Content Popularity Prediction Towards Location-Aware Mobile Edge Caching

Mobile edge caching aims to enable content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case with zero-mean noise, a ridge regression-based online algorithm with positive perturbation is proposed. Regret analysis indicates that the hit rate achieved by the proposed algorithm asymptotically approaches that of the optimal caching strategy in the long run. When the noise structure is unknown, an $H_{\infty }$ filter-based online algorithm is devised by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases and, hence, are robust to the time-varying user demands. The estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments using real-world datasets are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving a content hit rate that is comparable to that via the hindsight optimal strategy.