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作者:

Zhang, Yibo (Zhang, Yibo.) | Li, Qi (Li, Qi.) | Wang, Jingjing (Wang, Jingjing.) | Wang, Jiaxing (Wang, Jiaxing.) | Chen, Jianrui (Chen, Jianrui.) | Han, Zhu (Han, Zhu.)

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EI Scopus SCIE

摘要:

To meet the requirements of ubiquitous connectivity, the sixth-generation (6G) is expected to support higher frequencies such as millimeter waves and terahertz, as well as high-mobility scenarios like unmanned aerial vehicles and vehicular networks. However, communication scenarios under these conditions may suffer from beam misalignment due to high mobility, especially in high-frequency channels with narrow beam characteristics. Integrated sensing and communications (ISAC) enable hardware and spectrum resource sharing between radar sensing and wireless communication, and exhibit great potential in sensing-assisted communication. One pressing problem is that performance metrics for sensing and communication are often contradictory when they share the same hardware or wireless resources. Therefore, how to optimize the performance metrics between both is the key to fulfilling specific requirements. This article focuses on ISAC enabled predictive beamforming for 6G high-mobility scenarios, and investigates the technical characteristics, system architecture, and optimization schemes involved in the performance trade-offs. We first provide an overview of the unique characteristics of ISAC and predictive beamforming and highlight the benefits of combining them. We then investigate the design architectures and propose an optimization scheme that can achieve a performance trade-off between sensing and communication to maximize throughput. Finally, we highlight some challenging issues that need to be addressed in practical implementations.

关键词:

Sensors Radar Wireless communication Wireless sensor networks 6G mobile communication Array signal processing Millimeter wave communication

作者机构:

  • [ 1 ] [Zhang, Yibo]Beijing Informat Sci & Technol Univ, Sch Informat & Commun Engn, Beijing 100101, Peoples R China
  • [ 2 ] [Zhang, Yibo]Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
  • [ 3 ] [Wang, Jingjing]Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
  • [ 4 ] [Li, Qi]Beijing Univ Technol, Informat & Commun Engn, Beijing 100124, Peoples R China
  • [ 5 ] [Wang, Jingjing]Beihang Univ, Sch Cyber Sci & Technol, Beijing, Peoples R China
  • [ 6 ] [Wang, Jiaxing]Beihang Univ, Sch Cyber Sci & Technol, Beijing, Peoples R China
  • [ 7 ] [Chen, Jianrui]Beihang Univ, Sch Cyber Sci & Technol, Beijing, Peoples R China
  • [ 8 ] [Chen, Jianrui]Peng Cheng Lab, Shenzhen 518000, Peoples R China
  • [ 9 ] [Han, Zhu]Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
  • [ 10 ] [Han, Zhu]Kyung Hee Univ, Dept Comp Sci & Engn, Seoul 446701, South Korea

通讯作者信息:

  • [Wang, Jingjing]Beihang Univ, Sch Cyber Sci & Technol, Beijing, Peoples R China;;

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来源 :

IEEE NETWORK

ISSN: 0890-8044

年份: 2024

期: 4

卷: 38

页码: 292-300

9 . 3 0 0

JCR@2022

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SCOPUS被引频次: 5

ESI高被引论文在榜: 0 展开所有

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