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

TL;DR

This paper introduces a fast, efficient generative adversarial network-based heuristic algorithm for optimizing UAV-based network coverage, achieving near-optimal results with low computational complexity.

Contribution

It presents a novel GAN-based heuristic algorithm with a unique loss function for UAV coverage optimization, outperforming traditional algorithms in efficiency and near-optimality.

Findings

Converges close to the global optimum in simulations.

Maintains quadratic complexity regardless of user number.

Outperforms traditional algorithms in speed and accuracy.

Abstract

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.