Neural Networks in Telecommunications
Neural Networks in Telecommunications

Author: Ben Yuhas

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 374

ISBN-13: 1461527341

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Neural Networks in Telecommunications consists of a carefully edited collection of chapters that provides an overview of a wide range of telecommunications tasks being addressed with neural networks. These tasks range from the design and control of the underlying transport network to the filtering, interpretation and manipulation of the transported media. The chapters focus on specific applications, describe specific solutions and demonstrate the benefits that neural networks can provide. By doing this, the authors demonstrate that neural networks should be another tool in the telecommunications engineer's toolbox. Neural networks offer the computational power of nonlinear techniques, while providing a natural path to efficient massively-parallel hardware implementations. In addition, the ability of neural networks to learn allows them to be used on problems where straightforward heuristic or rule-based solutions do not exist. Together these capabilities mean that neural networks offer unique solutions to problems in telecommunications. For engineers and managers in telecommunications, Neural Networks in Telecommunications provides a single point of access to the work being done by leading researchers in this field, and furnishes an in-depth description of neural network applications.

Research Anthology on Artificial Neural Network Applications
Research Anthology on Artificial Neural Network Applications

Author: Management Association, Information Resources

Publisher: IGI Global

Published: 2021-07-16

Total Pages: 1575

ISBN-13: 1668424096

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Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.

Machine Learning for Future Wireless Communications
Machine Learning for Future Wireless Communications

Author: Fa-Long Luo

Publisher: John Wiley & Sons

Published: 2020-02-10

Total Pages: 490

ISBN-13: 1119562252

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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.

Machine Learning and Wireless Communications
Machine Learning and Wireless Communications

Author: Yonina C. Eldar

Publisher: Cambridge University Press

Published: 2022-06-30

Total Pages: 560

ISBN-13: 1108967736

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How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications – an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.

Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications
Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications

Author: Joshua Alspector

Publisher: Psychology Press

Published: 2013-06-17

Total Pages: 321

ISBN-13: 1134786107

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The world is witnessing the rapid evolution of its own nervous system by an unparalleled growth in communication technology. Like the evolution of the nervous systems in animals, this growth is being driven by a survival-of-the-fittest-mechanism. In telecommunications, the entities that fuel this growth are companies and nations who compete with each other. Companies with superior information systems can outrun and outsmart others because they serve their customers better. On the threshold of an explosion in the variety, speed and usefulness of telecommunication networks, neural network researchers can make important contributions to this emerging new telecommunications infrastructure. The first International Workshop on Applications of Neural Networks to Telecommunications (IWANNT) was planned in response to the telecommunications industry's needs for new adaptive technologies. This workshop featured 50 talks and posters that were selected by an organizing committee of experts in both telecommunications and neural networks. These proceedings will also be available on-line in an electronic format providing multimedia figures, cross-referencing, and annotation.

Applications of Neural Networks in Electromagnetics
Applications of Neural Networks in Electromagnetics

Author: Christos Christodoulou

Publisher: Artech House Publishers

Published: 2001

Total Pages: 544

ISBN-13:

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The high-speed capabilities and learning abilities of neural networks can be applied to quickly solving numerous complex optimization problems in electromagnetics, and this book shows you how. Even if you have no background in neural networks, this book helps you understand the basics of each main network architecture in use today, including its strengths and limitations. Moreover, it gives you the knowledge you need to identify situations when the use of neural networks is the best problem-solving option.

Applications of Artificial Neural Networks for Nonlinear Data
Applications of Artificial Neural Networks for Nonlinear Data

Author: Patel, Hiral Ashil

Publisher: IGI Global

Published: 2020-09-25

Total Pages: 315

ISBN-13: 1799840433

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Processing information and analyzing data efficiently and effectively is crucial for any company that wishes to stay competitive in its respective market. Nonlinear data presents new challenges to organizations, however, due to its complexity and unpredictability. The only technology that can properly handle this form of data is artificial neural networks. These modeling systems present a high level of benefits in analyzing complex data in a proficient manner, yet considerable research on the specific applications of these intelligent components is significantly deficient. Applications of Artificial Neural Networks for Nonlinear Data is a collection of innovative research on the contemporary nature of artificial neural networks and their specific implementations within data analysis. While highlighting topics including propagation functions, optimization techniques, and learning methodologies, this book is ideally designed for researchers, statisticians, academicians, developers, scientists, practitioners, students, and educators seeking current research on the use of artificial neural networks in diagnosing and solving nonparametric problems.

Neural Network Parallel Computing
Neural Network Parallel Computing

Author: Yoshiyasu Takefuji

Publisher: Springer Science & Business Media

Published: 1992-01-31

Total Pages: 254

ISBN-13: 9780792391906

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Neural Network Parallel Computing is the first book available to the professional market on neural network computing for optimization problems. This introductory book is not only for the novice reader, but for experts in a variety of areas including parallel computing, neural network computing, computer science, communications, graph theory, computer aided design for VLSI circuits, molecular biology, management science, and operations research. The goal of the book is to facilitate an understanding as to the uses of neural network models in real-world applications. Neural Network Parallel Computing presents a major breakthrough in science and a variety of engineering fields. The computational power of neural network computing is demonstrated by solving numerous problems such as N-queen, crossbar switch scheduling, four-coloring and k-colorability, graph planarization and channel routing, RNA secondary structure prediction, knight's tour, spare allocation, sorting and searching, and tiling. Neural Network Parallel Computing is an excellent reference for researchers in all areas covered by the book. Furthermore, the text may be used in a senior or graduate level course on the topic.