From PM Counters to Autonomous RAN — the practical guide to machine learning for radio network optimization. Real Python code, real operator data patterns, real deployment strategies.
CafeTele
One-time purchase — lifetime access, free updates
Not another "intro to ML" — this is ML applied to real telecom problems with real data
9+ runnable code examples — XGBoost propagation models, LSTM traffic forecasting, autoencoder anomaly detection, xApp development
Starts from PM counters, MDT, CDR — not toy datasets. Vendor-specific counter mapping (Ericsson, Huawei, Nokia)
Coverage, capacity, interference, handover, energy saving — the 5 highest-value AI use cases with production-proven approaches
NOC copilot, config assistant, 3GPP Q&A, automated reports. Plus RAG architecture to prevent hallucination
Complete rApp/xApp development guide. A1/E2 interfaces, traffic steering, QoS optimization on the Near-RT RIC platform
T-Mobile, Vodafone, Rakuten, SK Telecom, China Mobile, Telefonica, Jio, Deutsche Telekom — real results
CNN for coverage maps, LSTM for time series, autoencoders for anomaly detection, Transformers for log analysis
DQN/PPO for tilt optimization, multi-agent RL for coordinated multi-cell, digital twin training environment
One-time purchase — lifetime access — free updates
Launch price — limited time offer
Secure payment via Razorpay. Instant access after purchase.