Modeling and simulation– Modern Coding Systems and Applications (4V, 2Ü)

Course content

1. Basic models for modern coding systems
2. Principles of digital coding: encoding/decoding concepts, performance metrics, and simulation-based evaluation
3. Modern codes: Polar codes, LDPC codes, iterative decoding
4. Analog coding for over-the-air function computation
5. Machine learning-based coding methods: autoencoder-based communication systems, neural decoders, and data-driven performance optimization
6. Coding for modern storage and distributed systems: DNA storage, coded distributed computing, and network coding concepts
7. Coding for trustworthy and efficient networked applications: federated learning, privacy-aware distributed learning, and selected blockchain/network-coding applications
8. Modelling, simulation, and validation of coding systems: abstraction levels, performance-complexity trade-offs, benchmark scenarios, and interpretation of simulation results
9. Selected recent research papers on modern coding applications, including student presentations and moderated technical discussions

Literature

Richardson and Urbanke: Modern Coding Theory
MacKay: Information Theory, Inference, and Learning Algorithms
Selected recent articles from journals and conferences on modern coding systems and applications

After successful completion of the module, students are able to compare modern coding approaches with respect to reliability, efficiency, latency, complexity, scalability, and implementation constraints. They can interpret recent research papers and transfer the presented methods to emerging application areas such as wireless communication systems, DNA storage, over-the-air computation, federated learning, network coding, trustworthy distributed systems, smart grids, distributed energy infrastructures, autonomous robotic systems, and industrial automation networks.

In addition to the technical aspects, the course enables students to recognize and integrate the broader challenges of current and future digital societies into their professional practice, fostering social responsibility. Within the field of modern coding systems and their applications, this includes, for example, raising awareness of socially relevant issues such as data security, privacy, digital inclusion, sustainable communication and storage infrastructures, reliable information processing in networked systems, and resource-efficient digitalization of energy, automation, and robotic systems.

Module examination: Oral examination (max. 40 minutes) or written examination (max. 180 minutes).*

Coursework: Successful completion of coursework includes the preparation and presentation of a selected recent research article as well as active participation in a moderated technical discussion, for example in the role of session chair. Successful completion of the coursework is a prerequisite for admission to the module examination.

*The exact examination modalities will be announced no later than in the second course session.

None

Basic module in the Master’s degree program “Electrical Engineering and Information Technology” for all study profiles.
Compulsory elective module in the Master’s degree program “Automation and Robotics”, recommended for the profiles “Machine Learning” and “Robotics”.

Course materials, including lecture notes, tutorial sheets, and other materials, can be found in the Moodle workspace. Please follow these steps to register:
First, please register for the course (lecture and tutorial) in LSF as early as possible.
By registering in LSF, you will be activated for the Moodle workspace; this may take some time.
Lecture notes, tutorial sheets, slides, and other materials are available for download in the Moodle workspace.