Strategic Availability and Cost-Effective UAV-Based Flying Access Networks: S-Modular Game Analysis

Abstract

Nowadays, ubiquitous network access has become a reality, thanks to Unmanned Aerial Vehicles (UAVs) that have gained extreme popularity due to their flexible deployment and higher chance of line-of-sight links to ground users. Telecommunication service providers deploy UAVs to provide areal network access in remote rural areas, disaster-affected areas, or massive-attended events (sport venues, festivals, etc.), where full setup to provide temporary wireless coverage would be very expensive. Of course, a UAV is battery-powered with a limited energy budget for both mobility aspect and communication aspect. An efficient solution is to allow UAVs switching their radio modules to the sleep mode in order to extend the battery lifetime. This results in temporary unavailability of communication feature. Within such a situation, the ultimate deal for a UAV operator is to provide a cost-effective service with acceptable availability. This would allow meeting some target quality of service while having a good market share granting satisfactory benefits. In this article, we exhibit a new framework with many interesting insights into how to jointly define the availability and the access cost in UAV-empowered flying access networks to opportunistically cover a target geographical area. Yet, we construct a duopoly model to capture the adversarial behavior of service providers in terms of their pricing policies and their respective availability probabilities. Optimal periodic beaconing (advertising the presence of the UAV) is to be addressed, given the UAVs with limited battery capacity and their recharging constraints. A full analysis of the game, both in terms of equilibrium pricing and equilibrium availability, is derived. We show that the availability-pricing game exhibits some nice features as it is submodular with respect to the availability policy; whereas, it is supermodular with respect to the service fee. Furthermore, we implement a learning scheme using best response dynamics that allows operators to learn their joint pricing-availability strategies in a fast, accurate, and distributed fashion. Extensive simulations show convergence of the proposed scheme to the joint pricing-availability equilibrium and offer promising insights into how the game parameters should be chosen to efficiently control the duopoly game.