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--- |
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language: en |
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tags: |
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- mobility-prediction |
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- lstm |
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- deep-learning |
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- wireless-networks |
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- handover-prediction |
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license: mit |
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datasets: |
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- mobility-data |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- auc |
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--- |
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# Mobility Prediction Model |
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A deep learning-based system for predicting mobility patterns and handover requirements in wireless networks using LSTM neural networks. |
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## Overview |
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This project implements a sophisticated mobility prediction model that uses historical mobility data to predict when a handover between network cells will be needed. The model leverages LSTM (Long Short-Term Memory) networks to capture temporal patterns in user mobility and network conditions. |
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## Features |
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- LSTM-based deep learning model for sequence prediction |
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- Comprehensive feature engineering including: |
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- Spatial features (x, y coordinates) |
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- Temporal features (velocity, heading) |
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- Network metrics (signal strength, SINR, network load, throughput) |
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- Time-based cyclical features (hour of day, day of week) |
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- Categorical features (pattern type, device type) |
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- Advanced model architecture with: |
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- Dual LSTM layers with dropout for regularization |
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- Dense layers for final prediction |
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- Binary classification output |
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- Robust data preparation pipeline |
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- Early stopping and learning rate reduction callbacks |
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- Comprehensive model evaluation metrics |
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## Model Architecture |
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The model consists of: |
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- Input LSTM layer (64 units) |
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- Dropout layer (0.3) |
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- Second LSTM layer (32 units) |
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- Dropout layer (0.3) |
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- Dense layer (32 units, ReLU activation) |
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- Output layer (1 unit, Sigmoid activation) |
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## Performance Metrics |
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The model is evaluated using: |
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- Accuracy |
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- Precision |
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- Recall |
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- AUC (Area Under the Curve) |
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## Data Requirements |
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The model expects the following features: |
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- Spatial data: x, y coordinates |
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- Mobility metrics: velocity, heading |
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- Network metrics: signal_strength, sinr, network_load, throughput_mbps |
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- Temporal data: timestamp |
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- Categorical data: pattern_type, device_type (optional) |
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- Target variable: handover_needed |
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## Usage |
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```python |
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# Prepare your data |
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X, y, scaler, feature_names = prepare_lstm_data_robust( |
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data, |
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sequence_length=20, |
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prediction_horizon=5 |
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) |
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# Build and train the model |
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model = build_mobility_prediction_model(input_shape=(X.shape[1], X.shape[2])) |
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model.fit(X_train, y_train, validation_data=(X_val, y_val)) |
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``` |
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## Dependencies |
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- TensorFlow |
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- NumPy |
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- Pandas |
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- Scikit-learn |
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## Model Training |
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The model includes several training optimizations: |
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- Early stopping with patience=10 |
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- Learning rate reduction on plateau |
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- Batch size of 32 |
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- Adam optimizer with learning rate 0.001 |
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- Binary cross-entropy loss function |
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## Contributing |
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Contributions are welcome! Please feel free to submit a Pull Request. |
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