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

Researchers propose a Q-learning-based adaptive retraining approach using Reinforcement Learning (RL) to handle drift in Open Radio Access Networks (O-RAN). This approach aims to balance forecasting accuracy and retraining cost, reducing overhead compared to traditional methods while maintaining system performance.

What the Document Says

The paper discusses a problem of dynamic traffic variations leading to drift in Open Radio Access Networks (O-RAN), which degrades the performance of AI/ML models.
Traditional retraining approaches maintain forecasting accuracy but have high computational cost and may violate Service Level Agreements (SLAs).
The proposed approach uses Q-learning to formulate the retraining decision as a Markov Decision Process (MDP), where an RL agent learns a policy.
The policy balances forecasting accuracy and retraining cost.
A multi-expert Long Short-Term Memory (LSTM) ensemble is used to mitigate catastrophic forgetting and improve robustness across diverse traffic conditions.
Experimental results show that the proposed approach reduces retraining overhead compared to greedy and random baselines, while maintaining system performance within predefined limits.

Key Concepts Mentioned

Open Radio Access Networks (O-RAN)
Drift
Artificial Intelligence/Machine Learning (AI/ML)
Service Level Agreements (SLAs)
Q-learning
Markov Decision Process (MDP)
Reinforcement Learning (RL)
Long Short-Term Memory (LSTM)

Claims Made by the Document

The proposed approach reduces retraining overhead compared to traditional methods.
The proposed approach maintains system performance within predefined limits.

Evidence Used

Experimental results in the paper

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.