Repetition codes, a special case of block channel coding, proves not to improve coding efficiency.
Build a LabVIEW subVI to implement the error detection and correction mechanism for the (n,k) Hamming linear block code channel decoder.
Channel decoding must be coordinated with coding for accurate results.
So-called linear codes create error-correction bits by combining the data bits linearly. Topics discussed include generator matrices and the Hamming distance.
An introduction to the iterative decoding revolution. Learn about the experimental approach to error-correcting codes that has changed electronic communications. Topics include Turbo Codes, Low-Density Parity-Check Codes, and serially concatenated codes. Final projects involve the design of an error-correcting code, experiments to predict its performance, and the written and oral presentation of the results. Programming experience and a course in probability are required. Instruction and practice in oral and written communication provided. 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.
This course introduces the theory of error-correcting codes to computer scientists. This theory, dating back to the works of Shannon and Hamming from the late 40's, overflows with theorems, techniques, and notions of interest to theoretical computer scientists. The course will focus on results of asymptotic and algorithmic significance. Principal topics include: Construction and existence results for error-correcting codes. Limitations on the combinatorial performance of error-correcting codes. Decoding algorithms. Applications in computer science.
The course focuses on the creation, manipulation, transmission, and reception of information by electronic means. Elementary signal theory; time- and frequency-domain analysis; Sampling Theorem. Digital information theory; digital transmission of analog signals; error-correcting codes. A complete course with over 90 modules.
Build a LabVIEW subVI to create the generator matrix (G matrix) for the (n,k) Hamming linear block code.
Build a LabVIEW subVI to generate the (n,k) parameters for a Hamming linear block code given the exponent q. Also calculate the coding rate.
Build a LabVIEW subVI to multiply two matrices A and B under modulo-2 arithmetic.
Build a LabVIEW subVI to create the parity check matrix (H matrix) for the (n,k) Hamming linear block code.
Explains the repetition code for error correction.
Build a LabVIEW subVI to create the syndrome table for the (n,k) Hamming linear block code channel decoder.
