# Search result: Catalogue data in Autumn Semester 2018

Electrical Engineering and Information Technology Master | ||||||

Master Studies (Programme Regulations 2018) | ||||||

Communication The core courses and specialization courses below are a selection for students who wish to specialize 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. | ||||||

Core Courses 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. | ||||||

Foundation Core Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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227-0121-00L | Communication Systems | W | 6 credits | 4G | A. Wittneben | |

Abstract | Information Theory, Signal Space Analysis, Baseband Transmission, Passband Transmission, Example und Channel, Data Link Layer, MAC, Example Layer 2, Layer 3, Internet | |||||

Learning objective | Introduction into the fundamentals of digital communication systems. Selected examples on the application of the fundamental principles in existing and upcoming communication systems | |||||

Content | Covered are the lower three layer of the OSI reference model: the physical, the data link, and the network layer. The basic terms of information theory are introduced. After this, we focus on the methods for the point to point communication, which may be addressed elegantly and coherently in the signal space. Methods for error detection and correction as well as protocols for the retransmission of perturbed data will be covered. Also the medium access for systems with shared medium will be discussed. Finally, algorithms for routing and flow control will be treated. The application of the basic methods will be extensively explained using existing and future wireless and wired systems. | |||||

Lecture notes | Lecture Slides | |||||

Literature | [1] Simon Haykin, Communication Systems, 4. Auflage, John Wiley & Sons, 2001 [2] Andrew S. Tanenbaum, Computernetzwerke, 3. Auflage, Pearson Studium, 2003 [3] M. Bossert und M. Breitbach, Digitale Netze, 1. Auflage, Teubner, 1999 | |||||

227-0101-00L | Discrete-Time and Statistical Signal Processing | W | 6 credits | 4G | H.‑A. Loeliger | |

Abstract | The 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. | |||||

Learning objective | The 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 throughout the course is the stable and robust "inversion" of a linear filter. | |||||

Content | 1. 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 notes | Lecture Notes | |||||

Advanced Core Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

227-0301-00L | Optical Communication Fundamentals | W | 6 credits | 2V + 1U + 1P | J. Leuthold | |

Abstract | 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. | |||||

Learning objective | 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. | |||||

Content | * 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. | |||||

Lecture notes | Lecture notes are handed out. | |||||

Literature | Govind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010 | |||||

Prerequisites / Notice | Fundamentals of Electromagnetic Fields & Bachelor Lectures on Physics. | |||||

227-0417-00L | Information Theory I | W | 6 credits | 4G | A. Lapidoth | |

Abstract | 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. | |||||

Learning objective | The fundamentals of Information Theory including Shannon's source coding and channel coding theorems | |||||

Content | 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 | |||||

Literature | T.M. Cover and J. Thomas, Elements of Information Theory (second edition) | |||||

227-0427-00L | Signal Analysis, Models, and Machine Learning | W | 6 credits | 4G | H.‑A. Loeliger | |

Abstract | 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. | |||||

Learning objective | The course is an introduction to some basic topics in signal processing and machine learning. | |||||

Content | 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. | |||||

Lecture notes | Lecture notes. | |||||

Prerequisites / Notice | 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 SystemsDoes not take place this semester. | E- | 6 credits | 2V + 2U | A. Wittneben | |

Abstract | Wireless access systems support locally constrained wireless connectivity and mobile access to a backbone network (typically the Internet). In this course the student develops a comprehensive understanding of existing and upcoming wireless access technologies (including WiFi, Bluetooth, RFID, NFC, VANET) and related Physical Layer and Medium Access Control Layer problems and opportunities. | |||||

Learning objective | The course consists of two tracks. The track "Technology&Systems" is structured as regular lecture. In the introduction we will discuss the challenges and potential of pervasive wireless access and study some fundamentals of short/medium range wireless communications. The main body of this track is devoted to existing and upcoming systems. A comprehensive survey of Ultrawide band (UWB) as the promising transmission technology for pervasive wireless access completes this track. In the track "Simulate&Practice" we form student teams that implement and analyze functional blocks of the physical layer of various advanced wireless access systems based on MATLAB simulations. The track includes combination tasks where different teams combine their functional blocks (e.g. transmitter, receiver) in order to simulate the complete physical layer. | |||||

Content | 1. Short range wireless communication : fundamental Physical Layer challenges and solutions 2. Wireless Local Area Network (WLAN) 3. Vehicular Networks (VANET) 4. Ultra-Wideband (UWB) technology: fundamental principles, promises and solutions 5. Wireless Body Area Networks (WBAN) 6. Wireless Personal Area Networks (Bluetooth, Zigbee) 7. Radio Frequency Identification (RFID) and Near Field Communication (NFC) | |||||

Lecture notes | Lecture Slides and handouts. | |||||

Literature | Selected Books | |||||

Prerequisites / Notice | Requirements: Knowledge of fundamental principles of digital communication systems (e.g. 227-0121-00 G Kommunikationssysteme) is helpful but not mandatory. Lecture is given in English. |

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