Anticipatory Caching in Small Cell Networks: A Transfer Learning Approach

Locally caching contents at the network edge constitutes one of the five most disruptive paradigms in 5G networks [1]. Recent results have shown that dynamic caching can significantly offload different parts of the network including the Radio access networks (RANs) and core network (CN), by smartly prefetching and storing contents closer to the end-users. In parallel to that, the era of pushing contents through the network on a best-effort basis ignoring who end-users are and what they are doing with their devices has dawned, calling for a truly context-aware and proactive networking paradigm [2]. As a result, edge caching has taken the recent 5G literature by storm over the last few years as evidenced in [2]–[6]. In [6], the concept of femtocaching was proposed addressing the problem of capacity-limited backhaul links by embedding base stations with high-storage units. In [2], a novel edge-centric networking paradigm was proposed in which network nodes (i.e., base station (BS) and/or user terminals (UTs)) proactively cache judiciously selected contents at the network edge. Exploiting both spatial and social caching coupled with suitable device-to-device (D2D) communication was shown to efficiently offload the backhaul traffic and enhancing the overall network performance in terms of cache-hit-rates and users' satisfaction ratios. Therein, a proactive caching proce-dure is formulated as a supervised machine learning problem and collaborative filtering (CF) techniques are used to estimate the file popularity matrix exploiting users-files correlations. Notwithstanding this fact, the file popularity matrix remains typically large and sparse in practice, rendering CF techniques sub-optimal suffering from data sparseness and cold-start problem, which are major challenges in the machine learning community [7]. In this work, we build upon the work in [2] and propose a more efficient machine learning technique, using the frame-work of transfer learning (TL) [7]. Indeed, in many real-world applications, it is expensive or even impossible to collect and label training data to build suitable prediction models. With this in mind, TL is seen as a suitable framework which allows to exploit data from other rich information sources (referred to as source domain) to further improve the prediction task in the target domain. TL has been traditionally used in data mining This research has been supported by the ERC Starting Grant 305123 MORE (Advanced Mathematical Tools for Complex Network Engineering), the SHARING project under the Finland grant 128010 and the project BESTCOM. problems such as classification, regression and very recently in the context of CF [7]. 1) Transfer Learning: In a nutshell, TL can be classified into inductive, transductive and unsupervised transfer learning depending on the availability of the source and target domain labels. Research issues in transfer learning boil down to: 1) what to transfer, 2) how to transfer, and 3) when to transfer. While "what to transfer" studies which part of the knowledge can be transferred across domains or tasks, "when to transfer" deals with the issue of knowing when is best to transfer the knowledge to avoid negative transfer, notably when the source and target domains become unrelated. Finally, "how to transfer" deals with knowledge extraction which needs to be transferred. A comprehensive survey of transfer learning is found in [7]. 2) Contribution: The basic idea of the proposed learning approach is to alleviate the data sparsity problem encountered in most CF problems, by learning and transferring the rich contextual information (i.e., source domain), to better estimate the (large-scale) file popularity matrix in the target domain. We assume that the knowledge extracted from the source domain stems from the interaction of users accessing/sharing and recommending files within their social community via D2D (Web2.0-like). Instead of learning from scratch in the target domain, the nice feature of the TL approach lies in judiciously extracting collaborative social behavior information from the source domain to aid in the learning in the target domain, which will be explained in the sequel. To the best of our knowledge this is perhaps the first contribution of transfer learning in RANs. A. Network Model