Um reconhecimento de modulação digital baseado em característica para rede assistidas por VANT em um aprendizado de máquina

This is the result of the work home of the PO-233 discipline at ITA

Description

Inspired by Matlab`s example Modulation Classification with Deep Learning, this project has goals to identified the modulation schema by a machine learning. Implemented during the PO-233 discipline at ITA, I leave here the artifacts to reproduce the experiment.

Basically we generate 1000 frames to each digital modulation (8-PSK, 16-QAM, 64-QAM, BPSK, CPFSK, GFSK, QPSK and PAM4) on Matlab according to Modulation Classification with Deep Learning.

The frames produced has noises into signals as can be observed in Waves with Noises 1 and 2 figures. The waves are available on dataset/origin.

After we transform the bandpass signal to the time domain, we applied to diversify technics (Fourier and Wavelet transform, Statistical Features, Constellation Shape Features, Cyclostationarity, Zero-crossing, and S transform)) to extract the main features of the signal. It that result a new dataset available on dataset/transform. The dataset have 8000 frames (samples) with 7.192 features.

Build With

• Matlab (R2020b)
• Jupyter Lab
  • Python
  • R

Installation

Use the package manager R to install foobar.

install.packages("R.matlab")
install.packages("wavelets")
install.packages("tuneR")
install.packages("seewave")
install.packages("data.table")
install.packages("GENEAread")
devtools::install_github("marksendak/constellation")
install.packages("constellation")

Use o scikit-learn no ambiente de acordo com o tutorial, check here It's yet needed to install some packages on Python:

pip install mlxtend
pip install seaborn

Usage

To use <project_name>, follow these steps:

Step 1

• Open the Matlab and execute the waveform_generation.m script
• Copy the files results (frame*.mat) to database/origin

Step 2

• Open the transformData.ipynb file on Jupyter Lab and run it.
• Check if there are 1 file at database/transform

Step 3

• Execute the model analysis script to get the performance of Decision Tree and Random Forest

Contributing

To contribute to <project_name>, follow these steps:

1. Fork this repository.
2. Create a branch: git checkout -b <branch_name>.
3. Make your changes and commit them: git commit -m '<commit_message>'
4. Push to the original branch: git push origin <project_name>/<location>
5. Create the pull request.

Alternatively see the GitHub documentation on creating a pull request.

Contributors

Thanks to the following people who have contributed to this project:

• Flavio Souza e-mail or web profile
  • Felipe Verri (Advisor) e-mail or web profile
  • Lourenço Junior (Advisor) e-mail

License

APACHE