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Deep Learning-Based Cooperative automatic modulation classification Method for MIMO Systems

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第4期69卷 4575-4579页

作者： Wang, Yu Wang, Juan Zhang, Wei Yang, Jie Gui, Guan Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China

automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver, equipped with multiple antennas, cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types, based on these sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditionalAMCmethods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

关键词： automatic modulation classification multiple-input multiple-output (MIMO) deep learning (DL) convolutional neural network (CNN) cooperative decision

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Accumulated Polar Feature-Based Deep Learning for Efficient and Lightweight automatic modulation classification With Channel Compensation Mechanism

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第12期69卷 15472-15485页

作者： Teng, Chieh-Fang Chou, Ching-Yao Chen, Chun-Hsiang Wu, An-Yeu Natl Taiwan Univ Grad Inst Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan

In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role in future intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, DL-based approaches suffer from heavy training overhead, memory overhead, and computational complexity, which severely hinder practical applications for resource-limited scenarios, such as Internet-of-Things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. Furthermore, the overhead of online retraining under time-varying fading channels has not been studied in the prior arts. In this work, an accumulated polar feature-based DL with a channel compensation mechanism is proposed to cope with the aforementioned issues. Firstly, the simulation results show that learning features from the polar domain with historical data information can approach near-optimal performance while reducing training overhead by 99.8 times. Secondly, the proposed neural network-based channel estimator (NN-CE) can learn the channel response and compensate for the distorted channel with 13% improvement. Moreover, in applying this lightweight NN-CE in a time-varying fading channel, two efficient mechanisms of online retraining are proposed, which can reduce transmission overhead and retraining overhead by 90% and 76%, respectively. Finally, the performance of the proposed approach is evaluated and compared with prior arts on a public dataset to demonstrate its great efficiency and lightness. The lightweight and efficient learning features of the proposed mechanism will be very attractive for future resource-constrained/aware IoT and Vehicle-to-Everything (V2X) applications.

关键词： modulation Channel estimation Feature extraction Training Deep learning Fading channels Computational complexity automatic modulation classification polar coordinate deep learning convolutional neural network time-varying fading channel online retraining

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automatic modulation classification Based on Bispectrum and CNN 8

Automatic Modulation Classification Based on Bispectrum and ...

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IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Li, Yongbin Shao, Gaoping Wang, Bin PLA Strateg Support Force Informat Engn Univ Zhengzhou Henan Peoples R China

ISBN: (纸本)9781538681787

automatic modulation classification (AMC) is crucial for the subsequent analysis and processing of modulated signals. In this paper, we propose an AMC method based on bispectrum and convolutional neural network (CNN) Alexnet model (Bispectrum-Alexnet) after studying the latest achievements in related fields. The proposed Bispectrum-Alexnet method feeds the CNN with amplitude spectrums of bispectrum (ASB) belonging to different signals, enabling it to learn and extract deep features automatically from those images, and finally obtain the ideal label. To present our work more clearly, firstly, we analyze the feasibility of using ASB for classification, especially focusing on the physical meaning of ASB. Secondly, an introduction to the complete algorithm implementation is given, including bispectrum estimation and CNN architecture. Finally, this method was designed to distinguish among 7 popular modulation signals (BPSK, 2ASK, 2FSK, 4FSK, 8FSK, LFM, OFDM) to validate the recognition performance. Simulation results show that the overall accuracy ratio can reach 97.7% when the symbolic signal to noise ratio is no less than 5dB.

关键词： automatic modulation classification bispectrum CNN ALexnet

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automatic modulation classification Under Non-Gaussian Noise: A Deep Residual Learning Approach

Automatic Modulation Classification Under Non-Gaussian Noise...

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IEEE International Conference on Communications (IEEE ICC)

作者： Ma, Jitong Lin, Shih-Chun Gao, Hongjie Qiu, Tianshuang Dalian Univ Technol Dept Elect Informat & Elect Engn Dalian Peoples R China North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27606 USA

