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

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 de...

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Bibliographic Details
Published inarXiv.org
Main Authors Erdol, Hakan, Wang, Xiaoyang, Piechocki, Robert, Oikonomou, George, Parekh, Arjun
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.07.2024
Subjects
Algorithms
Distillation
Machine learning
Performance evaluation
Real time
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Summary: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.
ISSN:2331-8422