Download or read book Automatic Modulation Classification written by Zhechen Zhu and published by John Wiley & Sons. This book was released on 2015-02-16 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Learning based Automatic Modulation Classification written by Ameen Elsiddig Abdelmutalab and published by . This book was released on 2015 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.

Download or read book AMC2N Automatic Modulation Classification Using Feature Clustering Based Two Lane Capsule Networks written by Dhamyaa H. Al‑Nuaimi and published by Infinite Study. This book was released on with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust‑CNN (R‑CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F‑score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

Download or read book Automatic Modulation Recognition of Communication Signals written by Elsayed Azzouz and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Modulation Classification Using Deep Learning Based Models written by Hathal Alwageed and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book 2021 IEEE 18th India Council International Conference INDICON written by IEEE Staff and published by . This book was released on 2021-12-19 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracks for the Event AI and Data Science Robotics and Cybernetics Devices, Circuits and Systems Control and Instrumentation VLSI and Nanotechnology Power, Energy and Power Electronics Computational Biology and Biomedical Informatics Antenna and Microwave Techniques Communications Networks, IoT Computer Architecture and Embedded Systems Signal Processing and Multimedia Security and Privacy

Download or read book Low Complexity Algorithms for Automatic Modulation Classification Based on Machine Learning written by Mohanad Abu-Romoh and published by . This book was released on 2019 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we discuss two different approaches to modulation classifiers: we first propose a hybrid method for automatic modulation classification that lies in the intersection between likelihood-based and feature-based classifiers. Specifically, the proposed method relies on statistical moments along with a maximum likelihood engine. We show that the proposed method offers a good trade-off between classification accuracy and complexity relative to the Maximum Likelihood (ML) classifier. Furthermore, our classifier outperforms state-of-the-art machine learning classifiers, such as genetic programming-based K-nearest neighbor (GP-KNN) classifiers, the linear support vector machine (LSVM) classifier and the fold-based Kolmogorov-Smirnov (FB-KS) algorithm. In the second part of thesis, we propose a distribution-based modulation classifier using neural networks. We show that our proposed classifier outperforms state-of-the-art classifiers, even when the pool of possible candidate modulations are unknown to the receiver.

Download or read book Deep Learning and Polar Transformation to Achieve a Novel Adaptive Automatic Modulation Classification Framework written by Pejman Ghasemzadeh and published by . This book was released on 2020 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic modulation classification (AMC) is an approach that can be leveraged to identify an observed signal's most likely employed modulation scheme without any a priori knowledge of the intercepted signal. Of the three primary approaches proposed in literature, which are likelihood-based, distribution test-based, and feature-based (FB), the latter is considered to be the most promising approach for real-world implementations due to its favorable computational complexity and classification accuracy. FB AMC is comprised of two stages: feature extraction and labeling. In this thesis, we enhance the FB approach in both stages. In the feature extraction stage, we propose a new architecture in which it first removes the bias issue for the estimator of fourth-order cumulants, then extracts polar-transformed information of the received IQ waveform's samples, and finally forms a unique dataset to be used in the labeling stage. The labeling stage utilizes a deep learning architecture. Furthermore, we propose a new approach to increasing the classification accuracy in low signal-to-noise ratio conditions by employing a deep belief network platform in addition to the spiking neural network platform to overcome computational complexity concerns associated with deep learning architecture. In the process of evaluating the contributions, we first study each individual FB AMC classifier to derive the respective upper and lower performance bounds. We then propose an adaptive framework that is built upon and developed around these findings. This framework aims to efficiently classify the received signal's modulation scheme by intelligently switching between these different FB classifiers to achieve an optimal balance between classification accuracy and computational complexity for any observed channel conditions derived from the main receiver's equalizer. This framework also provides flexibility in deploying FB AMC classifiers in various environments. We conduct a performance analysis using this framework in which we employ the standard RadioML dataset to achieve a realistic evaluation. Numerical results indicate a notably higher classification accuracy by 16.02% on average when the deep belief network is employed, whereas the spiking neural network requires significantly less computational complexity by 34.31% to label the modulation scheme compared to the other platforms. Moreover, the analysis of employing framework exhibits higher efficiency versus employing an individual FB AMC classifier.

Download or read book Mobile Multimedia Communications written by Jinbo Xiong and published by Springer Nature. This book was released on 2021-11-02 with total page 899 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Mobile Multimedia Communications, Mobimedia 2021, held in July 2021. Due to COVID-19 pandemic the conference was held virtually. The 66 revised full papers presented were carefully selected from 166 submissions. The papers are organized in topical sections as follows: Internet of Things and Wireless Communications Communication; Strategy Optimization and Task Scheduling Oral Presentations; Privacy Computing Technology; Cyberspace Security and Access control; Neural Networks and Feature Learning Task Classification and Prediction; Object Recognition and Detection.

Download or read book Machine Learning Techniques for Automatic Modulation Classification written by and published by . This book was released on 2017 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning for Future Wireless Communications written by Fa-Long Luo and published by John Wiley & Sons. This book was released on 2020-02-10 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Engineering Applications of Neural Networks written by John Macintyre and published by Springer. This book was released on 2019-05-14 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Engineering Applications of Neural Networks, EANN 2019, held in Xersonisos, Crete, Greece, in May 2019. The 35 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on AI in energy management - industrial applications; biomedical - bioinformatics modeling; classification - learning; deep learning; deep learning - convolutional ANN; fuzzy - vulnerability - navigation modeling; machine learning modeling - optimization; ML - DL financial modeling; security - anomaly detection; 1st PEINT workshop.

Download or read book Sequential Decision Making for Automatic Modulation Classification written by Nicholas W. Waltman and published by . This book was released on 2019 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, an algorithm is introduced to use deep learning to perform automatic modulation classification in a sequential manner. At each time step, a decision is made whether to request more data or to return a classification decision. This allows for the data, and therefore time, needed to make a decision to be minimized while maintaining a high degree of accuracy. The performance of this algorithm is studied using multiple strategies and lists of modulations to be classified.

Download or read book 5G and Beyond Wireless Systems written by Manish Mandloi and published by Springer Nature. This book was released on 2020-08-11 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the fundamental concepts, recent advancements, and opportunities for future research in various key enabling technologies in next-generation wireless communications. The book serves as a comprehensive source of information in all areas of wireless communications with a particular emphasis on physical (PHY) layer techniques related to 5G wireless systems and beyond. In particular, this book focuses on different emerging techniques that can be adopted in 5G wireless networks. Some of those techniques include massive-MIMO, mm-Wave communications, spectrum sharing, device-to-device (D2D) and vehicular to anything (V2X) communications, radio-frequency (RF) based energy harvesting, and NOMA. Subsequent chapters cover the fundamentals and PHY layer design aspects of different techniques that can be useful for the readers to get familiar with the emerging technologies and their applications.

Download or read book Automatic Speech Recognition written by Dong Yu and published by Springer. This book was released on 2014-11-11 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

Download or read book An Introduction to Machine Learning written by Gopinath Rebala and published by Springer. This book was released on 2019-05-07 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.

Download or read book Deep Learning Based Approaches for Sentiment Analysis written by Basant Agarwal and published by Springer Nature. This book was released on 2020-01-24 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.