Stochastic geometry analysis of cellular networks / Bartłomiej Błaszczyszyn, INRIA, Paris, Martin Haenggi, University of Notre Dame, Paul Keeler, Weierstrass Institute for Applied Analysis and Statistics, Sayandev Mukherjee, DOCOMO Innovations, Inc.
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) distr...
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| Language: | English |
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Cambridge, United Kingdom ; New York, NY, USA :
Cambridge University Press,
2018.
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| 100 | 1 | |a Błaszczyszyn, Bartłomiej, |d 1967- |e author. | |
| 245 | 1 | 0 | |a Stochastic geometry analysis of cellular networks / |c Bartłomiej Błaszczyszyn, INRIA, Paris, Martin Haenggi, University of Notre Dame, Paul Keeler, Weierstrass Institute for Applied Analysis and Statistics, Sayandev Mukherjee, DOCOMO Innovations, Inc. |
| 264 | 1 | |a Cambridge, United Kingdom ; |a New York, NY, USA : |b Cambridge University Press, |c 2018. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a 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. | ||
| 505 | 0 | |a Machine generated contents note: pt. I Stochastic Geometry -- 1. Introduction -- 1.1. The Demand for Ubiquitous Connectivity -- 1.2. Technical Challenges for a Network Operator -- 1.3. The Case for Small-Cell Architectures -- 1.4. Future Wireless Networks Will Be Heterogeneous -- 1.5. Approaches to the Design of Future Wireless Networks -- 1.6. The Case against Pure Simulation-Based Investigation -- 1.7. The Case for an Analytical Approach to HetNet Design -- 1.8. The Stochastic Geometric Approach to HetNet Analysis -- 1.8.1.A preview of the main results in the book -- 1.8.2. Extension to non-Poisson point processes -- 1.8.3. Applications to link-level analysis -- 2. The Role of Stochastic Geometry in HetNet Analysis -- 2.1. The Hexagonal Cellular Concept -- 2.2. Propagation, Fading, and SINR -- 2.3. Base Station Locations Modeled by Point Processes -- 3.A Brief Course in Stochastic Geometry -- 3.1. Purpose -- 3.2. Fundamental Definitions and Notation -- 3.2.1. Definition | |
| 505 | 0 | |a Note continued: 3.2.2. Equivalence of random sets and random measures -- 3.2.3. Distribution of a point process -- 3.2.4. Palm measures -- 3.2.5. Functions of point processes and the Campbell-Mecke theorem -- 3.2.6. Moment measures and factorial moment measures and their densities -- 3.3. Marked Point Processes -- 3.4. The Poisson Point Process and Its Properties -- 3.4.1. Definition -- 3.4.2. Properties -- 3.4.3. The pgfl and the Campbell-Mecke theorem -- 3.5. Alternative Models -- 3.5.1. Determinantal point processes -- 3.5.2. Matern hard-core processes -- 3.5.3. Strauss processes -- 3.5.4. Shot noise Cox processes -- 3.5.5. The Poisson hole process -- 4. Statistics of Received Power at the Typical Location -- 4.1. Modeling Signal Propagation and Cells in Heterogeneus Networks -- 4.1.1. Stationary heterogeneous network with a propagation field -- 4.1.2. Typical network station and typical location in the network -- 4.1.3. Exchange formula | |
