Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
arXiv:2606.28274v1 Announce Type: new Abstract: Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular networks. However, prediction remains challenging because network demand is influenced by complex mobility patterns, congestion dynamics, and heterogeneous user behavior. This paper introduces the Parameter-Efficient Hybrid Transformer (PEHT), a network traffic prediction framework that integrates urban mobility and congestion information into a Transformer-based architecture. PEHT separates primary network communication features
The increasing complexity of urban environments and the demand for more efficient network resource allocation are driving innovations in predictive AI models. Advances in transformer architectures are enabling more sophisticated traffic prediction. This paper introduces a novel parameter-efficient approach, making it more feasible for real-world deployment.
Accurate network traffic prediction directly impacts the efficiency and reliability of cellular networks, which are critical infrastructure in modern cities. Improved prediction can lead to better resource management, reduced congestion, and enhanced user experience.
This Parameter-Efficient Hybrid Transformer (PEHT) offers a more refined and resource-optimized method for integrating complex urban data into network traffic predictions, potentially lowering the computational barrier for sophisticated AI-driven network management. It could lead to substantial improvements in the responsiveness and stability of urban cellular networks.
- · Telecommunication companies
- · Urban planners
- · Smart city technology providers
- · AI model developers
- · Legacy network management systems
- · Companies reliant on less efficient prediction models
Telecommunication operators can optimize network resource allocation more effectively, reducing operational costs and improving service quality.
Enhanced network reliability and performance could support the broader adoption of bandwidth-intensive smart city applications and autonomous systems.
More efficient urban resource utilization, driven by predictive analytics, may contribute to overall urban sustainability and reduced energy consumption in city infrastructure.
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