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Cache-enabled device-to-device (D2D) communications can boost networkthroughput. By pre-downloading contents to local caches of users, the contentrequested by a user can be transmitted via D2D links by other users inproximity. Prior works optimize the caching policy at users with the knowledgeof content popularity, defined as the probability distribution of request forevery file in a library from by all users. However, content popularity can notreflect the interest of each individual user and thus popularity-based cachingpolicy may not fully capture the performance gain introduced by caching. Inthis paper, we optimize caching policy for cache-enabled D2D by learning userpreference, defined as the conditional probability distribution of a user'srequest for a file given that the user sends a request. We first formulate anoptimization problem with given user preference to maximize the offloadingprobability, which is proved as NP-hard, and then provide a greedy algorithm tofind the solution. In order to predict the preference of each individual user,we model the user request behavior by probabilistic latent semantic analysis(pLSA), and then apply expectation maximization (EM) algorithm to estimate themodel parameters. Simulation results show that the user preference can belearnt quickly. Compared to the popularity-based caching policy, the offloadinggain achieved by the proposed policy can be remarkably improved even withpredicted user preference.

Caching Policy Optimization for D2D Communications by Learning User Preference