Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration

Cellular networks in cities are under constant, unpredictable stress — traffic jams, concerts, and commutes all reshape how and where data flows. Predicting this demand accurately is essential for carriers to allocate bandwidth intelligently. PEHT introduces a transformer-based model that separates core network traffic signals from external urban mobility and congestion data, then fuses them efficiently using Low-Rank Adaptation (LoRA) to keep the model lightweight. Tested on real Milan telecom data, it outperforms existing approaches. Practical uses include real-time dynamic spectrum allocation, 5G network planning, smart city infrastructure management, and adaptive resource scheduling in densely populated urban environments. Authors: Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast Paper: https://arxiv.org/abs/2606.28274v1