###### Abstract  
Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users' diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users' data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness (\(\gamma\)), and 6% performance gain in terms of backhaul saving vs. cache capacity. caching: network delay; small base station; 5G; dynamic data popularity; reinforcement learning; Q-learning +
Footnote †: journal: Applied  
fetch their required contents from the core network via serving SBSs, which not only incurs congestion at the serving SBS but also overburdens the backhaul link. It is an interesting fact that users' data demands have a redundancy trend towards social networking, popular videos, and online gaming [5]. However, networks are equipped with limited operational capacity, therefore they are unable to handle explosive data demands, resulting in data traffic congestion [6]. Modern data traffic manifests reuse of asynchronous contents [7], because a large amount of data traffic incurs a backhaul bottleneck problem, which degrades the Quality of Experience (QoE). To relieve the traffic burden over the backhaul link, caching appears to be a promising approach that can directly serve contents to the users [8]. Caching at the network edge is recognized as an efficient way to mitigate bottleneck issues that arise due to SBSs' densification [9]. The data placement at the SBS level has the potential to serve users with immediate effects, which can significantly reduce the network delay [10]. Therefore, the data placement at the SBS level has attracted tremendous attention from the research community. SBSs are located close to users, which can allow better data access by the users. Therefore, caching popular contents at the SBS level can fulfill users' demands with minimum delay [11]. There are three benefits of SBS level caching. First, popular contents are placed close to the users, which reduces latency. Second, redundant data transmission over backhaul and network congestion is reduced. Third, most of the users' data requests are fulfilled through cache resources rather than the core network, which alleviates the backhaul link traffic congestion. These aforementioned advantages motivate data service providers to adopt new advanced technologies to improve QoE. Due to the capacity constraint on the cache resources, it is imperative to utilize scarce cache resources in an intelligent manner [12]. For efficient data caching, the network must rely on accurate information in data requests for determining what and where to cache contents [13].