Collaborative Edge Caching in Context-Aware Device-to-Device Networks

Abstract

Caching content on the edge of a network can effectively localize traffic, reduce network latency, and improve network throughput. In this paper, we propose a posteriori caching mechanism rather than the mainstream apriority theory, in which the content placement strategy is determined based on the identical distribution of content popularity and user preference before the theoretical analysis of the placement gain is validated. We primarily investigate the optimal caching strategy subjected to the constraint of storage capacity in a heterogeneous network, where a macro base station (MBS), small base stations (SBSs), and user terminals (UTs) are integrated for proactive content storage in the physical layer. To maximize the local hit rate while reducing the transmission delay, we first analyze the optimal strategy by converting content placement into a 0–1 knapsack problem and address the optimization problem using the method of Lagrangian multipliers. We then determine the request probability for each user by exploiting the context information of the user's request history, user similarity, and social ties to achieve reasonably well-optimized caching performance. The caching policy is further optimized into a low-complexity heuristic algorithm with the knowledge of request probability and the optimal copy volumes. Finally, the simulation results show that the proposed cooperative caching algorithm improves the performance metric in terms of hit rate and transmission delay under different benchmarks.