Adaptive multi-level graph representation with optimization-aware attention for robust cell association in 5G V2X networks

Efficient cell association remains a fundamental challenge in fifth-generation (5G) vehicle-to-everything (V2X) systems due to rapid topology changes, heterogeneous deployments, and stringent latency requirements. Conventional learning-based approaches often rely on shallow representations or independent optimization strategies, limiting their adaptability in dense and highly dynamic environments. To address these issues, this study introduces a multi-level graph representation framework that models interactions between vehicles and base stations across hierarchical spatial structures. The proposed approach integrates contextual node embedding with attention-driven graph learning to capture mobility patterns, signal characteristics, and network load dependencies. Additionally, a training-stage optimization mechanism is incorporated to refine attention parameters, improving convergence behavior without increasing inference complexity. The framework is evaluated using a real-world vehicular mobility dataset, demonstrating consistent improvements in association stability, handover reliability, and overall network performance compared with existing deep learning and graph-based methods. Experimental results show gains in accuracy (94.17%) and F1-score (93.93%), indicating enhanced decision robustness under dynamic conditions. Although validation is conducted on an urban dataset, the proposed architecture provides a scalable foundation for adaptive cell selection in next-generation intelligent transportation systems.