Static retraining fails in dynamic 6G networks. Our predictive approach uses LOF and LSTM to detect traffic shifts and retrain just in time, reducing SLA violations by 40% compared to threshold-based methods. 🌟 Follow us on LinkedIn! Check the…
The Zero-Touch Network and Service Management (ZSM) framework is realized through O-RAN, where Non-RT and Near-RT RICs host AI/ML models. Our predictive retraining integrates seamlessly, using LSTM and LOF to keep models accurate and efficient in dynamic 6G environments. 🌟…
Detecting new traffic patterns in B5G networks requires unsupervised anomaly detection. We compared OC-SVM, Isolation Forest, and LOF—LOF won with 99% accuracy, enabling predictive retraining that adapts to dynamic QoS demands. 🌟 Follow us on LinkedIn! Check the updates…
Retraining AI/ML models on fixed thresholds or periodic schedules leads to SLA violations or unnecessary computational costs. Our predictive method adapts to real-time traffic changes, using LSTM and LOF to retrain only when needed—cutting violations by 40%. 🌟 Follow us…
