Introduction to Network Modeling

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Credits
6
Department
AC
Types
Specialization compulsory (Computer Networks and Distributed Systems)
Requirements
This subject has not requirements
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.

Teachers

Person in charge

  • Jorge García Vidal ( )

Weekly hours

Theory
4
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0

Competences

Technical Competences of each Specialization

Computer networks and distributed systems

  • CEE2.2 - Capability to understand models, problems and algorithms related to computer networks and to design and evaluate algorithms, protocols and systems that process the complexity of computer communications networks.

Generic Technical Competences

Generic

  • CG4 - Capacity for general and technical management of research, development and innovation projects, in companies and technology centers in the field of Informatics Engineering.

Transversal Competences

Appropiate attitude towards work

  • CTR5 - Capability to be motivated by professional achievement and to face new challenges, to have a broad vision of the possibilities of a career in the field of informatics engineering. Capability to be motivated by quality and continuous improvement, and to act strictly on professional development. Capability to adapt to technological or organizational changes. Capacity for working in absence of information and/or with time and/or resources constraints.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.

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

Basic results of discrete probability

Theory
6
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0

Examples of discrete probability models

Theory
9
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0

Basic results on continuous probability

Theory
9
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0

Examples of continuous-probability models

Theory
9
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0

Lineal systems and signal space

Theory
12
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0

Lineal system and signal space examples

Theory
9
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0

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

Bibliografy

Basic:

Web links