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
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.
