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Communication and Detection Theory
Spring 2015


⇒ to time table and download of class material

Aim

This introduction to Detection and Communication Theory offers a glimpse at analog communication, but mainly focuses on the foundations of modern digital communication. 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.

Contents

  • 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

Lectures Following Up:

  • Information Theory I (Prof. Lapidoth, 7th Semester)
  • Algebra and Error Correcting Codes (Prof. Loeliger, 8th Semester)

Prerequisites

  • Undergraduate studies
  • Solid foundation in probability and calculus
  • Pleasure with mathematics

Instructor

Prof. Stefan M. Moser
ETF E104
phone: 044 632 36 24
e-mail:

Teaching Assistant

Annina Bracher
Office: ETF E105
Phone: 044 632 28 99
e-mail:

Time and Place

  • Lecture: Tuesday, 13:15–15:00, ETZ E8
  • Exercise: Tuesday, 15:15–17:00 ETZ E8

Textbook

We use the book by Amos Lapidoth:

Amos Lapidoth: A Foundation in Digital Communication, Cambridge University Press, 2009. ISBN: 9780521193955.

Exercises

Printed copies of the exercises will be handed out in class. See time table below.

Examination

Written exam (3 hours).

Testat requirements

None.

Special Remarks

The lecture is in English.

Video Podcast

Video recordings of the lectures can be found here.

Time Table

Note that some linked documents in this table can only be downloaded within ETHZ!

W Date Topic Handouts Exercise Solutions Lecturer
1 17 Feb. Course information, point-to-point digital communication systems, functions, signals, time-reversal, the inner product, orthogonality, energy, the Fourier transform, bandwidth (Ch. 1, 2, 3, 5, 6) Syllabus Exercise 1 Solutions 1 Stefan M. Moser
2 24 Feb. Passband signals, bandwidth around the carrier frequency, analytic signal and baseband representation, inner products in passband and baseband (Ch. 7)   Exercise 2 Solutions 2 Stefan M. Moser
3 3 Mar. The geometry of the space L_2 and the sampling theorem (Ch. 4, 8)   Exercise 3 Solutions 3 Stefan M. Moser
4 10 Mar. Sampling real passband signals, mapping bits onto waveforms, a glimpse at stochastic processes (Ch. 9, 10, 12)   Exercise 4 Solutions 4 Stefan M. Moser
5 17 Mar. Nyquist's criterion, discrete-time stochastic processes: stationarity and wide-sense stationarity (Ch. 11, 13)   Exercise 5 Solutions 5 Stefan M. Moser
6 24 Mar. Energy and power in PAM signals (Ch. 14)   Exercise 6 Solutions 6 Stefan M. Moser
7 31 Mar. Operational power spectral density (OPSD) of a stochastic process, OPSD of a PAM signal (Ch. 15)   Exercise 7 Solutions 7 Stefan M. Moser
7 Apr. holidays    
8 14 Apr. QAM, complex random variables (Ch. 16, 17)   Exercise 8 Solutions 8 Stefan M. Moser
9 21 Apr. Energy, power, and operational PSD in QAM, univariate Gaussians and the Q-function (Ch. 18, 19)   Exercise 9 Solutions 9 Annina Bracher
10 28 Apr. Binary hypothesis testing (Ch. 20)   Exercise 10 Solutions 10 Annina Bracher
11 5 May Multi-hypothesis testing, the nearest-neighbour rule, the union-of-events bound (Ch. 21, 22)   Exercise 11 Solutions 11 Stefan M. Moser
12 12 May Multivariate Gaussian distribution (Ch. 23)   Exercise 12 Solutions 12 Stefan M. Moser
13 19 May Continuous-time stochastic processes, FDDs, Gaussian stochastic processes, linear functionals of stochastic processes (Ch. 25)   Exercise 13 Solutions 13 Stefan M. Moser
14 26 May Detection in white Gaussian noise (Ch. 26)   Exercise 14 Solutions 14 Stefan M. Moser

-||-   _|_ _|_     /    __|__   Stefan M. Moser
[-]     --__|__   /__\    /__   Senior Researcher & Lecturer, ETH Zurich, Switzerland
_|_     -- --|-    _     /  /   Adj. Professor, National Chiao Tung University (NCTU), Taiwan
/ \     []  \|    |_|   / \/    Web: http://moser-isi.ethz.ch/


Last modified: Fri Aug 21 11:44:44 UTC+8 2015