论文标题
PROSKY：整洁的6G天空中的Noma-Mmwave
ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G
论文作者
论文摘要
无人驾驶飞机（UAVS）的能力灵活而有效地提供了无处不在的连通性，已经受到了越来越多的研究关注。但是，要将无人机的性能提升到一个新的水平，需要将它们与其他一些技术合并，例如非正交多重访问（NOMA）和毫米波（MMWave），这两个技术都承诺高光谱效率（SE）。由于使用基于模型的技术可能无法有效地管理无人机，因此无人机不可避免地需要利用的另一种关键创新技术是人工智能（AI）。设计一种基于AI的技术，该技术可自适应地分配无线电资源，并将无人机放置在3D空间中以满足某些沟通目标，这是一个艰难的行程。在本文中，我们提出了一个神经进化，以增强拓扑结构框架（称为Prosky）来管理Noma-Mmwave-UAV网络。 Prosky比基于模型的方法表现出显着的性能改进。此外，Prosky在SE和能源效率EE中，同时相当公平，这是一个基于深厚的增强学习DRL的方案，在SE和能源效率EE中，学到的速度比胜过5.3倍。可以在此处使用Prosky源代码：https：//github.com/fouzibenfaid/prosky
Rendering to their abilities to provide ubiquitous connectivity, flexibly and cost effectively, unmanned aerial vehicles (UAVs) have been getting more and more research attention. To take the UAVs' performance to the next level, however, they need to be merged with some other technologies like non-orthogonal multiple access (NOMA) and millimeter wave (mmWave), which both promise high spectral efficiency (SE). As managing UAVs efficiently may not be possible using model-based techniques, another key innovative technology that UAVs will inevitably need to leverage is artificial intelligence (AI). Designing an AI-based technique that adaptively allocates radio resources and places UAVs in 3D space to meet certain communication objectives, however, is a tough row to hoe. In this paper, we propose a neuroevolution of augmenting topologies NEAT framework, referred to as ProSky, to manage NOMA-mmWave-UAV networks. ProSky exhibits a remarkable performance improvement over a model-based method. Moreover, ProSky learns 5.3 times faster than and outperforms, in both SE and energy efficiency EE while being reasonably fair, a deep reinforcement learning DRL based scheme. The ProSky source code is accessible to use here: https://github.com/Fouzibenfaid/ProSky
