An Introduction to Cellular Network Analysis Using Stochastic Geometry

✍ Scribed by Jeffrey G. Andrews, Abhishek K. Gupta, Ahmad Alammouri, Harpreet S. Dhillon

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 99
• Series: Synthesis Lectures on Learning, Networks, and Algorithms
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Preface
Acknowledgements
Contents
About the Authors
1 Key Background on Stochastic Geometry
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1.1 Point Process Essentials
1.1.1 Intensity Measure
1.1.2 Mean Summation Over a PP and Campbell's Theorem
1.1.3 Probability Generating Functional (PGFL)
1.1.4 Marked Point Process
1.1.5 Palm Distribution
1.1.6 Campbell-Mecke Theorem
1.1.7 Pair Correlation Function
1.2 The Poisson Point Process
1.2.1 Campbell's Theorem for a PPP
1.2.2 PGFL of a PPP
1.2.3 Slivnyak's Theorem
1.2.4 Campbell-Mecke Theorem for PPP
1.2.5 Properties of the PPP
1.2.6 Poisson Voronoi Tessellation
1.2.7 Useful Distance Distributions
2 Downlink Analysis
2.1 Downlink Model and Metrics
2.1.1 Distance to the Nearest Base Station (Serving Link Distance)
2.2 Interference Characterization
2.3 Coverage Probability (SINR Distribution)
2.4 Special Cases
2.4.1 Noise Still Present, α= 4
2.4.2 Interference-Limited, Any Path Loss Exponent
2.4.3 Interference-Limited, α= 4
2.5 Validation
2.6 Incorporating Shadowing
2.7 Coverage with a General Path Loss Model
2.8 Coverage Analysis for the Typical Cell
2.9 Area Spectral Efficiency
3 Uplink Analysis
3.1 Model and Preliminaries
3.2 Characterization of the PP of Interfering Users
3.3 Distribution of Distances R and Ri
3.4 Interference Characterization
3.5 Coverage Probability
3.6 Special Cases in Terms of ε
3.6.1 Full Channel Inversion (ε=1)
3.6.2 Fixed Transmit Power (ε=0)
3.6.3 Approximation for ε=1
3.7 Validation and Discussion
4 Heterogeneous Cellular Network Analysis
4.1 HetNet Model
4.2 Cell Association
4.3 Analysis for Average Power-Based Cell Association
4.3.1 Special Cases
4.4 Analysis for Instantaneous Power-Based Cell Selection
4.4.1 Special Cases
4.5 Interpretations and Impact on Network Throughput
5 Dense Cellular Networks
5.1 The Standard Power-Law Path Loss Model
5.2 Physically Feasible Path Loss Models
5.2.1 Definition
5.3 SINR and ASE Scaling Laws
5.3.1 Asymptotic Analysis
5.4 Final Remarks
6 Extensions
6.1 General Fading Models
6.2 Advanced Cell Selection Strategies
6.3 General Spatial Models
6.4 Multiple-Input Multiple-Output (MIMO)
6.5 Spectrum and Resource Sharing
6.6 Millimeter-Wave and TeraHertz
6.7 Modern Communication Paradigms Including Beyond-5G and 6G
6.8 Parting Remarks
Bibliography