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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.
This book is the study guide and textbook for the TCO Certified Telecommunications Network Specialist (CTNS) Certification, conforming to the lessons in the eight CTNS courses and their exams: 2241 Introduction to Broadband Converged IP Telecom 2206 Wireless Telecommunications 2221 Fundamentals of Voice over IP 2201 The PSTN 2212 OSI Layers and Protocol Stacks 2211 LANs, VLANs, Wireless and Optical Ethernet 2213 IP Addresses, Packets and Routers 2214 MPLS and Carrier Networks The selection of material, its order, timing, and explanations are field-tested to deliver the core knowledge set for today’s telecommunications. The courses deliver a solid foundation of knowledge in broadband, telecom, datacom and networking: the fundamentals, technologies, jargon and buzzwords, standard practices and most importantly, the underlying ideas, and how it all fits together… with TCO Certification to prove it! The first four CTNS courses are on telecommunications, beginning with Introduction to Broadband Converged IP Telecom, an introduction and first pass through all of the topics; followed by Wireless Telecommunications, then Introduction to Voice over IP, and The PSTN. The second half of CTNS is four courses focusing on the three main enabling technologies for the modern telecom network: Ethernet, IP and MPLS. We begin with the OSI model and its Layers to establish a framework for understanding what each does and how they work together... and all the other things that have to be done. This book is intended to enhance your learning and retention while taking the online courses. It is also useful as a day-to-day reference handbook and glossary. Our goal is to explain the big picture, the jargon and buzzwords, and put in place a very solid base of telecom knowledge spanning fundamentals to the latest technologies and how they are deployed – in plain English. Let's get started!
Telecommunications current and emerging, wired and wireless--is covered in-depth here with the broadest, deepest, most up-to-date telecom overview on the market by one of the field's leading trainers. Whether readers are new to telecommunications and IT or simply want an understandable, comprehensive review of the state-of-the-art technology, this book is for them.
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.
Have you ever wanted to know how modern digital communications systems work? Find out with this step-by-step guide to building a complete digital radio that includes every element of a typical, real-world communication system. Chapter by chapter, you will create a MATLAB realization of the various pieces of the system, exploring the key ideas along the way, as well as analyzing and assessing the performance of each component. Then, in the final chapters, you will discover how all the parts fit together and interact as you build the complete receiver. In addition to coverage of crucial issues, such as timing, carrier recovery and equalization, the text contains over 400 practical exercises, providing invaluable preparation for industry, where wireless communications and software radio are becoming increasingly important. A variety of extra resources are also provided online, including lecture slides and a solutions manual for instructors.
Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data. Applications of machine learning in wireless communications have been receiving a lot of attention, especially in the era of big data and IoT, where data mining and data analysis technologies are effective approaches to solving wireless system evaluation and design issues.
The understanding of communication refers to canonical schemes from technologies to decisions on where, how, and why the semic act gains or is at risk; to hypotheses and limits; and to normal and unconventional exchanges of senses, despite the confrontations between codes, coding, and decoding. In this book, communication is defined as concept, skill, potential, behavior, mechanism, category of exchange, phenomenon, tool, and variable. This sophisticated view differs from previous studies and assumes the multiple systems of systems and meanings generated by various fieldworks that require/reclaim their primacy over communication. Basic Communication and Assessment Prerequisites for the New Normal of Education discusses the rivalry paradigms, ambiguities, new meanings, and mechanisms of the crossroad between communication and assessment. This book makes an inventory of developments in the area as well as analyzes new edumetrics and psychometrics and inserts new best practices. This involves creating new conversational networks of global best practices and metaparadigms in order to solve current disparities and unsolved problems from the fieldwork. Covering topics such as chronic conditions, online educational environments, and self-assessment competencies, this text is ideal for teachers, parents, students, trainers, decision makers, researchers, and academicians.
Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. What You Will Learn Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with PythonSet up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenariosDevelop solutions for commercial-grade IoT or IIoT projectsImplement case studies in machine learning with IoT from scratch Who This Book Is For Raspberry Pi and Arduino enthusiasts and data science and machine learning professionals.