  This work considers the deployment of unmanned aerial vehicles (UAVs) over a
predefined area to serve a number of ground users. Due to the heterogeneous
nature of the network,the UAVs may cause severe interference to the
transmissions of each other. Hence, a judicious design of the user-UAV
association and UAV locations is desired. A potential game is defined where the
players are the UAVs. The potential function is the total sum-rate of the
users. The agents utility in the potential games is their marginal contribution
to the global welfare or their so-called wonderful life utility. A
game-theoretic learning algorithm, binary log-linear learning (BLLL), is then
applied to the problem. Given the potential game structure, a consequence of
our utility design, the stochastically stable states using BLLL are guaranteed
to be the potential maximizers. Hence, we optimally solve the user-UAV
association and 3D-location problem. Next, we exploit the sub-modular features
of the sum rate function for a given configuration of UAVs to design an
efficient greedy algorithm. Despite the simplicity of the greedy algorithm, it
comes with a guaranteed performance of $1-1/e$ of the optimal solution. To
further reduce the number of iterations, we propose another heuristic greedy
algorithm that provides very good results. Our simulations show that, in
practice, the proposed greedy approaches achieve significant performance in a
few number of iterations.
