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Explosive growth of mobile data demand may impose a heavy traffic burden onfronthaul links of cloud-based small cell networks (C-SCNs), which deterioratesusers' quality of service (QoS) and requires substantial power consumption.This paper proposes an efficient maximum distance separable (MDS) coded cachingframework for a cache-enabled C-SCNs, aiming at reducing long-term powerconsumption while satisfying users' QoS requirements in short-termtransmissions. To achieve this goal, the cache resource in small-cell basestations (SBSs) needs to be reasonably updated by taking into account users'content preferences, SBS collaboration, and characteristics of wireless links.Specifically, without assuming any prior knowledge of content popularity, weformulate a mixed timescale problem to jointly optimize cache updating,multicast beamformers in fronthaul and edge links, and SBS clustering.Nevertheless, this problem is anti-causal because an optimal cache updatingpolicy depends on future content requests and channel state information. Tohandle it, by properly leveraging historical observations, we propose atwo-stage updating scheme by using Frobenius-Norm penalty and inexact blockcoordinate descent method. Furthermore, we derive a learning-based design,which can obtain effective tradeoff between accuracy and computationalcomplexity. Simulation results demonstrate the effectiveness of the proposedtwo-stage framework.

Joint Long-Term Cache Updating and Short-Term Content Delivery in Cloud-Based Small Cell Networks