arXiv:2607.08443v1 Announce Type: cross Abstract: 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 for
The increasing complexity and dynamic nature of Open RAN deployments are making traditional AI/ML model retraining inefficient, necessitating adaptive and cost-effective solutions.
This development addresses a critical challenge in deploying AI/ML in dynamic network environments, ensuring performance and efficient resource utilization, which is vital for scalable and reliable future wireless infrastructure.
The adoption of reinforcement learning for adaptive drift handling could lead to more resilient and autonomous Open RAN operations, reducing manual intervention and operational costs.
- · Telecommunication companies
- · AI/ML model developers
- · Network operators
- · Reinforcement learning platforms
- · Traditional network optimization services
- · Manual IT operations
Improved stability and performance of AI/ML models in dynamic Open RAN environments.
Accelerated deployment and broader adoption of AI-driven automation in telecommunications infrastructure.
Reduced total cost of ownership for 5G/6G networks and enhanced service reliability for end-users.
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Read at arXiv cs.AI