https://github.com/mohamedmetwalli5/modulationclassification

Trainning and comparing four different models then try to combine and modify these architectures on a synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset for use in measuring performance across different signal and noise power scenarios to obtain better results.
https://github.com/mohamedmetwalli5/modulationclassification

latex pattern-recognition

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Trainning and comparing four different models then try to combine and modify these architectures on a synthetic dataset, generated with GNU Radio, consisting of 11 modulations. This is a variable-SNR dataset for use in measuring performance across different signal and noise power scenarios to obtain better results.

• Host: GitHub
• URL: https://github.com/mohamedmetwalli5/modulationclassification
• Owner: MohamedMetwalli5
• License: mit
• Created: 2022-05-21T18:08:22.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-06-28T01:31:02.000Z (over 2 years ago)
• Last Synced: 2024-11-11T04:46:24.285Z (3 months ago)
• Topics: latex, pattern-recognition
• Language: Jupyter Notebook
• Homepage:
• Size: 19.3 MB
• Stars: 3
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Modulation Classification

## Problem statement

  A synthetic dataset, generated with GNU Radio, consisting of 11 modulations.

  This is a variable-SNR dataset with moderate LO drift, light fading, and numerous

  different labeled SNR increments for use in measuring performance across

  different signal and noise power scenarios. It is required to train and compare

  three different models then try to combine and modify these architectures to

  obtain better results.

## Combining Data

  The raw data contained of 2 channels mainly but we could apply derivative or

  integration on the data to get more channels.

  we have tried the following combinations during training:

  * Raw data

  * Raw data + derivative

  * Raw data + Integration

  Combining all three together won’t fit in RAM because size of data is large.

## Data explanation

  RadioML2016b data set consists of 1,200,000 samples each sample has the shape

  128x2 it represents 128 complex time domain samples with 2 vectors to represent

  the real and imaginary parts of the time samples.

  The dataset consists of 10 different signals each one has 20 different level of noise

  applied to it between -20 and 18 db Here’s example of each class with different

  db levels:

  - blue curve represents real values of the signal while magenta represents imaginary

  values.

  ## Different models

  We have built 4 models:

  * CNN

  * Vanilla RNN

  * LSTM

  * CNN-LSTM which is called CLDNN

 ## Tuning Techniques

  It was required to tune the learning rate of each model so we have tried the

  most used learning rates:

  * 1e-1

  * 1e-2

  * 1e-3

  * 1e-4

  * 1e-5

  * 1e-6

  

  In most models 1e-4 was the best regarding to accuracy and loss but in LSTM

  model the default settings of Adam optimizer were better in integral model.

  We also used some callback function to optimize the tuning process.

  * EarlyStopping to stop training if the model didn’t enhance accuracy for 5

  epochs

  * Save checkpoint which saves a checkpoint only if the model has enhanced

  its accuracy

  

 ## CNN Model Structure

 ![image](https://user-images.githubusercontent.com/58489322/175792063-7ffae085-f224-4712-879c-290a837634ad.png)

 

 ## Vanilla RNN Model Structure

 ![image](https://user-images.githubusercontent.com/58489322/175792088-3c08b1c2-5020-40db-a111-4a76fdb825d0.png)

 

 ## LSTM Model Structure

 ![image](https://user-images.githubusercontent.com/58489322/175792099-4a71d45c-1d40-463b-8737-f982fc456e11.png)

 

 ## CNN-LSTM Model Structure

 ![wwwwwwwwwww](https://user-images.githubusercontent.com/58489322/175792191-ef649d3c-e47b-41a8-92d5-7a444df91b5e.png)

 ## References

 * T. O’shea, N. West “Radio Machine Learning Dataset Generation with

 GNU Radio”,

 https://pubs.gnuradio.org/index.php/grcon/article/download/11/10/

 

 * T. O’Shea, J. Corgan, and T. Clancy “Convolutional Radio Modulation

 Recognition Networks” https://arxiv.org/pdf/1602.04105.pdf

 

 * N. West, T. O’shea “Deep Architectures for Modulation Recognition”,

 https://arxiv.org/pdf/1703.09197.pdf

 

 * K. Karra, S. Kuzdeba, J. Peterson “Modulation recognition using

 hierarchical deep neural networks”

 http://ieeexplore.ieee.org/document/7920746/?anchor=authors