  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.
