Summer School | Robotics, Communication Networks and Innovation

After showing the history of information theory and communication systems, these lectures introduce the basics of stochastic modelling, e.g., basic rules of probability theory, random variables, random vectors and transformations and stochastic models for mobile radio channels. Then, we will learn about the elements of information transmission such as discrete models for entropy and transinformation, capacity, source coding, channel capacity and the fundamental theorem of channel coding. Throughout the course, we will also see many applications.

The second part addresses solving the occurring problems. In the last two decades, important problems in modern communication technology have been solved by means of optimization methods. Therefore, we provide you with the corresponding theory and efficient solution methods. These are demonstrated with a variety of current examples from information theory and communication technology, e.g. power control in wireless networks and rate adaptation in multicarrier systems. Special emphasis is put on modeling, convex optimization, Lagrange duality and Karush-Kuhn-Tucker conditions. Further, we will address linear programming, branch-and-bound, heuristics for channel allocation, simulated annealing and other randomized procedures.