Deep Learning-Driven Performance Prediction of Textile-Based Wearable Antennas

This project implements a Convolutional Neural Network (CNN) approach to predict the performance of textile-based wearable antennas operating in ISM bands (2.4 GHz and 5.8 GHz) for healthcare applications.

Project Overview

The increasing adoption of wearable technologies in healthcare has created a demand for efficient, safe, and flexible antenna designs. This project focuses on using deep learning to predict key antenna performance parameters, reducing the time and complexity associated with traditional full-wave electromagnetic simulations.

Key Features

• CNN-based performance prediction for textile-based wearable antennas
• Multi-output prediction of S11 (return loss), gain, radiation patterns, and SAR
• Synthetic data generation for training and testing
• Performance visualization and analysis tools
• SAR compliance checking against regulatory thresholds

Target Performance Metrics

The model aims to predict antenna designs meeting the following specifications:

• Gain: 3-5 dBi
• SAR: Below 1.6 W/kg (regulatory threshold)
• Mechanical bending radius: > 10 mm (for comfort and adaptability)

Project Structure

.
├── config/               # Configuration files
│   └── model_config.json # Model training configuration
├── data/                 # Dataset storage
│   ├── raw/              # Raw simulation and measurement data
│   └── processed/        # Processed data for model training
├── models/               # Saved model checkpoints
├── notebooks/            # Jupyter notebooks for experimentation
│   └── antenna_prediction_example.ipynb # Example workflow notebook
├── plots/                # Visualization outputs
├── src/                  # Source code
│   ├── data/             # Data processing scripts
│   │   ├── data_processor.py       # Data preprocessing module
│   │   └── generate_synthetic_data.py # Synthetic data generator
│   ├── features/         # Feature engineering 
│   ├── models/           # Model architectures
│   │   ├── cnn_model.py  # CNN model definition
│   │   ├── train_model.py # Training script
│   │   └── predict_model.py # Prediction script
│   └── visualization/    # Visualization utilities
│       └── visualizer.py # Visualization module
├── tests/                # Test cases
│   └── test_model.py     # Model architecture tests
├── run_workflow.sh       # End-to-end workflow script
├── requirements.txt      # Project dependencies
└── README.md             # Project documentation

Installation and Setup

1. Clone this repository:

git clone https://github.com/username/textile-antenna-prediction.git
cd textile-antenna-prediction

2. Install dependencies:

pip install -r requirements.txt

3. Create necessary directories (if not already present):

mkdir -p data/raw data/processed models plots

Usage

Quick Start

Run the entire workflow (data generation, model training, and prediction) using:

./run_workflow.sh

Step-by-Step Workflow

1. Generate synthetic data:

python src/data/generate_synthetic_data.py --num_samples 200 --output_dir data/raw

2. Train the model:

python src/models/train_model.py --config config/model_config.json

3. Make predictions:

python src/models/predict_model.py --model_dir models/model_YYYYMMDD_HHMMSS --params_file data/raw/antenna_params.csv --output_dir predictions

Jupyter Notebook

Explore the workflow interactively using the provided Jupyter notebook:

jupyter notebook notebooks/antenna_prediction_example.ipynb

Technical Approach

Deep Learning Model

The project implements two types of CNN models:

1. Single-output model: Predicts a specific antenna parameter (e.g., gain or SAR)
2. Multi-output model: Simultaneously predicts multiple parameters (S11, gain, SAR)

The CNN architecture consists of:

• Convolutional layers for feature extraction
• Batch normalization for training stability
• Dropout layers for regularization
• Dense layers for parameter prediction

Data Representation

Antenna radiation patterns are converted to image-like representations for CNN processing. S-parameters and other performance metrics are processed and scaled appropriately before training.

Performance Metrics

The model's performance is evaluated using:

• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)
• R² score

Applications

This project is particularly useful for:

1. Healthcare Wearables: Designing antennas for continuous health monitoring devices
2. Telemedicine: Reliable communication in remote healthcare applications
3. Positioning Systems: Location tracking for patient monitoring
4. Rapid Prototyping: Accelerated design cycle for wearable antenna development

Future Work

• Integration with electromagnetic simulation software for validation
• Extended model for different antenna types (beyond patch and monopole)
• Real-time adaptive tuning of antennas in dynamic body-worn environments
• Transfer learning from simulation data to improve prediction on real measurements
• Web interface for antenna designers to predict performance without simulation

References

1. IEEE standards for SAR limits in body-worn devices
2. ISM band regulations (2.4 GHz and 5.8 GHz)
3. Literature on textile-based wearable antennas
4. Deep learning approaches for electromagnetic design optimization

License

MIT

Acknowledgements

This project is inspired by the growing need for efficient design methodologies in the field of wearable antennas for healthcare applications.