frozenhairdryer / JCAS_multitarg

Corresponding code to WSA/SCC 2023 contribution

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JCAS_multitarg

Corresponding code to WSA/SCC 2023 contribution

Init

Before you start, the figure folder has to be created. Run:

cd Autoencoder
mkdir figures

If you are planning on running the code on cpu, run:

pip install -r requirements.txt

If you want to run on gpu, cupy is installed additionally. Run:

pip install -r requirements_gpu.txt

Additionally, install pytorch according to their website.

Overview

Training and plotting can be done by running split_test.py. We recommend running this file to get accustomed to the code.

Main Components are:

  • autoencoder_compare_cpr: evaluation script for sweeping number of samples
  • set folder: pickled versions of trained autoencoders
  • Folder Autoencoder:
    • training_routine has training function which is described below
    • NN_classes has the following functions and classes:
      • Encoder
      • Decoder (Comm Receiver)
      • custom binary loss function (equivalent to BCE loss)
      • Beamformer
      • Presence_Detector
      • Angle_Estimator

About

Corresponding code to WSA/SCC 2023 contribution

License:GNU General Public License v3.0


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