Amos Lapidoth: Catalogue data in Spring Semester 2020
|Name||Prof. Dr. Amos Lapidoth|
Inst. f. Signal-u.Inf.verarbeitung
ETH Zürich, ETF E 107
|Telephone||+41 44 632 51 92|
|Department||Information Technology and Electrical Engineering|
|227-0104-00L||Communication and Detection Theory||6 credits||4G||A. Lapidoth|
|Abstract||This course teaches the foundations of modern digital communications and detection theory. Topics include the geometry of the space of energy-limited signals; the baseband representation of passband signals, spectral efficiency and the Nyquist Criterion; the power and power spectral density of PAM and QAM; hypothesis testing; Gaussian stochastic processes; and detection in white Gaussian noise.|
|Objective||This is an introductory class to the field of wired and wireless communication. It offers a glimpse at classical analog modulation (AM, FM), but mainly focuses on aspects of modern digital communication, including modulation schemes, spectral efficiency, power budget analysis, block and convolu- tional codes, receiver design, and multi- accessing schemes such as TDMA, FDMA and Spread Spectrum.|
|Content||- Baseband representation of passband signals.|
- Bandwidth and inner products in baseband and passband.
- The geometry of the space of energy-limited signals.
- The Sampling Theorem as an orthonormal expansion.
- Sampling passband signals.
- Pulse Amplitude Modulation (PAM): energy, power, and power spectral density.
- Nyquist Pulses.
- Quadrature Amplitude Modulation (QAM).
- Hypothesis testing.
- The Bhattacharyya Bound.
- The multivariate Gaussian distribution
- Gaussian stochastic processes.
- Detection in white Gaussian noise.
|Literature||A. Lapidoth, A Foundation in Digital Communication, Cambridge University Press, 2nd edition (2017)|
|227-0420-00L||Information Theory II |
Does not take place this semester.
|6 credits||2V + 2U||A. Lapidoth|
|Abstract||This course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory.|
|Objective||The course has two objectives: to introduce the students to the key information theoretic results that underlay the design of communication systems and to equip the students with the tools that are needed to conduct research in Information Theory.|
|Content||Differential entropy, maximum entropy, the Gaussian channel and water filling, the entropy-power inequality, Sanov's Theorem, Fisher information, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, and the Gelfand-Pinsker problem.|
|Literature||T.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006|
|401-5680-00L||Foundations of Data Science Seminar||0 credits||P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, N. Meinshausen, G. Rätsch, C. Uhler, S. van de Geer, F. Yang|