xApp Distillation: AI-based Conflict Mitigation in B5G O-RAN

TL;DR

This paper introduces xApp distillation, a method to combine multiple AI-based network management applications in 5G B5G networks, reducing conflicts and outages by consolidating their knowledge into a single model.

Contribution

The paper presents a novel xApp distillation technique that mitigates conflicts among multiple AI-driven network management xApps in O-RAN by consolidating their capabilities into one model.

Findings

xApp distillation reduces network outages compared to conflict mitigation schemes.

The method effectively consolidates multiple xApps' knowledge.

Performance evaluations demonstrate significant improvements in conflict mitigation.

Abstract

The advancements of machine learning-based (ML) decision-making algorithms created various research and industrial opportunities. One of these areas is ML-based near-real-time network management applications (xApps) in Open-Radio Access Network (O-RAN). Normally, xApps are designed solely for the desired objectives, and fine-tuned for deployment. However, telecommunication companies can employ multiple xApps and deploy them in overlapping areas. Consider the different design objectives of xApps, the deployment might cause conflicts. To prevent such conflicts, we proposed the xApp distillation method that distills knowledge from multiple xApps, then uses this knowledge to train a single model that has retained the capabilities of Previous xApps. Performance evaluations show that compared conflict mitigation schemes can cause up to six times more network outages than xApp distillation in some cases.