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.

提出了一种xApp蒸馏方法，通过从多个xApp中蒸馏知识来训练一个保留了先前xApp能力的单一模型，相对于冲突缓解方案，在某些情况下xApp蒸馏可以减少多达六倍的网络故障。

xApp蒸馏：B5G O-RAN 中基于人工智能的冲突缓解