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.

翻译：基于机器学习（ML）的决策算法进展创造了众多研究与产业机遇。开放无线接入网络（O-RAN）中基于ML的近实时网络管理应用程序（xApp）正是该领域的重要方向。通常，xApp仅针对预设目标进行设计，并经过微调后部署。然而，电信运营商可能在重叠区域部署多个xApp。考虑到不同xApp的设计目标差异，此类部署可能引发冲突。为防止此类冲突，我们提出xApp蒸馏方法：该方法从多个xApp中提取知识，并利用这些知识训练单一模型，使其保留原有xApp的全部功能。性能评估表明，在某些场景下，现有冲突缓解方案导致的网络中断次数可达xApp蒸馏方案的六倍。