| 505 | 0 | |a Note continued: 4.1.4. Shot noise model of all signal powers in the network -- 4.1.5. Service zones or cells -- 4.1.6. Typical cell vs. zero-cell -- 4.1.7. Rate coverage -- 4.1.8. Cell loads -- 4.2. Heterogeneous Poisson Network Seen at the Typical Location -- 4.2.1. Projection process and a propagation invariance -- 4.2.2. Heterogeneous Poisson network -- 4.2.3. Poisson network equivalence -- 4.2.4. Incorporating propagation terms such as transmission powers and antenna gains -- 4.2.5. Intensity measure of a general projection process -- 4.3.Networks Appear Poisson Due to Random Propagation Effects -- 4.3.1. Projection process based on a deterministic configuration of base stations -- 4.3.2. Poisson model approximation -- 4.3.3. Order statistics of signals -- 4.3.4. Fitting the Poisson model -- 4.3.5. Poisson convergence -- 4.3.6. Possible extensions -- 4.4. Bibliographic Notes -- pt. II SINR Analysis -- 5. Downlink SINR: Fundamental Results -- 5.1. General Considerations | |
| 505 | 0 | |a Note continued: 5.1.1. SINR distribution -- 5.1.2. Signal-to-total-interference-plus-noise ratio -- 5.1.3. Choice of the base station -- 5.1.4. Simple and multiple coverage regime -- 5.1.5. Coverage probability exchange formula in the simple regime -- 5.1.6. Increasing model complexity -- 5.2. Basic Results for Poisson Network with Singular Path Loss Model -- 5.2.1. The singular path loss model -- 5.2.2. SINR with respect to the typical station -- 5.2.3. SINR with respect to the strongest station in the simple coverage regime -- 5.2.4. Coverage probability by the closest base station -- 5.2.5. Alternative derivation of coverage probability by the closest base station -- 5.2.6. Coverage probability with shadowing separated from fading -- 5.3. Multiple Coverage in Poisson Network with Singular Path Loss Model -- 5.3.1. Coverage number and k-coverage probability -- 5.3.2. Multiple coverage in heterogeneous network -- 5.3.3. Matrix formulation of the multiple coverage event | |
| 505 | 0 | |a Note continued: 7.1.1. Interference and SIR for the singular path loss model -- 7.1.2. Results for general path loss models -- 7.2. SINR Analysis for the Poisson Network with Advanced Signaling -- 7.2.1. MIMO analysis -- 7.2.2. Distribution of SINR -- 7.2.3.CoMP analysis -- 7.3. Multi-Link SINR Analysis: Area Spectral Efficiency and Energy Efficiency -- 7.3.1. Link-centric vs. cell-centric perspective -- 7.3.2. Performance metrics of interest to operators -- 7.3.3. Spectral efficiency as ergodic capacity -- 8. Extensions to Non-Poisson Models -- 8.1. Non-Poisson Point Processes -- 8.1.1. Motivation -- 8.1.2. Appropriate point processes -- 8.1.3. Choice of the base station and propagation effects -- 8.1.4. Determinantal models -- 8.1.5. Ginibre point process -- 8.1.6. General determinantal point process -- 8.1.7. Cox point processes -- 8.1.8. Neyman-Scott cluster processes -- 8.2. Approximate SIR Analysis for General Networks -- 8.2.1. Motivation | |
| 505 | 0 | |a Note continued: 8.2.2. Accuracy of the SIR distributions compared with real networks -- 8.2.3. ASAPPP -- 8.2.4. Why is ASAPPP so effective? -- 8.2.5. ASAPPP for HetNets -- 8.3. Bibliographic Notes. | |
| 650 | 0 | |a Wireless communication systems |x Mathematics. | |
| 650 | 0 | |a Stochastic models. | |
| 650 | 0 | |a Stochastic geometry. | |
| 650 | 7 | |a Stochastic geometry. |2 fast |0 (OCoLC)fst01133509 | |
| 650 | 7 | |a Stochastic models. |2 fast |0 (OCoLC)fst01737780 | |
| 700 | 1 | |a Haenggi, Martin, |e author. | |
| 700 | 1 | |a Keeler, Paul, |d 1981- |e author. | |
| 700 | 1 | |a Mukherjee, Sayandev, |d 1970- |e author. | |
| 776 | 0 | 8 | |i Print version: |a Błaszczyszyn, Bartłomiej, 1967- |t Stochastic geometry analysis of cellular networks. |d Cambridge, United Kingdom ; New York, NY, USA : Cambridge University Press, 2018 |z 1107162580 |z 9781107162587 |w (OCoLC)994359537 |
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