Automatic Modulation Classification Of Digital Communication Signals
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Automatic Modulation Classification Of Digital Communication Signals
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Author : Qijun Xu
language : en
Publisher:
Release Date : 2001
Automatic Modulation Classification Of Digital Communication Signals written by Qijun Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Automatic Modulation Classification
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Author : Zhechen Zhu
language : en
Publisher: John Wiley & Sons
Release Date : 2015-02-16
Automatic Modulation Classification written by Zhechen Zhu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-16 with Technology & Engineering categories.
Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability. This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algorithms, as well as guidance on state-of-the-art classification designs with specific military and civilian applications in mind. Key Features: Provides an important collection of AMC algorithms in five major categories, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, machine learning assisted classifiers and blind modulation classifiers Lists detailed implementation for each algorithm based on a unified theoretical background and a comprehensive theoretical and numerical performance comparison Gives clear guidance for the design of specific automatic modulation classifiers for different practical applications in both civilian and military communication systems Includes a MATLAB toolbox on a companion website offering the implementation of a selection of methods discussed in the book
Analysis Of Decision Theoretic Modulation Classification Methods For Digital Communication Signals
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Author :
language : en
Publisher:
Release Date : 2004
Analysis Of Decision Theoretic Modulation Classification Methods For Digital Communication Signals written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Automated modulation classification is a fundamental requirement for electronic support measures. Existing automated classifiers use a variety of different modulation recognition techniques. This paper reviews the category of decision-theoretic approaches and discusses the relationships between decision-theoretic methods and other statistical modulation classification methods.
Automatic Modulation Recognition Of Communication Signals
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Author : Elsayed Azzouz
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Automatic Modulation Recognition Of Communication Signals written by Elsayed Azzouz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-17 with Science categories.
Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a critical review of automatic modulation recognition. This includes techniques for recognising digitally modulated signals. The book also gives comprehensive treatment of using artificial neural networks for recognising modulation types. Automatic Modulation Recognition of Communications Signals is the first comprehensive book on automatic modulation recognition. It is essential reading for researchers and practising engineers in the field. It is also a valuable text for an advanced course on the subject.
Machine Learning Techniques For Automatic Modulation Classification
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Author :
language : en
Publisher:
Release Date : 2017
Machine Learning Techniques For Automatic Modulation Classification written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Electronic books categories.
Automatic Modulation Classification (AMC) is concerned with automatically identifying the modulation type of communication signals. AMC is the fundamental component of signal recovery systems and is also employed in jammers in military electronic warfare. Its potential to solve serious issues such as spectral congestion encourages one to develop systems that can quickly and efficiently identify the modulation class of intercepted signals. This thesis is dedicated to classifying digital signals into one of the eight classes: 8-Pulse shift keying (8-PSK), Binary pulse shift keying (BPSK), Continuous-phase frequency-shift keying (CPFSK), Gaussian frequency-shift keying (GFSK), 4-Pulse amplitude modulation (4-PAM), 16-Quadrature amplitude modulation (16-QAM), 64-QAM and Quadrature phase shift keying (QPSK). The classification task has been accomplished via machine learning techniques. The objective is to study and compare various classifiers for identifying the class of a digitally modulated signal. Machine learning classifiers k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forests and Artificial Neural Networks were implemented. The classifiers were trained to perform the task of AMC and their performances were examined and compared with each other. Manual feature engineering was done to train the classifiers. An alternate solution to feature engineering was presented in the form of feature learning from raw data.
Automatic Modulation Recognition Of Communication Signals
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Author : Elsayed Azzouz
language : en
Publisher: Springer
Release Date : 2013-01-17
Automatic Modulation Recognition Of Communication Signals written by Elsayed Azzouz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-17 with Science categories.
Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a critical review of automatic modulation recognition. This includes techniques for recognising digitally modulated signals. The book also gives comprehensive treatment of using artificial neural networks for recognising modulation types. Automatic Modulation Recognition of Communications Signals is the first comprehensive book on automatic modulation recognition. It is essential reading for researchers and practising engineers in the field. It is also a valuable text for an advanced course on the subject.
Learning Based Automatic Modulation Classification
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Author : Ameen Elsiddig Abdelmutalab
language : en
Publisher:
Release Date : 2015
Learning Based Automatic Modulation Classification written by Ameen Elsiddig Abdelmutalab and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Cognitive radio networks categories.
