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A Novel Lightweight Grouped Gated Recurrent Unit for automatic modulation classification

IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第8期13卷 2135-2139页

作者： Hu, Xin Gao, Gujiuxiang Li, Boyan Wang, Weidong Ghannouchi, Fadhel M. Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Univ Calgary Schulich Sch Engn Dept Elect & Comp Engn Intelligent RF Radio Lab Calgary AB T2N 1N4 Canada

With the aim of enhancing the performance of automatic modulation classification (AMC), modern researchers tend to adopt architectures based on Recurrent Neural Networks (RNNs) to better extract temporal features from signals. However, the widely adopted vanilla RNN structures incur high parameter and compute overheads, making them challenging to deploy in resource-limited scenarios. In this letter, a plug-and-play architecture based on Gated Recurrent Unit (GRU) is proposed, named Lightweight Grouped Gated Recurrent Unit (LGGRU), which proves highly effective for AMC. We first analyze the detrimental impact of the reset gate in vanilla GRU on feature extraction for modulation signals and suggest its removal. Additionally, a grouped update algorithm for hidden state is innovatively proposed to further reduce redundant parameters. Furthermore, LeakyReLU is employed instead of the hyperbolic tangent (tanh) activation function, making it more adaptable to resource-limited devices. Experimental results confirm the improvement in AMC accuracy with the proposed method, along with a significant reduction in parameter and compute overheads.

关键词： Logic gates modulation Computer architecture Feature extraction Task analysis Recurrent neural networks Long short term memory automatic modulation classification RNN GRU lightweight grouped update algorithm for hidden state

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MM-Net: A Multi-Modal Approach Toward automatic modulation classification

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IEEE COMMUNICATIONS LETTERS 2024年第2期28卷 328-331页

作者： Triaridis, Konstantinos Doumanidis, Constantine Chatzidiamantis, Nestor D. Karagiannidis, George K. Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece Lebanese Amer Univ LAU Artificial Intelligence & Cyber Syst Res Ctr Beirut 11022801 Lebanon

automatic modulation classification (AMC) has become an important component in communication systems for both civil and defense applications. The shortcomings of traditional approaches to AMC have led researchers to develop complex machine learning (ML)-based approaches. In this work, inspired by multi-modal approaches for general Computer Vision tasks like Semantic Segmentation, we propose MM-Net, a multimodal approach to AMC that uses domain-specific features in the form of Higher Order Cumulants (HOCs) to improve classification performance. Furthermore, we explore the usage of HOCs in existing Deep Learning (DL)-based applications for AMC. Simulation results show that for eight modulation classification, MM-Net achieves high classification accuracy even at low SNRs, demonstrating the robustness of the multimodal approach even under challenging channel conditions, while existing methods are improved by utilizing HOCs, especially at low SNR values.

关键词： Feature extraction modulation Task analysis Signal to noise ratio Convolutional neural networks Computer architecture Robustness automatic modulation classification machine learning deep learning cumulants convolutional neural networks transfer learning

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automatic modulation classification Based on Cauchy-Score Constellation and Lightweight Network Under Impulsive Noise

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IEEE WIRELESS COMMUNICATIONS LETTERS 2021年第11期10卷 2509-2513页

作者： Luan, Shengyang Gao, Yinrui Zhou, Jiachen Zhang, Zhaojun Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China

automatic modulation classification in the manner of pattern recognition has caught massive attention due to its simplicity in workflow design. Recently, enormous interest has been aroused because of the boosting development of deep learning and numerous network structures, leading to various substantial contributions. However, to the best of our knowledge, current approaches fail to handle two essential concerns simultaneously: the statistical distribution of the noise and the computational complexity due to the network structure. Since these factors play critical roles when facing practical applications, a modified constellation concept based on the Score function of Cauchy distribution is proposed as a robust feature to impulsive noise. Besides, a lightweight structure based on Shuffle Unit and Gated Recurrent Unit is proposed as the recognizer to lower the potential risk of overfitting and cope with the time-consuming problem. Monte-Carlo experiments involving multiple comparison algorithms are executed under different conditions, and results verify the superior performance of the proposed AMC scheme.

