This repository contains python code to generate results for experiments on generative modeling of radio frequency (RF) communication signals, specifically synthetic orthogonal-frequency division multiplexing (OFDM) signals. This code implements two novel generative adversarial network (GAN) models, a 1D and a 2D convolutional model, named PSK-GAN and STFT-GAN, respectively, as well as the WaveGAN model architecture as a baseline for comparison.
J. Sklar, A. Wunderlich, "Feasibility of Modeling Orthogonal Frequency-Division Multiplexing Communication Signals with Unsupervised Generative Adversarial Networks", Journal of Research of the National Institute of Standards and Technology, Volume 126, Article No. 126046 (2021) https://doi.org/10.6028/jres.126.046.
The software enables automated testing of many model configurations across different datasets. Model creation and training is implemented
using the Pytorch library. This repository contains files for initializing the experiment test runs (main.py), training of GAN models(gan_train.py), loading target distributions (data_loading.py), and evaluation(gan_evaluation.py) of generated distributions. The /utils directory contains
supporting modules for target dataset creation, and model evaluation. The models/ directory contains modules that create PSK-GAN, STFT-GAN,
and WaveGAN architectures.
Running main.py runs the default GAN configuration specified by the configuration dictionary ./experiment_resources/training_specs_dict.py.
Descriptions for the fields specified in ./experiment_resources/training_specs_dict.py are located in
./experiment_resources/configuration_dictionary_description.csv. Additionally, a set of model configurations can be run in an automated fashion
by passing a configuration table (csv file) as an argument to the main python module (ex. main.py --configs path_to_config_table.csv). Column labels
of a configuration table should correspond to desired keys in the GAN configuration dictionary that are to be changed across runs.
The training and test target datasets used in this study were synthesized using the script scripts/target_data_synth.py. To execute experiments, first run this script and place its contents in a subdirectory named Data/. When running the models, experimental results are saved in experiment_results/.
We use a conda virtual environment to manage the project library dependencies.
Run the following commands to install requirements to a new conda environment:
conda create --name <env> --file .experiment_resources/requirements.txt
conda activate <env>
pip install -r .experiment_resources/pip_requirements.txt
This code executes three experiments: (1) a data complexity experiment, (2) a modulation order experiment, and (3) a fading channel experiment. In order to reproduce results from each of the three experiments, run
main.py --configs ./experiment_resources/test_configs_complexity_PSKGAN.csv
main.py --configs ./experiment_resources/test_configs_complexity_WaveGAN.csv
main.py --configs ./experiment_resources/test_configs_complexity_STFTGAN.csv
main.py --configs ./experiment_resources/test_configs_modulation_STFTGAN.csv
main.py --configs ./experiment_resources/test_configs_channel_STFTGAN.csv
Aggregated plots across model runs are created using the script ./scripts/plotting_script.py.
Single process multi-GPU training is done using Pytorch's DataParallel method, in order to increase training speed. During code development, we found that multi-process multi-GPU training using the DistributedDataParallel method was not compatible with the gradient penalty operation (autograd.grad). Therefore, DistributedDataParallel is not recommended when using Wasserstein-GP loss.
Jack Sklar (jack.sklar@nist.gov) and Adam Wunderlich (adam.wunderlich@nist.gov)
Communications Technology Laboratory
National Institute of Standards and Technology
Boulder, Colorado
The authors thank Ian Wilkins and Sumeet Batra for their contributions to an early version of this software.
This software was developed by employees of the National Institute of Standards and Technology (NIST), an agency of the Federal Government and is being made available as a public service. Pursuant to title 17 United States Code Section 105, works of NIST employees are not subject to copyright protection in the United States. This software may be subject to foreign copyright. Permission in the United States and in foreign countries, to the extent that NIST may hold copyright, to use, copy, modify, create derivative works, and distribute this software and its documentation without fee is hereby granted on a non-exclusive basis, provided that this notice and disclaimer of warranty appears in all copies.
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