The aim of this course is to introduce students to iterative decoding algorithms and the codes to which they are applied, including Turbo Codes, Low-Density Parity-Check Codes, and Serially-Concatenated Codes. The course will begin with an introduction to the fundamental problems of Coding Theory and their mathematical formulations. This will be followed by a study of Belief Propagation–the probabilistic heuristic which underlies iterative decoding algorithms.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
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Course Credit: MIT
Course Curriculum
| Signing Up | 00:05:00 | ||
| Channels | 00:05:00 | ||
| Analysis of Repetition Code Meta-channel | 00:05:00 | ||
| Prior, Extrinsic and Posterior Probabilities, II | 00:10:00 | ||
| Parity Continued | 00:10:00 | ||
| Introduction | 00:05:00 | ||
| Markov Property | 00:05:00 | ||
| Vector Spaces | 00:05:00 | ||
| LDPC Codes | 00:05:00 | ||
| Belief Propagation on Trees | 00:05:00 | ||
| Representing Probabilities, Equality Nodes | 00:05:00 | ||
| The Binary Erasure Channel | 00:10:00 | ||
| Convolutional Codes | 00:05:00 | ||
| Remarks on Convolutional Codes | 00:10:00 | ||
| Decoding Modules | 00:05:00 | ||
| Developments in Iterative Decoding | 00:05:00 | ||
| Assessment | |||
| Submit Your Assignment | 00:00:00 | ||
| Certification | 00:00:00 | ||
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