Buch, Englisch, 206 Seiten, Format (B × H): 183 mm x 253 mm, Gewicht: 562 g
Buch, Englisch, 206 Seiten, Format (B × H): 183 mm x 253 mm, Gewicht: 562 g
ISBN: 978-1-107-16258-7
Verlag: Cambridge University Press
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.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
Weitere Infos & Material
Part I. Stochastic Geometry: 1. Introduction; 2. The role of stochastic geometry in HetNet analysis; 3. A brief course in stochastic geometry; 4. Statistics of received power at the typical location; Part II. SINR Analysis: 5. Downlink SINR: fundamental results; 6. Downlink SINR: advanced results; 7. Downlink SINR: further extensions; 8. Extensions to non-Poisson models.