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This work considers the deployment of unmanned aerial vehicles (UAVs) over apredefined area to serve a number of ground users. Due to the heterogeneousnature of the network,the UAVs may cause severe interference to thetransmissions of each other. Hence, a judicious design of the user-UAVassociation and UAV locations is desired. A potential game is defined where theplayers are the UAVs. The potential function is the total sum-rate of theusers. The agents utility in the potential games is their marginal contributionto the global welfare or their so-called wonderful life utility. Agame-theoretic learning algorithm, binary log-linear learning (BLLL), is thenapplied to the problem. Given the potential game structure, a consequence ofour utility design, the stochastically stable states using BLLL are guaranteedto be the potential maximizers. Hence, we optimally solve the user-UAVassociation and 3D-location problem. Next, we exploit the sub-modular featuresof the sum rate function for a given configuration of UAVs to design anefficient greedy algorithm. Despite the simplicity of the greedy algorithm, itcomes with a guaranteed performance of $1-1/e$ of the optimal solution. Tofurther reduce the number of iterations, we propose another heuristic greedyalgorithm that provides very good results. Our simulations show that, inpractice, the proposed greedy approaches achieve significant performance in afew number of iterations.

The Optimal and the Greedy: Drone Association and Positioning Schemes for Internet of UAVs