The motivation for developing this synthesis lecture was to provide a tutorial on queuing and trunking, with extensions to networks of queues, suitable for supplementing courses in communications, stochastic processes, and networking. An essential component of this lecture is MATLAB-based demonstrations and exercises, which can be easily modified to enable the student to observe and evaluate the impact of changing parameters, arrival and departure statistics, queuing disciplines, the number of servers, and other important aspects of the underlying system model. Much of the work in this lecture is based on Poisson statistics, since Poisson models are useful due to the fact that Poisson models are analytically tractable and provide a useful approximation for many applications. We recognize that the validity of Poisson statistics is questionable for a number of networking applications and therefore we briefly discuss self-similar models and the Hurst parameter, long-term dependent models, the Pareto distribution, and other related topics. Appropriate references are given for continued study on these topics. The initial chapters of this book consider individual queues in isolation. The systems studied consist of an arrival process, a single queue with a particular queuing discipline, and one or more servers. While this allows us to study the basic concepts of queuing and trunking, modern data networks consist of many queues that interact in complex ways. While many of these interactions defy analysis, the final chapter introduces a model of a network of queues in which, after being served in one queue, customers may join another queue. The key result for this model is known as Jackson's Theorem. Finally, we state the BCMP Theorem, which can be viewed as a further extension of Jackson's Theorem and present Kleinrock's formula, which can be viewed as the network version of Little's Theorem. Table of Contents: Introduction / Poisson, Erlang, and Pareto Distributions / A Brief Introduction to Queueing Theory / Blocking and Delay / Networks of Queues
The Phase-Locked Loop (PLL), and many of the devices used for frequency and phase tracking, carrier and symbol synchronization, demodulation, and frequency synthesis, are fundamental building blocks in today's complex communications systems. It is therefore essential for both students and practicing communications engineers interested in the design and implementation of modern communication systems to understand and have insight into the behavior of these important and ubiquitous devices. Since the PLL behaves as a nonlinear device (at least during acquisition), computer simulation can be used to great advantage in gaining insight into the behavior of the PLL and the devices derived from the PLL. The purpose of this Synthesis Lecture is to provide basic theoretical analyses of the PLL and devices derived from the PLL and simulation models suitable for supplementing undergraduate and graduate courses in communications. The Synthesis Lecture is also suitable for self study by practicing engineers. A significant component of this book is a set of basic MATLAB-based simulations that illustrate the operating characteristics of PLL-based devices and enable the reader to investigate the impact of varying system parameters. Rather than providing a comprehensive treatment of the underlying theory of phase-locked loops, theoretical analyses are provided in sufficient detail in order to explain how simulations are developed. The references point to sources currently available that treat this subject in considerable technical depth and are suitable for additional study. Download MATLAB codes (.zip) Table of Contents: Introduction / Basic PLL Theory / Structures Developed From The Basic PLL / Simulation Models / MATLAB Simulations / Noise Performance Analysis
Ziemer and Tranter provide a thorough treatment of the principles of communications at the physical layer suitable for college seniors, beginning graduate students, and practicing engineers. This is accomplished by providing overviews of the necessary background in signal, system, probability, and random process theory required for the analog and digital communications topics covered in the book. In addition to stressing fundamental concepts, the seventh edition features sections on important areas such as spread spectrum, cellular communications, and orthogonal frequency-division multiplexing. While the book is aimed at a two-semester course, more than enough material is provided for structuring courses according to students need and instructor preference.
Keeping up to date with the most current technologies in the field is essential for all effective electrical and computer engineers. The updated 7th edition of Principles of Communications presents the reader with more in-chapter examples, providing for a more supportive framework for learning. Readers are exposed to digital data transmission techniques earlier in the book, so they can appreciate the characteristics of digital communication systems prior to learning about probability and stochastic processes. They will also find expanded forward error correction code examples, and additional MATLAB problems.
The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm for estimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.
In sensor network applications, measured data are often meaningful only when the location is accurately known. In this booklet, we study research problems associated with node localization in wireless sensor networks. We describe sensor network localization problems in terms of a detection and estimation framework and we emphasize specifically a cooperative process where sensors with known locations are used to localize nodes at unknown locations. In this class of problems, even if the location of a node is known, the wireless links and transmission modalities between two nodes may be unknown. In this case, sensor nodes are used to detect the location and estimate pertinent data transmission activities between nodes. In addition to the broader problem of sensor localization, this booklet studies also specific localization measurements such as time of arrival (TOA), received signal strength (RSS), and direction of arrival (DOA). The sequential localization algorithm, which uses a subset of sensor nodes to estimate nearby sensor nodes' locations is discussed in detail. Extensive bibliography is given for those readers who want to delve further into specific topics.
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