Work

Poster: Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN

Year: 2025

Type: conference-paper

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 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.

DOI: 10.1145/3737900.3770175

Authors: Abdelaziz Salama, Mohammed M. H. Qazzaz, Zeinab Nezami, Maryam Hafeez, Syed Ali Raza Zaidi

Institution: University of Leeds

Language: English

Topic: Software-Defined Networks and 5G

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Funders: UK Research and Innovation, Engineering and Physical Sciences Research Council, HORIZON EUROPE Marie Sklodowska-Curie Actions