This monograph, "Stochastic Geometry and Wireless Networks, Volume I - Theory," by François Baccelli and Bartłomiej Blaszczyszyn, provides a comprehensive overview of the theoretical foundations of stochastic geometry in the context of wireless networks. The book is structured into two volumes, with Volume I focusing on the theory of spatial averages and Volume II on practical modeling and performance analysis. Volume I is divided into three parts: classical stochastic geometry models, SINR (Signal-to-Interference Ratio) stochastic geometry, and an appendix containing mathematical tools. The first part introduces classical stochastic geometry models, including the Poisson point process, marked point processes, shot-noise fields, and coverage processes. The Poisson point process is a central concept, representing a random distribution of points in space, often used to model the locations of nodes in wireless networks. The second part explores SINR-based models, analyzing how signal strength and interference affect network performance. It discusses the properties of SINR cells, their interactions, and the impact of interference on network connectivity. The third part provides mathematical tools essential for the analysis of stochastic geometry in wireless networks. The book emphasizes the use of stochastic geometry to model and analyze wireless networks, particularly for large-scale networks. It highlights the importance of spatial averages in understanding network performance characteristics such as connectivity, stability, and capacity. The authors also discuss the role of stochastic geometry in modeling various network architectures, including ad hoc and cellular networks. The monograph is written for readers with a background in applied probability and wireless communications, providing a detailed treatment of the theoretical underpinnings of stochastic geometry and its applications in wireless network analysis. The text is structured to facilitate both theoretical understanding and practical application, offering a systematic approach to evaluating network performance using stochastic geometry.This monograph, "Stochastic Geometry and Wireless Networks, Volume I - Theory," by François Baccelli and Bartłomiej Blaszczyszyn, provides a comprehensive overview of the theoretical foundations of stochastic geometry in the context of wireless networks. The book is structured into two volumes, with Volume I focusing on the theory of spatial averages and Volume II on practical modeling and performance analysis. Volume I is divided into three parts: classical stochastic geometry models, SINR (Signal-to-Interference Ratio) stochastic geometry, and an appendix containing mathematical tools. The first part introduces classical stochastic geometry models, including the Poisson point process, marked point processes, shot-noise fields, and coverage processes. The Poisson point process is a central concept, representing a random distribution of points in space, often used to model the locations of nodes in wireless networks. The second part explores SINR-based models, analyzing how signal strength and interference affect network performance. It discusses the properties of SINR cells, their interactions, and the impact of interference on network connectivity. The third part provides mathematical tools essential for the analysis of stochastic geometry in wireless networks. The book emphasizes the use of stochastic geometry to model and analyze wireless networks, particularly for large-scale networks. It highlights the importance of spatial averages in understanding network performance characteristics such as connectivity, stability, and capacity. The authors also discuss the role of stochastic geometry in modeling various network architectures, including ad hoc and cellular networks. The monograph is written for readers with a background in applied probability and wireless communications, providing a detailed treatment of the theoretical underpinnings of stochastic geometry and its applications in wireless network analysis. The text is structured to facilitate both theoretical understanding and practical application, offering a systematic approach to evaluating network performance using stochastic geometry.