论文标题
带有无人机辅助5G网络的延迟敏感服务交付
Latency-sensitive Service Delivery with UAV-Assisted 5G Networks
论文作者
论文摘要
在这封信中，开发了一个新颖的框架，以提供关键的分布的URLLC服务，该服务在覆盖区域中部署无人机（UAV）。为此，研究了资源优化问题，即资源块（RB）和功率分配，以及无人用的5G网络的最佳无人机部署策略，以共同最大化平均总和率，并最大程度地降低无人机的发射功率，同时满足URLLC要求。为了应对零星的URLLC流量问题，提出了一个有效的在线URLLC流量预测模型，该模型基于高斯流程回归（GPR），该模型得出了最佳的URLLC调度和传输功率策略。该法式问题被揭示为一种混合成员非线性编程（MINLP），该编程在引入连续的最小化算法之后解决。最后，提供了模拟结果以显示我们提出的解决方案方法的效率。
In this letter, a novel framework to deliver critical spread out URLLC services deploying unmanned aerial vehicles (UAVs) in an out-of-coverage area is developed. To this end, the resource optimization problem, i.e., resource blocks (RBs) and power allocation, and optimal UAV deployment strategy are studied for UAV-assisted 5G networks to jointly maximize the average sum-rate and minimize the transmit power of UAV while satisfying the URLLC requirements. To cope with the sporadic URLLC traffic problem, an efficient online URLLC traffic prediction model based on Gaussian Process Regression (GPR) is proposed which derives optimal URLLC scheduling and transmit power strategy. The formulated problem is revealed as a mixed-integer nonlinear programming (MINLP), which is solved following the introduced successive minimization algorithm. Finally, simulation results are provided to show our proposed solution approach's efficiency.
