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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.