A novel learning mechanism for traffic offloading with Small Cell as a Service

The densification of mobile networks with small cells is seen as the most promising solution to the explosive data traffic increase. Due to their financial implementation requirements, which could not be met by the service providers, the emergence of third parties that deploy and lease small cell networks opens up new business opportunities. In this paper, we study a proportionally fair auction scheme as an efficient way of small cell capacity distribution, both in network and financial terms. To improve the bidders' strategies, we propose a novel learning mechanism that alleviates the uncertainty incurred by variations in the traffic and the lack of information in the auctions. Extensive simulations prove the efficiency of our proposal, which also performs in equal terms with the ideal case of complete information.© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works