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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

Course Reviews

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