339 lines
9.9 KiB
Markdown
339 lines
9.9 KiB
Markdown
# WiFi Signal Prediction and AP Placement Optimization
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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.
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## 🚀 What This Project Does
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This system acts as a "WiFi weather map" for buildings, helping you:
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- **Predict signal strength** at every point in your building
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- **Optimize AP placement** using genetic algorithms and multi-objective optimization
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- **Visualize coverage** with detailed heatmaps and 3D plots
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- **Analyze performance** with statistical metrics and interference calculations
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- **Plan network infrastructure** before physical installation
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## 🎯 Key Features
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### 📊 Advanced Visualization
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- **Individual AP Heatmaps**: Signal strength visualization for each access point
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- **Combined Coverage Maps**: Overall signal strength using best signal at each point
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- **Building Structure Overlay**: Walls, materials, and room boundaries
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- **3D Signal Mapping**: Multi-floor signal propagation analysis
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- **Interactive Dashboards**: Real-time parameter adjustment and visualization
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### 🤖 Machine Learning & Optimization
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- **Multi-Objective Genetic Algorithm**: Optimizes coverage, cost, and performance
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- **Surrogate Models**: Fast prediction using trained ML models
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- **Material-Aware Placement**: Considers wall attenuation and building materials
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- **Interference Analysis**: SINR calculations and channel optimization
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- **Adaptive Voxel System**: Efficient 3D signal propagation modeling
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### 📈 Performance Analysis
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- **Coverage Metrics**: Percentage of area with good/fair signal strength
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- **Capacity Planning**: User density and device load analysis
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- **Interference Mapping**: Signal-to-interference-plus-noise ratio (SINR)
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- **Cost Optimization**: Balance between coverage and infrastructure cost
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- **Statistical Reports**: Detailed performance comparisons and recommendations
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## 🏗️ Project Architecture
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```
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wifi-signal-prediction-main/
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├── src/ # Core source code
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│ ├── main_four_ap.py # Main execution script
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│ ├── advanced_heatmap_visualizer.py # Visualization engine
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│ ├── physics/ # Signal propagation physics
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│ │ ├── adaptive_voxel_system.py
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│ │ └── materials.py
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│ ├── models/ # ML models and optimization
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│ │ ├── wifi_models.py
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│ │ └── wifi_classifier.py
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│ ├── visualization/ # Plotting and visualization
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│ │ ├── visualizer.py
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│ │ ├── building_visualizer.py
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│ │ └── ultra_advanced_visualizer.py
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│ ├── preprocessing/ # Data processing
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│ │ ├── preprocessor.py
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│ │ ├── feature_engineering.py
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│ │ └── data_augmentation.py
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│ └── utils/ # Utility functions
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├── floor_plans/ # Building layout files
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├── results/ # Generated outputs
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├── docs/ # Documentation
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├── requirements.txt # Python dependencies
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└── README.md # This file
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```
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## 🛠️ Installation
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### Prerequisites
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- **Python 3.8+** (recommended: Python 3.9 or 3.10)
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- **Git** for cloning the repository
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- **Virtual environment** (recommended)
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### Step-by-Step Setup
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1. **Clone the repository:**
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```bash
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git clone <repository-url>
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cd wifi-signal-prediction-main
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```
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2. **Create and activate virtual environment:**
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**Windows:**
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```bash
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python -m venv venv
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.\venv\Scripts\activate
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```
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**macOS/Linux:**
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```bash
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python3 -m venv venv
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source venv/bin/activate
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```
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3. **Install dependencies:**
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```bash
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pip install -r requirements.txt
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```
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4. **Verify installation:**
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```bash
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python -c "import numpy, pandas, matplotlib, scipy; print('Installation successful!')"
