Probability And Stochastic Processes With Applications to Communications, Systems And Networks

The present reprint contains all of the articles accepted and published in the Special Issue "Probability and Stochastic Processes with Applications to Communications, Systems and Networks" from the MDPI Mathematics journal. This Special Issue is devoted to probability, statistics, stochastic processes and their different applications in system and network analysis. The Special Issue includes works related to the analysis and applications of different queuing models, which begin with general approaches in modeling queuing systems and networks; an analysis of probabilistic and statistical methods in telecommunication; an asymptotic analysis of queuing networks in the condition of a large load; general complex networks and their structures, e.g., topology and graph theory; mathematical methods and models in smart cities; exclusive statistical methods, such as statistical estimates in bio/ecology, medicine and neural networks; and studies that estimate parameters in complex technical systems, etc. We hope that the scientific results collected in this reprint will help foster future research related to probability, stochastic processes and their applications.

This book provides engineers with focused treatment of the mathematics needed to understand probability, random variables, and stochastic processes, which are essential mathematical disciplines used in communications engineering. The author explains the basic concepts of these topics as plainly as possible so that people with no in-depth knowledge of these mathematical topics can better appreciate their applications in real problems.

The stability analysis of stochastic models for telecommunication systems is an intensively studied topic. The analysis is, as a rule, a difficult problem requiring a refined mathematical technique, especially when one endeavors beyond the framework of Markovian models.

Communication Systems is as an introductory textbook, presenting Fourier transform, convolution, and definitions of autocorrelation and power spectral density. It also introduces concepts of probability, random variables, and stochastic processes and their applications to the analysis of linear systems.

This is an introductory-level text on stochastic modeling. It is suited for undergraduate students in engineering, operations research, statistics, mathematics, actuarial science, business management, computer science, and public policy. It employs a large number of examples to teach the students to use stochastic models of real-life systems to predict their performance, and use this analysis to design better systems.

An interdisciplinary approach to understanding queueing and graphical networks
In today's era of interdisciplinary studies and research activities, network models are becoming increasingly important in various areas where they have not regularly been used.