SIMULATION AND ANALYSIS OF STOCHASTIC SIGNALS
Academic year2016/17
CourseTELECOMMUNICATIONS ENGINEERING
Code565II
Credits6
PeriodSemester 2
LanguageItalian
| Modules | Area | Type | Hours | Teacher(s) |
| ANALISI E SIMULAZIONE DI SEGNALI ALEATORI | ING-INF/03 | LEZIONI | 60 | |
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Knowledge
The course treats the basics of random processes theory that are of interest for the applications of telecommunication engineering. The scope of the course is get the students familiar with the modelling and analysis of stochastic phenomena. A relevant part of the course will be devoted computer generated examples, to improve programming skills for simulation and statistical analysis of random signals. Seven experimental drill-lessons will be devoted to this purpose.
A feature of the course is the emphasis given to illustrative design examples. To this purpose we have chosen to use MATLAB (acronym for MATrix LABoratory and product of The Math Works, Inc.), guided by the conviction that it provides a computationally efficient tool for a software laboratory, where the concepts of statistical signal processing are explored and the theoretical findings verified.
Assessment criteria of knowledge
During the final laboratory practical demonstration (2 hours), the student is asked to write a Matlab script and run it in order to demonstrate the ability to put into practice the basic principles of statistical signal theory illustrated throughout the course. During the oral exam, the student will be assessed on his/her ability in discussing the main course contents with competence, critical awareness and propriety of expression.
Methods:
- Final oral exam
- Final laboratory practical demonstration
Further information:
The final test is composed by a final laboratory practical demonstration followed by an oral exam. In general, both each part contributes 50% to the definition of the final grade.
Teaching methods
Delivery: face to face
Learning activities:
- attending lectures
- participation in discussions
- individual study
- group work
- Laboratory work
Attendance: Advised
Teaching methods:
- Lectures
- laboratory
- project work
Syllabus
RANDOM VARIABLES: Statistical characterization of random variables. Moments and central moments. Characteristic function and moment theorem. Skewness and kurtosis, and their use for partial characterization of non Gaussian random variables. De Moivre-Lapalace theorem. RANDOM VECTORS: Complements on random vectors. Conditional distributions. Bayes theorem and total probability theorem for continuous random variables. Conditional averages. Gaussian vectors, definition, properties and random generation. Histogram and scatter-plot of random data. Fundamental theorem for transformation of random vectors; linear transformation of Gaussian vectors. Convergence and approximation of a sum, law of large numbers. Central-limit theorem. Measure of a constant random signal embedded in Gaussian noise. DISCRETE-TIME RANDOM PROCESSES: Strict sense and wide sense stationarity. Autocorrelation function (ACF) and power spectra spectral density (PSD), Einstein-Wiener-Khintchine theorem.
Bibliography
M. Ciampi, L. Verrazzani, G. Del Corso: Teoria dei segnali: segnali aleatori, Edizioni ETS, Pisa, 1994.
M. Luise, M.G. Vitetta, "Teoria dei Segnali, 3a Edizione," McGraw-Hill, 2009.
S. Kay: Intuitive probability and random processes, Springer-Verlag, 2006.
M. Diani: Raccolta di esercizi di segnali aleatori, Seconda Ed., Servizio Editoriale Universitario (SEU), Settembre 2005.
Updated: 14/11/2016 17:27
