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

Marek RužiÄ Ka, Marcel VoloÅ¡in, Juraj Gazda, Taras Maksymyuk, Longzhe Han and MisCha Dohler

International Journal of Distributed Sensor Networks, 2022, vol. 18, issue 3, 15501477221075544

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.

Keywords: Generative adversarial network; unmanned aerial vehicle; algorithm optimization; coverage; machine learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:18:y:2022:i:3:p:15501477221075544

DOI: 10.1177/15501477221075544

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