2-UAV RAN 切片的混合解决方案

Nathan Boyer
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引用次数: 0

摘要

通过无人机向用户分发互联网是可能的。此外,第五代(5G)新无线电(NR)技术旨在适应广泛的应用和行业。NGNM 5G 白皮书》将这些垂直用例分为三类: - 增强型移动宽带(eMBB) - 大规模机器型通信(mMTC) - 超可靠低延迟通信(URLLC)。将物理网络划分为多个虚拟网络似乎是为每个应用提供定制服务并限制运营成本的最佳方式。这种设计就是众所周知的网络切片(textit{network slicing})。因此,每架无人机必须在 3 个用户类别之间分配带宽。整个问题(位置+带宽)可以定义为一个优化问题,但由于很难高效解决,因此在文献中几乎总是由人工智能来解决。在我的实习中,我想通过建立一个一方面涉及人工智能、另一方面涉及优化的混合解决方案,来证明将该问题视为优化问题仍然是有用的。与只使用人工智能的方法相比,我使用它取得了更好的结果,尽管代价是计算时间稍长(但仍然合理)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid solution for 2-UAV RAN slicing
It's possible to distribute the Internet to users via drones. However it is then necessary to place the drones according to the positions of the users. Moreover, the 5th Generation (5G) New Radio (NR) technology is designed to accommodate a wide range of applications and industries. The NGNM 5G White Paper \cite{5gwhitepaper} groups these vertical use cases into three categories: - enhanced Mobile Broadband (eMBB) - massive Machine Type Communication (mMTC) - Ultra-Reliable Low-latency Communication (URLLC). Partitioning the physical network into multiple virtual networks appears to be the best way to provide a customised service for each application and limit operational costs. This design is well known as \textit{network slicing}. Each drone must thus slice its bandwidth between each of the 3 user classes. This whole problem (placement + bandwidth) can be defined as an optimization problem, but since it is very hard to solve efficiently, it is almost always addressed by AI in the litterature. In my internship, I wanted to prove that viewing the problem as an optimization problem can still be useful, by building an hybrid solution involving on one hand AI and on the other optimization. I use it to achieve better results than approaches that use only AI, although at the cost of slightly larger (but still reasonable) computation times.
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