关键词： modulation Convolutional neural networks Robustness Logic gates Computational complexity Signal to noise ratio Pipelines Alpha-stable distribution automatic modulation classification constellation Cauchy distribution score function

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A Deep Learning-Based Novel Class Discovery Approach for automatic modulation classification

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IEEE COMMUNICATIONS LETTERS 2023年第11期27卷 3018-3022页

作者： Zhang, Rui Zhao, Yanlong Yin, Zhendong Li, Dasen Wu, Zhilu Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

The existing automatic modulation classification (AMC) methods require the training and testing datasets share a common set of modulation categories. However, the AMC model may encounter the need to discriminate novel classes in non-cooperative environments. To the best of our knowledge, the research reporting AMC in the class-disjoint environment has not been addressed yet. In this letter, a novel class discovery method is proposed for AMC leveraging the information contained in the labeled training dataset. Specifically, a 3-stage deep learning method is introduced to recognize samples of the known classes and cluster samples of novel classes. The extracted features and the pairwise similarity relationship are considered as the common knowledge between the two class-disjoint datasets and are utilized to help the construction and training of the classifier for novel classes. The simulation results validate the effectiveness and performance of the proposed method.

关键词： automatic modulation classification novel class discovery deep learning

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A Fast Multi-Loss Learning Deep Neural Network for automatic modulation classification

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第6期9卷 1503-1518页

作者： Chang, Shuo Yang, Zheng He, Jiashuo Li, Rong Huang, Sai Feng, Zhiyong Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China State Radio Monitoring Ctr Beijing Radio Monitoring Stn Beijing 100876 Peoples R China

automatic modulation classification (AMC) enables significant applications in both the military and civilian domains. Inspired by the great success of deep learning (DL), a dual-stream neural network using in-phase/quadrature (I/Q) and amplitude/phase (A/P) data has made a superior performance. However, this dual-stream model is oversized (large number of parameters) and time-consuming (high inference time). To deal with that, a novel lightweight single stream neural network made up of group convolutional layer and transformer encoder layer is proposed. Specifically, the group convolutional layer divides the input tensor into different groups for convolution, which needs fewer parameters and has lower computational complexity than commonly used convolutional layers. For the transformer encoder layer, it can generate all time steps' outputs in parallel by using multi-head attention scheme, which is impossible for the commonly used recurrent-like layers (i.e., the recurrent-like layers output all time steps one by one.). Furthermore, a novel class center distance expansion loss incorporating the cross entropy loss is proposed for model training, which can enlarge the distance of different class centers Finally, the overlapping risk of different class features is reduced. As a result, the inference time of the proposed FastMLDNN is 13x faster than MLDNN, and the model size is near 1/6 as MLDNN, which only sacrifices 0.12% in accuracy. The source code has been released in the github: https://***/Singingkettle/ChangShuoRadioRecognition/tree/main/configs/fastmldnn.

关键词： modulation Hidden Markov models Task analysis Feature extraction Entropy Convolution Transformers automatic modulation classification deep neural network group convolutional layer transformer layer multi-loss functions

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High-Order Convolutional Attention Networks for automatic modulation classification in Communication

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第7期22卷 4600-4610页

作者： Zhang, Duona Lu, Yuanyao Li, Yundong Ding, Wenrui Zhang, Baochang North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China Beihang Univ Inst Unmanned Syst Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Zhongguancun Lab Beijing 102206 Peoples R China

automatic modulation classification is a challenging and critical task in the field of communication. Deep convolutional networks (ConvNets) have been recently applied in cognitive radio and achieved remarkable performance. However, existing ConvNet-based methods mainly focus on the first-order architecture design, while fail to explore feature correlations of radio signal, which are particularly significant for useful information extraction in low signal-to-noise ratios (SNRs). In this paper, we propose high-order convolutional attention networks (HoCANs) for radio signal expression and feature correlation learning, based on a novel high-order attention mechanism to rescale the convolutional features along channel and sequence dimensions. High-order convolutional layer and covariance matrix after nonlinear transformation are led for tenser filtering with more discriminative representations of radio signals. Experiments have been conducted to validate the superiority of HoCANs which achieve state-of-the-art accuracy for automatic modulation classification on RADIOML 2018.01A dataset.

关键词： Convolution modulation Covariance matrices Deep learning Convolutional neural networks Correlation Computational modeling High-order convolution covariance matrix Index Terms attention mechanism automatic modulation classification intelligent communication

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automatic modulation classification using bimodal parallel multichannel deep learning framework for spatial multiplexing MIMO system

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PHYSICAL COMMUNICATION 2023年第1期59卷

作者： Pathak, Nakul K. Bajaj, Varun Indian Inst Informat Technol Design & Mfg Elect & Commun Discipline Jabalpur 482005 Madhya Pradesh India

