Caching of Bivariate Gaussians with Non-Uniform Preference Probabilities

Caching is technique that alleviates networks during peak hours by transmitting partial information before a request for any is made. We study this method in a lossy source coding setting with Gaussian databases. A good caching strategy minimizes the data still needed on average once the user requests a file. We identify two important parameters: the prior preference for a file and the correlation among files. This paper characterizes the trade-off between cache and average update communication rate to meet a user's demand using Gaussian codebooks. It is argued that what information needs to be cached not only depends on preference and correlation, but also on the size of the cache.


Published in:
Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux, 176-183
Presented at:
2017 Symposium on Information Theory and Signal Processing in the Benelux, Delft, the Netherlands, May 11-12, 2017
Year:
2017
ISBN:
978-94-6186-811-4
Keywords:
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 Record created 2017-05-15, last modified 2018-03-17

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