Lecture Slides
- Introduction
- Introduction Stochastic
- Basics of Information Theory
- Source Coding Theorem
- Typical Sequences
- Source Coding
- Arithmetic Coding
- Sources with Memory
- Channel Coding
- Channel Coding Theorem
- Zero Error Capacity
- MAP- and ML-Decoding
- Gaussian Channels
- Gaussian Channels II
- Multi-User Communication
- Multiple-Access Channel
- Diversity
- IT Security
Further materials
Exercise Sheets
- Sheet 1 (Introduction to probability theory, Expected value (2), Law of total expectation, Probability calculus, Moment generating function):
- Exercises Solutions
- Sheet 2 (Entropy and mutual information during data transmission, Entropy of the geometric distribution, Conditional entropies, Conditional mutual information, Kullback-Leibler distance and average mutual information):
- Exercises Solutions
- Sheet 3 (Typical sequences, Uniquely decodable and prefix-free codes, Shannon-Fano code, Huffman code):
- Exercises Solutions
- Sheet 4 (Efficiency of different source coding schemes, Markov-sources):
- Exercises Solutions
- Sheet 5 (Linear block codes, Shortened Hamming code, Jointly typical sequences):
- Exercises Solutions
- Sheet 6 (Binary symmetric erasure channel, Channel capacity for quantized AWGN-channel):
- Exercises Solutions
- Sheet 7 (Gaussian distribution maximizes entropy, The mutual information game):
- Exercises Solutions
- Sheet 8 (Waterfilling, Tomlinson-Harashima precoding):
- Exercises Solutions
- Sheet 9 (Degraded broadcast channels, Broadcast vs. TDMA):
- Exercises Solutions
- Sheet 10 (TDMA in the multiple-access channel, Square multiple-access channel, M-user multiple-access channel):
- Exercises Solutions
Lab
- Lab 1 (Lempel-Ziv):
- Introduction and TasksMaterials
- Lab 2 (Mutual information):
- Introduction and TasksMaterials