# Game Theory-Based Smart Mobile-Data Offloading Scheme in 5G Cellular Networks
## 1 Introduction  
Over the past few decades, the demands on wireless cellular networks (WCNs) have been increasing fast, with applications on UEs which are mobile devices used directly by end-users to communicate such as smart phones, tablets, and other new UEs. Mobile users in the networks rely more heavily to connect, interact, follow social media, watch live TV, and download music, etc. Moreover, according to a study by Cisco Systems, Inc. [1], global mobile-data traffic (MDT) has been growing explosively, and was expected to increase 7-fold between 2017 and 2022, reaching 77.5 exabytes per month by 2022. The ever-increasing MDT is one of the reasons end-user experience decreasing quality of service (QoS), and it creates challenges for cellular network operators (CNOs). To face this explosive traffic demand, CNOs need to upgrade their networks by either migrating to new-generation WCNs or developing enhancement techniques to significantly increase their network capacity. However, traditional methods, such as acquiring more licensed spectrum, developing new small-size cells, and upgrading technologies (e.g., from wide band code division multiple access [WCDMA] to Long Term Evolution [LTE]/LTE-Advanced [LTE-A]) are costly, time-consuming, and may not catch up to the pace of the traffic increase [2]. Clearly, CNOs must find novel methods to solve this problem, and mobile data offloading (MDO) appears to be one of the promising solutions that use complementary technologies (such as small cells and Wi-Fi networks) for delivering theMDT originally targeted at cellular networks. MDO helps the network to increase overall throughput, reduces content delivery time, extends network coverage, increases network availability, and provides better EE. The performance benefits of MDO through small cells and Wi-Fi networks have been proven in the literature [3, 4, 5, 6, 7, 8, 9]. However, due to the limitations of backhaul connection and cross- or co-tier interference issues in the small-cell network, as well as service coverage and mobility in Wi-Fi networks, MDO through these networks is costly and impractical. A promising solution that has been considered lately for offloading MDT is opportunistic communications [10], and D2D communications can also be used to facilitate opportunistic communications [11, 12]. D2D communications (also considered opportunistic) allows UEs in proximity to each other to exchange data directly without relying on infrastructure, and consequently, incurs very little or no monetary cost. Extensive MDT has led researchers and designers to begin developing fifth-generation (5G) networks [13, 14, 15]. The authors in [13] mention challenges and current trends toward converged the fifth-generation (5G) mobile networks. The 5G networks are expected to have higher capacity and throughput when compared with the Fourth Generation (4G). However, the systems of 5G networks will need to face some new technical challenges, like Machine to Machine (M2M) communication, energy efficiency, complete ubiquity, autonomous management and increasing mobile traffic demands. [PERSON] et al. [14] consider key five technologies that have the largest impact on progressing 5G: dense small-cell deployment, massive multiple-input multiple-output (M-MIMO), D2D, M2M, and millimeter-wave communications. Among the new features heralded by 5G, D2D communications could have a prominent role with systems or applications requiring low latency, and network traffic offloading. Moreover, computation offloading enabled by cloud/edge communication architecture can offload computation-excessive and latency-stringent applications to nearby devices through D2D communications or to nearby edge nodes through cellular or other wireless technologies [16, 17]. Therefore, from the benefits of D2D communication in 5G cellular networks, MDO through D2D communication and offloading is a promising solution in reducing network load as demand for mobile traffic is increasing.