Generative Adversarial Networks-Based AI/ML Model Adaptive Retraining for Beyond 5G Networks

Beyond fifth-generation (B5G) networks aim to support high data rates, low-latency applications, and massive machine communications. Artificial Intelligence (AI) and Machine Learning (ML) can help to improve B5G network performance and efficiency. However, dynamic service demands of B5G cause AI/ML performance degradation, resulting in violations of Service Level Agreements (SLA), 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 presents a novel algorithm that predicts when to retrain AI/ML models using the generative adversarial networks (GANs) architecture. The proposed predictive approach is evaluated for a Quality of Service (QoS) prediction use case on O-RAN Software Community (OSC) platform and compared to the predictive approach based on the classifier and the threshold approach. The results show that the proposed predictive approach outperforms both the classifier-based predictive and threshold approaches.