ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Ashit Kumar Subudhi, Bhargav Chirumamilla, Shubham Vaishnav, Mduduzi C. Hlophe, Praveen Kumar Donta, Andrea Fumagalli, Venkateswarlu Gudepu, Koteswararao Kondepu
• Venue: arXiv: cs.NI
• Published: 2026-07-09
• Collected: 2026-07-10
• URL: http://arxiv.org/abs/2607.08443v1
• PDF: https://arxiv.org/pdf/2607.08443v1
• DOI: UNKNOWN

Plain-Language Brief

The paper proposes an adaptive retraining method for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL). The goal is to balance forecasting accuracy and retraining cost in the presence of dynamic traffic variations that cause drift, which degrades AI/ML model performance. This approach uses a Q-learning based policy to make retraining decisions as a Markov Decision Process (MDP), employs a multi-expert Long Short-Term Memory (LSTM) ensemble to mitigate catastrophic forgetting and improve robustness, and demonstrates experimental results showing reduced retraining overhead compared to baselines while maintaining system performance.

What the Document Says

• The paper discusses a problem of drift in Open Radio Access Networks (O-RAN) that negatively affects AI/ML models performance due to dynamic traffic variations.
• A Q-learning based adaptive retraining approach is proposed to balance forecasting accuracy and retraining cost.
• The approach formulates the retraining decision as a Markov Decision Process (MDP), where an RL agent learns a policy.
• The policy is designed to manage trade-offs between forecasting accuracy and retraining cost.
• A multi-expert Long Short-Term Memory (LSTM) ensemble is incorporated to mitigate catastrophic forgetting and enhance robustness across various traffic conditions.
• Experimental results reveal the proposed approach reduces retraining overhead compared to greedy and random baselines, while keeping system performance within predefined limits.

Key Concepts Mentioned

• Open Radio Access Networks (O-RAN)
• Dynamic traffic variations
• Drift
• Artificial Intelligence/Machine Learning (AI/ML)
• Forecasting accuracy
• Retraining cost
• Service Level Agreements (SLAs)
• Q-learning
• Markov Decision Process (MDP)
• Reinforcement Learning (RL)
• Long Short-Term Memory (LSTM) ensemble
• Catastrophic forgetting

Claims Made by the Document

• Dynamic traffic variations in O-RAN cause drift that degrades AI/ML model performance.
• The proposed approach effectively reduces retraining overhead compared to baselines.

Evidence Used

• Experimental results presented in the paper support the claim of reduced retraining overhead.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 formulates the 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.