Content-aware user clustering and caching in wireless small cell networks

Abstract

In this paper, the problem of content-aware user clustering and content caching in wireless small cell networks is studied. In particular, a service delay minimization problem is formulated, aiming at optimally caching contents at the small cell base stations (SCBSs). To solve the optimization problem, we decouple it into two interrelated subproblems. First, a clustering algorithm is proposed grouping users with similar content popularity to associate similar users to the same SCBS, when possible. Second, a reinforcement learning algorithm is proposed to enable each SCBS to learn the popularity distribution of contents requested by its group of users and optimize its caching strategy accordingly. Simulation results show that by correlating the different popularity patterns of different users, the proposed scheme is able to minimize the service delay by 42% and 27%, while achieving a higher offloading gain of up to 280% and 90%, respectively, compared to random caching and unclustered learning schemes.

Information-centric mobile caching network frameworks and caching optimization: a survey

“…audio and video), when their position changes, they change the AP which they connect to. In mobile cellular networks, the APs contain NodeBs in 3G networks and eNBs in 4G LTE networks [29,30], macro base stations(MBSs)and small cell base stations(SBSs) which are proposed in heterogeneous cellular network(HCN) [4,[30][31][32][33][34][35], as well as Wi-Fi access points and worldwide interoperability for microwave access (WiMAX) base stations [30]. Figure 2 gives the typical architectures and scenarios of caching framework in mobile cellular networks.…”

“…The architecture consisting of a single MBS and a set of SBSs is provided as the cache-enabled architecture based on SBSs, such as picocells, microcells or femtocells overlaid on existing macro-cellular wireless systems. The request is sent to the SBS at first, and if the SBS owns a copy of the requested item, the request can be satisfied locally; otherwise, the request that cannot be satisfied by the SBS is routed to the MBS [32,34].…”

“…The metric of delay is discussed using optimization theory in [28,29,32,35,36] and game theory in [103]. Minimizing caching failure probability or maximizing caching hit rate are formulated in [33,34,53,62,104].…”

“…A joint user clustering and caching scheme for wireless heterogeneous networks is presented which small-cell users have different preferences over different content types in [32]. Service delay experienced by UE to retrieve the requested content from BS is defined as the ratio between content size and downlink transmission rate from base station (MBS or SBSs) to user, and the caching optimization problem is formulated to minimize the total delay by finding the optimal caching strategy.…”

Rate aware network codes for coordinated multi base-station networks

“…With the emergence of smart phones, the 4G and 5G cellular network architectures, the data download problem has become more attractive in the wireless scenarios. Consequently, the data download problem was modified from a classical full package download problem into a download completion problem [3,4]. The large storage capabilities of modern user equipment enable these devices to benefit from the already downloaded messages and request only the missed ones.…”