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Practical AMC model based on SAE with various optimisation methods under different noise environments

IET COMMUNICATIONS 2020年第22期14卷 4081-4088页

作者： Li, Zerun Wang, Qinglin Liu, Weisong Xu, Qiang Xing, Zuocheng Li, Yongzhong Natl Univ Def Technol Coll Comp 109 Deya Rd Changsha Peoples R China Natl Univ Def Technol State Key Lab Complex Electronmagnet Environm Eff 109 Deya Rd Changsha Peoples R China Changsha Univ Coll Comp Engn & Appl Math 98 Hongshan Rd Changsha Peoples R China

automatic modulation classification (AMC) has recently attracted widespread attention nowadays due to its desirable features of generalisability and requirement of little prior knowledge through artificial intelligence (AI) technology. The authors propose a stacked auto-encoder (SAE) based on various optimisation methods structure to intelligently process a feature space that includes spectral-based features and high-order cumulants. To unify the dimensionality of the features, they apply different normalisation methods to the feature space before training the SAE model to decide corresponding normalisations under different noise environments. Linear normalisation is superior when signal-to-noise ratio (SNR) is low, and standardisation is superior when SNR is between -1 and 4 dB. Regularisation works best when SNR is greater than 5 dB. To increase the recognition accuracy of the proposed model, they introduce the unconstrained optimisation theory to adjust the proposed SAE model, including Nelder-Mead method, Newton optimisation method, conjugate gradient method and quasi-Newton method. They observe that the quasi-Newton method offers desirable performance when optimising SAE model. It is the first time to compare these data normalisation methods and discuss unconstrained optimisation theory together to recognise modulation types. The recognition accuracy of this model for eight modulation types can reach 99.8% when SNR ranges from -5 to 10 dB.

关键词： feature extraction learning (artificial intelligence) signal classification higher order statistics Newton method conjugate gradient methods optimisation modulation neural nets optimising SAE model data normalisation methods unconstrained optimisation theory recognition accuracy SNR practical AMC model noise environments automatic modulation classification AI technology stacked auto‐ encoder feature space high‐ order cumulants linear normalisation signal‐ to‐ noise ratio Nelder‐ Mead method Newton optimisation method conjugate gradient method quasiNewton method optimisation method structure intelligently process spectral‐ based features

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UAV Communication Signal Recognition: A New Feature Representation and Deep-Learning Method

UAV Communication Signal Recognition: A New Feature Represen...

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作者： Li, Z. Brno University of Technology

As the threats from unmanned aerial vehicles (UAVs) increases gradually, to recognize and classify unknown UAVs have became more and more important in both civil and military security fields. classification of signal modulation types is one of the basic techniques for specific UAV recognition. In this paper, to represent the hidden features involved in the transmitted signals from UAVs, we propose a two-dimensional squeezing transform (TDST) to characterize the UAV communication signals in a compressed time-frequency plane. The new time-frequency representation, TDST, retains the instantaneous characteristics of the UAV signal, and is with excellent data reduction and noise suppression capabilities. The TDST plane of different modulation types are then considered as input features, and we propose a convolutional neural network (CNN) based on deep-learning to recognize the UAV signals. We design an interception system and consider 10 types of UAV signals with random initial phase, bandwidth and frequency offset. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm.

关键词： automatic modulation classification unmanned aerial vehicles squeezing transform convolutional neural network

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Tracking Performance of Online Large Margin Semi-supervised classifiers in automatic modulation classification

Tracking Performance of Online Large Margin Semi-supervised ...

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6th International Symposium on Telecommunications (IST) with Emphasis on Information and Communication Technology

作者： Hosseinzadeh, Hamidreza Razzazi, Farbod Haghbin, Afrooz Islamic Azad Univ Sci & Res Branch Dept Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781467320733

automatic classification of modulation type in detected signals is an intermediate step between signal detection and demodulation, and is also an essential task for an intelligent receiver in various civil and military applications. In this paper, we propose a new method to evaluate the tracking performance, and classification by using semi-supervised online passive-aggressive classifier. This classifier employs a self-training approach for tracking performance evaluation in AWGN channels with unknown signal to noise ratios. Simulation results shows that adding unlabeled input samples to the training set, improve the tracking capacity of the presented system. The selection of appropriate features helps the general system to work for a set of initial sample of each class. The simulation results show that the employing this learning method increase the accuracy level.

