  We consider a service cost minimization problem for resource-constrained
small-cell networks with caching, where the challenge mainly stems from (i) the
insufficient backhaul capacity and limited network bandwidth and (ii) the
limited storing capacity of small-cell base stations (SBSs). Besides, the
optimization problem is NP-hard since both the users' mobility patterns and
content preferences are unknown. In this paper, we develop a novel
mobility-aware joint routing and caching strategy to address the challenges.
The designed framework divides the entire geographical area into small sections
containing one SBS and several MUs. Based on the concept of one-stop-shop
(OSS), we propose a federated routing and popularity learning (FRPL) approach
in which the SBSs cooperatively learn the routing and preference of their
respective MUs and make a caching decision. The FRPL method completes multiple
tasks in one shot, thus reducing the average processing time per global
aggregation of learning. By exploiting the outcomes of FRPL together with the
estimated service edge of SBSs, the proposed cache placement solution greedily
approximates the minimizer of the challenging service cost optimization
problem. Theoretical and numerical analyses show the effectiveness of our
proposed approaches.
