Moses - Modern Signal Processing for Communications

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SS 2018 - SoSe 2020. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#40829 / #2

Validity:
SS 2018 - SoSe 2020

Default display language:
English

Module title:
Modern Signal Processing for Communications

Credits:
6

Responsible person:
Stanczak, Slawomir

Grading:
benotet

Type of exam:
Mündliche Prüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Telekommunikationssysteme

Area of expertise:
34331800 FG Netzwerk- und Informationstheorie

Prüfungsausschuss:
No information

Kontakt

Office:
HFT 6

Contact person:
Reinhardt, Kerstin

E-mail address:
sekretariat@netit.tu-berlin.de

Website:

http://www.netit.tu-berlin.de/

Learning Outcomes

After completion of this module, the students have the ability to apply various methods and tools of modern signal processing to solve problems in a broad area of wireless communications. Moreover, they will better understand the fundamental relationships in wireless networks and obtain valuable insights into the design and operation of such networks. Finally the lecture intends to convey a comprehensive understanding of selected theoretical concepts used in wireless network optimization such as random matrix theory and non-linear Perron-Frobenius theory.

Content

The learning content includes: - Modern signal processing methods for interference reduction in spread spectrum and MIMO systems, adaptive beamforming, PAPR reduction in OFDM systems, acoustic source localization with wireless sensor networks, environmental modeling in wireless multi-agent systems - Fundamentals of (convex) optimization theory, projection methods, principles of convex relaxation - Axiomatic framework for interference modeling, existence and uniqueness of fixed points, fixed-point algorithms, applications of standard interference functions - Non-linear Perron-Frobenius theory - (Non-asymptotic) random matrix theory

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Mathematical Introduction to Machine Learning	VL		WiSe	No information	2
Modern Signal Processing for Communications	VL	3433 L 8371	SoSe	No information	2

Workload and Credit Points

Mathematical Introduction to Machine Learning (VL):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Pre/post processing	15.0	4.0h	60.0h
			90.0h(~3 LP)

Modern Signal Processing for Communications (VL):

Workload description	Multiplier	Hours	Total
Präsenzzeit	15.0	2.0h	30.0h
Vor-/Nachbereitung	15.0	4.0h	60.0h
			90.0h(~3 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The module consists of conventional frontal teaching in class, developing theoretical and mathematical concepts, and a semester project where students work, possibly in groups, and are assigned a research paper in the area of wireless network optimization to read, understand, and prepare a talk.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Prerequisite for participation to courses are a mathematical background at the level of beginning MS students in Electrical Engineering (signals and systems, linear algebra and notions of matrix theory). The course is open to students enrolled in any MSc in EE CS, Mathematics and Physics.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Oral exam

Language

English

Duration/Extent

45 Minuten

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
2 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Course teaching and organization (not module examination enrollment at Examination office/Prüfungsamt) is supported by an ISIS course. Registration details are provided at the beginning of the module.

Recommended literature
David G. Luenberger, Optimization by Vector Space Methods, Wiley, 1998
Roman Vershynin, Introduction to the non-asymptotic analysis of random matrices, arXiv, 2011
Stanczak, Wiczanowski and Boche, Fundamentals of Resource Allocation in Wireless Networks: Theory and Algorithms, Springer 2009

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

Miscellaneous

No information

Modern Signal Processing for Communications