Author: Elsayed Azzouz Publisher: Springer Science & Business Media ISBN: 1475724691 Category : Science Languages : en Pages : 233
Book Description
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.
Author: Elsayed Azzouz Publisher: Springer ISBN: 9781475724707 Category : Science Languages : en Pages : 218
Book Description
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.
Author: Elsayed Azzouz Publisher: Springer Science & Business Media ISBN: 9780792397960 Category : Science Languages : en Pages : 242
Book Description
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.
Author: Zhechen Zhu Publisher: John Wiley & Sons ISBN: 1118906497 Category : Technology & Engineering Languages : en Pages : 204
Book Description
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
Author: Tejashri Kuber Publisher: ISBN: Category : Modulators (Electronics) Languages : en Pages : 35
Book Description
An Automatic Modulation Recognition (AMR) process using the Discrete Wavelet Transform (DWT) is presented in this work. The AMR algorithm involves the use of wavelet domain signal templates derived from digitally modulated signals that are used to transmit binary data. The signal templates, locally stored in a receiver, are cross-correlated with the incoming noisy, received signal after it has been transformed into the wavelet domain. The signal template that yields the largest cross-correlation value determines the type of digital modulation that had been employed at the transmitter. The specific binary-valued digital modulation schemes considered in this work include BASK, BFSK and BPSK. The discrete wavelet used for the creation of the signal templates is the Haar, or Daubechies 1, wavelet. Extensive computer simulations have been performed to evaluate the modulation recognition performance of the AMR algorithm as a function of channel SNR. It has been determined that the rate of correct classification for BASK signals is 68% for an SNR = 5 dB and 90% for an SNR = 10 dB SNR. The rate of correct classification for BFSK signals is 71% for an SNR = 5 dB and 92% for an SNR = 10 dB. Correct classification of BPSK signals is 71% for an SNR = 5 dB and 92% for an SNR = 10 dB. In comparison to alternative AMR methods reported in the literature, the AMR algorithm developed in this study produces reliable results even at relatively low values of SNR which are characteristic of realistic communications channels.
Author: Fa-Long Luo Publisher: John Wiley & Sons ISBN: 1119562252 Category : Technology & Engineering Languages : en Pages : 490
Book Description
A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
Author: Elsayed Azzouz
Author: Elsayed Azzouz
Author: Elsayed Azzouz
Author: Zhechen Zhu
Author: Xiang Nan Liu
Author: IEEE Staff
Author: Tejashri Kuber
Author: Qijun Xu
Author: Fa-Long Luo
Author: Zhechen Zhu