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

Author

Listed:
  • Marek RužiÄ ka
  • Marcel VoloÅ¡in
  • Juraj Gazda
  • Taras Maksymyuk
  • Longzhe Han
  • MisCha Dohler

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.

Suggested Citation

  • Marek RužiÄ ka & Marcel VoloÅ¡in & Juraj Gazda & Taras Maksymyuk & Longzhe Han & MisCha Dohler, 2022. "Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501477221, March.
    • Handle: RePEc:sae:intdis:v:18:y:2022:i:3:p:15501477221075544
    DOI: 10.1177/15501477221075544
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    References listed on IDEAS

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    1. Hatem Fayed & Amir Atiya, 2013. "Erratum to: A mixed breadth-depth first strategy for the branch and bound tree of Euclidean k-center problems," Computational Optimization and Applications, Springer, vol. 54(3), pages 705-705, April.
    2. Hatem Fayed & Amir Atiya, 2013. "A mixed breadth-depth first strategy for the branch and bound tree of Euclidean k-center problems," Computational Optimization and Applications, Springer, vol. 54(3), pages 675-703, April.
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