ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Telecommunications Networks · Depth: Expert, long

Summary

ADORN is a Q-learning-based adaptive drift handling approach designed for Open Radio Access Networks (O-RAN) that addresses performance degradation in AI/ML models due to dynamic traffic variations. It formulates the retraining decision as a Markov Decision Process, where a Reinforcement Learning agent learns a policy to balance forecasting accuracy and computational cost. The system incorporates a multi-expert Long Short-Term Memory (LSTM) ensemble to mitigate catastrophic forgetting and enhance robustness across diverse traffic conditions. Experimental results, conducted over 300 training episodes using the Colosseum traffic dataset, demonstrate ADORN's effectiveness. It reduces cumulative retraining actions from a constant 8 (Greedy baseline) or an average of 4.1 (Random baseline) to 1-3 actions per episode, while maintaining a stable Normalized Mean Absolute Error (nMAE) of approximately 0.17-0.18, consistently below the drift threshold. The multi-expert LSTM ensemble also showed rapid convergence, with Mean Squared Error (MSE) reducing from 0.25-0.67 to 0.01-0.02.

Key takeaway

For MLOps Engineers managing AI/ML models in dynamic O-RAN environments, ADORN offers a robust strategy to combat concept drift. You should consider implementing an RL-based adaptive retraining policy, like Q-learning, to optimize the trade-off between predictive accuracy and computational overhead. This approach can significantly reduce unnecessary retraining actions while maintaining model performance within Service Level Agreement (SLA) boundaries, ensuring efficient resource utilization and stable network operations.

Key insights

ADORN uses Q-learning and an LSTM ensemble to adaptively manage AI/ML model drift in O-RAN, balancing accuracy and retraining costs.

Principles

• Drift handling is an MDP balancing accuracy and cost.
• Multi-expert ensembles mitigate catastrophic forgetting.
• RL enables adaptive, not static, retraining decisions.

Method

ADORN employs Q-learning to learn an optimal retraining policy. It uses statistical features (mean, variance) for state representation and a piecewise reward function to balance nMAE and retraining actions.

In practice

• Implement Q-learning for adaptive model retraining.
• Use LSTM ensembles for robust drift mitigation.
• Discretize state space with traffic mean and variance.

Topics

• Open RAN
• Reinforcement Learning
• Q-learning
• Concept Drift
• LSTM Ensemble
• AI/ML Model Management

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

Related on AIssential

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.