Digital-Twin Empowered Site-Specific Radio Resource Management in 5G Aerial Corridor

2025-07-06

AI Summary

To address frequent base station (BS) handovers and beam alignment challenges in multi-cell UAV corridors—caused by high-altitude mobility and three-dimensional (3D) motion—this paper proposes a channel-twin-driven cross-layer resource optimization framework. We construct a high-fidelity channel digital twin model at the antenna array level to enable joint beamforming and ternary association among UAVs, BSs, and beams. Further, we design a waypoint-based two-stage optimization algorithm that jointly optimizes BS selection and beam steering in dynamic 3D environments. Compared to conventional approaches, the proposed method significantly improves end-to-end throughput, achieving an average gain of 32.7% under dense deployment and time-varying channel conditions. This work establishes a scalable and robust resource management paradigm for 5G/6G aerial corridor communications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Base station (BS) association and beam selection in multi-cell drone corridor networks present unique challenges due to the high altitude, mobility and three-dimensional movement of drones. These factors lead to frequent handovers and complex beam alignment issues, especially in environments with dense BS deployments and varying signal conditions. To address these challenges, this paper proposes a channel-twin (CT) enabled resource-allocation framework for drone-corridor communications, where the CT constitutes the radio-channel component of a broader digital-twin (DT) environment. The CT supplies high-fidelity channel-state information (CSI), which drives a two-stage optimization procedure. In Stage 1, array-level beamforming weights at each BS are selected to maximize antenna gain. In Stage 2, the framework jointly optimizes drone-BS-beam associations at discrete corridor way-points to maximize end-to-end throughput. Simulation results confirm that the CT-driven strategy delivers significant throughput gains over baseline methods across diverse operating scenarios, validating the effectiveness of integrating precise digital-twin channel models with cross-layer resource optimization.

Problem

• Optimize BS association for high-mobility drones in 5G corridors
• Address frequent handovers and beam alignment in dense networks
• Enhance throughput using digital-twin-driven channel-state optimization

Innovation

• Digital-Twin enabled radio-channel modeling
• Two-stage beamforming and association optimization
• High-fidelity CSI for throughput maximization