AI/ML-driven optimization: anomaly detection, predictive optimization

ML Anomaly Detection

AI and Machine Learning are transforming network optimization from reactive to proactive. Anomaly detection uses ML models to learn each cell's normal behavior baseline — KPI ranges, daily and weekly traffic patterns, expected correlations between metrics. Techniques include autoencoders (neural networks that learn to reconstruct normal patterns; anomalies produce high reconstruction error), isolation forests (tree-based algorithms that isolate outliers requiring few splits), and LSTM networks (recurrent neural networks that learn temporal patterns and flag deviations). Unlike threshold-based…