Introducción al Modelado de Redes

Usted está aquí

Inicio » Estudios » Másteres » Máster en Innovación e Investigación en Informática » Plan de estudios » Asignaturas » Introducción al Modelado de Redes
Créditos
6
Tipos
Optativa
Requisitos
Esta asignatura no tiene requisitos
Departamento
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.

Horas semanales

Teoría
4
Problemas
0
Laboratorio
0
Aprendizaje dirigido
0
Aprendizaje autónomo
0

Objetivos

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

Contenidos

  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.

Actividades

Actividad Acto evaluativo


Basic results of discrete probability



Teoría
6h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Examples of discrete probability models



Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Basic results on continuous probability



Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Examples of continuous-probability models



Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Lineal systems and signal space



Teoría
12h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Lineal system and signal space examples



Teoría
9h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Metodología docente

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.

Método de evaluación

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

Bibliografía

Básica:

Web links