Introduction to Network Modelling

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Credits
6
Types
Elective
Requirements
This subject has not requirements
Department
AC
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

  1. The main goal of the course is to develop in the students quantitative modeling skills, based on probabilistic techniques.
    Related competences:

Contents

  1. 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.
  2. 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.
  3. 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

Activity Evaluation 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:

F= 0.25xP+0.25xD+0.5xE

Bibliography

Basic:

Web links