Advanced Topics in Communications Theory

Course content

In this module, students learn the current advanced topics in communications theory. This comprises current methods and tools from statistical signal processing as well as statistical and information theoretic modelling of communications systems (e.g. arbitrarily varying channels, copula), the analysis and design of communications systems using machine learning (reinforcement learning, deep neural networks, and others). The module entitles students to study current research questions from communications theory by appying modern and solid methods. The exercise is implemented as a "reading class", in which current research publications are presented by the students in short presentations. 

• Mathematical modelling of communications systems
• Performance analaysis of communications systems
• Coding and transmission over arbitrarily varying channels
• Multiuser networks and statistical dependent channels
• Bayesian inference and statistic
• Fisher information and Cramer-Rao bound
• Deep Neural Networks and global programming
• Reinforcement learning for optimization of complex networks