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

Area of Science:

• Wireless Communications
• Intelligent Transportation Systems
• Machine Learning

Background:

• Efficient cell association is crucial for 5G vehicle-to-everything (V2X) systems, facing challenges from rapid topology changes and latency demands.
• Existing learning-based methods struggle with adaptability in dense, dynamic V2X environments due to shallow representations and independent optimization.
• Heterogeneous deployments and stringent latency requirements further complicate reliable cell selection in vehicular networks.

Purpose of the Study:

• To introduce a novel multi-level graph representation framework for adaptive cell selection in 5G V2X systems.
• To enhance the adaptability and robustness of cell association strategies in highly dynamic vehicular environments.
• To improve association stability, handover reliability, and overall network performance in next-generation intelligent transportation systems.

Main Methods:

• Developed a multi-level graph representation modeling vehicle-base station interactions across hierarchical spatial structures.
• Integrated contextual node embedding with attention-driven graph learning to capture mobility, signal, and network load dependencies.
• Incorporated a training-stage optimization mechanism to refine attention parameters for improved convergence without increasing inference complexity.

Main Results:

• The proposed framework demonstrated consistent improvements in association stability and handover reliability.
• Achieved significant gains in accuracy (94.17%) and F1-score (93.93%) compared to existing methods.
• Validated performance using a real-world vehicular mobility dataset, showing enhanced decision robustness under dynamic conditions.

Conclusions:

• The multi-level graph framework effectively addresses challenges in 5G V2X cell association.
• The approach offers a scalable foundation for adaptive cell selection in intelligent transportation systems.
• The integration of graph learning and attention mechanisms provides robust performance in dynamic vehicular networks.