# Fiches de cours

## Signal processing for communications

Prandoni Paolo

English

#### Summary

Students learn digital signal processing theory, including discrete time, Fourier analysis, filter design, adaptive filtering, sampling, interpolation and quantization; they are introduced to image processing and data communication system design.

#### Content

1. Basic discrete-time signals and systems: signal classes and operations on discrete-time signals, signals as vectors in Hilbert space
2. Fourier Analysis: properties of Fourier transforms, DFT, DTFT; FFT.
3. Discrete-Time Systems: LTI filters, convolution and modulation; difference equations; FIR vs IIR, stability issues.
4. Z-transform: properties and regions of convergence, applications to linear systems.
5. Filter Design: FIR design methods, IIR design methods, filter structures.
6. Stochastic and Adaptive Signal Processing: random processes, spectral representation, Optimal Least Squares adaptive filters.
7. Interpolation and Sampling: the continuous-time paradigm, interpolationthe sampling theorem, aliasing.
8. Quantization: A/D and D/A converters.
9. Multi-rate signal processing: upsampling and downsampling, oversampling.
10. Multi-dimensional signals and processing: introduction to Image Processing.
11. Practical applications: digital communication system design, ADSL.

#### Keywords

signal processing, discrete-time, continuous-time, filter, filter design, sampling, aliasing, DSP, Fourier transform, FFT, modem, ADSL

#### Learning Prerequisites

##### Required courses

calculus, linear algebra

##### Recommended courses

Circuits and systems, basic probability theory

##### Important concepts to start the course

vectors and vector spaces, functions and sequences, infinite series

#### Learning Outcomes

By the end of the course, the student must be able to:
• Identify signals and signal types
• Recognize signal processing problems
• Apply the correct analysis tools to specific signals
• Check system stability
• Manipulate rational transfer functions
• Implement signal processing algorithms
• Design digital filters
• Interpret complex signal processing systems

#### Transversal skills

• Use a work methodology appropriate to the task.
• Assess one's own level of skill acquisition, and plan their on-going learning goals.
• Use both general and domain specific IT resources and tools

#### Teaching methods

Course with exercises sessions and coding examples and exercises in Python (Jupyter Notebooks)

#### Expected student activities

complete weekly homework, explore and modify Jupyter Notebook examples

#### Assessment methods

final exam fully determines final grade.

#### Supervision

 Office hours Yes Assistants Yes Forum Yes

#### Resources

##### Bibliography

Signal processing for Communications, EPFL Press, 2008, by P. Prandoni and M. Vetterli. The book is available for sale in printed form online and in bookstores; in iBook format on the Apple store and is also available as a free pdf file at http://www.sp4comm.org/

##### Notes/Handbook

lecture slides available for download at the beginning of the semester.

A complete online DSP MOOC is available on Coursera.

#### Prerequisite for

adaptive signal processing, image processing, audio processing, advanced signal processing

### Dans les plans d'études

• Auditeurs en ligne, 2018-2019, Semestre printemps
• Semestre
Printemps
• Forme de l'examen
Ecrit
• Crédits
6
• Matière examinée
Signal processing for communications
• Cours
4 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Semestre
Printemps
• Forme de l'examen
Ecrit
• Crédits
6
• Matière examinée
Signal processing for communications
• Cours
4 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Passerelle HES - SC, 2018-2019, Semestre printemps
• Semestre
Printemps
• Forme de l'examen
Ecrit
• Crédits
6
• Matière examinée
Signal processing for communications
• Cours
4 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Semestre
Printemps
• Forme de l'examen
Ecrit
• Crédits
6
• Matière examinée
Signal processing for communications
• Cours
4 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Semestre
Printemps
• Forme de l'examen
Ecrit
• Crédits
6
• Matière examinée
Signal processing for communications
• Cours
4 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Semestre
Printemps
• Forme de l'examen
Ecrit
• Crédits
6
• Matière examinée
Signal processing for communications
• Cours
4 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines
• Semestre
Printemps
• Forme de l'examen
Ecrit
• Crédits
6
• Matière examinée
Signal processing for communications
• Cours
4 Heure(s) hebdo x 14 semaines
• Exercices
2 Heure(s) hebdo x 14 semaines

### Semaine de référence

LuMaMeJeVe
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
En construction

Cours
Exercice, TP
Projet, autre

### légende

• Semestre d'automne
• Session d'hiver
• Semestre de printemps
• Session d'été
• Cours en français
• Cours en anglais
• Cours en allemand