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automatic modulation classification Using Involution Enabled Residual Networks

IEEE WIRELESS COMMUNICATIONS LETTERS 2021年第11期10卷 2417-2420页

作者： Zhang, Hao Yuan, Lu Wu, Guangyu Zhou, Fuhui Wu, Qihui Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum Nanjing 210000 Peoples R China Univ Sci & Technol China Dept Comp Sci & Technol Hefei 230026 Peoples R China

automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications. Many deep learning based models especially convolution neural networks (CNNs) have been proposed for AMC. However, the computation cost is very high, which makes them inappropriate for beyond the fifth generation wireless communication networks that have stringent requirements on the classification accuracy and computing time. In order to tackle those challenges, a novel involution enabled AMC scheme is proposed by using the bottleneck structure of the residual networks. Involution is utilized instead of convolution to enhance the discrimination capability and expressiveness of the model by incorporating a self-attention mechanism. Simulation results demonstrate that our proposed scheme achieves superior classification performance and faster convergence speed comparing with other benchmark schemes.

关键词： Convolution Kernel modulation Feature extraction Residual neural networks Computational modeling Wireless communication automatic modulation classification deep learning involution residual networks

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automatic modulation classification Using Moments and Likelihood Maximization

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IEEE COMMUNICATIONS LETTERS 2018年第5期22卷 938-941页

作者： Abu-Romoh, Mohannad Aboutaleb, Ahmed Rezki, Zouheir Univ Idaho Dept Elect & Comp Engn Moscow ID 83844 USA

Motivated by the fact that moments of the received signal are easy to compute and can provide a simple way to automatically classify the modulation of the transmitted signal, we propose a hybrid method for automatic modulation classification that lies in the intersection between likelihood-based and feature-based classifiers. Specifically, the proposed method relies on statistical moments along with a maximum likelihood engine. We show that the proposed method offers a good trade-off between classification accuracy and complexity relative to the maximum likelihood classifier. Furthermore, our classifier outperforms state-of-the-art machine learning classifiers, such as genetic programming-based K-nearest neighbor classifiers, the linear support vector machine classifier and the fold-based Kolmogorov-Smirnov algorithm.

关键词： automatic modulation classification support vector machines genetic programming machine learning moments-based classification

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automatic modulation classification Using Convolutional Neural Network With Features Fusion of SPWVD and BJD

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2019年第3期5卷 469-478页

作者： Zhang, Zufan Wang, Chun Gan, Chenquan Sun, Shaohui Wang, Mengjun Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China China Acad Telecommun Technol State Key Lab Wireless Mobile Commun Beijing 100191 Peoples R China

automatic modulation classification (AMC) is becoming increasingly important in spectrum monitoring and cognitive radio. However, most existing modulation classification algorithms neglect the complementarities between different features and the importance of features fusion. To remedy these flaws, this paper presents a scheme of features fusion for AMC using convolutional neural network (CNN). The approach attempts to fuse different images and handcrafted features of signals to obtain more discriminating features. First, eight handcrafted features and different images features are both extracted. In the latter, signals are converted into two kinds of time-frequency images by smooth pseudo-wigner-vine distribution and Born Jordan distribution, and a fine-tuned CNN model is utilized to extract image features. Second, the joint features are formed by combination of each of image and handcrafted features, and a multimodality fusion model is applied to fuse the joint features to yield further improvement. Finally, simulation results reveal the superior performance of the proposed scheme. It is worth mentioning that the classification accuracy can reach 92.5% with signal-to-noise ratio at -4 dB.

关键词： automatic modulation classification time frequency distribution convolutional neural network multimodality fusion

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automatic modulation classification in Impulsive Noise: Log-Domain 3-D Constellation Diagrams and Multiscale Dual-Convolutional 3DCNN

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第4期10卷 1204-1217页

作者： Bai, Jiansheng Yao, Jinjie Guo, Yurong Yang, Zhiliang Wang, Liming North Univ China State Key Lab Elect Testing Technol Taiyuan 030051 Shanxi Peoples R China

automatic modulation classification (AMC) is vital in cognitive communication systems. Existing AMC methods are mainly designed for Gaussian noise channels, but research shows that non-Gaussian impulsive noise in wireless communication systems cannot be ignored. This paper proposes a novel AMC method based on Log-domain 3D constellation diagrams and multi-scale dual-convolutional 3DCNN (3D Convolutional Neural Networks). Firstly, we adopt non-linear logarithmic function transformation to effectively suppress non-Gaussian impulsive noise and transform multi-type modulated signals from the signal time domain to the graph domain, constructing Log-domain constellations. Secondly, the 2D constellations are projected onto three-dimensional space to form logarithmic domain 3D constellation diagrams, aiming to provide more discriminative feature dimensions for deep learning networks. Then, a new multi-scale dual-convolutional 3DCNN (MDC-3DCNN) is proposed, where the number and scale of dual-convolutional structures are the hyperparameters of MDC-3DCNN, to achieve feature extractions on both spatial and planar dimensions simultaneously for modulation classification. Numerical simulation results and actual measurement experiments demonstrate that the proposed method can effectively perform high-precision and robust classification of modulated signals in non-Gaussian impulsive noise channels.

