Sabbry, N.H.; Levina, A. Navigating through Noise Comparative Analysis of Using Convolutional Codes vs. Other Coding Methods in GPS Systems. Appl. Sci.2023, 13, 11164.
Sabbry, N.H.; Levina, A. Navigating through Noise Comparative Analysis of Using Convolutional Codes vs. Other Coding Methods in GPS Systems. Appl. Sci. 2023, 13, 11164.
Sabbry, N.H.; Levina, A. Navigating through Noise Comparative Analysis of Using Convolutional Codes vs. Other Coding Methods in GPS Systems. Appl. Sci.2023, 13, 11164.
Sabbry, N.H.; Levina, A. Navigating through Noise Comparative Analysis of Using Convolutional Codes vs. Other Coding Methods in GPS Systems. Appl. Sci. 2023, 13, 11164.
Abstract
This research highlights the importance of error-correcting codes in ensuring secure and efficient data transmission over noisy channels. It discusses the application of Convolutional codes and the Viterbi algorithm for error detecting and correcting in applications where the data rate is high and the error probability is relatively low. This research extends beyond previous studies by conducting a comparative analysis of Convolutional codes, the Viterbi algorithm, and alternative coding methods utilized in satellite communication systems, by evaluating their effectiveness and efficiency relative to other coding approaches like BCH, LDPC, and turbo codes. The evaluation encompasses key performance metrics including error correction capabilities, computational complexity, and robustness against various error types. Additionally, practical implementation factors such as power consumption, computational requirements, and compatibility with existing hardware are considered. The analysis sheds light on the advantages and disadvantages of each coding method, offering insights into why convolutional codes and the Viterbi algorithm are particularly suitable for GPS systems.
Keywords
global positioning system GPS; error-correcting codes; convolutional codes; viterbi algorithm
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