ISBN: (纸本)9781538680889

During the last few years, automatic modulation classification (AMC) has attracted widespread attention in both civilian and military applications. Conventional AMC schemes are primarily developed under Gaussian noise assumptions. However, recent empirical studies show that non-Gaussian noise has emerged in a variety of wireless networked systems. The bursty nature of non-Gaussian noise fundamentally challenges the applicability of the conventional AMC schemes. In order to improve the classification performance under non-Gaussian noise, in this paper, a novel modulation classification method is proposed by using cyclic correntropy spectrum (CCES) and deep residual neural network (ResNet). First, CCES is introduced to effectively suppress non-Gaussian noise through the designated Gaussian kernel. CCES also provides significantly different CCES graphs with respect to different modulation schemes, enabling AMC to directly operate with the graphs without further feature extraction. Next, based on the CCES graphs, an end-to-end deep ResNet-based AMC is developed to recognize the correct modulation by iteratively evaluating the residual information in a cascade of multiple learning layers. Experimental results confirm that the proposed algorithm outperforms existing designs with much higher classification accuracy, i.e., 3 dB less in the required generalized signal to noise ratio for 100% accuracy, in non-Gaussian noise environments.

关键词： automatic modulation classification cyclic correntropy spectrum deep residual neural network non-Gaussian noise

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Accuracy Analysis of Feature-Based automatic modulation classification via Deep Neural Network

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SENSORS 2021年第24期21卷 8252-8252页

作者： Ge, Zhan Jiang, Hongyu Guo, Youwei Zhou, Jie China Acad Engn Phys Inst Elect Engn Mianyang 621000 Peoples R China

A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of FB-AMC among the most commonly used features, including higher-order cumulants (HOC), features-based fuzzy c-means clustering (FCM), grid-like constellation diagram (GCD), cumulative distribution function (CDF), and raw IQ data. A novel end-to-end modulation classifier based on deep learning, named CCT classifier, which can automatically identify unknown modulation schemes from extracted features using a general architecture, was proposed. Features except GCD are first converted into two-dimensional representations. Then, each feature is fed into the CCT classifier for modulation classification. In addition, Gaussian channel, phase offset, frequency offset, non-Gaussian channel, and flat-fading channel are also introduced to compare the performance of different features. Additionally, transfer learning is introduced to reduce training time. Experimental results showed that the features HOC, raw IQ data, and GCD obtained better classification performance than CDF and FCM under Gaussian channel, while CDF and FCM were less sensitive to the given phase offset and frequency offset. Moreover, CDF was an effective feature for AMC under non-Gaussian and flat-fading channels, and the raw IQ data can be applied to different channels' conditions. Finally, it showed that compared with the existing CNN and K-S classifiers, the proposed CCT classifier significantly improved the classification performance for MQAM at N = 512, reaching about 3.2% and 2.1% under Gaussian channel, respectively.

关键词： automatic modulation classification deep learning higher-order cumulants fuzzy c-means clustering non-Gaussian channel flat-fading

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LSTM-based automatic modulation classification 15

LSTM-based Automatic Modulation Classification

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15th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Zhou, Quan Jing, Xiaojun He, Yuan Cui, Yuanhao Kadoch, Michel Cheriet, Mohamed Beijing Univ Posts & Telecommun Beijing Peoples R China Univ Quebec ETS Montreal PQ Canada

ISBN: (纸本)9781728157849

Recently, automatic modulation classification (AMC) has been studied by more and more researchers, and a host of methods based on deep learning have been proposed. Different from image data, signal data is a sequence that changes with time, and has temporal characteristics. Like the spatial feature, the temporal feature cannot be ignored. In this paper, we add a Long Short-Term Memory (LSTM) module to the convolutional neural network (CNN) to learn the time-related features of wireless signals. LSTM learns the dependency relationship between the current element and the elements before-after through the gating structure. Experimental results show that our proposed methods perform better than the model without LSTM. The proposed network Incept-LSTM has a classification accuracy of 97.5% at a high signal-to-noise ratio (SNR). Compared to the model without LSTM, the classification accuracy of our proposed model is improved by 0.1% to 5.7%. At the same time, we compared the classification accuracy under different SNRs to explore its impact on classification performance. We found the classiticalion ability of LSTM is more prominent at the low SNRs.

