Caching Policy Optimization for D2D Communications by Learning User Preference

Cache-enabled device-to-device (D2D) communications can boost network throughput. By pre-downloading contents to local caches of users, the content requested by a user can be transmitted via D2D links by other users in proximity. Prior works optimize the caching policy at users with the knowledge of content popularity, defined as the probability distribution that each file in a library is requested by all users. However, content popularity can not reflect the interest of each individual user and thus existing caching policy based on popularity may not fully capture the performance gain introduced by caching. In this paper, we optimize caching policy for cache-enabled D2D by learning user preference, which is defined as the conditional probability distribution of a user's request given that the user sends a request. We first formulate an optimization problem with given user preference to maximize the offloading probability, which is proved as NP-hard, and then provide a greedy algorithm to find 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 the model parameters. Simulation results show that using pLSA can learn user preference quickly. Compared to existing caching policy exploiting content popularity, the offloading gain achieved by the proposed policy can be remarkably improved even with predicted user preference.