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https://github.com/matlab-deep-learning/csinet-channel-compression-in-matlab-using-keras

This example shows how to co-execute MATLAB and Python to simulate the effect of channel estimate compression on precoding in a MIMO OFDM channel.
https://github.com/matlab-deep-learning/csinet-channel-compression-in-matlab-using-keras

communications csinet deep-learning keras matlab matlab-deep-learning tensorflow

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This example shows how to co-execute MATLAB and Python to simulate the effect of channel estimate compression on precoding in a MIMO OFDM channel.

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# Simulate Channel Compression Effect on Precoding Using MATLAB and Keras with CSINet



This example shows how to co-execute MATLAB® and Python® to simulate the effect of channel estimate compression on precoding in a MIMO OFDM channel. It shows how to:

* Generate CDL channel estimates in MATLAB using the [5G Toolbox™](https://www.mathworks.com/products/5g.html)

* Load and test a pre-trained CSINet Keras™ model using co-execution with Python

* Fine tune the model weights through transfer learning using co-execution with Python

* Simulate the effect of channel estimate compression on precoding in MATLAB using the [5G Toolbox](https://www.mathworks.com/products/5g.html) and [Communications Toolbox™](https://www.mathworks.com/products/communications.html)



The following figure summarizes the operations executed in MATLAB (marked in blue) versus those executed in Keras (marked in grey).



![](images/Workflow_Coexecution.png)



## Overview



There are different options for accessing deep learning models within MATLAB, including:



1. Using models created in MATLAB using [Deep Learning Toolbox™](https://www.mathworks.com/products/deep-learning.html)

2. [Converting](https://www.mathworks.com/help/deeplearning/import-deep-neural-networks.html) models from other frameworks into MATLAB

3. Co-executing models from other frameworks with MATLAB



This example provides an overview of the third approach. Co-execution is useful for leveraging [wireless products in MATLAB](https://www.mathworks.com/solutions/wireless-communications.html) to test existing deep learning models from other frameworks in an end-to-end link simulation. 

This workflow also allows engineering teams working with MATLAB & Python deep learning frameworks to easily combine their work in one environment.

Approaches one & two are useful for building deep learning models from scratch or for using additional capabilities from the [Deep Learning Toolbox](https://www.mathworks.com/products/deep-learning.html) with pre-trained models, such as [Deep Learning Code Generation](https://www.mathworks.com/help/deeplearning/code-generation.html) or [deep learning models in Simulink®](https://www.mathworks.com/help/deeplearning/ug/classify-images-in-simulink-with-imported-tensorflow-network.html). 



### Requirements

To run this example, you need:

- [MATLAB](http://www.mathworks.com) R2022b or later

- [Communications Toolbox](https://www.mathworks.com/products/communications.html)

- [5G Toolbox](https://www.mathworks.com/products/5g.html)

- [Python](https://www.python.org/downloads/release/python-3104/) (tested with version 3.10.4)

- [TensorFlow™](https://www.tensorflow.org/) (tested with version 2.10.0)

- [Keras](https://keras.io/) (tested with version 2.10.0)

- [Numpy™](https://numpy.org/) (tested with version 1.23.4)



For more information about installing Python, see [Install Supported Python Implementation](https://www.mathworks.com/help/matlab/matlab_external/install-supported-python-implementation.html).



## Running the Example

Open and run the live script `SimChanCompEffOnPrecodingUsingMATLABAndKerasWithCSINetExample.mlx`. 

To generate a new CDL channel estimates dataset, use the live script `GenerateCSINetDataSet.mlx`. 

The live scripts use the helper files `preprocessChannelEstimate.m` and `postprocessChannelEstimate.m` for pre/post-processing the training and testing data sets.



### Visualize the channel compression effect

By the end of the example, you will be able to visualize the effect of channel feedback compression with CSINet [1] on the received constellation of an OFDM MIMO channel with zero-forcing precoding.

The following figure shows the effect of using CSINet for channel feedback with compression rate 1/4 and normalized mean square error -42 dB in a CDL channel with the following parameters:

* Tx Antennas: 32

* Rx Antennas: 2

* Delay Profile: CDL-B

* RMS delay spread: 100 ns

* Max delay after truncation: 32

* Max Doppler: 2 Hz

* Resource blocks: 48 

* Subcarrier spacing: 30 KHz






## References

[1] Wen, Chao-Kai, Wan-Ting Shih, and Shi Jin, "Deep learning for massive MIMO CSI feedback," IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 748-751, Oct. 2018.



Copyright 2023, The MathWorks, Inc.



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