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 R2≈0.91--0.93 with high efficiency for regular traffic, while complex models reach near-perfect accuracy (R2=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.
@article{arxiv.2510.00851,
title = {Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN},
author = {Abdelaziz Salama and Mohammed M. H. Qazzaz and Zeinab Nezami and Maryam Hafeez and Syed Ali Raza Zaidi},
journal= {arXiv preprint arXiv:2510.00851},
year = {2025}
}