lecture: HS 2010 (großer Hörsaal)
practical fire drill: Emmy-Noether-Str. behind the SFG Building

The date for the initial organisational meeting of this lab, including grouping, will be advertised shortly. Room NW1 N1250.

The ICT lab consists of multiple parts, which are organized by two departments: the RF department and the dept. of communications engineering. Please refer to the individual departments for further information:

http://www.hf.uni-bremen.de/
http://www.ant.uni-bremen.de/courses/ictlab/

Dieser Kurs knüpft an das Modul Digitale Signalverarbeitung an und behandelt fortgeschrittene Themen der digitalen Signalverarbeitung wie die diskrete Fouriertransformation (DFT), ihre Anwendungen, lineare Schätzprobleme und die Spektralschätzung.

ONLINE

Block-Course (during the semester break)

Elective course for M.Sc. CIT, Electrical Engineering & Information Technology and Space-ST.

Contents:
• Why swarm exploration – some motivational examples with applications in space and on Earth
• Preliminaries: selected mathematical formalism (vector spaces, matrices, representation and approximations in vector spaces, least squares, convex optimization)
• Recap of probability and statistics (calculus of probabilities, moments, Bayesian theory)
• Machine learning tools (supervised learning, linear regression, kernel methods, neural networks, impact of regularization, sparsity and compressed sensing)
• Models for multi-agent networks (connected network models, distributed inference strategies)
• Distributed machine learning and exploration (models for static spatial regression, information-theoretic exploration approaches, Bayesian sequential methods for learning and exploration)
• Discussion of several practical examples of swarm exploration solutions (cooperative localization, information-driven sparse mapping of magnetic fields, exploration of sparse gas sources using a swarm of mobile robots)
As outcome, the students should be able to:
• Understand key concepts in distributed information processing over networks,
• Explain and apply mathematical tools needed to implement classical machine learning algorithms in distributed settings

Mobile communication plays a significant role in our daily life. Over the last three decades, mobile applications have been tremendously enhanced from voice-only over mobile broadband to applications for the mobile internet. Currently, the next generation of cellular systems, the so-called '5th Generation (5G)' is developed and the first commercial 5G networks are deployed since 2019. 5G will play an outstanding role for the Internet of Things and will redefine a wide range of industries by enabling new use cases. Meanwhile, the research for further enhancements of 5G, called 5G-Advanced, and even 6G systems has started.

This lecture will provide the technical foundation of 5G and 6G mobile communication systems. In the first part of the lecture, the fundamental concepts of the network architecture, the radio access network, and the PHY & MAC layer technologies are covered. The second part of the lecture focuses on selected topics like machine-type communication, device-to-device communication, and artificial intelligence & machine learning. Also, an outlook to future research for 6G systems will be provided. First deployments of 5G-Advanced and 6G are expected in 2026 and 2030, respectively.

The lecture will be continuously updated shifting the focus from 5G-Advanced to 6G systems over the next years.

Anzahl der Teilnehmer ist begrenzt.

Block-course towards the end of the lecture period.