Stochastic Geometry Analysis of Space-Air-Ground Networks - Hardcover

Über die Autorin bzw. den Autor

Minwei Shi received the B.S., M.S., and Ph.D. degrees from Beijing Institute of Technology, Beijing, China, in 2015, 2018, and 2023, respectively. From 2018 to 2019, he was with Datang Mobile Communications Equipment Co., Ltd., Beijing. He is currently held a postdoctoral position with Beijing Institute of Technology. His research interests include modeling and performance analysis of wireless communication networks.

Kai Yang received the Ph.D. degree from Beijing Institute of Technology, Beijing, China, in 2010. From 2010 to 2013, he was with Alcatel-Lucent Shanghai Bell, Shanghai, China. Now, he is a professor in the School of Information and Electronics in Beijing Institute of Technology. His current research interests include resource allocation, convex optimization, massive MIMO, mmWave systems, and interference mitigation.

He Zhou received the B.E. and M.S. degrees from Harbin Institute of Technology, Harbin, China, in 2016 and 2018, respectively, and his Ph.D. degree from Beijing Institute of Technology, Beijing, China, in 2023. He is currently with Beijing Institute of Spacecraft System Engineering. His research interests include mobility management, satellite communication, and space-air-ground integrated networks.

Von der hinteren Coverseite

This book presents a comprehensive framework for the theoretical analysis of space-air-ground networks using stochastic geometry. This analytical approach is indispensable for evaluating the performance of large-scale space-air-ground networks, which serve as critical facilitators for the advancement of the sixth-generation wireless communication aimed at providing high-speed broadband coverage for remote areas. By incorporating the features of topology and channel model in different tiers, this book investigates the key performance metrics in terms of load balancing, coverage assessment, and mobility management. The developed mechanisms provide effective design insights for space-air-ground networks while obviating the need for complex system-level simulations.