Two Time-Scale Caching Placement and User Association in Dynamic Cellular Networks

Abstract

With the rapid growth of data traffic in cellular networks, edge caching has become an emerging technology for traffic offloading. We investigate the caching placement and content delivery in cacheenabling cellular networks. To cope with the time-varying content popularity and user location in practical scenarios, we formulate a long-term joint dynamic optimization problem of caching placement and user association for minimizing the content delivery delay which considers both content transmission delay and content update delay. To solve this challenging problem, we decompose the optimization problem into two sub-problems, the user association sub-problem in a short time scale and the caching placement in a long time scale. Specifically, we propose a low complexity user association algorithm for a given caching placement in the short time scale. Then we develop a deep deterministic policy gradient based caching placement algorithm which involves the short time-scale user association decisions in the long time scale. Finally, we propose a joint user association and caching placement algorithm to obtain a sub-optimal solution for the proposed problem. We illustrate the convergence and performance of the proposed algorithm by simulation results. Simulation results show that compared with the benchmark algorithms, the proposed algorithm reduces the long-term content delivery delay in dynamic networks effectively.

A bi-objective optimization model for the UAV caching at amusement parks in fog computing

“…The unmanned ground vehicles accomplished the computation tasks o oaded from UAVs and UAVs maximized their utilities. A long-term joint dynamic optimization problem of caching placement and user association is presented in [14]. It minimized the content delivery delay.…”

Joint AP‐user association and caching strategy for delivery delay minimization in cell‐free massive MIMO systems

“…To improve delay performance, A few works have investigated the joint AP-user association and caching policy [10][11][12][13][14][15][16]. Teng et al formulated a joint optimization of the user association and caching to minimize the download delay under the constraints of storage size [10].…”

“…Liu et al [15] performed joint optimization of user association and cache deployment by the convex approximation method. Zhang et al [16] proposed a two-time scale joint caching and user association scheme based on belief propagation and deep reinforcement learning algorithms to optimize the content delivery latency of the system. Unlike traditional cellular networks, CF massive MIMO systems have the characteristics of multi-AP deployment and user-centric, which has a significant effect on problem-solving.…”

“…To improve delay performance, A few works have investigated the joint AP‐user association and caching policy [10–16]. Teng et al.…”

“…Differing from the delay models of traditional cellular networks [10, 11, 16], we analyze how the delay is critically affected by the cooperative caching between APs and associations between multi‐AP and user. Based on the established delay model, we propose a joint optimization problem of the caching placement and AP‐user association to minimize the total content delivery delay under the constraint of each AP's caching and fronthaul capacity. 2)Given the multi‐AP deployment and user‐centric features of the CF massive MIMO systems, adopting the optimization schemes [10–16] designed for cellular networks cannot effectively solve the proposed problem. To address this problem, we first decompose the original problem into an AP‐user association sub‐problem and a caching deployment sub‐problem.…”