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The challenge of dynamic traffic demand in mobile networks is tackled bymoving cells based on unmanned aerial vehicles. Considering the tremendouspotential of unmanned aerial vehicles in the future, we propose a new heuristicalgorithm for coverage optimization. The proposed algorithm is implementedbased on a conditional generative adversarial neural network, with a uniquemultilayer sum-pooling loss function. To assess the performance of the proposedapproach, we compare it with the optimal core-set algorithm and quasi-optimalspiral algorithm. Simulation results show that the proposed approach convergesto the quasi-optimal solution with a negligible difference from the globaloptimum while maintaining a quadratic complexity regardless of the number ofusers.

Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle-based network coverage optimization