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automatic modulation classification Using Compressive Convolutional Neural Network

IEEE ACCESS 2019年 7卷 79636-79643页

作者： Huang, Sai Chai, Lu Li, Zening Zhang, Di Yao, Yuanyuan Zhang, Yifan Feng, Zhiyong Beijing Univ Posts & Telecommun Key Lab Univ Wireless Commun Minist Educ Beijing 100876 Peoples R China Univ Denver Daniel Felix Ritchie Sch Engn & Comp Sci Denver CO 80208 USA Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Henan Peoples R China Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Beijing 100101 Peoples R China

The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC). In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e., regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs), are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high-dimensional features for modulation classification. Additionally, CCNN utilizes intra-class compactness and inter-class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods.

关键词： automatic modulation classification compressive loss constraint deep convolutional neural network multiple constellation images

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automatic modulation classification of radar signals using the Rihaczek distribution and Hough transform

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IET RADAR SONAR AND NAVIGATION 2012年第5期6卷 322-331页

作者： Zeng, D. Zeng, X. Cheng, H. Tang, B. Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Peoples R China

It is an important work to classify the modulation type of the intercepted radar signal for an electronic intelligence (ELINT) receiver in a non-cooperative environment. The authors use the Rihaczek distribution (RD) and the Hough transform (HT) to concentrate the energy in time-frequency plane and derive two new characteristic features, namely the ratio of the minimum to the maximum of the HT and the peak number of the HT of the real part of the RD, to improve the probabilities of successful recognition (PSRs) to recognise the classical low probability of intercept (LPI) radar signals. The first feature is especially suitable for the linear frequency modulation (LFM), whereas the second one is specifically designed for frequency shift keying (FSK). The choice of thresholds and the effects of signal parameters are analysed. Simulations show that the PSRs can reach 90% when the signal-to-noise ratio (SNR) is above -4 dB. The proposed algorithm is better than the previous algorithms by just using ambiguity function.

关键词： radar signal processing Radar theory electronic intelligence receiver signal-to-noise ratio LFM FSK Signal processing and detection Hough transforms probability LPI radar signals classical low probability of intercept modulation and coding methods statistical distributions Rihaczek distribution time-frequency plane ambiguity function radar receivers ELINT receiver Hough transform intercepted radar signal linear frequency modulation automatic modulation classification Integral transforms frequency shift keying PSR SNR probabilities of successful recognition signal classification Other topics in statistics

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automatic modulation classification for Adaptive OFDM Systems Using Convolutional Neural Networks With Residual Learning

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IEEE ACCESS 2023年 11卷 61013-61024页

作者： Kumar, Anand Srinivas, Keerthi Kumar Majhi, Sudhan Indian Inst Technol IIT Patna Dept Elect Engn EE Patna 801106 India Altiostar Bengaluru 560102 India Indian Inst Sci IISc Dept Elect Commun Engn Bengaluru 560012 India

automatic modulation classification (AMC) is becoming a promising technique for future adaptive wireless transceiver systems. The existing blind modulation classification (BMC) methods for orthogonal frequency division multiplexing (OFDM) fail to achieve the required performance by using statistical-based methods. Thus, the modulation classification research community is trying to adopt the deep learning (DL) method to improve the modulation classification accuracy. However, most of the existing DL methods for AMC of OFDM that involve the extraction of statistical features from the signal do not work for adaptive transceiver systems where the signal parameters are changed dynamically. In this paper, we design and implement AMC for adaptive OFDM systems by using a convolutional neural network (CNN) with residual learning. The proposed AMC can identify the modulation format of the received OFDM signal with different number of subcarriers, randomized carrier frequency offset (CFO), symbol timing offset (STO), phase offset, and unknown channel state information. We use residual learning to mitigate the effect of varying CFO, STO, and AWGN noise on the received OFDM signal. A larger pool of modulation schemes such as binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK, p /4-QPSK, minimum shift keying, 8-PSK, 16-quadrature amplitude modulation (QAM), and 64-QAM are being considered for the proposed AMC for OFDM system in a dynamic environment. The performance and complexity of the proposed AMC are compared with the existing statistical feature-based and DL-based approaches. The proposed AMC for the OFDM system is also verified on the real-time data set generated from the universal software radio peripheral testbed setup.

