Basic (i.e., grad) knowledge of probability and statistics. Some previous knowledge about communication systems is also recommended, as they constitute the main cases of study.

Fundamentals of Markov chain theory, and the ability to use the main results to model communication system. Performance analysis and capacity planning of queues and networks of queues, ability to identify weakenesses, bottlenecks and congestion problems. Performance analysis (reliability, goodput) of classic communication systems.

Overview of probability. Exponential random variable and Poisson arrival process Discrete-time Markov chains. Continuous-time Markov chains Queueing theory. Networks of queues.

The course consists on two types of lectures: 1. theoretical lectures, where the key results will be developed, and 2. practical lectures, where those principles will be used to solve the proposed problems.