关键词： automatic modulation classification Deep Learning Long Short-Term Memory

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Exploiting a low-cost CNN with skip connection for robust automatic modulation classification

Exploiting a low-cost CNN with skip connection for robust au...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Thien Huynh-The Hua, Cam-Hao Kim, Jae-Woo Kim, Seung-Hwan Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi Si Gyeongsangbuk D South Korea Kyung Hee Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781728131061

Recently, deep learning (DL) is an innovative machine learning (ML) technique that has gained the outstanding achievements in computer vision and natural language processing. This work takes advantage of DL for effectively handling automatic modulation classification (AMC), which is the fundamental function of numerous cognitive radio-based and spectrum sensing-based applications in many modern communication systems. Concretely, a novel deep convolutional neural network (DCNN) is proposed for learning a classification model from a massive amount of modulated signals, in which the network architecture has several convolutional blocks specialized to simultaneously capture the temporal intra-signal correlations and the spatial inter-signal relations. To this end, each block comprises various convolutional layers of asymmetric convolution kernels, whose outputs are gathered via a concatenation layer. For the enrichment of multi-scale deep feature and the prevention of gradient vanishing problem, these blocks are associated by skip connections to take into account the useful residual information. In experiments, the proposed CNN-based AMC method achieves the overall 24-modulation classification rate of 88.22% at 10dB SNR on the well-known DeepSig dataset.

关键词： automatic modulation classification deep learning convolutional neural network skip connection

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Deep Convolutional Neural Network with Wavelet Decomposition for automatic modulation classification 15

Deep Convolutional Neural Network with Wavelet Decomposition...

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15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Wang, Hongyu Ding, Wenrui Zhang, Duona Zhang, Baochang Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Beihang Univ Unmanned Syst Res Inst Beijing Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728151694

In cognitive radio, signal recognition is an important technology and modulation recognition plays a key role in it. With the development of artificial intelligence, deep learning algorithms applied in automatic modulation recognition have developed quickly, whereas they usually depend on a large number of labeled samples for training. Few samples directly affect the network convergence, which will lead to network overfilling and cannot achieve good results. The loss of prior information makes feature extraction more difficult. In this paper, we propose a wavelet-decomposition-based algorithm for modulation recognition to solve the small sample size problem. To obtain rich information relatively, we adopt the wavelet function to analyze signals from multiple scales, extract the time domain features of the signals without any prior information by Residual Blocks, and fuse these features by attention blocks. In order to reduce the risk of overfilling, we use the Batch Normalization layer, Global Average Pooling, and Additional Random Noise to improve robustness. The proposed algorithm has a classification accuracy of 95.6% with only 20 samples per category when the SNR is 20dB, which outstrips other classical methods under the same condition.

关键词： automatic modulation classification wavelet decomposition attention block small sample size problem

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Features Fusion based automatic modulation classification Using Convolutional Neural Network

Features Fusion based Automatic Modulation Classification Us...

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39th IEEE International Conference on Computer Communications (IEEE INFOCOM)

作者： Lin, Chunsheng Huang, Juanjuan Huang, Sai Yao, Yuanyuan Guo, Xin Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Beijing 100101 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

ISBN: (纸本)9781728186955

The modulation format is a key parameter that. influences the monitoring of the intercepted signals. automatic modulation classification (AMC) is utilized to recognize the modulation format of the intercepted signals. However, most recent AMC methods neglect the complementarity accross different features. In this paper, we propose a novel feature fusion based AMC scheme using the convolutional neural network (FFCNN). Fused feature is generated by concatenating the two-dimensional spectrum correlation function (SCF) images and the graphic constellation (GC) images. Moreover, the FFCNN classifier is adopted to obtain more discriminative representations, leading to improved final modulation classification performance. Extensive simulations demonstrate that the proposed FFCNN scheme outperforms other recent methods.

关键词： automatic modulation classification Convolutional neural network Graphic constellation Spectrum correlation function

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AMC-IoT: automatic modulation classification Using Efficient Convolutional Neural Networks for Low Powered IoT Devices 11

AMC-IoT: Automatic Modulation Classification Using Efficient...

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11th International Conference on Information and Communication Technology Convergence (ICTC) - Data, Network, and AI in the age of Untact (ICTC)

作者： Usman, Muhammad Lee, Jeong-A Chosun Univ Dept Comp Engn Gwangju South Korea

ISBN: (纸本)9781728167589

automatic modulation classification (AMC) is used to identify the modulation for the received signal. IoT devices use modern communication methods which are based on multiple input multiple output (MIMO) in which the signals are received from various sources. The identification of modulation is vital. Feature based AMC methods combined with deep learning techniques has the potential to meet the latency requirement in the IoT applications. An efficient convolutional neural network based on depthwise separable convolution has been proposed to classify the modulation of the received signals. The proposed architecture has 58% less parameters than the conventional convolutional architecture and the performance is comparable.

关键词： automatic modulation classification convolutional neural networks depthwise separable IoT

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