inRAN: Interpretable Online Bayesian Learning for Network Automation in Open Radio Access Networks

2026-01-06

arXiv.org

AI Summary

This work addresses the limited deployability of existing deep neural network–based approaches in Open RAN due to their lack of interpretability and transparency. To overcome this, the authors propose inRAN, a novel framework that leverages Kolmogorov–Arnold Networks (KANs) to construct interpretable surrogate models. The framework integrates genetic search with trust-region methods to enable safe optimization, while incorporating online dynamic tracking and an adaptive threshold-shifting mechanism to handle non-stationary network dynamics. Evaluated on a real-world O-RAN testbed in a network slicing scenario, inRAN satisfies chance constraints with a 92.67% guarantee rate and demonstrates significantly higher resource utilization efficiency compared to state-of-the-art methods.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Non-convex Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web search models and ranking

Abstract

Emerging AI/ML techniques have been showing great potential in automating network control in open radio access networks (Open RAN). However, existing approaches heavily rely on blackbox policies parameterized by deep neural networks, which inherently lack interpretability, explainability, and transparency, and create substantial obstacles in practical network deployment. In this paper, we propose inRAN, a novel interpretable online Bayesian learning framework for network automation in Open RAN. The core idea is to integrate interpretable surrogate models and safe optimization solvers to continually optimize control actions, while adapting to non-stationary dynamics in real-world networks. We achieve the inRAN framework with three key components: 1) an interpretable surrogate model via ensembling Kolmogorov-Arnold Networks (KANs); 2) safe optimization solvers via integrating genetic search and trust-region descent method; 3) an online dynamics tracker via continual model learning and adaptive threshold offset. We implement inRAN in an end-to-end O-RAN-compliant network testbed, and conduct extensive over-the-air experiments with the focused use case of network slicing. The results show that, inRAN substantially outperforms state-of-the-art works, by guaranteeing the chance-based constraint with a 92.67% assurance ratio with comparative resource usage throughout the online network control, under unforeseeable time-evolving network dynamics.

Problem

• interpretability
• network automation
• Open RAN
• black-box models
• explainability

Innovation

• interpretable AI
• online Bayesian learning
• Kolmogorov-Arnold Networks
• safe optimization
• Open RAN