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A Unified Implementation of Several Baseline Deep Learning Models for Automatic Modulation Recognition
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# 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](https://www.sciencedirect.com/science/article/pii/S1051200422002676?via%3Dihub) If you have any question, please contact e-mail: [email protected] # 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](https://pubs.gnuradio.org/index.php/grcon/article/view/11), [RML2018.01a](https://ieeexplore.ieee.org/abstract/document/8267032)| [RML](http://radioml.com) | If RML2018 dataset is too large, you can use SubsampleRML2018.py to sample the dataset to get a partial dataset for experimentation. | | [HisarMod2019.1](https://ieeexplore.ieee.org/abstract/document/9128408) | [HisarMod](http://dx.doi.org/10.21227/8k12-2g70) | In our experiments, the dataset was converted from a .CSV file to a .MAT
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matched fp:42dcd1e2d3f6e28c, llm:Description: 'A Unified Implementation of Several Baseline Deep Learning Models for Automatic Modulation Recognition' (repo metadata). Language: Python. No topics provided.
matched fp:42dcd1e2d3f6e28c, llm:Description: 'A Unified Implementation of Several Baseline Deep Learning Models for Automatic Modulation Recognition' (repo metadata). Language: Python. No topics provided.
matched fp:42dcd1e2d3f6e28c, llm:Description: 'A Unified Implementation of Several Baseline Deep Learning Models for Automatic Modulation Recognition' (repo metadata). Language: Python. No topics provided.