xApp Distillation: AI-based Conflict Mitigation in B5G O-RAN
This addresses conflict mitigation for telecommunication companies deploying AI-based applications in 5G networks, representing an incremental improvement over existing methods.
The paper tackles the problem of conflicts arising from deploying multiple machine learning-based network management applications (xApps) with different objectives in overlapping areas of B5G O-RAN, proposing an xApp distillation method that trains a single model to retain their capabilities, resulting in up to six times fewer network outages compared to other schemes in some cases.
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.