wifi-signal-prediction/README.md

WiFi Signal Prediction and AP Placement Optimization

A comprehensive Python-based system for predicting WiFi signal strength, optimizing access point (AP) placement, and generating detailed visualizations for indoor wireless network planning. This project combines advanced physics-based signal propagation modeling with machine learning optimization to help network engineers and IT professionals design optimal WiFi coverage.

🚀 What This Project Does

This system acts as a "WiFi weather map" for buildings, helping you:

• Predict signal strength at every point in your building
• Optimize AP placement using genetic algorithms and multi-objective optimization
• Visualize coverage with detailed heatmaps and 3D plots
• Analyze performance with statistical metrics and interference calculations
• Plan network infrastructure before physical installation

🎯 Key Features

📊 Advanced Visualization

• Individual AP Heatmaps: Signal strength visualization for each access point
• Combined Coverage Maps: Overall signal strength using best signal at each point
• Building Structure Overlay: Walls, materials, and room boundaries
• 3D Signal Mapping: Multi-floor signal propagation analysis
• Interactive Dashboards: Real-time parameter adjustment and visualization

🤖 Machine Learning & Optimization

• Multi-Objective Genetic Algorithm: Optimizes coverage, cost, and performance
• Surrogate Models: Fast prediction using trained ML models
• Material-Aware Placement: Considers wall attenuation and building materials
• Interference Analysis: SINR calculations and channel optimization
• Adaptive Voxel System: Efficient 3D signal propagation modeling

📈 Performance Analysis

• Coverage Metrics: Percentage of area with good/fair signal strength
• Capacity Planning: User density and device load analysis
• Interference Mapping: Signal-to-interference-plus-noise ratio (SINR)
• Cost Optimization: Balance between coverage and infrastructure cost
• Statistical Reports: Detailed performance comparisons and recommendations

🏗️ Project Architecture

wifi-signal-prediction-main/
├── src/                           # Core source code
│   ├── main_four_ap.py           # Main execution script
│   ├── advanced_heatmap_visualizer.py  # Visualization engine
│   ├── physics/                   # Signal propagation physics
│   │   ├── adaptive_voxel_system.py
│   │   └── materials.py
│   ├── models/                    # ML models and optimization
│   │   ├── wifi_models.py
│   │   └── wifi_classifier.py
│   ├── visualization/             # Plotting and visualization
│   │   ├── visualizer.py
│   │   ├── building_visualizer.py
│   │   └── ultra_advanced_visualizer.py
│   ├── preprocessing/             # Data processing
│   │   ├── preprocessor.py
│   │   ├── feature_engineering.py
│   │   └── data_augmentation.py
│   └── utils/                     # Utility functions
├── floor_plans/                   # Building layout files
├── results/                       # Generated outputs
├── docs/                          # Documentation
├── requirements.txt               # Python dependencies
└── README.md                      # This file

🛠️ Installation

Prerequisites

• Python 3.8+ (recommended: Python 3.9 or 3.10)
• Git for cloning the repository
• Virtual environment (recommended)

Step-by-Step Setup

1. Clone the repository:

git clone <repository-url>
cd wifi-signal-prediction-main

2. Create and activate virtual environment:

Windows:

python -m venv venv
.\venv\Scripts\activate

macOS/Linux:

python3 -m venv venv
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Verify installation:

python -c "import numpy, pandas, matplotlib, scipy; print('Installation successful!')"

🚀 How to Run

Basic Usage (Quick Start)

Run the main script with default settings:

python src/main_four_ap.py

This will:

1. Load default building layout (50m × 30m)
2. Place 4 access points optimally
3. Generate signal strength predictions
4. Create comprehensive visualizations
5. Save results to results/ directory

Advanced Usage

1. Custom Building Layout

python src/main_four_ap.py --config floor_plans/custom_layout.json

2. Specify Number of APs

python src/main_four_ap.py --num_aps 6 --target_coverage 0.95

3. Optimization Mode

python src/main_four_ap.py --optimize --pop_size 50 --generations 100

4. 3D Analysis

python src/main_four_ap.py --3d --building_height 10.0

Command Line Options

Option	Description	Default
--num_aps	Number of access points	4
--target_coverage	Target coverage percentage	0.9
--optimize	Enable genetic algorithm optimization	False
--3d	Enable 3D analysis	False
--quick_mode	Fast mode with reduced resolution	False
--output_dir	Output directory	results/
--config	Configuration file path	None

