Abstract
Cooperative caching between multiple small base stations (SBSs) plays a critical role for easing the traffic congestion of the backhaul link. Previous works assume that cooperative caching policies can achieve good performance when each user in the region is mainly served by its nearest SBS, which may not be the case in small cell networks with heterogeneous channel qualities. In this paper, we study cooperative caching problem in a small cell network with heterogeneous channel qualities when content popularity profile is unknown. These two features impose new challenges in optimizing content placement in multiple SBSs. To address this problem, we first propose a bayes-based learning algorithm that learn the popularity profile by sampling from a Beta distribution at each time period. Based on the estimated popularity profile, we then optimize the content placement at each time period by caching contents with higher popularity/size ratio in SBSs with better channel qualities. Numerical results show that the proposed algorithms outperforms three baselines in terms of average transmission delay and cache hit rate.
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