关键词： OFDM modulation Adaptive systems Task analysis Symbols Mathematical models Convolutional neural networks automatic modulation classification CNN deep learning residual learning

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automatic modulation classification Using Combination of Genetic Programming and KNN

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2012年第8期11卷 2742-2750页

作者： Aslam, Muhammad Waqar Zhu, Zhechen Nandi, Asoke Kumar Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3BX Merseyside England Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla Finland

automatic modulation classification (AMC) is an intermediate step between signal detection and demodulation. It is a very important process for a receiver that has no, or limited, knowledge of received signals. It is important for many areas such as spectrum management, interference identification and for various other civilian and military applications. This paper explores the use of Genetic Programming (GP) in combination with K-nearest neighbor (KNN) for AMC. KNN has been used to evaluate fitness of GP individuals during the training phase. Additionally, in the testing phase, KNN has been used for deducing the classification performance of the best individual produced by GP. Four modulation types are considered here: BPSK, QPSK, QAM16 and QAM64. Cumulants have been used as input features for GP. The classification process has been divided into two-stages for improving the classification accuracy. Simulation results demonstrate that the proposed method provides better classification performance compared to other recent methods.

关键词： automatic modulation classification Genetic programming K-nearest neighbor classification using genetic programming Higher order cumulants

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automatic modulation classification Methods for Wireless OFDM Systems in TDD Mode

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IEEE TRANSACTIONS ON COMMUNICATIONS 2010年第9期58卷 2480-2485页

作者： Haering, L. Chen, Y. Czylwik, A. Univ Duisburg Essen Dept Commun Syst Duisburg Germany

This paper presents a novel automatic modulation classification (AMC) algorithm in wireless time division duplex (TDD) orthogonal frequency division multiplexing (OFDM) systems with adaptive modulation (AM). Based upon the maximum-a-posteriori (MAP) criterion, a classification method for digital quadrature amplitude modulation (QAM) schemes is developed that exploits the channel reciprocity in TDD systems and the receiver knowledge about the overall transmission data rate. The AMC algorithm enables the application of adaptive modulation in wireless OFDM systems without loss in the effective data rate caused by signaling of the bit allocation table (BAT). Numerical results show almost no performance loss in terms of the packet error ratio (PER) compared to the case with perfect knowledge of the modulation schemes if the proposed AMC technique is used.

关键词： automatic modulation classification adaptive modulation orthogonal frequency division multiplexing time division duplex maximum-likelihood maximum a-posteriori

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automatic modulation classification of composite FM/PM speech signals in sensor arrays over flat fading channel

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IET COMMUNICATIONS 2007年第2期1卷 157-164页

作者： El-Mahdy, A. E. Mil Tech Coll Dept Elect Engn Cairo Egypt

A method for automatic classification of composite FM/PM speech signals is proposed here. The method classifies a number of intercepted signals into FM or PM, provided that each signal is either speech FM or PM. The signals are transmitted through flat fading channels and contaminated with white Gaussian noise. A linear array consisting of several sensors is constructed to intercept these signal. The minimum description length criterion is used to estimate the number of intercepted signals. The maximum likelihood approach is used to estimate the direction of arrival of each signal, the fading channel, coefficients and the noise variance. The classification procedure is based on estimation of the phase waveform which contains the speech signal. An iterative algorithm is derived to perform this estimate for each signal and then the resulting estimate is used to develop a method to determine, for each signal, if it is FM or PM. The method is based on evaluating the power spectral density of the instantaneous frequency of the intercepted signal which is distinctive in speech FM and PM signals. Computer simulations are performed to validate the theoretical developments.

关键词： iterative methods maximum likelihood estimation speech processing fading channels direction of arrival estimation sensor arrays frequency modulation composite FM-PM speech signal phase modulation direction-of-arrival estimation signal classification maximum likelihood approach Gaussian noise flat fading channel array signal processing power spectral density iterative algorithm automatic modulation classification

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y automatic modulation classification Based on Novel Feature Extraction Algorithms

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IEEE ACCESS 2020年 8卷 16362-16371页

作者： Zhang, Xiaolin Sun, Jianting Zhang, Xiaotong Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

In order to solve the problems that the accuracy of modulation recognition algorithms of MSK and MQAM signals is not ideal under the condition of low signal-to-noise ratio (SNR) in Additional White Gaussian Noise (AWGN) environment, two novel features, the differential nonlinear phase Peak Factor (PF) and the reciprocal of amplitude envelope variance of cyclic spectrum at zero frequency cross section after difference and forth power processing, are constructed, which can complete the recognition of MSK signal and the classification of MPSK and MQAM signals respectively. This paper proposes a mixed recognition algorithm based on the two new features and other classical features, and design a tree shaped multi-layer smooth support vector machine classifier based on feature selection algorithm (FS_DT-SSVM) to recognize eleven kinds of digital modulation signals. The simulation results illustrate that the algorithm can achieve the classification of the modulation signals {2FSK, MSK, 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM} with small SNR. When the SNR is not less than -1dB, the recognition rate of the classifier for all signals exceed 97%, which validates the effectiveness of the proposed modulation recognition algorithm.