📊 Understanding the Output

Generated Files

1. Visualization Plots (results/plots/):

   • coverage_combined.png - Overall coverage heatmap
   • ap_individual_*.png - Individual AP coverage maps
   • signal_distribution.png - Signal strength histograms
   • interference_map.png - Interference analysis
   • capacity_analysis.png - User capacity planning

2. Data Files (results/data/):

   • signal_predictions.csv - Raw signal strength data
   • ap_locations.json - Optimized AP positions
   • performance_metrics.json - Statistical analysis
   • optimization_results.json - Genetic algorithm results

3. Reports (results/reports/):

   • coverage_report.html - Interactive HTML report
   • performance_summary.txt - Text summary
   • recommendations.md - Actionable recommendations

Key Metrics Explained

• Coverage Percentage: Area with signal ≥ -70 dBm (good) or ≥ -80 dBm (fair)
• Average Signal Strength: Mean RSSI across all points
• SINR: Signal-to-interference-plus-noise ratio
• Capacity: Maximum supported users per AP
• Cost Efficiency: Coverage per dollar spent

🔧 Configuration

Building Layout Configuration

Create a JSON file to define your building:

{
  "building_width": 50.0,
  "building_length": 30.0,
  "building_height": 3.0,
  "materials": {
    "walls": {"attenuation": 6.0, "thickness": 0.2},
    "windows": {"attenuation": 2.0, "thickness": 0.01},
    "doors": {"attenuation": 3.0, "thickness": 0.05}
  },
  "rooms": [
    {
      "name": "Conference Room",
      "polygon": [[0, 0], [10, 0], [10, 8], [0, 8]],
      "material": "drywall"
    }
  ]
}

Optimization Parameters

# In your script or config file
optimization_config = {
    "population_size": 40,
    "generations": 30,
    "crossover_prob": 0.5,
    "mutation_prob": 0.3,
    "min_aps": 2,
    "max_aps": 10,
    "ap_cost": 500,
    "power_cost_per_dbm": 2
}

🧪 Advanced Features

1. Material-Aware Signal Propagation

The system models different building materials:

• Concrete walls: High attenuation (6-8 dB)
• Glass windows: Low attenuation (2-3 dB)
• Drywall: Medium attenuation (3-5 dB)
• Wooden doors: Variable attenuation (3-6 dB)

2. Multi-Objective Optimization

Genetic algorithm optimizes:

• Coverage maximization
• Cost minimization
• Interference reduction
• Capacity planning

3. 3D Signal Analysis

• Multi-floor signal propagation
• Vertical signal attenuation
• Ceiling and floor effects
• Elevation-based optimization

4. Real-Time Visualization

• Interactive parameter adjustment
• Live coverage updates
• Performance monitoring
• Export capabilities

📈 Performance Results

Based on extensive testing:

Model Accuracy

• Random Forest: RMSE 0.01, R² 1.00 (Best)
• SVM: RMSE 0.10, R² 0.99
• KNN: RMSE 0.15, R² 0.98

Optimization Performance

• Coverage Improvement: 15-25% over random placement
• Cost Reduction: 20-30% through optimal AP count
• Interference Reduction: 40-60% through channel planning

🐛 Troubleshooting

Common Issues

1. Import Errors:

pip install --upgrade pip
pip install -r requirements.txt --force-reinstall

2. Memory Issues:

python src/main_four_ap.py --quick_mode

3. Visualization Errors:

pip install matplotlib --upgrade

4. Slow Performance:

python src/main_four_ap.py --quick_mode --num_aps 2

Debug Mode

python src/main_four_ap.py --debug --verbose

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Setup

pip install -r requirements.txt
pip install pytest black flake8

📚 Documentation

• Technical Details: See SUMMARY.md
• API Reference: Check docstrings in source code
• Examples: Look in docs/examples/
• Research Papers: Referenced in docs/papers/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Contributors and maintainers
• Research community for signal propagation models
• Open source libraries: NumPy, Pandas, Matplotlib, SciPy, DEAP
• Academic institutions for theoretical foundations

📞 Support

• Issues: Create a GitHub issue
• Discussions: Use GitHub Discussions
• Email: Contact maintainers directly

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Ready to optimize your WiFi network? Start with python src/main_four_ap.py and see the magic happen! 🚀