Modern Channel Coding, Inference and Learning

Aim

After completed course the student should be able to:

• define correctly and explain about the following notions: Hamming distance, linear error-correcting code, LDPC code, “Turbo” code, optimal decoding, iterative decoding, decoding region, channel capacity, density evolution, Monte Carlo simulations, marginalization, neural network;
• passably implement decoding algorithms for modern channel codes as well as plot and analyze performance of those;
• fairly well handle necessary mathematical tools: random variables variables, Bayesian inference, Monte Carlo methods, neural networks;
• independently use advanced channel coding techniques in practical applications;
• implement K-means clustering algorithms for sets of data points;

Content

• Introduction to information theory and fundamental limits for communication over noisy channels;
• Modern error-correcting codes: LDPC codes and "Turbo" codes;
• Optimal decoding: ML- och MAP- decoding;
• Iterative decoding algorithms and analysis av their performance;
• Bayesian inference and examples of its applications;
• K-means clustering algorithms;
• Exact marginalization;
• Monte Carlo methods for simulation of physical systems;
• Introduction to neural networks: single neurons and examples;
• Capacity of a single neuron;