Amir Ahangarzadeh, Matin Hashemi, S. Alireza Nezamalhosseini, "Accurate Modulation Classification Under Impaired Wireless Channels via Shallow Convolutional Neural Networks", Physical Communication, 2022.
Classifying the modulation type of radio signals plays an important role in current and future wireless communication systems. We present a modulation classification method based on convolutional neural networks that reaches high accuracy in face of various channel characteristics and signal conditions without requiring the network to have a very large depth. Experiment results show that the proposed method reaches accurate classification under different system impairment settings that include sampling rate offset, carrier frequency offset, multi-path fading, and additive white Gaussian noise. For instance, compared to a state-of-the-art method, accuracy is improved up to 25% in classifying difficult modulation types under system impairments. Source code of the proposed method is available online.
Step 1: Download the test dataset, trained model, and python source code from here.
Step 2: Put all three files (.py, .h5, and .dat) in one folder, and then, run the python file.
Please use the following entry to cite our work in your publications:
@article{ahangarzadeh2022pc,
author = {Amir Ahangarzadeh and Matin Hashemi and S. Alireza Nezamalhosseini},
title = {Accurate Modulation Classification Under Impaired Wireless Channels via Shallow Convolutional Neural Networks},
journal = {Physical Communication},
year = {2022},
}