Wireless Network Performance Enhancement via Directional Antennas Models, Protocols, and Systems

Directional antenna technologies have made significant advancements in the last decade. These advances have opened the door to many exciting new design opportunities for wireless networks to enhance quality of service (QoS), performance, and network capacity. In this book, experts from around the world present the latest research and development in wireless networks with directional antennas. Their contributed chapters provide detailed coverage of the models, algorithms, protocols, and applications of wireless networks with various types of directional antennas operating at different frequency bands.

Wireless Network Performance Enhancement via Directional Antennas: Models, Protocols, and Systems

identifies several interesting research problems in this important field, providing an opportunity to learn about solid solutions to these issues. It also looks at a number of practical hardware designs for the deployment of next-generation antennas, as well as efficient network protocols for exploitation of directional communications.

The book is organized into six sections:

• Directional Antennas
• – covers the hardware design of different types of antennas











• Directional MAC
• – focuses on the principles of designing medium access control (MAC) protocols for directional networks











• Millimeter Wave
• – explores different design aspects of millimeter wave (mm-Wave) systems, which operate in higher-frequency bands (such as 60 GHz)











• MIMO
• – explains how to establish a multiple-input, multiple-output (MIMO) antenna system and describes how it operates in a cognitive radio network











• Advanced Topics
• – looks at additional topics such as beamforming in cognitive radio networks, multicast algorithm development, network topology management for connectivity, and sensor network lifetime issues











• Applications
• – illustrates some important applications, such as military networks and airborne networking, that benefit from directional networking designs

With this book, researchers and engineers will be well-equipped to advance the research and development in this important field. If you’re new to this field, you will find this book to be a valuable reference on basic directional networking principles, engineering design, and challenges.

John D. Matyjas earned his PhD in electrical engineering from State University of New York at Buffalo in 2004. Currently, he is serving as the Connectivity & Dissemination Core Technical Competency Lead at the Air Force Research Laboratory (AFRL) in Rome, New York. His research interests include dynamic multiple-access communications and networking, spectrum mutability, statistical signal processing and optimization, and neural networks. He served on the IEEE Transactions on Wireless Communications Editorial Advisory Board from 2012-2014. Dr. Matyjas is the recipient of the 2012 IEEE R1 Technology Innovation Award, 2012 AFRL Harry Davis Award for "Excellence in Basic Research," and the 2010 IEEE International Communications Conference Best Paper Award. He is an IEEE senior member, chair of the IEEE Mohawk Valley Signal Processing Society, and member of Tau Beta Pi and Eta Kappa Nu.

Fei Hu

is a professor in the Department of Electrical and Computer Engineering at The University of Alabama, Tuscaloosa. He earned his PhD at Tongji University (Shanghai, China) in the field of signal processing (1999), and at Clarkson University (New York) in electrical and computer engineering (2002). He has published over 200 journal/conference papers and books. Dr. Hu’s research has been supported by U.S. National Science Foundation, Cisco, Sprint, and other sources. His research expertise can be summarized as 3S: Security, Signals, Sensors: (1) Security: How to overcome different cyber attacks in a complex wireless or wired network. Dr. Hu’s recent research focuses on cyber-physical system security and medical security issues. (2) Signals: This mainly refers to intelligent signal processing

, that is, using machine learning algorithms to process sensing signals in a smart way in order to extract patterns (i.e., pattern recognition). (3) Sensors: This includes microsensor design and wireless sensor networking issues.