Technology and Network Evolution

Todays, market forecasts point out an explosion of wireless traffic. The traffic generated by wired devices is much lesser than that of the traffic generated by wireless devices. Even though, the long term evolution

(LTE) as a latest advanced networks, mobile networks will not be able to sustain the demanded rates. To overcome this lacking, small cell base station networks (SCBSNs) have been proposed as a solution, and they are expected to be the successor of Long Term

Evolution (LTE) networks

Deploying such high data rate SCBSNs to satisfy this demand needs high-speed dedicated backhaul.

For costly nature of this requirement, the current state of the art proposes to add high storage units (i.e., hard-disks, solid-state drives) to small cells base station and use these units for caching purposes. For decreasing the cost and additive benefits, the work in proposes to utilize such a dense infrastructure opportunistically for cloud storage scenarios either for caching or persistent storage. In parallel, another line of research focuses on content caching to handle network resources efficiently and can be seen as a complementary .

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Here, we complementarily merge the two approaches for SCBSNs. We focus on SCBSNs where we have small base stations deployed with high storage units but have limited backhaul links. In fact, we have the following annotations:

•  In typical networks, which are called here reactive networks, user requests are satisfied right after they are initiated. Compare with proactive SCBSNs can track, learn and then create a user request prediction model. So, we get flexibility in scheduling efficient resources.
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•  Although human activities is highly predictable and correlated, actually, predicting the accurate time of user requests might not be achievable. Nevertheless, statistical patterns such as file popularity distributions may help to permit a certain level of prediction. Doing this, the predicted files can be cached in SCBSNs. So, the backhaul can be offloaded and movable users can have a higher level of satisfaction.
•  The caching in SCBSNs can be low-cost like the storage units have become exceptionally cheap. Such as, putting two terabytes of storage in a SCBS costs approximately 120 dollars.
•  Generally the network operators deploy transparent caching proxies to accelerate service requests and reduce bandwidth expenses. We examine that these kinds of caching approaches can be a complementary way of cost reduction by migrating the role of these proxies to SCBSNs.

Given these annotations, the aim of this work is to examine the impact of caching in SCBSNs. Searching for caching as a way of being proactive; we also introduce an algorithm called MyCaching. The method relies on the popularity statistics of the requested files. We then compare our results with PropCaching.

Road Map: We discuss the problem scenario and describe our system model in Section 2. We formulate an optimization problem and clarify the MyCaching algorithm in Section 3. Related numerical results are given in Section 4. Finally, we conclude in Section 5.

The mathematical notation used in this paper is the following. Lower case and uppercase italic symbols (e.g., a, A) are scalar values. A lower case boldface symbol (e.g., a) denotes a row vector and an upper case boldface symbol (e.g., A) a matrix. 1M?N represents an M ? N matrix with all ones, and 0M?N is again M x N sized matrix but with entries set to zeros. ? (a) is a diagonal matrix constructed from vector a. The indicator function 1{.>.} returns 1 when the inequality holds, otherwise 0. Finally, the transpose of a is denoted with at.