Performance analysis of mobility prediction based proactive wireless caching

We study a mobility prediction based proactive wireless caching scheme for two-tier cellular networks consisting of a base station (BS) tier and a device-to-device (D2D) tier. Two scenarios are considered: popular contents cached only at BSs, and popular contents cached at both BSs and MTs. We model user mobility as a Markov renewal process to predict user moving paths and residence time. Then we analyse the hit-rate performance to evaluate the presented schemes. By formulating content placement to maximize the hit-rate as optimization problems, we provide the optimal solution for the first scenario and develop a greedy mobility prediction based proactive wireless caching (MPPC) scheme for the second. Through analysis we show that the hit-rate achieved by MPPC is at least exp(1)-1/exp(1) of the optimal hit-rate. The numeric results show that the MPPC can dramatically improve the hit-rate performance, compared with random caching and most popular caching (MPC) schemes. We show that the hit-rate achieved by MPPC outperformances MPC by 26% at most when MTs are not able to cache. Besides we present the impact of the moving speed on the hit-rate performance of MPPC for MTs.