ECTS - University of Pisa
Gli obiettivi del corso sono:
The objectives of the course are:
Durante l'esame finale lo studente deve essere in grado di dimostrare sia il livello di conoscenza e di comprensione del materiale del corso che le conoscenze acquiste durante le ore di laboratorio.
Metodo di verifica
During the final exam the student must be able to demonstrate both the level of knowledge and understanding of the course material and the knowledge acquired during the laboratory hours.
Verification method:
Lo studente che supera con successo l'esame avrà acquisito le conoscenze necessarie all'analisi dei segnali e sistemi e le tecnologie di base per il trasferimento dei dati nei sistemi di comunicazioni.
The student who successfully passes the exam will have acquired the knowledge necessary for the analysis of signals and systems and the basic technologies for data transfer in communications systems.
Valutazione di piccoli progetti di programmazione assegnati durante il corso e esame finale orale.
Evaluation of small programming projects assigned during the course and final oral exam.
Gli studenti apprenderanno le tecniche fondamentali per l'analisi dei segnali/sistemi e la trasmissione dei dati nei sistemi di comunicazione, e alcuni risultati allo stato dell'arte.
Students will learn the fundamental techniques for signal / systems analysis and data transmission in communication systems, and some state of the art results.
La verifica viene effettuata in occasione della prova orale ma anche durante il corso, sia nell'ambito delle lezioni teoriche che delle esercitazioni in MATLAB.
The verification is carried out on the occasion of the oral test but also during the course, both in the context of the theoretical lessons and the exercises in MATLAB.
Conoscenze di base di analisi matematica e teoria della probabilità. Conoscenze di base del linguaggio di programmazione MATLAB.
Basic knowledge of mathematical analysis and probability theory. Basic knowledge of the MATLAB programming language.
Modalità di svolgimento delle lezioni: lezioni frontali, con ausilio di slide (in Inglese)
Modalità di apprendimento:
Presenza alle lezioni: Consigliata
Metodi di insegnamento:
Forme aggiuntive di interazione con gli studenti:
Lectures: lectures, with the help of slides (in English)
Learning mode:
Attendance at lessons: Recommended
Teaching methods:
Additional forms of interaction with students:
Il programma del corso è il seguente:
1. Fourier Analysis of Signals and Systems: The Fourier Transform, Transmission of Signals through Linear Time-Invariant System, Canonical Representation of Band-Pass Signals, Linear Modulation Theory, Numerical Computation of the Fourier Transform
2. Probability Theory and Bayesian Inference: Probability Theory, Random Variables, The Gaussian Distribution, The Central Limit Theorem, Bayesian Inference
3. Stocastic Processes: Definition, Strictly Stationary and Weakly Stationary Processes, Ergodic Processes, Transmission of a Weakly Stationary Process through a Linear Time-invariant Filter, Power Spectral Density of a Weakly Stationary Process, The Gaussian Process, Noise
4. Basics of Information Theory: Entropy, Lossless Data Compression Algorithms, Channel Capacity
5. Conversion of Analog Waveforms into Coded Pulses: Sampling Theory, Pulse-Amplitude Modulation, Phase-Shift Keying Techniques, Quadrature Amplitude Modulation
6. Signaling over Fading Channels: large scale and small scale fading. Orthogonal Frequency Division Multiplexing, Spread Spectrum Signals.
7. Error Control Coding: Linear Block Codes, Convolutional Codes, Turbo Codes, Low-Density Parity -Check Codes
8. Cellular networks: 3G, 4G, 5G and their multiplexing and multiple access technologies.
1. Fourier Analysis of Signals and Systems: The Fourier Transform, Transmission of Signals through Linear Time-Invariant System, Canonical Representation of Band-Pass Signals, Linear Modulation Theory, Numerical Computation of the Fourier Transform
2. Probability Theory and Bayesian Inference: Probability Theory, Random Variables, The Gaussian Distribution, The Central Limit Theorem, Bayesian Inference
3. Stocastic Processes: Definition, Strictly Stationary and Weakly Stationary Processes, Ergodic Processes, Transmission of a Weakly Stationary Process through a Linear Time-invariant Filter, Power Spectral Density of a Weakly Stationary Process, The Gaussian Process, Noise
4. Basics of Information Theory: Entropy, Lossless Data Compression Algorithms, Channel Capacity
5. Conversion of Analog Waveforms into Coded Pulses: Sampling Theory, Pulse-Amplitude Modulation, Phase-Shift Keying Techniques, Quadrature Amplitude Modulation
6. Signaling over Fading Channels: large scale and small scale fading. Orthogonal Frequency Division Multiplexing, Spread Spectrum Signals.
7. Error Control Coding: Linear Block Codes, Convolutional Codes, Turbo Codes, Low-Density Parity -Check Codes
8. Cellular networks: 3G, 4G, 5G and their multiplexing and multiple access technologies.
Le lezioni faranno uso delle slides, aclune note aggiuntive preparate dai docenti, e da ulteriore materiale didattico che sarà presentato durante in corso, fra cui il seguente libro di testo:
Slides presented during the lectures and other reference material that will be indicated during the course, including the folling book:
I docenti hanno predisposto delle slides a supporto dello studio individuale per i non frequentanti. Al fine di milgiorare l'apprendimento, gli studenti non frequentanti sono inoltre invitati a contattare i docenti (via email) per spiegazioni aggiuntive (e/o approfondimenti).
The teachers prepared slides to support the individual study for non-attending students. In order to improve learning, non-attending students are also invited to contact the teachers (via email) for additional explanations (and / or insights).
Esame orale, e progetto individuale in Matlab.
Oral exam, and individual project in Matlab.
