Lehrinhalte
The module ``Wireless Communications Systems (TI)'' consists of 3 courses of 3 Pts each, for a total of
9 Pts (ECTS). Two of these courses are required:
1) Wireless Digital Communications Lab I;
2) Foundations of Digital and Wireless Communications.
Foundations of Digital and Wireless Communications provides the basics of digital and wireless
communications. In particular, the material covered by the course includes basic principles
of communication over time-varying wireless channels, performance measures, stochastic modeling of
wireless channels, the notions of frequency, time and spatial diversity, basic transmit and receive
diversity techniques, the impact of channel state information, basic spread-spectrum techniques,
OFDM principle and basic multiaccess techniques.
The Lab-based course Wireless Digital Communications Lab I
provides the students with a hands-on practical training based on Matlab, on the
algorithms studied in the theory developed in ``Foundations of Digital and Wireless Communications.''
One elective course can be chosen from a list.
In the list of elective courses, one important foundation theoretical course is
Information Theory, which presents the fundamentals of Information Theory. In particular,
information measures and information sources, and the fundamental limits of data compression,
channel coding and (lossy) source coding. Relations to channel coding theory, communication
theory and signal processing (notably, audio, image and video coding) are evidenced.
Other elective include: Estimation and Decision Theory for Communication Systems (providing the fundamentals of statistical signal processing applied to
communication problems), Projection Methods in Signal Processing and Communications (providing a
theoretical framework for optimization in vector spaces, essential for signal processing and communication
algorithms), Compressed Sensing (also known as sparse signal reconstruction, provides the basics of a
new area in signal processing focused on the estimation of inherently redundant signals when only a small
number of linear projections are available, which is gaining more and more importance in areas such as
image coding, features identification, machine learning schemes, channel estimation in wireless
communications, and computation in big-data sets), Network Information Theory (this presents the
extension of classical information theory to network problems, i.e., problems involving more than one
source and one destination, which is at the basis of modern and efficient system design in wireless
networks),
Modern Channel Coding (a comprehensive overview of ``graph-based'' modern channel codes and their
iterative decoding algorithms, such as Turbo Codes, LDPC codes, Spatially coupled LDPC codes),
Cross-Layer Optimization and Resource Allocation in Wireless Networks (methods to formulate and solve
optimization problems related to wireless networks, including decentralized solutions suitable to on-line
protocol implementation), Wireless Communication Systems (a deep overview of present and future
wireless communication standards, their design rationale and their main working principles), Physical-
Layer Security (information-theoretic methods to achieve secure communication over a wireless channel),
MIMO Systems and Adaptive Transmission (schemes and techniques for multiple-antenna systems, which
have become very popular in recent years and have been massively adopted in wireless communication
standards such as IEEE 802.11n, 802.11ac, and 3GPP LTE-Advanced).
Last but not least, the Lab-based course Wireless Digital Communications Lab II
provides additional hands-on practical training based on Matlab, on the
algorithms studied in the theory developed in ``Foundations of Digital and Wireless Communications.''
