Coursebooks

Signal processing for communications

COM-303

Lecturer(s) :

Prandoni Paolo

Language:

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:

Transversal skills

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/

Ressources en bibliothèque
Notes/Handbook

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

A complete online DSP MOOC is available on Coursera.

Websites

Prerequisite for

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

In the programs

  • Auditeurs en ligne, 2018-2019, Spring semester
    • Semester
       Spring
    • Exam form
       Written
    • Credits
      6
    • Subject examined
      Signal processing for communications
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Written
    • Credits
      6
    • Subject examined
      Signal processing for communications
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Passerelle HES - SC, 2018-2019, Spring semester
    • Semester
       Spring
    • Exam form
       Written
    • Credits
      6
    • Subject examined
      Signal processing for communications
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Written
    • Credits
      6
    • Subject examined
      Signal processing for communications
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Written
    • Credits
      6
    • Subject examined
      Signal processing for communications
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Written
    • Credits
      6
    • Subject examined
      Signal processing for communications
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Written
    • Credits
      6
    • Subject examined
      Signal processing for communications
    • Lecture
      4 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
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     
Under construction
 
      Lecture
      Exercise, TP
      Project, other

legend

  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German