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.