论文标题
基于无人机的网络覆盖范围优化的快速和计算有效的生成对抗网络算法
Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle-based network coverage optimization
论文作者
论文摘要
移动网络中动态交通需求的挑战是通过基于无人驾驶汽车移动电池来应对的。考虑到将来无人飞行器的巨大潜力，我们提出了一种新的启发式算法，以进行覆盖优化。提出的算法是基于条件生成对抗神经网络实现的，具有独特的多层总和损失函数。为了评估所提出方法的性能，我们将其与最佳核心算法和准最佳螺旋算法进行了比较。仿真结果表明，所提出的方法会收敛到准最佳解决方案，而与全局最佳的差异相差微不足道，同时保持二次复杂性，无论用户数量多少。
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.
