Predictive RAN Optimization through Reinforcement Learning Intelligence

Goal:

Help operators dynamically allocate spectrum and capacity based on predicted network stress, ensuring better customer experience and cost efficiency.

Impact Keywords:

• RAN Optimization
• Predictive Analytics
• Dynamic Resource Allocation
• Operational Intelligence

Approach:

LunarTech Lab built a machine-learning framework that learns network behavior and predicts congestion hotspots before they occur.

1. Multi-Source Data Integration:
2. Combined tower telemetry, weather data, and mobility traces into a single spatiotemporal dataset.
3. Predictive Modeling Engine:
4. Developed gradient boosting and LSTM-based models to forecast cell utilization up to 24 hours ahead, identifying early stress indicators.
5. Reinforcement-Learning Optimization:
6. Simulated thousands of parameter-adjustment actions (e.g., handover thresholds, antenna tilts) to determine optimal configurations.
7. Visual Network Intelligence Dashboard:
8. Delivered an intuitive interface showing predicted issues, confidence levels, and suggested optimization actions per cell site.
9. Knowledge Transfer:
10. Trained client’s RAN engineers to interpret model outputs and retrain on local datasets using internal infrastructure.

Summary:

This project brought predictive intelligence into telecom operations — enabling data-driven RAN management, improving uptime, and reducing manual optimization cycles by 40%.