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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.
Pavlos Nikolopoulos, Muhammad Abdullah
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