectively addressing urban coverage disparities. Numerical simulations show that RA-IRS consumes only $3.3 \%$ of the energy used by U-IRS over a 12-hour service duration. Additionally, RAIRS demonstrates superior traffic serviceability compared to fixed terrestrial IRSs. It efficiently handles more than twice the traffic demand in areas of high heterogeneity and achieves approximately $\mathbf{5 0 \%}$ greater signal quality gain. These outcomes underscore RAIRS’s enhanced adaptability and its effectiveness in improving coverage and QoS in complex urban environments.