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Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Mai 2013
Genre:	Schulbücher
	C / Computer Communication Networks / Computer communication systems / Cybernetics & systems theory / engineering / Information and Communication, Circuits / Information theory / Kybernetik und Systemtheorie / Mathematical Applications in Computer Science / Network hardware / Netzwerk-Hardware / Probabilities / Probability & statistics / Probability Theory / Probability Theory and Stochastic Processes / Stochastics / Stochastik / System Theory / Systems Theory, Control / Wahrscheinlichkeitsrechnung und Statistik mehr...
ISBN:	9781461347811
EAN-Code:	9781461347811
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Serie:	Information Technology: Transmission, Processing and Storage
Dimensionen:	H 254 mm / B 178 mm / D
Gewicht:	876 gr
Seiten:	441
Illustration:	XXI, 441 p.
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This book is an expanded third edition of the book Performance Analysis of Digital Transmission Systems, originally published in 1990. Second edition of the book titled Digital Transmission Systems: Performance Analysis and Modeling was published in 1998. The book is intended for those who design communication systems and networks. A computer network designer is interested in selecting communication channels, error protection schemes, and link control protocols. To do this efficiently, one needs a mathematical model that accurately predicts system behavior. Two basic problems arise in mathematical modeling: the problem of identifying a system and the problem of applying a model to the system analysis. System identification consists of selecting a class of mathematical objects to describe fundamental properties of the system behavior. We use a specific class of hidden Markov models (HMMs) to model communication systems. This model was introduced by C. E. Shannon more than 50 years ago as a Noisy Discrete Channel with a finite number of states. The model is described by a finite number of matrices whose elements are estimated on the basis of experimental data. We develop several methods of model identification and show their relationship to other methods of data analysis, such as spectral methods, autoregressive moving average CARMA) approximations, and rational transfer function approximations.