关键词： automatic modulation classification Tracking performance evaluation Semi-supervised learning Passive-aggressive classifier Online learning

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Comparisons on Deep Learning Methods for NOMA Scheme classification in Cellular Downlink 15

Comparisons on Deep Learning Methods for NOMA Scheme Classif...

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

作者： Dong, Junwei Wang, Aihua Ding, Peili Ye, Neng Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Shaoxing Shangyu Inst Qual Inspect & Metrol Testi Shaoxing Peoples R China

ISBN: (纸本)9781728157849

Various non-orthogonal multiple access (NOMA) schemes have been proposed in multi-user downlink scenarios for the enhanced spectrum efficiency. While coherent detection at the receiver requires the knowledge of transmission configuration, the direct transmissions of the deployed NOMA scheme leads to heavy signaling overhead. To this end, this paper proposes an automatic classification framework for diversified NOMA schemes based on deep learning, which can effectively determine the NOMA encoding method without prior knowledge. Specifically, we resort to fully-connected deep neural network (FC-DNN) and long short-term memory (LSTM), which respectively use a feed-forward structure and a recurrent structure to deal with multiple received signal packets of NOMA. We then analyze the performance of the above two methods in both ideal and non-ideal scenarios, including phase and frequency offsets. The results indicate that LSTM can identify five different NOMA schemes with over 90% accuracy in the ideal case, and outperforms FC-DNN especially in low signal-to-noise region. However, FC-DNN achieves better performance than LSTM in nonideal cases.

关键词： 5G NR NOMA deep learning LSTM automatic modulation classification

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classification Model of Wireless Signals Based on Higher-order Statistics 15

Classification Model of Wireless Signals Based on Higher-ord...

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

作者： Shi, Fangning Jing, Xiaojun He, Yuan Kadoch, Michel Cheriet, Mohamed Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv BUPT Minist Educ Beijing Peoples R China Univ Quebec ETS Montreal PQ Canada

ISBN: (纸本)9781728157849

automatic modulation classification technology is an indispensable step in cognitive radio, and its recognition accuracy is related to the orderly progress of subsequent communications. In this paper, we introduce the higher-order statistics into residual neural network for the precise classification of different modulation types. The classification technology can recognize 24 digital and analog modulation types under both synthetic simulated channel effects and over-the-air recordings. We also consider a rigorous baseline method using residual neural network and compare performance between two approaches under a wide range of signal-to-noise ratio. Experimental results show that our proposed method achieves an average accuracy of 96.4% and obtains better performance in correct classification probability than the baseline method, especially in lower signal-to-noise ratio.

关键词： automatic modulation classification cognitive radio the higher-order statistics residual neural network simulated channel effects

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Performance Comparison of Adaptive modulation and Coding in OFDM Systems Using Signaling and automatic modulation classification

Performance Comparison of Adaptive Modulation and Coding in ...

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International OFDM Workshop

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

ISBN: (纸本)9783800733606

This paper compares the system performance of signaling and automatic modulation classification in adaptive orthogonal frequency division multiplexing (OFDM)-based systems in time-division duplex (TDD) mode. In previous theoretical contributions, transmitter-side joint adaptive modulation and coding (AM+C) techniques have been proposed which can be used to either increase the data rate or, alternatively, improve the link reliability. Since the receiver must be synchronized to the transmit parameters, the required signaling overhead reduces the benefit of adaptive modulation and coding when applied to practical systems. Numerical results in terms of the effective data bandwidth efficiency reveal the potential of recently developed automatic modulation classifiers as an alternative to conventional signaling schemes.

关键词： OFDM Adaptive modulation and coding automatic modulation classification Signaling

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Noise-Robust Feature Combination Method for modulation classification Under Fading Channels 88

Noise-Robust Feature Combination Method for Modulation Class...

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88th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Zhou, Siyang Wu, Zhilu Yin, Zhendong Yang, Zhutian Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9781538663585

automatic modulation classification (AMC) plays an important role in cognitive radio and is widely studied recent years. However, most existing AMC schemes must be deployed under their training SNRs, which makes them highly dependent on the accuracy of channel estimation. The classifiers may need to be re-trained to fit the varying channel condition. To address this problem, a feature combination method aiming to find noise robust features under fading channels is proposed in this paper. Stacked auto encoder is deployed to explore robust features from an extracted feature set, and these new features is then used to train a support vector machine (SVM). Numerical results shows that the generalization ability of SVMs trained with new features can be significantly improved;therefore the method is robust to SNR variation.

