Advanced Topics in Signal Processing
Lecturer: Frank Fitzek
Target Audience: Master EI and MSCE
Next Exam: tbd. (no responsibility is taken for the correctness of this information)
Additional Information: TUMonline and Moodle
Lectures/Tutorials in Summer Semester 2018
This course introduces the students to the challenges and approaches of the state of the art implementations of network coding. The course is taught not just through lectures, but also with hands-‐on exercises using the KODO software library. KODO is available in C++, Python and as web tool and is therefore addressing the different implementation skills of the student.
The initial lectures refresh the knowledge of the students of the theoretical background of network coding, e.g., the min-‐cut max-‐flow of a network, inter-‐flow network coding, and intra-‐flow Random Linear Network Coding (RLNC). The student is then introduced to the state of the art software library KODO and the advanced implementations of network coding such as systematic, sparse, tunable sparse, sliding window, etc. The course also covers the benefits of network coding in distributed storage applications. By the end of the course, the student will be introduced to advanced applications of network coding, e.g., Coded TCP, MORE, FULCRUM.
The exercises will teach the students how to use sockets in python as well as the python bindings of the KODO software library for implementing unicast and broadcast communication applications.