Peterson-Gorenstein-Zierler algorithm for differential convolutional codes
Jos\'e G\'omez-Torrecillas, F. J. Lobillo, Gabriel Navarro and, Jos\'e Patricio S\'anchez-Hern\'andez
TL;DR
This paper introduces differential convolutional codes with specific Hamming distances and develops an algebraic decoding algorithm inspired by the Peterson-Gorenstein-Zierler method, enhancing decoding efficiency.
Contribution
It presents a novel class of differential convolutional codes and an algebraic decoding algorithm tailored for them, extending existing coding theory techniques.
Findings
Successful design of differential convolutional codes with desired Hamming distances
Development of an algebraic decoding algorithm inspired by Peterson-Gorenstein-Zierler
Potential improvements in decoding performance for convolutional codes
Abstract
Differential Convolutional Codes with designed Hamming distance are defined, and an algebraic decoding algorithm, inspired by Peterson-Gorenstein-Zierler's algorithm, is designed for them.
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