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33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Gros, Alexander Moeyaert, Veronique Megret, Patrice UMONS Fac Engn Electromagnetism & Telecommun Dept Mons Belgium

ISBN: (数字)9781665480536

ISBN: (纸本)9781665480536

The AMR (automatic modulation Recognition) domain has recently shown an increase of interest, particularly as an application for monitoring the physical layer of wireless transmissions. In this work, it is proposed to study the combination of B/EMD (Bivariate/Empirical Mode Decomposition) and CNN (Convolutional Neural Network) in order to improve the modulated signals recognition rates. On the one hand, B/EMD is able to decompose signals into a finite number of IMFs (Intrinsic Mode Functions) whose features can be analyzed. On the other hand, CNNs are already used in the scientific literature to classify modulations. Adopting a publicly available data base containing numerous modulated signals under varying channel conditions, it is possible to decompose the complex signals using B/EMD and inserting them into a CNN. It is shown that the shape of the data has an important impact on the classification. Furthermore, the performance is compared and it is shown that employing the IMFs provided by a BEMD (Bivariate Empirical Mode Decomposition) instead of the original IQ signal improves the overall accuracy of the recognition by 2% in the frame of the parameters used in this paper. More than that, it is also able to increase the recognition rates up to a maximum of 4.4% for higher signal to noise ratio values.

关键词： automatic modulation recognition AMR automatic modulation classification AMC empirical mode decomposition EMD BEMD decomposition convolutional neural networks CNN

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Faking Signals to Fool Deep Neural Networks in AMC via Few Data Points

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IEEE ACCESS 2021年 9卷 124425-124433页

作者： Ma, Hongbin Yang, Shuyuan He, Guangjun Wu, Ruowu Hao, Xiaojun Li, Tingpeng Feng, Zhixi State Key Lab Complex Electromagnet Environm Effe Luoyang 471003 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China State Key Lab Space Ground Integrated Informat Te Beijing 100086 Peoples R China

The recent years has witnessed a rapid development of Deep Learning (DL) based Automation modulation classification (AMC) methods, which has proved to outperform traditional classification approaches. In order to disturb the deep neural networks for AMC, in this paper, we propose an adversarial attack method to generate fake signals for fooling DL-based classifiers. Firstly, some constraints on visual difference and recoverability of fake signals are defined. Next, a Few Data Point Attacker (FDPA) is proposed to generate fake signals with few perturbed data points via differential evolution algorithm. Some experiments are taken on a public dataset, RML 2016.10a, and the results show that fake signals generated by the FDPA can remarkably reduce the accuracies of three types of DL-based AMC classifiers, a Convolutional Neural Network (CNN) based classifier, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) based classifier, and a classifier combined with CNN and LSTM-RNN. The code will be available.

关键词： modulation Visualization Perturbation methods Convolutional neural networks Deep learning Statistics Sociology automatic modulation classification fake signals few data point attacker (FDPA) differential evolution

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Data-and-Knowledge Dual-Driven automatic modulation Recognition for Wireless Communication Networks

Data-and-Knowledge Dual-Driven Automatic Modulation Recognit...

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IEEE International Conference on Communications (ICC)

作者： Ding, Rui Zhang, Hao Zhou, Fuhui Wu, Qihui Han, Zhu Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Univ Houston Houston TX 77004 USA

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations. The visual model is utilized to extract visual features. The attribute learning model is used to learn the attribute semantic representations. The transformation model is proposed to convert the attribute representation into the visual space. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy, especially in the low SNR. Moreover, the confusion among high-order modulations is reduced by using our proposed scheme compared with other traditional schemes.

关键词： automatic modulation classification data-and-knowledge dual-driven low signal-to-noise radio

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Combining Deep Learning and Linear Processing for modulation classification and Symbol Decoding

Combining Deep Learning and Linear Processing for Modulation...

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IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Hanna, Samer Dick, Chris Cabric, Danijela Univ Calif Los Angeles Elect & Comp Engn Dept Los Angeles CA 90095 USA Xilinx Inc San Jose CA USA

ISBN: (纸本)9781728182988

Deep learning has been recently applied to many problems in wireless communications including modulation classification and symbol decoding. Many of the existing end-to-end learning approaches demonstrated robustness to signal distortions like frequency and timing errors, and outperformed classical signal processing techniques with sufficient training. However, deep learning approaches typically require hundreds of thousands of floating points operations for inference, which is orders of magnitude higher than classical signal processing approaches and thus do not scale well for long sequences. Additionally, they typically operate as a black box and without insight on how their final output was obtained, they can't be integrated with existing approaches. In this paper, we propose a novel neural network architecture that combines deep learning with linear signal processing typically done at the receiver to realize joint modulation classification and symbol recovery. The proposed method estimates signal parameters by learning and corrects signal distortions like carrier frequency offset and multipath fading by linear processing. Using this hybrid approach, we leverage the power of deep learning while retaining the efficiency of conventional receiver processing techniques for long sequences. The proposed hybrid approach provides good accuracy in signal distortion estimation leading to promising results in terms of symbol error rate. For modulation classification accuracy, it outperforms many state of the art deep learning networks.

