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Edge-caching is recognized as an efficient technique for future cellularnetworks to improve network capacity and user-perceived quality of experience.To enhance the performance of caching systems, designing an accurate contentrequest prediction algorithm plays an important role. In this paper, we developa flexible model, a Poisson regressor based on a Gaussian process, for thecontent request distribution.  The first important advantage of the proposed model is that it encourages thealready existing or seen contents with similar features to be correlated in thefeature space and therefore it acts as a regularizer for the estimation.Second, it allows to predict the popularities of newly-added or unseen contentswhose statistical data is not available in advance. In order to learn the modelparameters, which yield the Poisson arrival rates or alternatively the content\textit{popularities}, we invoke the Bayesian approach which is robust againstover-fitting.  However, the resulting posterior distribution is analytically intractable tocompute. To tackle this, we apply a Markov Chain Monte Carlo (MCMC) method toapproximate this distribution which is also asymptotically exact. Nevertheless,the MCMC is computationally demanding especially when the number of contents islarge. Thus, we employ the Variational Bayes (VB) method as an alternative lowcomplexity solution. More specifically, the VB method addresses theapproximation of the posterior distribution through an optimization problem.Subsequently, we present a fast block-coordinate descent algorithm to solvethis optimization problem. Finally, extensive simulation results both onsynthetic and real-world datasets are provided to show the accuracy of ourprediction algorithm and the cache hit ratio (CHR) gain compared to existingmethods from the literature.

A Bayesian Poisson-Gaussian Process Model for Popularity Learning in Edge-Caching Networks