关键词： Feature extraction modulation Constellation diagram Three-dimensional displays Receivers Signal to noise ratio Communication systems automatic modulation classification constellation diagrams impulsive noise non-Gaussian noise 3DCNN feature extraction

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automatic modulation classification Using CNN-LSTM Based Dual-Stream Structure

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第11期69卷 13521-13531页

作者： Zhang, Zufan Luo, Hao Wang, Chun Gan, Chenquan Xiang, Yong Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Chongqing Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China Minist Educ Engn Res Ctr Mobile Commun Chongqing 400065 Peoples R China Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia

Deep learning (DL) has recently aroused substantial concern due to its successful implementations in many fields. Currently, there are few studies on the applications of DL in the automatic modulation classification (AMC), which plays a critical role in non-cooperation communications. Besides, most previous work ignores the feature interaction, and only considers spatial or temporal attributes of signals. Combining the advantages of the convolutional neural network (CNN), and the long short-term memory (LSTM), this paper addresses the AMC using CNN-LSTM based dual-stream structure, which efficiently explores the feature interaction, and the spatial-temporal properties of raw complex temporal signals. Specifically, a preprocessing step is first implemented to convert signals into the temporal in-phase/quadrature (I/Q) format, and the amplitude/phase (A/P) representation, which facilitates the acquirement of more effective features for classification. To extract features from each signal pattern, each stream is composed of CNN, and LSTM (denoted as CNN-LSTM). Most importantly, the features learned from two streams interact in pairs, which increases the diversity of features and thereby further improves the performance. Finally, some comparisons with previous work are performed. The experimental results not only demonstrate the superior performance of the proposed method compared with the existing state-of-the-art methods, but also reveal the potential of DL-based approaches for AMC.

关键词： Feature extraction modulation Wireless communication Convolutional neural networks Data models Training automatic modulation classification con-volutional neural network dual-stream structure long short term memory network signal representation

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automatic modulation classification Using Interacting Multiple Model Kalman Filter for Channel Estimation

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第9期68卷 8928-8939页

作者： Salam, Ahmed O. Abdul Sheriff, Ray E. Hue, Yim-Fun Al-Araji, Saleh R. Mezher, Kahtan Univ Bradford Fac Engn & Informat Bradford BD7 1DP W Yorkshire England Univ Bolton Sch Engn Bolton BL3 5AB England Khalifa Univ Coll Engn Abu Dhabi 127788 U Arab Emirates

A rigorous model for automatic modulation classification (AMC) in cognitive radio systems is proposed in this paper. This is achieved by exploiting the Kalman filter (KF) integrated with an adaptive interacting multiple model (IMM) for resilient estimation of the channel state information. A novel approach is proposed, in adding up the square root singular values of the decomposed channel using the singular value decompositions algorithm. This new scheme, termed Frobenius eigenmode transmission, is chiefly intended to maintain the total power of all individual effective eigenmodes, as opposed to keeping only the dominant one. The analysis is applied over multiple-input multiple-output (MIMO) antennas in combination with a Rayleigh fading channel using a quasi-likelihood ratio test algorithm for AMC. The expectation-maximization is employed for recursive computation of the underlying estimation and classification algorithms. Novel simulations demonstrate the advantages of the combined IMM-KF structure when compared to the perfectly known channel and maximum likelihood estimate, in terms of achieving the targeted optimal performance with the desirable benefit of less computational complexity loads.

