Clustering for Designing Error Correcting Codes


In this thesis we address the problem of designing code1 fo; specific applications. To do so we make use oft he relationship bet ween dus ten and codes. Designing a block code over any finite dimensional space may be thought of as forming the corresponding number of elnsters over the par titular dimensional space, In literature we have a numb w of algorithms available for clustering. We have examined the performance of a number of such algorithms, such a Linde BuzbGr ay, Simnlat ed Annealing, Simulated Annealing with LindeJIuzb Gray, Det erminstie Annealing, ete, for design of codes, But dl these dgorithmms make use of the Eucledian squared error distance measure for clustering. This di~tantana measure does not match with the dist ance measure of intere~t in the error correcting scenario, namely, Hamming distance. Consequently we have developed an algorithm that can be used for clustering wi ti Hamming dis t a m as the distance measure, Also, it has been observed that stochastic algorithms, such as Simulated Annealing fail to ploduee optimum codes due to very slow convergence near the end. As a remedy, we havt proposed a modjfieation based on the code structure, for such algorithms for code design which makes it possible to converge to the optimum eodea

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@inproceedings{Joseph1994ClusteringFD, title={Clustering for Designing Error Correcting Codes}, author={Binoy Joseph}, year={1994} }