Druck auf Anfrage Neuware - Printed after ordering - This book provides an accessible yet rigorous first reference for readers interested in learning how to model and analyze cellular network performance using stochastic geometry. In addition to the canonical downlink and uplink settings, analyses of heterogeneous cellular networks and dense cellular networks are also included. For each of these settings, the focus is on the calculation of coverage probability, which gives the complementary cumulative distribution function (ccdf) of signal-to-interference-and-noise ratio (SINR) and is the complement of the outage probability. Using this, other key performance metrics, such as the area spectral efficiency, are also derived. These metrics are especially useful in understanding the effect of densification on network performance. In order to make this a truly self-contained reference, all the required background material from stochastic geometry is introduced in a coherent and digestible manner.This Book:Provides an approachable introduction to the analysis of cellular networks and illuminates key system dependenciesFeatures an approach based on stochastic geometry as applied to cellular networks including both downlink and uplinkFocuses on the statistical distribution of signal-to-interference-and-noise ratio (SINR) and related metrics.

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Detalles bibliogr�ficos

T�tulo: An Introduction to Cellular Network Analysis Using Stochastic Geometry
Editor: Springer International Publishing
Fecha de publicaci�n: 2023
Idioma: Ingl�s
ISBN 10: 3031297423
ISBN 13: 9783031297427
Encuadernaci�n: Buch
Estado: Neu

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Sinopsis

This book provides an accessible yet rigorous first reference for readers interested in learning how to model and analyze cellular network performance using stochastic geometry. In addition to the canonical downlink and uplink settings, analyses of heterogeneous cellular networks and dense cellular networks are also included. For each of these settings, the focus is on the calculation of coverage probability, which gives the complementary cumulative distribution function (ccdf) of signal-to-interference-and-noise ratio (SINR) and is the complement of the outage probability. Using this, other key performance metrics, such as the area spectral efficiency, are also derived. These metrics are especially useful in understanding the effect of densification on network performance. In order to make this a truly self-contained reference, all the required background material from stochastic geometry is introduced in a coherent and digestible manner.

This Book:

Provides an approachable introduction to the analysis of cellular networks and illuminates key system dependencies
Features an approach based on stochastic geometry as applied to cellular networks including both downlink and uplink
Focuses on the statistical distribution of signal-to-interference-and-noise ratio (SINR) and related metrics

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Jeffrey G. Andrews, Ph.D, is the Truchard Family Endowed Chair in Engineering and Director of 6G@UT at The University of Texas at Austin. He received a B.S. in Engineering with High Distinction from Harvey Mudd College and a M.S. and Ph.D in Electrical Engineering from Stanford University.

Abhishek K. Gupta, Ph.D, is an Assistant Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. He received a B.Tech.- M.Tech dual degree in Electrical Engineering from IIT Kanpur and a Ph.D in Electrical and Computer Engineering from The University of Texas at Austin.

Ahmad AlAmmouri, PhD, is a Senior Research Engineer with the Samsung Research America in Plano, Texas. He received his B.Sc. from the University of Jordan, an M.Sc. from King Abdullah University of Science and Technology (KAUST), and a Ph.D from The University of Texas at Austin.

Harpreet S. Dhillon, PhD, is a Professor of Electrical and Computer Engineering at Virginia Tech and the Associate Director of Wireless@VT. He received a B.Tech. in Electronics and Communication Engineering from IIT Guwahati, an M.S. in Electrical Engineering from Virginia Tech, and a Ph.D in Electrical Engineering from The University of Texas at Austin.

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