Coding Theory

Course Purpose
The goal of this course is to understand the limit and possibility of channel coding under noisy channels, and study explicit construction of coding schemes that allows practical and computationally efficient encoding and decoding algorithms.
Learning Goals
Students should acquire knowledge on the limit and possibility of channel coding, and understand the principles of encoding and decoding, and their properties of the various error correcting codes including the Hamming code, Reed-Muller code, convolutional codes, turbo codes, LDPC codes, and polar codes.
Topic
Session 1Lecture outline / Basics of error correction code
Session 2Information theory and channel coding
Session 3Error correction code concept and method
Session 4Mathematical foundation (Finite field, linear algebra, etc.)
Session 5Definition of linear code
Session 6Properties of linear code
Session 7Specific examples of linear codes: Hamming code, Reed-Muller code
Session 8Specific examples of linear codes: Low density parity check code
Session 9Specific examples of linear codes: Polar code
Session 10Convolutional code
Session 11Maximum likelihood decoding method of convolutional code (Vitterbi algorithm)
Session 12Finite body
Session 13Reed-Solomon code
Session 14Reed-Solomon code decoding method
＊＊This content is based on April 1, 2024. For the latest syllabus information and details, please check the syllabus information inquiry page provided by the university.＊＊