  Mobile edge caching enables content delivery directly within the radio access
network, which effectively alleviates the backhaul burden and reduces
round-trip latency. To fully exploit the edge resources, the most popular
contents should be identified and cached. Observing that content popularity
varies greatly at different locations, to maximize local hit rate, this paper
proposes an online learning algorithm that dynamically predicts content hit
rate, and makes location-differentiated caching decisions. Specifically, a
linear model is used to estimate the future hit rate. Considering the
variations in user demand, a perturbation is added to the estimation to account
for 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 asymptotically
approaches the optimal policy in the long term. Extensive simulations based on
real world traces show that, the proposed algorithm achieves higher hit rate
and better adaptiveness to content popularity fluctuation, compared with other
schemes.