关键词： automatic modulation classification Kalman filter interacting multiple model channel estimation

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automatic modulation classification for MIMO System Based on the Mutual Information Feature Extraction

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IEEE ACCESS 2024年 12卷 68463-68470页

作者： Ussipov, N. Akhtanov, S. Zhanabaev, Z. Turlykozhayeva, D. Karibayev, B. Namazbayev, T. Almen, D. Akhmetali, A. Tang, Xiao Al Farabi Kazakh Natl Univ Fac Phys & Technol Alma Ata 050040 Kazakhstan Almaty Univ Power Engn & Telecommun Dept Telecommun Engn Alma Ata 050013 Kazakhstan Northwestern Polytechin Univ Sch Elect & Informat Xian 710071 Peoples R China

automatic modulation classification (AMC) is an essential technology that is widely applied into various communications scenarios. In recent years, many Machine Learning and Deep-Learning methods have been introduced into AMC, and a lot of them apply different approaches to eliminate interference in complex Multiple-Input and Multiple-Output (MIMO) signals and improve classification performance. However, in practical communication systems, the perfect elimination of MIMO signal interference is impossible, and therefore classification performance suffers. In this paper, we propose a new AMC algorithm for MIMO system based on mutual information (MI) features extraction, which does not require a large amount of training data and the elimination of MIMO signal interference. In this approach, features based on mutual information are extracted using In-Phase and Quadrature (IQ) constellation diagrams of MIMO signals, which have not been explored previously. Our method can be effective since mutual information considers the interdependencies among variables and measures how much information about one variable reduces uncertainty about another, providing a valuable perspective for extracting higher-level and interesting features from the data. The effectiveness of our method is evaluated on several model and real-world datasets, and its applicability is proven.

关键词： automatic modulation classification classifier feature extraction mutual information entropy complex MIMO signals

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automatic modulation classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints

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IEEE SIGNAL PROCESSING LETTERS 2017年第11期24卷 1626-1630页

作者： Ali, Afan Fan Yangyu Northwestern Polytech Univ Sch Elect & Informat Engn Xian 710072 Shaanxi Peoples R China

We demonstrate a novel method for the automatic modulation classification based on a deep learning autoencoder network, trained by a nonnegativity constraint algorithm. The learning algorithm aims to constrain the negative weights, learns features that amount to a part-based representation of data, and disentangles a more meaningful hidden structure. The performance of this algorithm is tested on the fourth-order cumulants of the modulated signals. The results indicate that the autoencoder with nonnegativity constraint (ANC) improves the sparsity and minimizes the reconstruction error in comparison with the conventional sparse autoencoder. The classification accuracy of an ANC based deep network shows improved accuracy under limited signal length and fading channel.

关键词： Autoencoder automatic modulation classification cumulants deep learning networks nonnegativity constraints

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automatic modulation classification Using Contrastive Fully Convolutional Network

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IEEE WIRELESS COMMUNICATIONS LETTERS 2019年第4期8卷 1044-1047页

作者： Huang, Sai Jiang, Yizhou Gao, Yue Feng, Zhiyong Zhang, Ping Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

automatic modulation classification (AMC) aims at identifying the modulation format of the received signal. In this letter, we propose a novel grid constellation matrix (GCM)-based AMC method using a contrastive fully convolutional network (CFCN). We use GCMs as the input of the network, which are extracted from the received signals using low-complexity preprocessing. Moreover, a loss function with contrastive loss is designed to train the CFCN, which boosts the discrepancies among different modulations and obtains discriminative representations. Extensive simulations demonstrate that CFCN performs superior classification performance and better robustness to model mismatches with low training time comparing with other recent methods.

关键词： automatic modulation classification contrastive loss deep learning fully convolutional network

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automatic modulation classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第5期69卷 5688-5692页

作者： Wang, Yu Gui, Jie Yin, Yue Wang, Juan Sun, Jinlong Gui, Guan Gacanin, Haris Sari, Hikmet Adachi, Fumiyuki Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA Rhein Westfal TH Aachen Fac Elect Engn & Informat Technol D-5552062 Aachen Germany Tohoku Univ Res Org Elect Commun ROEC Sendai Miyagi 9808577 Japan

automatic modulation classification (AMC) is one of the most critical technologies for non-cooperative communication systems. Recently, deep learning (DL) based AMC (DL-AMC) methods have attracted significant attention due to their preferable performance. However, the study of most of DL-AMC methods are concentrated in the single-input and single-output (SISO) systems, while there are only a few works on DL-based AMC methods in multiple-input and multiple-output (MIMO) systems. Therefore, we propose in this work a convolutional neural network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC) method for MIMO systems. Simulation results demonstrate that the CNN/ZF-AMC method achieves better performance than the artificial neural network (ANN) with high order cumulants (HOC)-based AMC method under the condition of the perfect channel state information (CSI). Moreover, we also explore the impact of the imperfect CSI on the performance of the CNN/ZF-AMC method. Simulation results demonstrated that the classification performance is not only influenced by the imperfect CSI, but also associated with the number of the transmit and receive antennas.

关键词： automatic modulation classification deep learning zero-forcing equalization channel statement information multiple-input and multiple-output systems

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