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automatic modulation classification Using Combination of Genetic Programming and KNN

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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automatic modulation classification: A Deep Learning Enabled Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第11期67卷 10760-10772页

作者： Meng, Fan Chen, Peng Wu, Lenan Wang, Xianbin Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Jiangsu Peoples R China Western Univ Dept Elect & Comp Engn London ON N6A 3K7 Canada

automatic modulation classification (AMC), which plays critical roles in both civilian and military applications, is investigated in this paper through a deep learning approach. Conventional AMCs can be categorized into maximum likelihood (ML) based (ML-AMC) and feature-based AMC. However, the practical deployment of ML-AMCs is difficult due to its high computational complexity, and the manually extracted features require expert knowledge. Therefore, an end-to-end convolution neural network (CNN) based AMC (CNN-AMC) is proposed, which automatically extracts features from the long symbol-rate observation sequence along with the estimated signal-to-noise ratio (SNR). With CNN-AMC, a unit classifier is adopted to accommodate the varying input dimensions. The direct training of CNN-AMC is challenging with the complicated model and complex tasks, so a novel two-step training is proposed, and the transfer learning is also introduced to improve the efficiency of retraining. Different digital modulation schemes have been considered in distinct scenarios, and the simulation results show that the CNN-AMC can outperform the feature-based method, and obtain a closer approximation to the optimal ML-AMC. Besides, CNN-AMCs have the certain robustness to estimation error on carrier phase offset and SNR. With parallel computation, the deep-learning-based approach is about 40 to 1700 times faster than the ML-AMC regarding inference speed.

关键词： automatic modulation classification convolution neural network deep learning two-step training

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automatic modulation classification Based on Deep Residual Networks With Multimodal Information

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2021年第1期7卷 21-33页

作者： Qi, Peihan Zhou, Xiaoyu Zheng, Shilian Li, Zan Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China 011 Res Ctr Sci & Technol Commun Informat Secur Control Lab Jiaxing 314033 Peoples R China

automatic modulation classification (AMC) is becoming increasingly important for its fundamental role in dynamic spectrum access, which can support 5G wireless communications to refarm the spectrum resource with low utilization. In order to achieve a better classification performance, several AMC methods based on prototype and variant of convolutional neural networks (CNNs) have been proposed. However, most existing AMC methods based on CNNs only use monomodal information from either time domain or frequency domain. The complementary processing gain, which can be obatianed by fusing multimodal information from multiple transformation domain together, is neglected. To address the issue, we exploit a waveform-spectrum multimodal fusion (WSMF) method to realize AMC based on deep residual networks (Resnet). After extracting features from multimodal information using Resnet, we adopt a feature fusion strategy to merge multimodal features of signals to obtain more discriminating features. Simulation results demonstrate the superior performance of our proposed WSMF method compared with traditional CNNs based AMC method using single modality information. Our proposed method can distinguish among sixteen modulation signals, and it works well even for higher-order digital modulation types like 256QAM and 1024QAM.

关键词： automatic modulation classification 5G wireless communications convolutional neural network multimodal information feature fusion

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automatic modulation classification with deep learning-based frequency selection filters

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ELECTRONICS LETTERS 2020年第21期56卷 1144-1145页

作者： Liu, Weisong Huang, Zhitao Li, Xueqiong Wang, Xiang Li, Baoguo Natl Univ Def Technol State Key Lab Complex Electromagnet Environm Effe Changsha Peoples R China

automatic modulation classification (AMC) is an important and challenging task that aims to discriminate modulation formats of received signals, such as military communications, cognitive radio and spectrum management. With the development of deep learning techniques, research in AMC has gained promising results because of its powerful representation and classification abilities. In this Letter, the authors present a new network architecture that combines a frequency selection module and a convolutional neural network (CNN). This scheme not only processes raw signal data with carriers to increase the in-band signal-to-noise ratio but also converge faster than traditional CNN. Experiments demonstrate the effectiveness and efficiency of the proposed model.

关键词： neural nets signal classification cognitive radio modulation learning (artificial intelligence) military communication modulation formats received signals cognitive radio spectrum management deep learning techniques AMC powerful representation classification abilities frequency selection module raw signal data in-band signal-to-noise ratio automatic modulation classification deep learning-based frequency selection filters important task

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