Research Article Access Point Backhaul Resource Aggregation as a Many-to-One Matching Game in Wireless Local Area Networks Kawther Hassine, 1 Mounir Frikha, 1 and Tijani Chahed 2 1 Higher School of Communications of Tunis (Sup’Com), Tunis, Tunisia 2 Institut Mines-Telecom, Telecom SudParis, Paris, France Correspondence should be addressed to Kawther Hassine; kaouther.hassine@supcom.tn Received 22 September 2016; Revised 9 March 2017; Accepted 10 April 2017; Published 15 May 2017 Academic Editor: Michael McGuire Copyright © 2017 Kawther Hassine et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis paper studies backhaul bandwidth aggregation in the context of a wireless local area network composed of two diferent types of access points: those with spare backhaul capacity (which we term providers) and those in shortage of it (benefciaries); the aim is to transfer excess capacity from providers to benefciaries. We model the system as a matching game with many-to-one setting wherein several providers can be matched to one benefciary and adopt the so-called deferred acceptance algorithm to reach an optimal and stable solution. We consider two favors, when the benefciaries are limited in their resource demands and when they are not, and two scenarios, when resources are abundant and when they are scarce. Our results show that the many-to-one setting outperforms the one-to-one case in terms of overall throughput gain, resource usage, and individual benefciaries satisfaction by up to 50%, whether resources are scarce or abundant. As of the limited versus nonlimited case, the former ensures more fair sharing of spectral resources and higher satisfaction percentage between benefciaries. 1. Introduction Te current and recent trends in mobile networks are towards exponential increase in the use of spectral resources. Although current capacity requirements seem to be contain- able, bandwidth consumption will continue to increase with future generations of wireless networks. In the context of a heterogeneous small cell-based archi- tecture, there are three basic areas of connectivity, namely, Personal Area Networks (PANS), wireless local area networks (WLANs), and cellular coverage. Under this umbrella, small cell-based mobile networks are principally used to bridge connections to the device by means of capillary networks. Meanwhile, trafc ofoad and local access is ofen covered by means of a short-range wireless technology. In this context, rather than deploying their own edging sites, operators trends are towards more efcient and cost- efective solutions. Te latter include using WiFi in the last mile coverage. In fact, WiFi WLANs will be responsible for ofoading trafc from and to macrocells by trafc amounts up to 52% by 2018 (source: Cisco VNI Global Mobile Data Trafc Forecast 2013–2018). Besides, voice trafc lost from LTE systems to WiFi networks will reach 53% by 2020 (source: Cisco VNI Mobile, 2016). From this perspective, pressure is increasing on existing WiFi backhaul segments. Tis portion of the network that starts at the radio access network and ends in the mobile core network (see Figure 1) is the key answer to sustain trafc growth and relieve pressure of both mobile and wireless networks. Tis is where new ways to manage backhaul resources need to be considered. In the context of wireless local area networks, a major research trend targets better management of the available backhaul resource at access points (APs) through the so- called backhaul bandwidth aggregation. With no major short-term investments and no altering to network topology, this technique enables wireless clients to connect simulta- neously to diferent APs and cumulate available backhaul capacities in order to improve their transmission rates (see Figure 2). Backhaul bandwidth aggregation techniques can be broadly classifed in two categories: client-oriented and Hindawi Wireless Communications and Mobile Computing Volume 2017, Article ID 3523868, 11 pages https://doi.org/10.1155/2017/3523868