Design and Implementation of Remote-Control System for Unmanned Aerial Vehicles Based on 6G Network Slicing

Unmanned Aerial Vehicles (UAVs) are quickly finding application in monitoring, shipping, and ecological surveillance, which require proper path planning and real-time management. The implementation and design of a remote-control system (RCS) for UAVs is a key part of enhancing the efficiency and reliability of UAV operations especially in dynamic and challenging conditions. Network slicing is an option for meeting the many and diverse communication requirements of UAVs as 6G network slicing is introduced. This study aims at employing Greedy Rank Cuckoo Search Optimization (GRCSO) approach in a 6G network slicing architecture to optimize the positioning of paths for UAVs. The proposed approach employs GRCSO to optimize the position of the UAV path, as it can balance exploration and exploitation to accomplish the global optimization. The system is simulated with a 6G network slicing and built on a Python platform and is measured by parameters such as latency, energy consumption, and path accuracy. The findings indicate that the GRCSO-based RCS is superior to other optimization methods, as it is 0.20[Formula: see text]m more precise in the path and its latency is half as small at 1.0[Formula: see text]s, so the system is suitable in the case of real-time UAV missions under adverse conditions. It demonstrates that 6G network slicing in addition to highly developed optimization methods should be used to enhance the functioning of UAVs to achieve significant efficiency and reliability benefits.