{"ID":2862074,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00851","arxiv_id":"2510.00851","title":"Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN","abstract":"Next-generation wireless networks require intelligent traffic prediction to enable autonomous resource management and handle diverse, dynamic service demands. The Open Radio Access Network (O-RAN) framework provides a promising foundation for embedding machine learning intelligence through its disaggregated architecture and programmable interfaces. This work applies a Neural Architecture Search (NAS)-based framework that dynamically selects and orchestrates efficient Long Short-Term Memory (LSTM) architectures for traffic prediction in O-RAN environments. Our approach leverages the O-RAN paradigm by separating architecture optimisation (via non-RT RIC rApps) from real-time inference (via near-RT RIC xApps), enabling adaptive model deployment based on traffic conditions and resource constraints. Experimental evaluation across six LSTM architectures demonstrates that lightweight models achieve $R^2 \\approx 0.91$--$0.93$ with high efficiency for regular traffic, while complex models reach near-perfect accuracy ($R^2 = 0.989$--$0.996$) during critical scenarios. Our NAS-based orchestration achieves a 70-75\\% reduction in computational complexity compared to static high-performance models, while maintaining high prediction accuracy when required, thereby enabling scalable deployment in real-world edge environments.","short_abstract":"Next-generation wireless networks require intelligent traffic prediction to enable autonomous resource management and handle diverse, dynamic service demands. The Open Radio Access Network (O-RAN) framework provides a promising foundation for embedding machine learning intelligence through its disaggregated architectur...","url_abs":"https://arxiv.org/abs/2510.00851","url_pdf":"https://arxiv.org/pdf/2510.00851v1","authors":"[\"Abdelaziz Salama\",\"Mohammed M. H. Qazzaz\",\"Zeinab Nezami\",\"Maryam Hafeez\",\"Syed Ali Raza Zaidi\"]","published":"2025-10-01T13:05:59Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}