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

Unmanned Aerial Vehicles (UAVs) integrated with Intelligent Reflecting Surfaces (IRS) offer promising solutions for enhancing radio access networks in dense urban microcell environments. Yet, UAV-mounted IRS (U-IRS) designs suffer from limited availability due to high energy consumption for continuous hovering, whilst fixed terrestrial IRS deployments provide energy efficiency at the cost of flexibility. Recognizing that the key challenge is to balance the mobility of UAV-mounted IRS with the energy efficiency of terrestrial IRS, we propose a novel Robotic Aerial Intelligent Reflecting Surface (RA-IRS) that employs a mechanical anchoring mechanism to secure the IRS without the need for sustained hovering, thereby significantly reducing energy consumption while retaining the mobility required to serve dynamic hotspot areas. A multi-epoch optimization framework is developed to jointly determine RA-IRS deployment, visiting order, hotspot selection, and active/passive beamforming with the goal of maximizing served traffic demand within an urban microcell. Statistical channel information initially guides RA-IRS anchoring and trajectory design via two sequential linear programs, followed by an alternating-optimization scheme that refines hotspot selection and active/passive beamforming under instantaneous channel conditions. Numerical evaluations demonstrate that with several RA-IRS devices being deployed in the microcell, the served traffic reaching 1.3–1.45 times the baseline traffic observed without deployment, depending on the heterogeneity of the scenario. Compared to UAV-mounted IRS systems, RA-IRS achieve up to 96% energy savings, while under stringent QoS constraints—where terrestrial IRS yield only marginal improvements—RA-IRS deliver gains that are two to three times higher. Together, these benefits effectively balance the trade-offs between mobility and energy efficiency in dense urban networks.