
Course unit
NETWORK ANALYSIS AND SIMULATION
INP3049930, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Core courses 
INGINF/03 
Telecommunications 
6.0 
Course unit organization
Period 
Second semester 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Lecture 
6.0 
48 
102.0 
No turn 
Examination board
Board 
From 
To 
Members of the board 
2 A.A. 2018/2019 
01/10/2018 
15/03/2020 
ZORZI
MICHELE
(Presidente)
ZANELLA
ANDREA
(Membro Effettivo)
BADIA
LEONARDO
(Supplente)
CALVAGNO
GIANCARLO
(Supplente)
CORVAJA
ROBERTO
(Supplente)
ERSEGHE
TOMASO
(Supplente)
LAURENTI
NICOLA
(Supplente)
ROSSI
MICHELE
(Supplente)
TOMASIN
STEFANO
(Supplente)
VANGELISTA
LORENZO
(Supplente)

1 A.A. 2017/2018 
01/10/2017 
15/03/2019 
ZORZI
MICHELE
(Presidente)
ZANELLA
ANDREA
(Membro Effettivo)
BADIA
LEONARDO
(Supplente)
CALVAGNO
GIANCARLO
(Supplente)
CORVAJA
ROBERTO
(Supplente)
ERSEGHE
TOMASO
(Supplente)
LAURENTI
NICOLA
(Supplente)
MILANI
SIMONE
(Supplente)
ROSSI
MICHELE
(Supplente)
TOMASIN
STEFANO
(Supplente)
VANGELISTA
LORENZO
(Supplente)
ZANUTTIGH
PIETRO
(Supplente)

Prerequisites:

The course requires preliminary knowledge of: Mathematical Analysis, Probability, random variables and random processes, networks and protocols. It also assumes a minimum level of programming experience and a basic knowledge of some language (eg, MATLAB, C, Python). 
Target skills and knowledge:

The training objective of the course involves the acquisition of the following knowledge and skills:
1. Understanding the main problems related to simulation and performance analysis through random computer experiments
2. Understanding the probabilistic fundamentals underlying the theory of statistical confidence
3. Knowing how to design simple simulation programs, write the relative code, and be able to correctly interpret and represent the results obtained
4. To gain experience with a complex network simulator (Omnet++) through appropriate laboratory experiments
5. To demonstrate the knowledge of the methodologies of the course and the ability to use a simulation tool to solve a problem of interest, presenting and discussing its formulation and results 
Examination methods:

The assessment of the knowledge and skills acquired is carried out through the development and presentation of a project. About half way through the course, students are offered some possible topics for a project to be developed by the end of the course, individually or in groups of 2 or 3 people. The project, which should engage every student for 4050 hours in total, must be illustrated in a written document and presented with slides. The presentation and discussion of the project, possibly supplemented by a discussion of the homeworks, constitutes the final exam. 
Assessment criteria:

The evaluation of the acquired knowledge and skills will be carried out considering:
1. The completeness and depth of the knowledge of the topics covered during the course.
2. The ability to apply the theoretical concepts treated during the course to specific practical problems
3. The ability to obtain correct numerical results in the proposed exercises
4. The ability to develop, describe and present the final project. 
Course unit contents:

1. Introduction to simulation, methodologies
2. Data analysis, confidence intervals for various metrics
3. Random number generators and their properties
4. Generation of random variables with any distribution
5. MonteCarlo simulations, dynamic simulations, eventdriven simulations
6. Variance reduction techniques
7. Calculation of complex integrals with Gauss quadrature formulas
8. Application examples: SIR in cellular systems, Slotted ALOHA on radio channel, performance of a geographic routing protocol
9. Various simulations using Omnet ++ (laboratory) 
Planned learning activities and teaching methods:

The course is made of 32 hours of lectures and 16 hours of laboratory activities.
Some classes are carried out using slides, which makes it easier to use complex figures. The more theoretical classes are instead carried out on the blackboard, as it is believed that this method of delivery allows to maintain the right rhythm of presentation of the topics and to keep the students more engaged. In any case, we will try to maintain an interactive teaching style, stimulating students to intervene and discuss with the teacher and with each other.
The laboratory activities are based on the Omnet++ simulator and require the student to simulate progressively more complex systems and to perform simple parametric studies through simulation.
The course includes four mandatory homeworks, which consist mainly of programming exercises and visualization of results, to verify the acquisition of the concepts developed in class and to stimulate the students to carry out practical activities.
Finally, the course includes the development of a final project whose presentation and discussion constitutes the final exam. 
Additional notes about suggested reading:

The teaching material, including all the transparencies used in class, the texts that deal with the topics developed, scientific articles covered in class, exercises, datasets to be displayed, is entirely available on the website of the course on the elearning platform. 
Textbooks (and optional supplementary readings) 

J.Y. Le Boudec, Performance Evaluation of Computer and Communication Systems. Lausanne, Switzerland: EPFL Press, 2010.

S. Ross, Simulation. : Wiley, 2006. 4th ed.

A.M. Law, Simulation Modeling and Analysis. : McGrawHill, 2006. 4th ed.

Innovative teaching methods: Teaching and learning strategies
 Lecturing
 Laboratory
 Problem based learning
 Working in group
 Problem solving
 Loading of files and pages (web pages, Moodle, ...)
 Omnet++ network simulator
Innovative teaching methods: Software or applications used
 Moodle (files, quizzes, workshops, ...)
 Matlab
Sustainable Development Goals (SDGs)

