Beschreibung
This book is an introduction to Markov chain modeling with applications to communication networks. It begins with a general introduction to performance modeling in Chapter 1 where we introduce different performance models. We then introduce basic ideas of Markov chain modeling: Markov property, discrete time Markov chain (DTMC) and continuous time Markov chain (CTMC). We also discuss how to find the steady state distributions from these Markov chains and how they can be used to compute the system performance metric. The solution methodologies include a balance equation technique, limiting probability technique, and the uniformization. We try to minimize the theoretical aspects of the Markov chain so that the book is easily accessible to readers without deep mathematical backgrounds. We then introduce how to develop a Markov chain model with simple applications: a forwarding system, a cellular system blocking, slotted ALOHA, Wi-Fi model, and multichannel based LAN model. The examples cover CTMC, DTMC, birth-death process and non birth-death process. We then introduce more difficult examples in Chapter 4, which are related to wireless LAN networks: the Bianchi model and Multi-Channel MAC model with fixed duration. These models are more advanced than those introduced in Chapter 3 because they require more advanced concepts such as renewal-reward theorem and the queueing network model. We introduce these concepts in the appendix as needed so that readers can follow them without difficulty. We hope that this textbook will be helpful to students, researchers, and network practitioners who want to understand and use mathematical modeling techniques. Table of Contents: Performance Modeling / Markov Chain Modeling / Developing Markov Chain Performance Models / Advanced Markov Chain Models
Autorenporträt
Tim Fingscheidt received the Dipl.-Ing. degree in Electrical Engineering in 1993 and the Ph.D. degree in 1998 from RWTH Aachen University, Germany, both with distinction. He joined AT&T Labs, Florham Park, NJ, USA, for a PostDoc in 1998 and Siemens AG (Mobile Devices), Munich, Germany, in 1999, heading a signal processing development team. After a stay with Siemens Corporate Technology, Munich, Germany, from 2005 to 2006, he became Full Professor with the Institute for Communications Technology, Technische Universität (TU) Braunschweig, Germany, holding the Chair of "Signal Processing and Machine Learning". His research interests are machine learning in vision and time series such as speech, with focus on environment perception, signal classification, coding, and enhancement. He is founder of the TU Braunschweig Deep Learning Lab (tubs.DLL), a graduate student research thinks tank being active in publicly funded and industry research projects. Many of his projects have been dealing with automotive applications. Since 2018, he has been actively involved in the large-scale national research projects AI Platform Concept, AI Validation, AI Delta Learning, and AI Data Tooling, contributing research in robust semantic segmentation, monocular depth estimation, domain adaptation, corner case detection, and learned image coding. Prof. Fingscheidt received numerous national and international awards for his publications; among these, three CVPR workshop best paper awards in 2019, 2020, and 2021. He is interested in where academia meets industry and where machine learning meets highly automated driving. Hanno Gottschalk studied Physics and Mathematics and received diploma degrees from the Ruhr University Bochum in 1995 and 1997, respectively. After finishing his Ph.D. on Mathematical Physics in 1999, he joined the University La Sapienza of Rome for a PostDoc year, before continuing his academic career as PostDoc at Bonn University, where he habilitated in mathematics in 2003. Since 2005, he was lecturer (C2) at the University of Bonn and joined Siemens Energy from 2007-2011 as a Core Competency Owner for probabilistic design. Since 2011, he is Professor for stochastics at the University of Wuppertal. In 2018, he became co-founding Director of the Interdisciplinary Center for Machine Learning and Data Analytics (IZMD) of the University of Wuppertal. His research in the field of deep learning is focused on uncertainty and safety for deep learning perception algorithms. Applications lie in the field of false positive and false negative prediction and detection and retrieval of out of distribution objects. Apart from bi-lateral work with Volkswagen and Aptiv, he is member of the AI Validation, AI Delta Learning, and AI Data Tooling consortia within the AI family of large-scale projects. Hanno Gottschalk brings his special knowledge as statistician and mathematician to the field of automated driving and combines this with cutting edge technology in deep learning. Sebastian Houben studied Mathematics and Computer Science at the University in Hagen and graduated in 2009. He pursued Ph.D. studies at the Ruhr University of Bochum graduating with distinction in 2015. After his postdoctoral studies at the University of Bonn, he was appointed Junior Professor for Applied Computer Science at the Ruhr University of Bochum where he headed the Group of Real-time Computer Vision. As of early 2020, he is a senior researcher with the Fraunhofer Institute for Intelligent Analysis and Information Systems. His research interests cover computer vision and environment perception in autonomous robotics, in particular in the field of automated driving. Within the consortium KI-Absicherung and the competency center Machine-Learning-Rhein-Ruhr (ML2R), he represents the topic Trustworthy AI and is particularly interested in practical methods for explainability of black-box models, uncertainty estimation in neural networks, and vi
Herstellerkennzeichnung:
Springer Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE
E-Mail: juergen.hartmann@springer.com
