Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle-based network coverage optimization
This addresses coverage optimization for mobile networks using UAVs, but it appears incremental as it builds on existing methods like core-set and spiral algorithms.
The paper tackles dynamic traffic demand in mobile networks by using unmanned aerial vehicles for coverage optimization, proposing a heuristic algorithm based on a conditional generative adversarial network with a unique multilayer sum-pooling loss function. Simulation results show it converges to a quasi-optimal solution with negligible difference from the global optimum while maintaining quadratic complexity regardless of user count.
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.