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---
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.
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