Daniel Chew - Wireless Coexistence: Standards, Challenges, and Intelligent Solutions

1. Chapter 5
2. Chapter 6
3. Chapter 8
4. Chapter 9
5. Chapter 10

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 5
6. Chapter 6
7. Chapter 7
8. Chapter 8
9. Chapter 9
10. Chapter 10

IEEE Press
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Piscataway, NJ 08854

IEEE Press Editorial Board
Ekram Hossain, Editor in Chief

Daniel Chew

Johns Hopkins University Applied Physics Lab
Darnestown, MD, USA

Andrew L. Adams

Johns Hopkins University Applied Physics Lab
Darnestown, MD, USA

Jason Uher

Johns Hopkins University Applied Physics Lab
Baltimore, MD, USA

Copyright 2021 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.

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Library of Congress CataloginginPublication Data:

Names: Chew, Daniel (Electrical engineer), author. | Adams, Andrew L., author. | Uher, Jason, author.
Title: Wireless coexistence : standards, challenges, and intelligent solutions / Daniel Chew, Andrew L. Adams, Jason Uher.
Description: Hoboken, New Jersey : Wiley, [2021] | Includes bibliographical references and index.
Identifiers: LCCN 2021003859 (print) | LCCN 2021003860 (ebook) | ISBN 9781119584186 (hardback) | ISBN 9781119584223 (adobe pdf) | ISBN 9781119584124 (epub)
Subjects: LCSH: Wireless communication systems.
Classification: LCC TK5103.2 .C45375 2021 (print) | LCC TK5103.2 (ebook) | DDC 384.54/524dc23
LC record available at https://lccn.loc.gov/2021003859
LC ebook record available at https://lccn.loc.gov/2021003860

Cover Design: Wiley
Cover Image: Elnur/Shutterstock

Daniel Chew is a member of the Senior Professional Staff at The Johns Hopkins University Applied Physics Laboratory and teaches in the Engineering for Professionals program at Johns Hopkins University. He received a Bachelor's in Electrical Engineering from the University of Delaware, in 1998, and a Masters of Science in Electrical and Computer Engineering from Johns Hopkins University, in 2008. He has been a Lecturer at Johns Hopkins University since 2011. He is the author of the book The Wireless Internet of Things: A Guide to the Lower Layers (WileyIEEE, 2018). His professional interests include RadioFrequency Machine Learning, Digital Signal Processing, the Internet of Things, and SoftwareDefined Radios.

Andrew Adams holds a B.S.E.E. from the University of Maryland at College Park, and an M.S.E.E. from Johns Hopkins University. He is currently a member of the Senior Professional Staff at the Johns Hopkins University Applied Physics Laboratory, and is a U.S. Navy veteran. Andrew also teaches Software Defined Radio at Johns Hopkins University. His professional experience includes Machine Learning for Signal Processing, Wireless Communications, Digital Signal Processing, and RadioFrequency (RF) Hardware Design. His research interests include Intelligent Wireless Systems and Neuromorphic Computing

Jason Uher received his PhD from the University of Nebraska, in 2012, joining the Senior Professional Staff at The Johns Hopkins University Applied Physics Laboratory thereafter. Since then, has applied cutting edge research to the nations critical problems across multiple networking domains. His research includes topics such as PHY/MAC layer security and anonymity, software defined radio processing techniques, system complexity analysis, and wireless networking protocol evaluation.

Wireless communications has evolved considerably since we began practicing in the mid 1990s. Many of us started on Digital Signal Processors from Analog Devices, Texas Instruments, or Motorola, where we implemented basic signal processing functions in C using fixedpoint math. If realtime processing requirements were ultimately not met, we downgraded specific functions to assembly language as needed. At the time, those were considered cuttingedge devices and standard implementation practices. Coming out of college, even with a Masters in Electrical Engineering, we knew just enough to get started, and had to learn tricks of the trade and best practices, on the job, from more experienced Communications engineers. We are sure many have a similar background.

Fast forward to today, and the landscape is very different. The widespread availability of Software Defined Radio (SDR) hardware and software development environments have allowed students, early career engineers, practitioners in other disciplines, and maybe more importantly entrepreneurs to implement and deploy wireless capabilities much, much faster than was possible 20 years ago. Furthermore, advanced highlevel tool chains allow us to implement signal processing functionality on Field Programmable Gate Arrays (FPGAs) and Graphical Processing Units (GPUs), with limited experience on either platform; something unheard of even 10 years ago. More recently, GPUs have been targeted for Machine Learning (ML) and other highly parallelizable computing operations. ML is increasingly playing a larger role in wireless communications applications, which we investigate in this text.