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