文件列表:

CODEMETA.yaml (1641, 2022-01-06)
LICENSE.md (2128, 2022-01-06)
data_loading.py (11111, 2022-01-06)
experiment_resources (0, 2022-01-06)
experiment_resources\configuration_dictionary_description.csv (2380, 2022-01-06)
experiment_resources\pip_requirements.txt (108, 2022-01-06)
experiment_resources\requirements.txt (4638, 2022-01-06)
experiment_resources\test_configs_channel_STFTGAN.csv (110, 2022-01-06)
experiment_resources\test_configs_complexity_PSKGAN.csv (763, 2022-01-06)
experiment_resources\test_configs_complexity_STFTGAN.csv (768, 2022-01-06)
experiment_resources\test_configs_complexity_WaveGAN.csv (724, 2022-01-06)
experiment_resources\test_configs_impairments_STFTGAN.csv (77, 2022-01-06)
experiment_resources\test_configs_impairments_STFTGAN_2.csv (102, 2022-01-06)
experiment_resources\test_configs_modulation_STFTGAN.csv (266, 2022-01-06)
experiment_resources\trainspecs_dict.py (2824, 2022-01-06)
gan_evaluation.py (21778, 2022-01-06)
gan_models (0, 2022-01-06)
gan_models\DirectWaveGAN.py (8668, 2022-01-06)
gan_models\SFTFGAN.py (5580, 2022-01-06)
gan_models\__init__.py (0, 2022-01-06)
gan_train.py (17095, 2022-01-06)
licensing_statement.txt (1626, 2022-01-06)
main.py (6699, 2022-01-06)
scripts (0, 2022-01-06)
scripts\plotting_script.py (35278, 2022-01-06)
scripts\target_data_synth.py (3635, 2022-01-06)
utils (0, 2022-01-06)
utils\__init__.py (18, 2022-01-06)
utils\channel_models.py (5146, 2022-01-06)
utils\construct_ofdm.py (14645, 2022-01-06)
utils\evaluate_ofdm.py (21010, 2022-01-06)
utils\gan_configurator.py (3726, 2022-01-06)
utils\impairments.py (1422, 2022-01-06)
utils\synthetic_ofdm_modem.py (22422, 2022-01-06)

# **Software for Modeling OFDM Communication Signals with Generative Adversarial Networks** 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 2D convolutional model, named **PSK-GAN** and **STFT-GAN**, respectively, as well as the **WaveGAN** model architecture as a baseline for comparison. All three GAN models are trained on synthetic datasets over a range of OFDM parameters and conditions and evaluate their performance with simulated datasets. ## Software Implementation 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` and are provided in a separate gzip file, `target_distributions.tar.gz`. To execute experiments, first unzip this file and place its contents in a subdirectory named `Data/`. When running the models, experimental results are saved in `experiment_results/`, which is divided into sub-folders corresponding to each experiment. Each experiment folder contains sub-folders with results from three trials of each test configuration. Each test-trial folder contains saved GAN models, training metadata, as well as evaluations of the generated distributions. Previously-computed experimental results are saved in `experiment_results.tar.gz` ## Requirements We use a `conda` virtual environment to manage the project library dependencies. Run the following commands to install requirements to a new conda environment: ```setup conda create --name --file .experiment_resources/requirements.txt conda activate pip install -r .experiment_resources/pip_requirements.txt ``` ## Running Experiments 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 ```angular2html 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`. ## Implementation Notes Single process multi-GPU training is done using Pytorch's DataParallel method, in order to increase training speed. (Multi-process multi-GPU (DistributedDataParallel) is not compatible with the gradient penalty operation (autograd.grad) and is not recommended when using Wasserstein-GP loss). ## Authors Jack Sklar (jack.sklar@nist.gov) and Adam Wunderlich (adam.wunderlich@nist.gov) \ Communications Technology Laboratory \ National Institute of Standards and Technology \ Boulder, Colorado ## Acknowledgements The authors thank Ian Wilkins and Sumeet Batra for their contributions to an early version of this software. ## Licensing Statement 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. THE SOFTWARE IS PROVIDED 'AS IS' WITHOUT ANY WARRANTY OF ANY KIND, EITHER EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY THAT THE SOFTWARE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND FREEDOM FROM INFRINGEMENT, AND ANY WARRANTY THAT THE DOCUMENTATION WILL CONFORM TO THE SOFTWARE, OR ANY WARRANTY THAT THE SOFTWARE WILL BE ERROR FREE. IN NO EVENT SHALL NIST BE LIABLE FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO, DIRECT, INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES, ARISING OUT OF, RESULTING FROM, OR IN ANY WAY CONNECTED WITH THIS SOFTWARE, WHETHER OR NOT BASED UPON WARRANTY, CONTRACT, TORT, OR OTHERWISE, WHETHER OR NOT INJURY WAS SUSTAINED BY PERSONS OR PROPERTY OR OTHERWISE, AND WHETHER OR NOT LOSS WAS SUSTAINED FROM, OR AROSE OUT OF THE RESULTS OF, OR USE OF, THE SOFTWARE OR SERVICES PROVIDED HEREUNDER.

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