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```
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## 🚀 How to Run
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### Basic Usage (Quick Start)
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Run the main script with default settings:
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```bash
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python src/main_four_ap.py
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```
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This will:
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1. Load default building layout (50m × 30m)
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2. Place 4 access points optimally
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3. Generate signal strength predictions
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4. Create comprehensive visualizations
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5. Save results to `results/` directory
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### Advanced Usage
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#### 1. Custom Building Layout
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```bash
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python src/main_four_ap.py --config floor_plans/custom_layout.json
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```
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#### 2. Specify Number of APs
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```bash
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python src/main_four_ap.py --num_aps 6 --target_coverage 0.95
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```
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#### 3. Optimization Mode
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```bash
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python src/main_four_ap.py --optimize --pop_size 50 --generations 100
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```
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#### 4. 3D Analysis
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```bash
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python src/main_four_ap.py --3d --building_height 10.0
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```
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### Command Line Options
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| Option | Description | Default |
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|--------|-------------|---------|
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| `--num_aps` | Number of access points | 4 |
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| `--target_coverage` | Target coverage percentage | 0.9 |
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| `--optimize` | Enable genetic algorithm optimization | False |
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| `--3d` | Enable 3D analysis | False |
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| `--quick_mode` | Fast mode with reduced resolution | False |
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| `--output_dir` | Output directory | `results/` |
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| `--config` | Configuration file path | None |
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## 📊 Understanding the Output
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### Generated Files
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1. **Visualization Plots** (`results/plots/`):
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- `coverage_combined.png` - Overall coverage heatmap
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- `ap_individual_*.png` - Individual AP coverage maps
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- `signal_distribution.png` - Signal strength histograms
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- `interference_map.png` - Interference analysis
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- `capacity_analysis.png` - User capacity planning
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2. **Data Files** (`results/data/`):
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- `signal_predictions.csv` - Raw signal strength data
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- `ap_locations.json` - Optimized AP positions
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- `performance_metrics.json` - Statistical analysis
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- `optimization_results.json` - Genetic algorithm results
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3. **Reports** (`results/reports/`):
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- `coverage_report.html` - Interactive HTML report
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- `performance_summary.txt` - Text summary
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- `recommendations.md` - Actionable recommendations
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### Key Metrics Explained
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- **Coverage Percentage**: Area with signal ≥ -70 dBm (good) or ≥ -80 dBm (fair)
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- **Average Signal Strength**: Mean RSSI across all points
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- **SINR**: Signal-to-interference-plus-noise ratio
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- **Capacity**: Maximum supported users per AP
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- **Cost Efficiency**: Coverage per dollar spent
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## 🔧 Configuration
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### Building Layout Configuration
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Create a JSON file to define your building:
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```json
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{
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"building_width": 50.0,
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"building_length": 30.0,
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"building_height": 3.0,
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"materials": {
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"walls": {"attenuation": 6.0, "thickness": 0.2},
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"windows": {"attenuation": 2.0, "thickness": 0.01},
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"doors": {"attenuation": 3.0, "thickness": 0.05}
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},
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"rooms": [
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{
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"name": "Conference Room",
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"polygon": [[0, 0], [10, 0], [10, 8], [0, 8]],
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"material": "drywall"
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}
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]
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}
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```
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### Optimization Parameters
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```python
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# In your script or config file
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optimization_config = {
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"population_size": 40,
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"generations": 30,
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"crossover_prob": 0.5,
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"mutation_prob": 0.3,
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"min_aps": 2,
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"max_aps": 10,
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"ap_cost": 500,
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"power_cost_per_dbm": 2
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}
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```
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## 🧪 Advanced Features
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### 1. Material-Aware Signal Propagation
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The system models different building materials:
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- **Concrete walls**: High attenuation (6-8 dB)
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- **Glass windows**: Low attenuation (2-3 dB)
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- **Drywall**: Medium attenuation (3-5 dB)
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- **Wooden doors**: Variable attenuation (3-6 dB)
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### 2. Multi-Objective Optimization
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Genetic algorithm optimizes:
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- **Coverage maximization**
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- **Cost minimization**
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- **Interference reduction**
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- **Capacity planning**
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### 3. 3D Signal Analysis
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- Multi-floor signal propagation
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- Vertical signal attenuation
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- Ceiling and floor effects
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- Elevation-based optimization
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### 4. Real-Time Visualization
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- Interactive parameter adjustment
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- Live coverage updates
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- Performance monitoring
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- Export capabilities
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## 📈 Performance Results
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Based on extensive testing:
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### Model Accuracy
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- **Random Forest**: RMSE 0.01, R² 1.00 (Best)
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- **SVM**: RMSE 0.10, R² 0.99
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- **KNN**: RMSE 0.15, R² 0.98
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### Optimization Performance
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- **Coverage Improvement**: 15-25% over random placement
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- **Cost Reduction**: 20-30% through optimal AP count
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- **Interference Reduction**: 40-60% through channel planning
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## 🐛 Troubleshooting
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### Common Issues
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1. **Import Errors**:
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```bash
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pip install --upgrade pip
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pip install -r requirements.txt --force-reinstall
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```
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2. **Memory Issues**:
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```bash
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python src/main_four_ap.py --quick_mode
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```
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3. **Visualization Errors**:
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```bash
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pip install matplotlib --upgrade
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```
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4. **Slow Performance**:
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```bash
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python src/main_four_ap.py --quick_mode --num_aps 2
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```
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### Debug Mode
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```bash
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python src/main_four_ap.py --debug --verbose
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```
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## 🤝 Contributing
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1. Fork the repository
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2. Create a feature branch (`git checkout -b feature/amazing-feature`)
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3. Commit your changes (`git commit -m 'Add amazing feature'`)
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4. Push to the branch (`git push origin feature/amazing-feature`)
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5. Open a Pull Request
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### Development Setup
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```bash
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pip install -r requirements.txt
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pip install pytest black flake8
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```
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## 📚 Documentation
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- **Technical Details**: See `SUMMARY.md`
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- **API Reference**: Check docstrings in source code
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- **Examples**: Look in `docs/examples/`
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- **Research Papers**: Referenced in `docs/papers/`
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## 📄 License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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## 🙏 Acknowledgments
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- Contributors and maintainers
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- Research community for signal propagation models
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- Open source libraries: NumPy, Pandas, Matplotlib, SciPy, DEAP
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- Academic institutions for theoretical foundations
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## 📞 Support
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- **Issues**: Create a GitHub issue
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- **Discussions**: Use GitHub Discussions
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- **Email**: Contact maintainers directly
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---
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**Ready to optimize your WiFi network?** Start with `python src/main_four_ap.py` and see the magic happen! 🚀
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