ANFIS-based Regression for vBS Computing Usage Prediction in Open Radio Access Networks

Vilchez V.; Hinojosa E.; Grande R.E.D.; Astudillo C.A. · Proceedings - IEEE Symposium on Computers and Communications (2025) · DOI: 10.1109/iscc65549.2025.11326189

Resumen

The 5 G networks and their Open Radio Access Networks (O-RAN) architecture face significant challenges in resource management due to their extended flexibility and technological diversity. O-RAN's open, disaggregated architecture creates a heterogeneous environment that requires effective integration and analysis of data from various components. In this context, accurately predicting the computational utilization of virtual base stations (vBS) emerges as a critical challenge, essential for optimizing resource allocation and addressing the dynamic demands of 5 G and 6 G O-RAN networks. Traditional forecasting techniques often struggle with the complexity and variability of data in this scenario, necessitating advanced AI and ML approaches. We propose an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for multi-target regression to predict CPU utilization in vBS. By combining neural networks and fuzzy logic, ANFIS enhances both prediction accuracy and interpretability, making it ideal for the complexities of O-RAN 5G/6G networks. Our model, tested on publicly available O-RAN datasets, outperforms traditional ML methods. These results position ANFIS as an effective tool for optimizing resource management and enabling transparent decision-making in 5G/6G infrastructures, providing valuable support for network operators seeking efficient and scalable solutions in the evolving ORAN landscape.

Instituciones: Universidad Nacional de San Agustín de Arequipa