# Generative-AI for AI/ML Model Adaptive Retraining in Beyond 5G Networks

arXiv: https://arxiv.org/abs/2408.14827 · HTML: https://mlsanity.com/paper/2408.14827/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models](https://mlsanity.com/paper/2604.17770/) (2026)

## Abstract (arXiv)

Beyond fifth-generation (B5G) networks aim to support high data rates, low-latency applications, and massive machine communications. Artificial Intelligence/Machine Learning (AI/ML) can help to improve B5G network performance and efficiency. However, dynamic service demands of B5G use cases cause AI/ML model performance degradation, resulting in Service Level Agreements (SLA) violations, over- or under-provisioning of resources, etc. Retraining is essential to address the performance degradation of the AI/ML models. Existing threshold and periodic retraining approaches have potential disadvantages, such as SLA violations and inefficient resource utilization for setting a threshold parameter in a dynamic environment. This paper proposes a novel approach that predicts when to retrain AI/ML models using Generative Artificial Intelligence. The proposed predictive approach is evaluated for a Quality of Service Prediction use case on the Open Radio Access Network (O-RAN) Software Community platform and compared to the predictive approach based on the classifier and a threshold approach. Also, a realtime dataset from the Colosseum testbed is considered to evaluate Network Slicing (NS) use case with the proposed predictive approach. The results show that the proposed predictive approach outperforms both the classifier-based predictive and threshold approaches.