AI-Powered O-RAN Topology Detection, Capacity Estimation, Traffic Analytics, and Explainable Network Intelligence
The Intelligent Fronthaul Network Optimization Platform is a full-stack telecommunications analytics system developed to improve visibility, planning, and optimization of O-RAN fronthaul networks.
The platform processes large-scale traffic datasets collected from multiple Radio Units (RUs), automatically identifies network connectivity patterns, estimates optimal fronthaul link capacities using probabilistic techniques, and generates explainable AI insights for intelligent decision-making.
Designed for modern telecom environments, the solution combines data analytics, machine learning, statistical modeling, network visualization, and explainability into a unified platform.
Modern O-RAN deployments generate large volumes of traffic data that make network planning and optimization increasingly complex.
Network operators require:
- Accurate topology discovery
- Efficient capacity planning
- Congestion prediction
- Resource optimization
- Transparent AI-driven decision support
Traditional approaches often rely on manual analysis and static assumptions.
This platform addresses these challenges through automated topology inference, probabilistic capacity estimation, and explainable analytics.
- Identifies fronthaul connectivity patterns
- Infers network links from packet-loss correlations
- Maps relationships between Radio Units and transport links
- Monte Carlo-based capacity simulation
- Statistical confidence intervals
- Congestion-aware resource planning
- Buffer-aware network analysis
- SHAP-based feature importance analysis
- Transparent decision-making support
- Causal relationship discovery
- Model interpretability
- Throughput analysis
- Congestion monitoring
- Peak utilization detection
- Traffic trend visualization
- Real-time analytics visualization
- Capacity insights
- Historical analysis tracking
- Performance monitoring
- Interactive topology graphs
- Radio Unit connectivity mapping
- Fronthaul link representation
- Dynamic network exploration
┌─────────────────┐
│ Traffic Data │
│ (.dat Files) │
└────────┬────────┘
│
▼
┌─────────────────────┐
│ Data Processing │
│ & Aggregation │
└────────┬────────────┘
│
┌──────────────────┼──────────────────┐
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Topology │ │ Capacity │ │ Traffic │
│ Detection │ │ Estimation │ │ Analytics │
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘
│ │ │
└──────────┬──────┴─────────┬───────┘
▼ ▼
┌─────────────────────┐
│ Explainable AI │
│ SHAP Analytics │
└─────────┬───────────┘
│
▼
┌──────────────────┐
│ React Dashboard │
└──────────────────┘
- Python
- FastAPI
- Pandas
- NumPy
- Scikit-Learn
- NetworkX
- React
- Vite
- Tailwind CSS
- React Flow
- Recharts
- SHAP Explainability
- Statistical Modeling
- Monte Carlo Simulation
- Capacity Optimization
- Traffic Correlation Analysis
project-root/
│
├── backend/
│ ├── main.py
│ ├── analysis.py
│ ├── optimization.py
│ ├── parsing.py
│ ├── requirements.txt
│ └── output/
│
├── frontend/
│ ├── src/
│ ├── public/
│ └── package.json
│
├── throughput/
│ └── throughput-cell-*.dat
│
├── packet_stats/
│ └── pkt-stats-cell-*.dat
│
└── README.md
Provides a centralized overview of:
- Network utilization
- Capacity recommendations
- Throughput statistics
- Traffic patterns
- Analysis history
Automatically identifies:
- Fronthaul links
- Radio Unit relationships
- Shared infrastructure dependencies
- Connectivity structures
Supports:
- Buffer-aware estimation
- Loss-constrained optimization
- Statistical confidence bounds
- Monte Carlo simulations
Provides:
- Feature importance ranking
- SHAP visualizations
- Causal insights
- Model transparency
Monitors:
- Throughput variation
- Peak demand
- Congestion events
- Resource utilization
- Load throughput datasets
- Load packet statistics datasets
- Normalize network measurements
- Aggregate traffic information
- Detect connectivity relationships
- Infer network topology
- Estimate optimal capacities
- Generate explainable insights
- Visualize results through dashboard
- Python 3.9+
- Node.js 18+
- npm
pip install -r requirements.txt
uvicorn main:app --reloadBackend will be available at:
http://127.0.0.1:8000
cd frontend
npm install
npm run devFrontend will be available at:
http://localhost:5173
Optimize O-RAN fronthaul deployments using capacity recommendations.
Estimate bandwidth requirements under varying traffic conditions.
Detect and mitigate network bottlenecks.
Provide transparent AI-driven recommendations for operators.
Track network performance and utilization trends.
- Multi-cell traffic analysis
- Automated topology inference
- Probabilistic capacity estimation
- Explainable AI integration
- Interactive visual analytics
- Telecom-grade architecture
- Real-time streaming analytics
- Predictive congestion forecasting
- Reinforcement learning optimization
- Multi-site network orchestration
- Cloud-native deployment support
- Advanced anomaly detection
- Chaithanya R Hegde
- Rucha
- Arohi Rawat
MIT License
This project was developed as part of a telecommunications-focused innovation initiative exploring intelligent O-RAN fronthaul optimization through data analytics, machine learning, statistical modeling, and explainable AI.