  The challenge of dynamic traffic demand in mobile networks is tackled by
moving cells based on unmanned aerial vehicles. Considering the tremendous
potential of unmanned aerial vehicles in the future, we propose a new heuristic
algorithm for coverage optimization. The proposed algorithm is implemented
based on a conditional generative adversarial neural network, with a unique
multilayer sum-pooling loss function. To assess the performance of the proposed
approach, we compare it with the optimal core-set algorithm and quasi-optimal
spiral algorithm. Simulation results show that the proposed approach converges
to the quasi-optimal solution with a negligible difference from the global
optimum while maintaining a quadratic complexity regardless of the number of
users.
