论文标题
CNN辅助通道和载波频率偏移量估计HAP-LEO链接
CNN-aided Channel and Carrier Frequency Offset Estimation for HAPS-LEO Links
论文作者
论文摘要
低地球轨道（LEO）卫星巨型构成网络旨在通过预计在不到十年的时间内预计50,000颗卫星来满足高连通性需求。为了充分利用这种大规模的动态网络，由平流层节点（特别是高空平台站（HAP））组成的空气网络可以极大地帮助许多方面，包括移动性管理。 HAP-LEO网络将受到时变条件的约束，在本文中，我们引入了一种基于人工智能（AI）的方法，以实现唯一的渠道估计和同步问题。首先，通过使用拟议的基于卷积神经网络的估计量，可以最大程度地降低带有残留多普勒效应的通道均衡和载体频率偏移。然后，使用非正交多重访问方法提高光谱效率来使数据速率复杂化。我们观察到，提出的AI授权HAP-LEO网络不仅可以每秒提供高数据吞吐量，而且由于敏捷信号重建过程，服务质量更高。
Low Earth orbit (LEO) satellite mega-constellation networks aim to address the high connectivity demands with a projected 50,000 satellites in less than a decade. To fully utilize such a large-scale dynamic network, an air network composed of stratospheric nodes, specifically high altitude platform station (HAPS), can help significantly with a number of aspects including mobility management. HAPS-LEO network will be subject to time-varying conditions, and in this paper, we introduce an artificial intelligence (AI)-based approach for the unique channel estimation and synchronization problems. First, channel equalization and carrier frequency offset with residual Doppler effects are minimized by using the proposed convolutional neural networks based estimator. Then, the data rate is compounded by increasing spectral efficiency using non-orthogonal multiple access method. We observed that the proposed AI-empowered HAPS-LEO network provides not only a high data throughput per second but also higher service quality thanks to the agile signal reconstruction process.
