Extreme Value Statistics and Error Correcting Codes
S. Rouhani, J. Davoudi
TL;DR
This paper applies extreme value statistics to Derrida's model for error correcting codes, deriving the optimal signal-to-noise ratio for improved communication reliability.
Contribution
It introduces a novel application of extreme value statistics to Derrida's model, optimizing error correction performance.
Findings
Derived the optimal signal-to-noise ratio using extreme value statistics.
Demonstrated the effectiveness of Derrida's model in error correction.
Provided a new analytical framework for code construction.
Abstract
Derrida's model can be used for construction and transmission of error correcting codes, in an optimal way. we use the extreme statistics method to derive the optimal signal to noise ratio.
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Taxonomy
TopicsNumerical Methods and Algorithms · Algorithms and Data Compression · Neural Networks and Applications