227-0101-AAL  Discrete-Time and Statistical Signal Processing

SemesterSpring Semester 2020
LecturersH.‑A. Loeliger
Periodicityevery semester recurring course
Language of instructionEnglish
CommentEnrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement.

Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.


227-0101-AA RDiscrete-Time and Statistical Signal Processing
Self-study course. No presence required.
The underlying lecture is offered in autumn semester.
112s hrsH.‑A. Loeliger

Catalogue data

AbstractThe course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.
ObjectiveThe course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme is the stable and robust "inversion" of a linear filter.
Content1. Discrete-time linear systems and filters:
state-space realizations, z-transform and spectrum,
decimation and interpolation, digital filter design,
stable realizations and robust inversion.

2. The discrete Fourier transform and its use for digital filtering.

3. The statistical perspective:
probability, random variables, discrete-time stochastic processes;
detection and estimation: MAP, ML, Bayesian MMSE, LMMSE;
Wiener filter, LMS adaptive filter, Viterbi algorithm.
Lecture notesLecture Notes

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersH.-A. Loeliger
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 180 minutes
Written aidsLecture Notes (not including problems and solutions) and pesonal notes (max. 4 pages). No electronic devices. (Pocket calculators will be handed out, if necessary.)
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkhttp://www.isi.ee.ethz.ch/teaching/courses/dssp.html
Only public learning materials are listed.


No information on groups available.


There are no additional restrictions for the registration.

Offered in

Electrical Engineering and Information Technology MasterCourse Units for Additional Admission RequirementsE-Information