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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.
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Description
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 content
- Signing Up Unlimited
- Channels Unlimited
- Analysis of Repetition Code Meta-channel Unlimited
- Prior, Extrinsic and Posterior Probabilities, II Unlimited
- Parity Continued Unlimited
- Introduction Unlimited
- Markov Property Unlimited
- Vector Spaces Unlimited
- LDPC Codes Unlimited
- Belief Propagation on Trees Unlimited
- Representing Probabilities, Equality Nodes Unlimited
- The Binary Erasure Channel Unlimited
- Convolutional Codes Unlimited
- Remarks on Convolutional Codes Unlimited
- Decoding Modules Unlimited
- Developments in Iterative Decoding Unlimited
Instructor
