ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning

2026-07-09 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Networking & Internet Architecture · Depth: Expert, quick

Summary

ADORN, an Adaptive Drift handling for Open RAN using Reinforcement Learning system, proposes a Q-learning-based adaptive retraining approach to mitigate performance degradation in AI/ML models caused by dynamic traffic variations. This system formulates the retraining decision as a Markov Decision Process (MDP), where a Reinforcement Learning (RL) agent learns an optimal policy to balance forecasting accuracy with computational retraining costs. A key component of ADORN is its multi-expert Long Short-Term Memory (LSTM) ensemble, designed to prevent catastrophic forgetting and enhance model robustness across diverse traffic conditions. Experimental evaluations demonstrate that ADORN effectively reduces retraining overhead when compared to greedy and random baseline methods, while consistently maintaining system performance within predefined Service Level Agreement (SLA) limits.

Key takeaway

For MLOps Engineers managing AI/ML models in dynamic Open RAN environments, ADORN offers a robust strategy. You should consider implementing adaptive retraining policies using Q-learning to optimize the trade-off between model accuracy and operational costs. Integrating multi-expert LSTM ensembles can significantly enhance model robustness against traffic drift, preventing catastrophic forgetting and ensuring SLA compliance without excessive retraining overhead.

Key insights

ADORN uses RL and an LSTM ensemble to adaptively retrain AI/ML models in Open RAN, balancing accuracy and cost.

Principles

• Retraining decisions can be modeled as an MDP.
• RL agents can balance accuracy and computational cost.
• Multi-expert ensembles mitigate catastrophic forgetting.

Method

ADORN employs a Q-learning-based RL agent to learn a retraining policy within an MDP framework. It integrates a multi-expert LSTM ensemble for robust performance and to prevent catastrophic forgetting.

In practice

• Implement Q-learning for adaptive model retraining.
• Utilize multi-expert LSTMs for drift robustness.
• Balance forecasting accuracy with retraining cost.

Topics

• Open RAN
• Reinforcement Learning
• Q-learning
• LSTM Ensemble
• AI/ML Model Drift
• Adaptive Retraining

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.