The course covers some basic modeling techniques used in networking research. In particular it discusses discrete and continuous probability models, linear systems and signal space. These concepts are introduced through classical examples taken from different research areas, including traffic modelling, wireless transmission systems, smartphone sensor data filtering, switching systems, address lookup algorithms, optical switching, anti-spam filters, etc.
Weekly hours
Theory
4
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0
Objectives
The main goal of the course is to develop in the students quantitative modeling skills, based on probabilistic techniques.
Related competences:
Contents
Discrete probability models
Basic results. Examples: IQ switch max throughput, hash tables and ethernet switching. Anticolision methods in RFID tags. Blocking probabilities in optical switches. TCP window model. Bayesian antispam filters. Fountain codes.
Continuous probability models
Basic results. Exponential and Poisson distribution. Palm's theorem. PASTA. Residual times paradox. Large number laws. Normal distribution and Central Limit theorem. Multivariate Gaussian distributions. Examples: Basic teletraffic models. Path estability in MANETs. Epidemic models in networks. Additive Gaussian Noise. Filtering smartphone sensor data.
Lineal systems and signal space
Lineal spaces and lineal systems. Orthogonality. Fourier Series. Sampling theorem. Fast Fourier Transform. Random processes. Examples: Wireless transmission. IEEE 802.11g and 802.11n. Image compression.
Activities
ActivityEvaluation act
Basic results of discrete probability
Theory
6h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h
Examples of discrete probability models
Theory
9h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h
Basic results on continuous probability
Theory
9h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h
Examples of continuous-probability models
Theory
9h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h
Lineal systems and signal space
Theory
12h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h
Lineal system and signal space examples
Theory
9h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h
Teaching methodology
During the initial sessions of each theme, the main results will be explained in the blackboard. During the other sessions, will discuss in the classroom performance models taken from research papers.
Evaluation methodology
The evaluation is based on three different activities
- Short presentations of research papers (P)
- A detailed study of one paper (D)
- A final exam (E)
Each of the three activities will be evaluated (0=
The final mark for the course (F) will be:
An Introduction to probability theory and its applications (vol 1 and 2) -
Feller, William,
John Wiley and Sons, cop. 1968. ISBN: 0471257087 http://cataleg.upc.edu/record=b1005063~S1*cat