关键词： automatic modulation classification blind symbol decoding deep learning

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MAP-BASED automatic modulation classification FOR WIRELESS ADAPTIVE OFDM SYSTEMS

MAP-BASED AUTOMATIC MODULATION CLASSIFICATION FOR WIRELESS A...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： L. Haring C. Kisters Chair of Communication Systems University Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781479903573

This manuscript addresses the maximum-a-posteriori-(MAP)-based automatic modulation classification (AMC) for wireless orthogonal frequency division multiplexing (OFDM) systems with adaptive coding and modulation (ACM). The proposed classifier for quadrature amplitude modulation (QAM) schemes which utilizes the channel reciprocity in time-division duplex (TDD) systems requires the knowledge about the joint probabilities of the subcarrier-wise bit efficiencies at the transmitter and receiver side. In contrast to prior heuristic approaches, these probabilities are calculated analytically (with some approximations) if the transmitter and receiver apply the same bit loading algorithm on their erroneously estimated channel state information. Numerical results reveal that the precise knowledge of the joint probabilities improves the reliability of the automatic modulation classifier significantly, especially for high signal-to-noise power ratios (SNR).

关键词： OFDM automatic modulation classification Adaptive coding and modulation TDD Maximum-a-posteriori

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A novel deep learning automatic modulation classifier with fusion of multichannel information using GRU

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2023年第1期2023卷 66页

作者： Sun, Siqi Wang, Yongyu North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai Peoples R China

automatic modulation classification (AMC) plays a vital role in modern communication systems, which can support wireless communication systems with limited spectrum resource. This paper proposes an AMC method, which integrates gated recurrent unit (GRU) and convolutional neural network (CNN) to utilize the complementary input features of received signals for spatiotemporal feature extraction and classification. Different from other state-of-the-art (SoA) frameworks, the proposed AMC classifier, named as fusion GRU deep learning neural network (FGDNN), aggregates firstly temporal features with GRUs and then extracts spatial features with CNNs. The GRUs can store temporal dynamic features, and facilitate to capture the characteristics of correlation and dependence among input features. The method is tested extensively with comparisons in order to verify its effectiveness. Experiment results show that the recognition rates of our method outperform other deep learning frameworks.

关键词： automatic modulation classification Multichannel Input features Temporal dynamics Gated recurrent unit

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An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2021年 2卷 2243-2264页

作者： Wong, Lauren J. Clark, William H. Flowers, Bryse Buehrer, R. Michael Headley, William C. Michaels, Alan J. Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24060 USA Virginia Tech Hume Ctr Natl Secur & Technol Blacksburg VA 24060 USA Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA

While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications, a field loosely termed Radio Frequency Machine Learning (RFML). In particular, recent research has shown DL to be an enabling technology for Cognitive Radio (CR) applications as well as a useful tool for supplementing expertly defined algorithms for spectrum awareness applications such as signal detection, estimation, and classification. A major driver for the usage of RFML is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative raw Radio Frequency (RF) data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware requirements, that must be taken into account before deploying RFML systems in real-world wireless communication applications that largely go unaddressed in the current literature. This paper examines the prior works related to these major research considerations, with focus on the dependencies between them and factors unique to the RFML space.

关键词： Survey deep learning neural networks radio frequency machine learning spectrum awareness dynamic spectrum access cognitive radio automatic modulation classification specific emitter identification signal detection

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A Novel Low-Complexity Cyclostationary Feature Detection Using Sub-Nyquist Samples for Wideband Spectrum Sensing

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2021年第12期40卷 6371-6386页

作者： Mathew, Sneha Gem Samuel, Chris Prema Indian Inst Space Sci & Technol Thiruvananthapuram 695547 Kerala India

In this paper, we propose a novel low-complexity scheme to extract cyclic features of wideband signals from sub-Nyquist samples as Nyquist rates push contemporary analog-to-digital converters to their performance limits. Due to the sparse spectrum occupancy in wideband, sub-Nyquist sampling is performed and cyclostationary feature extraction is achieved at baseband to identify the modulation scheme with low computational complexity. The multichannel sub-Nyquist sampling structure of the modulated wideband converter (MWC) is used to perform spectrum sensing. automatic modulation identification and classification are done based on cyclostationary feature detection for the efficient use of available spectrum bands. The computational complexity is reduced as the aliased copy of the signal in baseband obtained in each channel of MWC is only used to estimate the cyclic spectrum of the signal and identify the modulation scheme. Simulations performed validate the proposed method for various modulation schemes.

