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Top1. Introduction
Fifth-generation (5G) and beyond wireless communication networks are expected to improve the actual infrastructure by providing higher data rates, offering a higher quality of service (QoS) in crowded areas, and lower the blind spots of the actual networks (Osseiran et al. 2014). UAVs have been introduced to achieve the above objective. The advantages of using UAVs for wireless communications are their flexible positioning, a lower-cost deployment, Swift and easy deployment, Maneuverability, Extend coverage, Enhance connectivity, and approach ground users. For example, they may be used, as temporary base stations for on-demand situations (Hayajneh et al. 2018), for adaptive fronthauling/backhauling (Dong et al. 2018), and for data acquisition for the Internet-of-Things (Motlagh et al. 2016). Despite their expected benefits, UAVs act as aerial base stations for providing coverage for mobile users on the ground constitutes a major challenge in UAV-based networks.
The Google Loon project (Guo et al. 2014) is founded on balloon deployment to deliver ubiquitous networking. The balloon will be deployed at high altitudes in the stratosphere to deliver Internet access, especially in rural and poorly covered areas. Facebook has the Drone project (Facebook 2014), its own vision for providing Internet access. This could potentially lead content providers such as Google and Facebook to become independent Internet service providers to distribute their content.
In these scenarios, UAVs need to fly beyond the marked area and send beacons to announce their presence to the ground users. The more the drones send beacons, they have a great chance to be detected and, then, chosen by the ground users. However, sending beacons requires a high-energy, which reaches an expensive service cost. In this general context, UAV needs to determine the price and the quality of service of the provided service while remaining competitive. At the same time, they need to set the beaconing duration of the UAV’s (the time needed for beaconing). Here, a natural question occurs: how can compete UAV operators set attractive service prices, good quality of service and fix an optimal beaconing duration in order to attract more ground users?
This paper develops a new network economic game theory model of UAVs act as flying base stations. A non-cooperative game among the UAVs is then formulated to determine the UAVs’ beaconing duration, service price and quality of service (QoS). The existence and uniqueness of the Nash equilibrium point are shown in a competitive contest among UAVs, which means that there exists a stable state where all UAVs do not have an incentive to change their strategies. Therefore, our model ensures the existence of an equilibrium for keeping the economy stable and achieving economic growth. In addition, a distributed algorithm is introduced to converge to the Nash equilibrium point. Then, a Bertrand game model with fairness concern is established, and its equilibrium price is derived and analyzed. Numerical examples are conducted to determine the factors that affect UAVs strategies and show that fairness concerns of UAV have a significant influence on their expected profits. We believe that this research study will shed light on the application of UAV as a flying base station.
The rest of this article is organized as follows: In Section 2 reviews of some related works. In Section 3, we present the problem formulation by modeling the ground users behavior, service probability and we describe the utility model. In Section 4 we present the theoretical analysis of the non-cooperative game considered in this study and the algorithm used to learn Nash Equilibrium point. In Section 5 game model with fairness concern. The simulation results are presented in section 6, followed by the conclusion in Section 7.