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CISOSE26 Local AI Uedu Code UG26
政治大學 AQI 43 26°C PM2.5 5
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Uedu Open / Error-Correcting Codes Laboratory
18.413

Error-Correcting Codes Laboratory

Prof. Daniel Spielman | Spring 2004
Data Science, Analytics & Computer Technology Algorithms and Data Structures Networks and Security Computer Science Science & Math Mathematics Engineering Computation
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CC BY-NC-SA 4.0
Course introduction
This course introduces 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. Belief Propagation will then be applied to the decoding of Turbo, LDPC, and Serially-Concatenated codes. The technical portion of the course will conclude with a study of tools for explaining and predicting the behavior of iterative decoding algorithms, including EXIT charts and Density Evolution.
Course Information
SourceMIT 開放式課程
DepartmentMathematics
LanguageEnglish
Number of videos0
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