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 for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL) to address the issue of drift that degrades the performance of AI/ML models. This approach aims to balance forecasting accuracy and retraining cost by formulating the retraining decision as a Markov Decision Process (MDP). The proposed method also incorporates a multi-expert Long Short-Term Memory (LSTM) ensemble to enhance robustness across diverse traffic conditions.

What the Document Says

The text discusses a research work about an adaptive retraining approach for Open Radio Access Networks (O-RAN).
The approach uses Q-learning and is based on Reinforcement Learning (RL).
The goal is to balance forecasting accuracy and retraining cost by formulating the retraining decision as a Markov Decision Process (MDP).
A Reinforcement Learning (RL) agent learns a policy in this approach.
The proposed approach incorporates a multi-expert Long Short-Term Memory (LSTM) ensemble to mitigate catastrophic forgetting and improve robustness.

Key Concepts Mentioned

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

Claims Made by the Document

Dynamic traffic variations in O-RAN lead to drift that degrades AI/ML models' performance.
Traditional retraining approaches maintain forecasting accuracy but incur high computational cost and may violate SLAs.

Evidence Used

The text mentions the negative impact of dynamic traffic variations on AI/ML models' performance.
The text states that traditional retraining approaches have high computational costs and may violate SLAs.

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.