Deployment of High Altitude Platforms network: A game theoretic approach

Abstract

The High Altitude Platforms (HAPs) have been considered as a promising technique for providing wireless communication and other services. They operate at an altitude from 17km to 22km, which exploit both advantages of satellites and terrestrial wireless communication networks. It is possible to deploy HAPs network rapidly and adjust them dynamically to cover specific regions requiring communication services. In this paper, the problem of deploying HAPs network to provide wireless communication for terrestrial users is investigated. The aim of deployment is to providing communication services to ground users as many as possible with quality of service (QoS) guaranteed. To find an optimal solution, we model the problem as a potential game. And a restricted spatial adaptive play (RSAP) learning algorithm is introduced for the game. Using this algorithm, the HAPs can be deployed to cover the user areas in a self-organized manner with high QoS achieved. Finally, the simulation results also demonstrate that the HAPs network can achieve the optimal deployment.

Improvement of the Global Connectivity Using Integrated Satellite-Airborne-Terrestrial Networks With Resource Optimization

“…Several papers in the literature have studied the deployment of the HAPs [11], [12], [14]. For instance, the work proposed in [14] investigates the HAPs' deployment taking into consideration the quality-of-service (QoS) of ground users.…”

“…Several papers in the literature have studied the deployment of the HAPs [11], [12], [14]. For instance, the work proposed in [14] investigates the HAPs' deployment taking into consideration the quality-of-service (QoS) of ground users. The authors propose a self-organized game theory model, where the HAPs are modeled as rational and self-organized players with the goal of achieving optimal configuration of the HAPs that maximizing the users' QoS.…”

“…In fact, by fixing the transmit powers, it can be seen that the optimization problem becomes linear binary optimization problem in terms of ǫ S su,n and δ wl , except (12) which is non linear with respect to ǫ S su,n due to the existence of the I M mu term in (10). On the other hand, by fixing ǫ S su,n and δ wl , the optimization problem is convex problem in P S su,n except constraints (12) and (14). Therefore, in order to solve the problem iteratively (solving the associations and them transmit powers), the goal is to linearize (12) respects to ǫ S su,n with fixed P S su,n .…”

“…Therefore, in order to solve the problem iteratively (solving the associations and them transmit powers), the goal is to linearize (12) respects to ǫ S su,n with fixed P S su,n . Then, approximate (12) and (14) to convex ones respects to P S su,n with fixed ǫ S su,n and δ wl .…”

“…The noise power N 0 is assumed to be −174 dBm. In Table II, we present the values of the remaining environmental parameters used in the simulations unless otherwise stated [14], [15], [23]. W. The figure also shows the BH association of HAPs with the GWs.…”