关键词： automatic modulation classification feature extraction feature combination stacked auto-encoder

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Multi-Receiver modulation classification for Non-Cooperative Underwater Acoustic Communications

Multi-Receiver Modulation Classification for Non-Cooperative...

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OCEANS - MTS/IEEE Kobe Techno-Oceans Conference (OTO)

作者： Zhang, Lan Feng, Wei Xu, Xiaomei Lu, Annan Zhou, Qing Sci & Technol Commun Informat Secur Control Lab Jiaxing 314000 Peoples R China China Elect Technol Grp Corp Res Inst 36 Jiaxing 341000 Peoples R China Xiamen Univ Minist Educ Key Lab Underwater Acoust Commun & Marine Informa Xiamen 361005 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9781538616543

In this paper, we design a non-cooperative underwater acoustic communications system employing automatic modulation classification (AMC) and blind channel estimation (BCE). The proposed system is based on the multi-receiver and the blind channel estimation exploits cross-relation between the receivers and the sparsity structure of the underwater acoustic channels. Based on the estimated channel impulse response, the classification ability of the featured-based AMC algorithm and the performance of the blind demodulator in multipath sparse channels is investigated. The simulation results show that the proposed system provides a good classification and demodulation performance while exhibiting significantly accurate blind channel estimation.

关键词： Non-cooperative underwater acoustic communication automatic modulation classification Blind channel estimation Multi-receiver

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Graph-based automatic modulation Classifier for M-ary Generalized QAM Signals 3

Graph-based Automatic Modulation Classifier for M-ary Genera...

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3rd International Conference on Imaging, Signal Processing and Communication (ICISPC)

作者： Yan, Xiao Zhang, Guoyu Luo, Jie Wu, Hsiao-Chun Wang, Qian Wu, Yiyan Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu Peoples R China Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA Commun Res Ctr Ottawa ON Canada

ISBN: (纸本)9781728136639

This paper presents a novel automatic modulation classification (AMC) method using graph-based constellation analysis, to classify M-ary Generalized Quadrature Amplitude modulation (GQAM) signals which employ uniform or non-uniform constellations for the first time. In our framework, a unified grid model is first built from the GQAM signal with the maximum constellation size in the modulation candidate set, and exploited to transform the received signal into graph domain. The graph representation of the received signal is established by mapping its recovered symbol points on the I/Q plane into the unified grid model, and then the eigenvalue(s) and eigenvector(s) of its corresponding adjacency matrix are computed. The modulation feature vector is constructed according to the eigenvector(s) corresponding to the maximum eigenvalue(s). The modulation type is eventually identified by searching the minimum angle between training features and test feature. Monte Carlo simulation results demonstrate that the proposed method can effectively classify the GQAM even in low signal-to-noise ratio.

关键词： automatic modulation classification generalized QAM unified grid model adjacency matrix feature vector angle

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Signaling-Free Bit and Power Loading in MIMO OFDM Systems 11

Signaling-Free Bit and Power Loading in MIMO OFDM Systems

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11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

作者： Haering, L. Univ Duisburg Essen Chair Commun Syst Essen Germany

ISBN: (纸本)9781728157634

This manuscript proposes a novel bit and power loading algorithm for multiple-input multiple-output orthogonal frequency division multiplexing systems in time-division duplex mode, in which the modulation schemes are automatically classified at the receiver. Due to decoupling the spatial streams by linear precoding, an adaptation at the transmitter side is enabled which targets a constant minimum distance of all constellation sets at the receiver side. Indeed, this fact supports the distinctiveness of different modulation schemes at the receiver. Simulations of typical indoor propagation scenarios reveal an increased modulation classification reliability compared to previous methods. Furthermore, numerical results show that the proposed adaptive transmission scheme clearly outperforms both a signaling-based adaptive and a non-adaptive concept, especially at lower signal-to-noise power ratios.

关键词： automatic modulation classification bit loading power loading MIMO OFDM

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