"Automatic Modulation Classification (AMC) is a new technology implemented into communication receivers to automatically determine the modulation type of a received signal. One of the main applications of AMC is in adaptive modulation systems, where the modulation scheme is changed dynamically according to the changes in the wireless channel. However, this requires the receiver to be continuously informed about the modulation type, resulting in a loss of bandwidth efficiency. The existence of smart receivers that can automatically recognize the modulation type improves the utilization of available bandwidth. In this thesis, a new AMC algorithm based on a Hierarchical Polynomial Classifier structure is introduced. The proposed system is tested for classifying BPSK, QPSK, 8-PSK, 16-QAM, 64-QAM and 256-QAM modulation types in Additive White Gaussian Noise (AWGN) and flat fading environments. Moreover, the system uses High Order Cumulants (HOCs) of the received signal as discriminant features to distinguish between the different digital modulation types. The proposed system divides the overall modulation classification problem into hierarchical binary sub-classification tasks. In each binary sub-classification, the HOC inputs are expanded into a higher dimensional space in which the two classes are linearly separable. Furthermore, the signal-to-noise ratio of the received signal is estimated and fed to the proposed classifier to improve the classification accuracy. Another modification is added to the proposed system by using stepwise regression optimization for feature selection. Hence, the input features to the classifier are chosen to give the highest classification accuracy while maintaining a minimum number of possible features. Extensive simulations showed that a significant improvement in classification accuracy and reduction in the system complexity is obtained compared to the previously suggested systems in the literature."--Abstract.
Advanced Techniques For Automatic Classification Of Digitally Modulated Communication Signals
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Author : Liang Hong
language : en
Publisher:
Release Date : 2002
Advanced Techniques For Automatic Classification Of Digitally Modulated Communication Signals written by Liang Hong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Automatic classification categories.
Automatic classification of the modulation type of a received signal is an indispensable step in many communication systems. It provides necessary information for data demodulation, information extraction and signal exploitation. In recent years, modulation classification is one of the most promising research areas and has found a variety of military and commercial applications. In this research, a set of advanced techniques are proposed and investigated for automatic classification of digitally modulated signals. For inter-class classification at moderate to high signal-to-noise ratio (SNR) environment, we propose to use the wavelet transform to discriminate among quadrature amplitude modulation (QAM), phase shift keying (PSK) and frequency shift keying (FSK) signals. The wavelet transform can effectively extract the transient characteristics from different modulation types for simple identification. Then we focus on intra-class classification between binary PSK (BPSK) and quadrature PSK (QPSK) at moderate to low SNR environment. At low SNR environment, the performance of the classifier using wavelet transform degrades quickly, because the extracted features are masked by the noise and difficult to recognize. On the other hand, the decision theoretic technique that is based on likelihood function works well at all SNR environment. We developed the composite hypothesis tests to identify between BPSK and unbalanced QPSK signals, and to discriminate between BPSK and QPSK signals without prior knowledge of signal level. Furthermore, we applied the composite hypothesis testing approach to operate on antenna array outputs for the purpose of increasing the accuracy of BPSK and QPSK identification when only a short data record is available. The above decision theoretic based classifiers require some unknown parameters that must be estimated before the classification decision can be made. Hence, Cramer-Rao lower bound is derived to evaluate the performance of the proposed estimators in obtaining the unknown parameters.
Classification Of Digital Communication Signal Modulation Schemes In Multipath Environments Using Higher Order Statistics
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Author : Meena Sreekantamurthy
language : en
Publisher:
Release Date : 2015
Classification Of Digital Communication Signal Modulation Schemes In Multipath Environments Using Higher Order Statistics written by Meena Sreekantamurthy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Digital communications categories.
Digital Communications 2
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Author : Mylène Pischella
language : en
Publisher: John Wiley & Sons
Release Date : 2015-10-12
Digital Communications 2 written by Mylène Pischella and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-12 with Technology & Engineering categories.
This second volume covers the following blocks in the chain of communication: the modulation baseband and transposed band, synchronization and channel estimation as well as detection. Variants of these blocks, the multicarrier modulation and coded modulations are used in current systems or future.