Suchergebnis: Katalogdaten im Herbstsemester 2020
|Elektrotechnik und Informationstechnologie Master|
|Master-Studium (Studienreglement 2018)|
The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Communication", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html.
The individual study plan is subject to the tutor's approval.
These core courses are particularly recommended for the field of "Communication".
You may choose core courses form other fields in agreement with your tutor.
A minimum of 24 credits must be obtained from core courses during the MSc EEIT.
| Advanced Core Courses|
Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only.
|227-0301-00L||Optical Communication Fundamentals||W||6 KP||2V + 1U + 1P||J. Leuthold|
|Kurzbeschreibung||The path of an analog signal in the transmitter to the digital world in a communication link and back to the analog world at the receiver is discussed. The lecture covers the fundamentals of all important optical and optoelectronic components in a fiber communication system. This includes the transmitter, the fiber channel and the receiver with the electronic digital signal processing elements.|
|Lernziel||An in-depth understanding on how information is transmitted from source to destination. Also the mathematical framework to describe the important elements will be passed on. Students attending the lecture will further get engaged in critical discussion on societal, economical and environmental aspects related to the on-going exponential growth in the field of communications.|
|Inhalt||* Chapter 1: Introduction: Analog/Digital conversion, The communication channel, Shannon channel capacity, Capacity requirements.|
* Chapter 2: The Transmitter: Components of a transmitter, Lasers, The spectrum of a signal, Optical modulators, Modulation formats.
* Chapter 3: The Optical Fiber Channel: Geometrical optics, The wave equations in a fiber, Fiber modes, Fiber propagation, Fiber losses, Nonlinear effects in a fiber.
* Chapter 4: The Receiver: Photodiodes, Receiver noise, Detector schemes (direct detection, coherent detection), Bit-error ratios and error estimations.
* Chapter 5: Digital Signal Processing Techniques: Digital signal processing in a coherent receiver, Error detection teqchniques, Error correction coding.
* Chapter 6: Pulse Shaping and Multiplexing Techniques: WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA.
* Chapter 7: Optical Amplifiers : Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers.
|Skript||Lecture notes are handed out.|
|Literatur||Govind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010|
|Voraussetzungen / Besonderes||Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.|
|227-0417-00L||Information Theory I||W||6 KP||4G||A. Lapidoth|
|Kurzbeschreibung||This course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equi-partition property, Huffman coding, channel capacity, the channel coding theorem, the source-channel separation theorem, and feedback capacity.|
|Lernziel||The fundamentals of Information Theory including Shannon's source coding and channel coding theorems|
|Inhalt||The entropy rate of a source, Typical sequences, the asymptotic equi-partition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the source-channel separation theorem, feedback capacity|
|Literatur||T.M. Cover and J. Thomas, Elements of Information Theory (second edition)|
|227-0427-00L||Signal Analysis, Models, and Machine Learning|
Findet dieses Semester nicht statt.
This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester).
|W||6 KP||4G||H.‑A. Loeliger|
|Kurzbeschreibung||Mathematical methods in signal processing and machine learning. |
I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity.
II. Learning linear and nonlinear functions and filters: neural networks, kernel methods.
III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events.
|Lernziel||The course is an introduction to some basic topics in signal processing and machine learning.|
|Inhalt||Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis.|
Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods.
Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events.
|Voraussetzungen / Besonderes||Prerequisites: |
- local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.)
- others: solid basics in linear algebra and probability theory
|227-0439-00L||Wireless Access Systems||W||6 KP||2V + 2U||A. Wittneben|
|Kurzbeschreibung||The lecture course covers current and upcoming wireless systems for data communication and localization in diverse applications. Important topics are broadband data networks, indoor localization, internet-of-things, biomedical sensor networks and smart grid communications. The course consists of two tracks, the lecture part “Technology & Systems” and the group exercise part “Simulate & Practice”.|
|Lernziel||General learning goals of the course: |
By the end of this course, students will be able to
- understand and illustrate the physical layer and MAC layer limits and challenges of wireless systems with emphasis on data communication and localization
- understand and explain the functioning of the most widely used wireless systems
- model and simulate the physical layer of state-of-the-art wireless systems
- explain challenges and solutions of indoor localization
- understand research challenges of future wireless networks
Specific learning goals include:
- Understanding the principles of OFDM and analyzing its performance on the physical layer
- Understanding and evaluating the challenges regarding current applications of wireless networks, e.g. for the internet-of-things, smart grid communication, biomedical sensor communication
- Illustrating the characteristics of the wireless channel
- Simulation of localization and user tracking based on wireless systems
- Explaining the basics of smart grid communications approaches (including narrowband PLC, G3-PLC)
- Wireless communication: fundamental Physical layer and MAC layer limits and challenges
- Basics of OFDM
- Wireless systems: WiFi / WLAN
- Wireless systems: Bluetooth, RFID (Radio Frequency Identification) and NFC (Near Field Communication)
- Indoor localization based on wireless systems
- Internet-of-things: Challenges and solutions regarding wireless data communication and localization
- Smart grid communications
- Biomedical sensor communication
- Next generation designs (glimpse on current research topics)
The goal of the course is to explain and analyze modern and future wireless systems for data communication and localization. The course covers designs for generic applications (e.g. WiFi, Bluetooth) as well as systems optimized for specific applications (e.g. biomedical sensor networks, smart grid communications).
The course consists of two parallel tracks. The track "Technology&Systems" is structured as regular lecture. In the introduction, we discuss the challenges and potential of wireless access and study some fundamental limits of wireless communications and localization approaches.
The second part of this track is devoted to the most widely used wireless systems, WiFi/WLAN, Bluetooth, RFID, NFC. Furthermore, we study the potential of using existing wireless communication systems for indoor localization.
The third part follows with an introduction to the internet-of-things, where we focus on data communication and localization challenges and solutions in wireless networks with a massive number of nodes. Next, we study communication technologies for the smart grid, which combine wireless as well as power line communication approaches to optimize availability and efficiency.
The track is completed by a comprehensive survey of short-range magneto-inductive micro sensor networks for communication and localization - as a promising technology for biomedical sensor communication (in-body, out-of-body).
In the track "Simulate&Practice" we form student teams to simulate and analyze functional blocks of the physical layer of advanced wireless systems (based on MATLAB simulations). The track includes combination tasks in which different teams combine their functional blocks (e.g. transmitter, receiver) in order to simulate the complete physical layer of a wireless system. The focus is on data communication and localization. The tasks include modeling and simulating of single-carrier systems (as, e.g., used in Bluetooth), multi-carrier OFDM systems (e.g. used in WiFi or power line communication), and indoor localization approaches (e.g. relevant for IoT and sensor networks).
|Skript||Lecture slides are available.|
|Literatur||Will be announced in the lecture.|
|Voraussetzungen / Besonderes||English|
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