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

ISBN-13: 9780367575243 / Angielski / Miękka / 2020 / 528 str.

John D. Matyjas; Fei Hu; Sunil Kumar

Opis

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

Spis treści

Directional Antennas. Introduction: Switched/Steered Directional Antennas for Networking. Design and Optimization of Wideband Log-Periodic Dipole Arrays under Requirements for High Gain, High Front-to-Back Ratio, Optimal Gain Flatness, and Low Side Lobe Level: The Application of Invasive Weed Optimization. Directional MAC. Discovery Strategies for a Directional Wake-Up Radio in Mobile Networks. Medium Access Control for Wireless Networks with Directional Antennas. IEEE 802.11ad Wireless Local Area Network and Its MAC Performance. Millimeter Wave. MAC Layer Protocols for Wireless Networks with Directional Antennas. Millimeter-Wave Wireless Networks: A Medium Access Control Perspective. Directional MAC Protocols for 60 GHz Millimeter Wave WLANs. Performance Improvements of mm-Wave Wireless Personal Area Networks Using Beamforming and Beamswitching. Applications of Directional Networking in Military Systems. MIMO. Design and Implementation of Directional Antenna-Based LOS-MIMO System for Gbps Wireless Backhaul. MIMO and Cooperation in Cognitive Radio-Based Wireless Networks: State-of-the-Art and Perspectives. Advanced Topics. Directional Antennas and Beamforming for Cognitive Radio-Based Wireless Networks. Multicast Algorithm Design for Energy-Constrained Multihop Wireless Networks with Directional Antennas. Connectivity of Large-Scale Wireless Networks with Directional Antennas. Bounds on the Lifetime of Wireless Sensor Networks with Lossy Links and Directional Antennas. Applications. Utilization of Directional Antennas in Flying Ad Hoc Networks: Challenges and Design Guidelines. Military Networks Enabled by Directional Antennas. Military Applications of Directional Mesh Networking. Collaborative and Opportunistic Content Dissemination via Directional Antennas. The Evolution of Directional Networking Systems Architecture.

O autorze

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.