论文标题

未来高空平台站(HAP)网络的愿景和框架

A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

论文作者

Kurt, Gunes, Khoshkholgh, Mohammad G., Alfattani, Safwan, Ibrahim, Ahmed, Darwish, Tasneem S. J., Alam, Md Sahabul, Yanikomeroglu, Halim, Yongacoglu, Abbas

论文摘要

高空平台站(HAPS)是一个网络节点,在平流层在20公里约20公里的高度上运行,对提供通信服务起着重要作用。由自主航空电子学,阵列天线,太阳能电池板效率水平和电池能量密度的技术创新引起的,并由蓬勃发展的行业生态系统促进,HAP已成为下一代无线网络的不可或缺的组成部分。在本文中,我们为未来的HAP网络提供了一个愿景和框架,并由全面和最先进的文献综述支持。我们强调了HAPS系统的未实现潜力,并详细介绍了它们为大都市地区服务的独特能力。讨论了HAPS能量和有效载荷系统中最新的进步和有希望的技术。提出了新兴的可重构智能表面(RSS)技术在HAPS系统的通信有效载荷中的整合,以提供具有成本效益的部署。介绍了HAPS系统中无线电资源管理的详细概述,并与协同的物理层技术一起介绍了,包括比尼奎斯特(FTN)更快的信号传导。描述了HAPS系统中缩短管理的许多方面。强调了人工智能(AI)在HAP中的显着贡献,包括设计,拓扑管理,交接和资源分配方面的机器学习。我们提供的文献的广泛概述对于证实我们的愿景至关重要,该愿景描绘了未来10年(下一代网络)以及随后的10年(下一代新一代网络)中预期的部署机会和挑战。

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).

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