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Stochastic Geometry Analysis of Cellular Networks

$104.00 ( ) USD

  • Date Published: March 2018
  • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • format: Adobe eBook Reader
  • isbn: 9781108340854
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About the Authors
  • Achieve faster and more efficient network design and optimization with this comprehensive guide. Some of the most prominent researchers in the field explain the very latest analytic techniques and results from stochastic geometry for modelling the signal-to-interference-plus-noise ratio (SINR) distribution in heterogeneous cellular networks. This book will help readers to understand the effects of combining different system deployment parameters on key performance indicators such as coverage and capacity, enabling the efficient allocation of simulation resources. In addition to covering results for network models based on the Poisson point process, this book presents recent results for when non-Poisson base station configurations appear Poisson, due to random propagation effects such as fading and shadowing, as well as non-Poisson models for base station configurations, with a focus on determinantal point processes and tractable approximation methods. Theoretical results are illustrated with practical Long-Term Evolution (LTE) applications and compared with real-world deployment results.

    • Presents the very latest results on signal-to-interference-plus-noise ratio (SINR) distribution in heterogeneous cellular networks obtained using the tools of stochastic geometry
    • Describes analytical techniques that are complementary to traditional simulation-based methods
    • Includes applications for next-generation (5G) deployments
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    Reviews & endorsements

    'These four renowned experts deliver a comprehensive yet curated treatment on the modelling and analysis of modern cellular networks using stochastic geometry, which has been one of the most important recent lines of wireless research. Highly recommended for interested researchers and engineers. Can serve as a useful companion to Haenggi’s landmark stochastic geometry textbook, which had fairly minimal treatment of cellular networks.' Jeff Andrews, University of Texas, Austin

    Customer reviews

    16th Oct 2018 by Nurika

    I want to learn more,and i think this is good.I can leran too much from this .

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    Product details

    • Date Published: March 2018
    • format: Adobe eBook Reader
    • isbn: 9781108340854
    • contains: 21 b/w illus. 1 table
    • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • Table of Contents

    Part I. Stochastic Geometry:
    1. Introduction
    2. The role of stochastic geometry in HetNet analysis
    3. A brief course in stochastic geometry
    4. Statistics of received power at the typical location
    Part II. SINR Analysis:
    5. Downlink SINR: fundamental results
    6. Downlink SINR: advanced results
    7. Downlink SINR: further extensions
    8. Extensions to non-Poisson models.

  • Authors

    Bartłomiej Błaszczyszyn, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
    Bartlomiej Blaszczyszyn is Research Director at the Institut national de recherche en informatique et en automatique (INRIA), France and a faculty member of the joint INRIA-ENS research group, DYOGENE.

    Martin Haenggi, University of Notre Dame, Indiana
    Martin Haenggi is the Frank M. Freimann Professor of Electrical Engineering and a Concurrent Professor of Applied and Computational Mathematics at the University of Notre Dame, Indiana, and the author of Stochastic Geometry for Wireless Networks (Cambridge, 2012).

    Paul Keeler, Weierstrass Institute for Applied Analysis and Statistics
    Paul Keeler is a postdoctoral researcher at the Weierstrass Institute for Applied Analysis and Statistics, Germany.

    Sayandev Mukherjee, DOCOMO Innovations, Inc., Palo Alto
    Sayandev Mukherjee is a Senior Research Engineer at DOCOMO Innovations Inc., and the author of Analytical Modeling of Heterogeneous Cellular Networks (Cambridge, 2014).

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