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Mobile edge caching enables content delivery directly within the radio accessnetwork, which effectively alleviates the backhaul burden and reducesround-trip latency. To fully exploit the edge resources, the most popularcontents should be identified and cached. Observing that content popularityvaries greatly at different locations, to maximize local hit rate, this paperproposes an online learning algorithm that dynamically predicts content hitrate, and makes location-differentiated caching decisions. Specifically, alinear model is used to estimate the future hit rate. Considering thevariations in user demand, a perturbation is added to the estimation to accountfor uncertainty. The proposed learning algorithm requires no training phase,and hence is adaptive to the time-varying content popularity profile.Theoretical analysis indicates that the proposed algorithm asymptoticallyapproaches the optimal policy in the long term. Extensive simulations based onreal world traces show that, the proposed algorithm achieves higher hit rateand better adaptiveness to content popularity fluctuation, compared with otherschemes.

Dynamic Mobile Edge Caching with Location Differentiation