关键词： Cognitive radio Spectrum sensing Sub-Nyquist sampling Modulated wideband converter Cyclostationarity automatic modulation classification

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automatic modulation DETECTION USING NON-LINEAR TRANSFORMATION DATA EXTRACTION AND NEURAL NETWORK classification 4

AUTOMATIC MODULATION DETECTION USING NON-LINEAR TRANSFORMATI...

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4th International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC)

作者： Kurniansyah, Hernawan Wijanto, Heroe Suratman, Fiky Yosef Telkom Univ Fac Elect Engn Bandung Indonesia

ISBN: (纸本)9781728119939

Adaptive modulation and Coding (AMC) is a system that performed adaptive modulation scheme in which sets of modulation used by the same system adapted to the conditions of the transmission channel. This research want to make an AMC system that can distinguish Amplitude modulation (AM), Lower Sideband (LSB), Upper Sideband (USB), Binary Phase-shift Keying (BPSK), Quartenary Phase-shift Keying (QPSK), and 8-Phase-shift Keying (8PSK) modulation automatically. AMC has an important role in the military. Modern electronic warfare, called the Electronic Warfare (EW) consists of three main components, these are Electronic Support (ES), Electronic Attack (EA) and Electronic Protect (EP). In electronic support, the main purpose is to collect the information of the received radio signal, so the AMC system can be used for this. With the AMC system, the modulation type is known to do the demodulation process, so the overlapped information can be known. The feature extraction process is one of the most important processes in AMC System. In this research, feature extraction performed is a high-order statistical feature in the time domain. The statistical order used is order 4. Information signals are passed on the transmission channel in the presence of AWGN noise interference with variable signal quality of 0 dB to 40 dB. Artificial neural network algorithm is used to classify modulation with a learning rate of 0.5 and the maximum number of epochs is 1000. By using the 4th order statistical feature, the AMC system on this research can distinguish AM, LSB, USB, BPSK, QPSK, and 8PSK modulation. This research focus on modulation that is used in HF military radio. The accuracy rate of this system in performing modulation classification without using non-linear transformations is 65.5% on 10 dB signal quality. Then, the accuracy of AMC by using non-linear transformations on the received signal reaches 88.8% on the 10 dB signal quality.

关键词： automatic modulation classification Spectral Based Feature High Order Statistics Artificial Neural Network

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Estimation of bias in numerical values of normalized sixth-order cumulants' structures for various signal constellations

Estimation of bias in numerical values of normalized sixth-o...

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International Conference on Computational Performance Evaluation (ComPE)

作者： Bozovic, Rade Orlic, Vladimir D. ALFA BK Univ Fac Informat Technol Belgrade Serbia Vlatacom Inst High Technol Belgrade Serbia

ISBN: (纸本)9781665436564

automatic modulation classification (AMC) represents technique for the process of modulation format recognition of signals considered to be a priori unknown, which finds wide usage in a numerous wireless systems and applications. Due to the low algorithm complexity and other practical aspects, AMC algorithms based on higher-order statistics are very popular. In this paper, properties of AMC algorithms based on sixth-order cumulants were explored in context of real and complex signals, i.e. different Pulse Amplitude modulation (PAM) and Quadrature Amplitude modulation (QAM) signal constellations. This was done through Monte Carlo simulations in a propagation conditions of Additive White Gaussian Noise (AWGN), but also in the channel with multipath propagation, with main focus on numerical quantification of the presence of bias - an interesting effect reported in standard cumulant structures. One new, recently published, approach in AMC, where unbiased nature of classification features is reported, was tested in the same context. Detailed numerical estimations for both standard and unbiased AMC algorithms are presented, confirming that bias issues can be resolved with new sixth-order cumulants formula and thus consequently better performances in classification of real and complex signals can be ensured, in all considered propagation scenarios.

关键词： automatic modulation classification AWGN cumulants multipath PAM QAM bias

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