Using MATLAB with Python
Collaborate with colleagues who work in other deep learning frameworks to train and test PyTorch®, TensorFlow™, or ONNX™ models using Python coexecution or the import and export functions.
Use MATLAB to generate data sets to train Python models by leveraging the Wireless Communications toolboxes.
Test Python models in link and system level simulations.
Integrate your work in Model Based Design workflows. For more information, see Deep Learning with Simulink (Deep Learning Toolbox).
Leverage the software-defined radio (SDR) support to test your design with over-the-air (OTA) signals.
After qualifying your design, you can import and deploy your system onto several possible platforms. For more information, see Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX (Deep Learning Toolbox).
Topics
- PyTorch Coexecution
AI for wireless workflows using coexecution of MATLAB and PyTorch. (Since R2025a)
Featured Examples
Train PyTorch Channel Prediction Models
Train PyTorch-based channel prediction neural networks using data generated in MATLAB.
Offline Training and Testing of PyTorch Model for CSI Feedback Compression
Train an autoencoder-based PyTorch neural network offline and test for CSI compression.
Online Training and Testing of PyTorch Model for CSI Feedback Compression
Train an autoencoder-based PyTorch neural network online and test for CSI compression.
Apply Transfer Learning on PyTorch Model to Identify 5G and LTE Signals
Coexecution with Python to identify 5G NR and LTE signals by using the transfer learning technique on a pre-trained PyTorch™ semantic segmentation network for spectrum sensing.
Import TensorFlow Channel Feedback Compression Network and Deploy to GPU
Generate GPU specific C++ code for a pretrained TensorFlow channel state feedback autoencoder.
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