关键词： automatic modulation classification novel feature extraction smooth support vector machine feature selection

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automatic modulation classification of Overlapped Sources Using Multi-Gene Genetic Programming With Structural Rick Minimization principle

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IEEE ACCESS 2018年 6卷 48827-48839页

作者： Huang, Sai Jiang, Yizhou Qin, Xiaoqi Gao, Yue Feng, Zhiyong Zhang, Ping Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum Minist Ind & Informat Technol Nanjing 211106 Jiangsu Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

As the spectrum environment becomes increasingly crowded and complicated, primary users may be interfered by secondary users and other illegal users. automatic modulation classification (AMC) of a single source cannot recognize the overlapped sources. Consequently, the AMC of overlapped sources attracts much attention. In this paper, we propose a genetic programming-based modulation classification method for overlapped sources (GPOS). The proposed GPOS consists of two stages, the training stage, and the classification stage. In the training stage, multi-gene genetic programming (MGP)-based feature engineering transforms sample estimates of cumulants into highly discriminative MGP-features iteratively, until optimal MGP-features (OMGP-features) are obtained, where the structural risk minimization principle (SRMP) is employed to evaluate the classification performance of MGP-features and train the classifier. Moreover, a self-adaptive genetic operation is designed to accelerate the feature engineering process. In the classification stage, the classification decision is made by the trained classifier using the OMGP-features. Through simulation results, we demonstrate that the proposed scheme outperforms other existing methods in terms of classification performance and robustness in case of varying power ratios and fading channel.

关键词： automatic modulation classification cumulant multi-gene genetic programming overlapped signal classification structural risk minimization principle

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automatic modulation classification for low-power IoT applications

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IEEE LATIN AMERICA TRANSACTIONS 2024年第3期22卷 204-212页

作者： Mondino-Llermanos, Yasmin R. Corral-Briones, Graciela Natl Univ Cordoba Cordoba Argentina

The Internet of Things (IoT) has swiftly become one of the most important technologies in recent years. Radio spectrum access represents a stern challenge for the IoT as a consequence of the increased use of connected devices. This is particularly true for IoT devices operating in the unlicensed band where the huge demand for wireless connectivity will require techniques that use the spectrum efficiently. Avoiding training sequences enables a more efficient spectrum usage and has the additional advantage of reducing the power consumption of IoT devices, but it requires modulation identification mechanisms. This paper presents a simple yet efficient method to classify received signals according to their modulation type. We propose the application of a single hidden layer neural network with a small number of trainable parameters for performing the classification between seven different modulation types. The designed classifier achieves a maximum accuracy of 95% when the signal-to-noise ratio (SNR) of the input data is 12 dB, and in the presence of multi-path fading, sample rate offset and carrier frequency offset.

关键词： internet of things radio spectrum access automatic modulation classification feature extraction mutual information artificial neural network

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automatic modulation classification of digital modulations in presence of HF noise

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2012年第1期2012卷 1-14页

作者： Alharbi, Hazza Mobien, Shoaib Alshebeili, Saleh Alturki, Fahd KACST Technol Innovat Ctr Radio Frequency & Photo Riyadh Saudi Arabia Prince Sultan Adv Technol Res Inst STC Chair Riyadh Saudi Arabia King Saud Univ Dept Elect Engn Riyadh Saudi Arabia

Designing an automatic modulation classifier (AMC) for high frequency (HF) band is a research challenge. This is due to the recent observation that noise distribution in HF band is changing over time. Existing AMCs are often designed for one type of noise distribution, e.g., additive white Gaussian noise. This means their performance is severely compromised in the presence of HF noise. Therefore, an AMC capable of mitigating the time-varying nature of HF noise is required. This article presents a robust AMC method for the classification of FSK, PSK, OQPSK, QAM, and amplitude-phase shift keying modulations in presence of HF noise using feature-based methods. Here, extracted features are insensitive to symbol synchronization and carrier frequency and phase offsets. The proposed AMC method is simple to implement as it uses decision-tree approach with pre-computed thresholds for signal classification. In addition, it is capable to classify type and order of modulation in both Gaussian and non-Gaussian environments.

关键词： automatic modulation classification Feature-based classification Bi-kappa noise HF communications

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