In recent years, automatic modulation classification (AMC) has proved its importance for the military as well as civil applications and deep learning (DL) based AMC has attracted wide attention. But existing methods neglect to consider the advantages of both multimodality and complementarities simultaneously in a single DL framework for multiple-input multiple-output (MIMO) system. To mitigate this, bimodal multichannel configurable DL-based AMC has been presented for the MIMO system under perfect channel state information with zero forcing equalizer. The proposed DL framework consists of two parallel structures of multichannel convolutional layers in which one multichannel structure is fed with in-phase/quadrature (I/Q) as first modal information while another multichannel structure accepts amplitude/phase as second modal information. Features extracted from this parallel structure then pass through long short-term memory (LSTM) layers for further extracting temporal information effectively. Finally, classification is accomplished through fully connected layers. Simulation results manifest the robustness of the proposed framework that achieves an average accuracy of about 0.6% to 12% higher compared to the state-of-the-art DL models. Simulations also illustrate the impact of antenna diversities with spatial multiplexing on the classification. (c) 2023 Elsevier B.V. All rights reserved.

关键词： automatic modulation classification Bimodal multichannel deep learning model Multiple-input multiple-output system Zero forcing equalizer

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Robust automatic modulation classification in Low Signal to Noise Ratio

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IEEE ACCESS 2023年 11卷 7860-7872页

作者： An, To Truong Lee, Byung Moo Sejong Univ Seoul South Korea Sejong Univ Dept Intelligent Mechatron Engn & Convergence Engn Seoul 05006 South Korea

In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.

关键词： modulation Feature extraction Signal to noise ratio Convolutional neural networks classification algorithms Signal processing algorithms Deep learning automatic modulation classification autoencoder denoiser denoise signal convolutional neural network automatic modulation classification

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automatic modulation classification in Impulsive Noise: Hyperbolic-Tangent Cyclic Spectrum and Multibranch Attention Shuffle Network

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Ma, Jitong Hu, Mutian Wang, Tianyu Yang, Zhengyan Wan, Liangtian Qiu, Tianshuang Dalian Maritime Univ Informat Sci & Technol Coll Dalian 116024 Peoples R China Guangxi Univ Sci & Technol Sch Automat Liuzhou 545000 Peoples R China Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116026 Peoples R China Dalian Maritime Univ Coll Transportat Engn Dalian 116024 Peoples R China

automatic modulation classification plays an essential role in cognitive communication systems. Traditional automatic modulation classification approaches are primarily developed under Gaussian noise assumptions. Nevertheless, recent empirical studies show that impulsive noise has emerged in numerous wireless communication systems. The bursty nature of impulsive noise fundamentally challenges the applicability of the traditional automatic modulation classification approaches. To accurately identify the modulation schemes in impulsive noise environment, in this article, we propose a novel modulation classification approach through using hyperbolic-tangent cyclic spectrum and multibranch attention shuffle neural networks. First, based on the designed hyperbolic-tangent autocorrelation function, hyperbolic-tangent cyclic spectrum is proposed to effectively suppress the impulsive noise and extract the discriminating features. Then, based on the hyperbolic-tangent cyclic spectrum, a novel deep shuffle neural network is proposed as a classifier to perform the modulation classification through the multibranch attention mechanism to reweight all the features. Both the numerical and real-data experimental results demonstrate that the proposed algorithm can correctly classify modulation schemes with high accuracy and robustness in impulsive noise.

关键词： modulation Feature extraction Wireless sensor networks Electromagnetics Interference Gaussian noise Deep learning automatic modulation classification feature extraction hyperbolic-tangent cyclic spectrum impulsive noise multibranch attention mechanism shuffle network

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Multiscale Correlation Networks Based on Deep Learning for automatic modulation classification

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IEEE SIGNAL PROCESSING LETTERS 2023年 30卷 633-637页

作者： Xiao, Jing Wang, Yufeng Zhang, Duona Ma, Qinyan Ding, Wenrui Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Inst Unmanned Syst Beijing 100191 Peoples R China North China Univ Technol Sch Informat Beijing 100144 Peoples R China

automatic modulation classification (AMC) is a challenging yet significant technique for communication systems. Deep learning methods, though widely employed for AMC, are challenged by the poor representation in noisy scenarios. In this letter, we propose a novel Multiscale Correlation Networks (MSCNs) approach to enhance noise suppression and bolster representation power for AMC. MSCNs leverage learning correlation wavelet transform to redistribute radio signal and noise features across various scales, and incorporate multiscale correlation and attention mechanisms to characterize feature properties in terms of frequency. Experiments reveal that MSCNs achieve an overall classification rate of 91.36% at 10 dB using 152 k training samples on the public RadioML 2018.01 A benchmark.

关键词： automatic modulation classification deep learning multiscale correlation noise suppression stationary wavelet transform

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