Sparse Random Linear Network Coding for Data Compression in WSNs

Abstract : This paper addresses the information theoretical analysis of data compression achieved by random linear network coding in wireless sensor networks. A sparse network coding matrix is considered with columns having possibly different sparsity factors. For stationary and ergodic sources, necessary and sufficient conditions are provided on the number of required measurements to achieve asymptotically vanishing reconstruction error. To ensure the asymptotically optimal compression ratio, the sparsity factor can be arbitrary close to zero in absence of additive noise. In presence of noise, a sufficient condition on the sparsity of the coding matrix is also proposed.
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Communication dans un congrès
2016 IEEE International Symposium on Information Theory (ISIT), Jul 2016, Barcelona, Spain. 〈10.1109/isit.2016.7541795 〉
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Soumis le : lundi 6 juin 2016 - 17:56:30
Dernière modification le : lundi 8 octobre 2018 - 16:30:47

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Wenjie Li, Francesca Bassi, Michel Kieffer. Sparse Random Linear Network Coding for Data Compression in WSNs. 2016 IEEE International Symposium on Information Theory (ISIT), Jul 2016, Barcelona, Spain. 〈10.1109/isit.2016.7541795 〉. 〈hal-01327498〉

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