[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84368-en":3,"doc-seo-84368-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84368,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","ADORN: Adaptive Drift Handling for Open RAN Using Reinforcement Learning","Dynamic traffic variations in Open Radio Access Networks (O-RAN) cause drift that degrades Artificial Intelligence/Machine Learning model performance. Conventional retraining keeps forecasting accuracy but can be computationally expensive and may breach Service Level Agreements (SLAs). ADORN introduces a Q-learning-driven adaptive retraining strategy that casts retraining selection as a Markov Decision Process, optimizing forecasting accuracy versus retraining cost. A multi-expert LSTM ensemble reduces catastrophic forgetting and improves robustness across diverse traffic patterns, reducing overhead while keeping performance within predefined limits.","ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning  \nAshit Kumar Subudhi∗ , Bhargav Chirumamilla‡, Shubham Vaishnav¶ , Mduduzi C. Hlophe†, Praveen Kumar Donta¶ , Andrea Fumagalli§ , Venkateswarlu Gudepu§ , Koteswararao Kondepu∗  \n∗ Indian Institute of Technology Dharwad, Karnataka, India  \n† University of Pretoria, Pretoria, South Africa ‡ Johns Hopkins University, USA ¶ Stockholm University, Sweden  \n§ Open Networking Advanced Research (OpNeAR) Lab, The University of Texas at Dallas, TX, USA  \nEmail: [ashit.subudhi.21@iitdh.ac.in](ashit.subudhi.21@iitdh.ac.in)  \narXiv :2607 .08443v 1 [ cs .NI] 9 Jul 2026  \nAbstract—Dynamic traffic variations in Open Radio Access Networks (O-RAN) lead to drift, which degrades the performance of Artificial Intelligence/Machine Learning (AI/ML) models. Traditional retraining approaches maintain forecasting accuracy but incur high computational cost and may lead to violations of Service Level Agreements (SLAs). This work proposes a Q-learning-based adaptive retraining approach that formulatesthe retraining decision as a Markov Decision Process (MDP), where a Reinforcement Learning (RL) agent learns a policy that balances forecasting accuracy and retraining cost. The proposed approach incorporates a multi-expert Long Short-Term Memory (LSTM) ensemble to mitigate catastrophic forgetting and improve robustness across diverse traffic conditions. Experimental results show that the proposed approach effectively reduces retraining overhead compared to greedy and random baselines, while maintaining system performance within predefined limits.  \nIndex Terms—Reinforcement Learning, Drift, Long Short Term Memory Ensemble, Open RAN, Q-Learning  \nI. INTRODUCTION  \nThe advent of fifth-generation and beyond (B5G) networks marks a significant leap forward in telecommunications and enables a wide range of new services—network slicing, autonomous vehicles, Augmented and Virtual Reality (AR/VR), and e-Health. The B5G networks support high data rates, ultra-low latency, and connectivity for a massive number of devices, which are critical requirements for addressing diverse and dynamic user service demands. However, traditional Radio Access Network (RAN) architectures rely on proprietary hardware with closed and embedded interfaces, which limits flexibility and restricts configurability to meet B5G network requirements [1] .  \nThe O-RAN Alliance introduces RAN Intelligent Controllers (RICs) that enable data collection across the RAN through standardized open interfaces and incorporate intelligence using Artificial Intelligence and Machine Learning (AI/ML) algorithms. However, AI/ML model performance relies on the characteristics of the training data. The arrival of new and previously unseen user traffic patterns introduces user data traffic changes, which degrade AI/ML model performance, referred to as drift. Drift caused by dynamic fluctuations in user traffic produces false insights and inaccurate decisions, leading to poor network management, severe service interruptions, inefficient resource allocation, and overall network  \nperformance degradation. Such effects result in violations of Service-Level Agreements (SLAs) [2], along with reduced network reliability, increased operational costs, and degraded user experience. Such consequences can be catastrophic for time-critical applications—smart ambulances—where driftinduced performance degradation leads to failures in meeting stringent throughput and response-time requirements.  \nDrift handling for B5G networks primarily relies on performance monitoring, statistical analysis, and data-driven approaches. Threshold-based approaches detect drift when AI/ML model performance metrics — accuracy, precision, recall, or error rate—exceed predefined thresholds, with traditional approaches including Drift Detection Method (DDM) and Early Drift Detection Method (EDDM) [3] . Statistical approaches such as the Fisher score evaluate changes in feature user ","cbCairWJI53m7TXh","https://ap.wps.com/l/cbCairWJI53m7TXh","pdf",495498,1,6,"English","en",105,"# Introduction\n## Drift in Open RAN and its Impact\n## Limitations of Existing Drift Detection and Retraining\n## Reinforcement Learning for Adaptive Retraining","[{\"question\":\"What problem does ADORN address in Open RAN systems?\",\"answer\":\"ADORN addresses drift caused by dynamic and previously unseen traffic patterns in Open Radio Access Networks, which degrades AI/ML model performance and harms network management and reliability.\"},{\"question\":\"How does ADORN decide when to retrain AI/ML models?\",\"answer\":\"ADORN formulates the retraining decision as a Markov Decision Process and uses a Q-learning reinforcement learning agent to learn a policy that balances forecasting accuracy and retraining cost.\"},{\"question\":\"Why is a multi-expert LSTM ensemble used in ADORN?\",\"answer\":\"A 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