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  • 标题:Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization
  • 本地全文:下载
  • 作者:Marek Ružička ; Marcel Vološin ; Juraj Gazda
  • 期刊名称:International Journal of Distributed Sensor Networks
  • 印刷版ISSN:1550-1329
  • 电子版ISSN:1550-1477
  • 出版年度:2022
  • 卷号:18
  • 期号:3
  • 页码:1-9
  • DOI:10.1177/15501477221075544
  • 语种:English
  • 出版社:Hindawi Publishing Corporation
  • 摘要: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.
  • 关键词:Generative adversarial network;unmanned aerial vehicle;algorithm optimization;coverage;machine learning
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