Learning outcomes

At the end of this lecture, a student should be able to 

  • solve stochastic processes,
  • observe by adequate simulation or by use of theory, the behavior of Markov systems, 
  • use discrete-event simulation paradigms to analyse a particular queueing system, 
  • realise a statistical analysis of obtained performance measures, 
  • valide his simulation tool.

 

 

 

Content

Our objective is to help students to understand and to solve random processes that may encounter in communication systems. 

We give theory of renewal and Markov processes and basics of queueing theory, 

We highlight the algorithmic and implementational aspects of the specification model. We will highlight algorithmic aspects of discrete-event simulation technics. Langage of use is R exclusively. 

The lecture is organized only if enough students decide to follow it. 

Assessment method

Written exam.

 

Sources, references and any support material

 S. Resnick. Adventures in Stochastic Processes. Birkhäuse, 2005.

 I. Adan et J. Resing. Queueing Theory. Available on line, Netherlands, 2002

H. Kobayashi et B.L. Mark. System Modeling and Analysis. Fondations of System Performance Evaluation. Pearson, 2009.

Lawrence M. Leemis and Stephen K. Park, Discrete-Event Simulation: A First Course, Prentice Hall, 2006.

 

Language of instruction

Français