Covering point process theory, random geometric graphs and coverage processes, this rigorous introduction to stochastic geometry will enable you to obtain powerful, general estimates and bounds of wireless network performance and make good design choices for future wireless architectures and protocols that efficiently manage interference effects. Practical engineering applications are integrated with mathematical theory, with an understanding of probability the only prerequisite. At the same time, stochastic geometry is connected to percolation theory and the theory of random geometric graphs and accompanied by a brief introduction to the R statistical computing language. Combining theory and hands-on analytical techniques with practical examples and exercises, this is a comprehensive guide to the spatial stochastic models essential for modelling and analysis of wireless network performance.

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.

Interference is the main performance-limiting factor of large wireless communication systems. To analyze and design these networks, it is thus imperative that the interference is statistically characterized or bounded in the presence of various sources of uncertainty, including the users' positions, their patterns of activity, and the channel fading states. Interference in Large Wireless Networks addresses this problem using basic probability and tools from stochastic geometry. Starting with regular networks and the popular Poisson model, it discusses increasingly more general networks. In addition to the interference itself, it also derives explicit expressions for outage probabilities, which are indispensable for the optimization of higher-level metrics such as the transport capacity or end-to-end delay. The book includes an appendix that reviews the underlying mathematical tools, which makes it self-contained and suitable for graduate students, researchers, and wireless engineers alike.