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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • E. S. Phalguna Krishna,
  • Kamaraj Kanagaraj,
  • N. V. RajaSekhar Reddy,
  • SK Khaja Shareef,
  • Syed Ziaur Rahman,
  • Suryanarayana Vadhri,
  • M. Janardhan

摘要

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.