Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges

Source code for the paper "Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges", which is published in Digital Signal Processing.

Representative and up-to-date models in the AMR field are implemented on four different datasets (RML2016.10a, RML2016.10b, RML2018.01a, HisarMod2019.1), providing a unified reference for interested researchers.

The article is available here:Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges

If you have any question, please contact e-mail: zhangxx8023@gmail.com

Abstract

Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing. Recent breakthroughs in deep learning (DL) have laid the foundation for developing high-performance DL-AMR approaches for communications systems. Comparing with traditional modulation detection methods, DL-AMR approaches have achieved promising performance including high recognition accuracy and low false alarms due to the strong feature extraction and classification abilities of deep neural networks. Despite the promising potential, DL-AMR approaches also bring concerns to complexity and explainability, which affect the practical deployment in wireless communications systems. This paper aims to present a review of the current DL-AMR research, with a focus on appropriate DL models and benchmark datasets. We further provide comprehensive experiments to compare the state of the art models for single-input-single-output (SISO) systems from both accuracy and complexity perspectives, and propose to apply DL-AMR in the new multiple-input-multiple-output (MIMO) scenario with precoding. Finally, existing challenges and possible future research directions are discussed.

Content

Experimental comparison for SISO system

Accuracy

Fig.1 Recognition accuracy comparison of the state-of-the-art models on (a) RML2016.10a, (b) RML2016.10b, (c) RML2018.01a, (d) HisarMod2019.1.

Parameter Comparison

Table1 Model size and complexity comparison on the four datasets (A: RML2016.10a, B: RML2016.10b, C: RML2018.01a, D: HisarMod2019.1).

Confusion matrix

Fig.2 Confusion matrices. A, B and C represent the confusion matrices obtained on the RML2016.10a, RML2016.10b, and RML2018.01a, respectively. The numerical indexes 1 - 14 denote CNN1, CNN2, MCNET, IC-AMCNET, ResNet, DenseNet, GRU, LSTM, DAE, MCLDNN, CLDNN, CLDNN2, CGDNet, PET-CGDNN.

Dataset

Table2 Main AMR open datasets for SISO systems.

Dataset	Link	Notes
RML2016.10a, RML2016.10b, RML2018.01a	RML	If RML2018 dataset is too large, you can use SubsampleRML2018.py to sample the dataset to get a partial dataset for experimentation.
HisarMod2019.1	HisarMod	In our experiments, the dataset was converted from a .CSV file to a .MAT file, which can be found in Link.

Related Papers

Model	Paper name	Publication year
CNN1	Convolutional Radio Modulation Recognition Networks	2016
CNN2	Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels	2020
MCNET	MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification	2020
IC-AMCNET	CNN-Based Automatic Modulation Classification for Beyond 5G Communications	2020
ResNet	Deep neural network architectures for modulation classification	2017
DenseNet	Deep neural network architectures for modulation classification	2017
GRU	Automatic Modulation Classification using Recurrent Neural Networks	2017
LSTM	Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors	2018
DAE	Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder	2022
MCLDNN	A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition	2020
CLDNN	Deep Architectures for Modulation Recognition	2017
CLDNN2	Deep neural network architectures for modulation classification	2017
CGDNet	CGDNet: Efficient Hybrid Deep Learning Model for Robust Automatic Modulation Recognition	2021
PET-CGDNN	An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation	2021
1DCNN-PF	Automatic Modulation Classification Using Parallel Fusion of Convolutional Neural Networks	2019

Environment

These models are implemented in Keras, and the environment setting is:

Python 3.6.10
TensorFlow-gpu 1.14.0
Keras-gpu 2.2.4

Remarks

You will need to download the appropriate dataset and change the flie path to the corresponding dataset in your code. There is no guarantee that the code can run sucessfully under other environmental configurations, but there may be performance differences due to different hardware conditions.

About DAE: In the author's open source code, decoder uses the TimeDistributed layer. In our initial implementation, decoder unfolds the data and uses a fully connected layer to reconstruct the input, so the difference is described here. (Source code for DAE) We updated the DAE source code and experimental results with TimeDistributed layer as decoder in our website.

Acknowledgement

Our code is partly based on leena201818. Thanks leena201818 and wzjialang for their great work!

Citation

Please cite the literature we refer to if they are helpful to your work. If our work is helpful to your research, please cite:

@article{ZHANG2022103650,
    title={Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges},
    author={Fuxin Zhang and Chunbo Luo and Jialang Xu and Yang Luo and FuChun Zheng},
    journal={Digital Signal Processing},
    year={2022},
    doi = {https://doi.org/10.1016/j.dsp.2022.103650}
}

FireTracer / AMR-Benchmark