400 research outputs found
Content-Aware User Clustering and Caching in Wireless Small Cell Networks
In this paper, the problem of content-aware user clustering and content
caching in wireless small cell networks is studied. In particular, a service
delay minimization problem is formulated, aiming at optimally caching contents
at the small cell base stations (SCBSs). To solve the optimization problem, we
decouple it into two interrelated subproblems. First, a clustering algorithm is
proposed grouping users with similar content popularity to associate similar
users to the same SCBS, when possible. Second, a reinforcement learning
algorithm is proposed to enable each SCBS to learn the popularity distribution
of contents requested by its group of users and optimize its caching strategy
accordingly. Simulation results show that by correlating the different
popularity patterns of different users, the proposed scheme is able to minimize
the service delay by 42% and 27%, while achieving a higher offloading gain of
up to 280% and 90%, respectively, compared to random caching and unclustered
learning schemes.Comment: In the IEEE 11th International Symposium on Wireless Communication
Systems (ISWCS) 201
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