Integration of Communication, Sensing and Computing: the Vision and Key Technologies of 6G

被引：0
|
作者：
Yan S. [1 ]
Peng M.-G. [1 ]
Wang W.-B. [1 ]
机构：
[1] State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2021年 / 44卷 / 04期
关键词：
Communication detection; Communication-sensing-computing integration; Distributed computing power; The sixth generation of mobile communications system;
D O I：
10.13190/j.jbupt.2021-081
中图分类号：
学科分类号：
摘要：
The emerging intelligent services such as self-driving, unmanned aerial vehicle emergency communication, immersive extended reality, industrial Internet of things, which rely on multi-dimensional information perception and super computing power, put forward high requirements for transmission rate, end-to-end delay, reliability and power consumption. To meet these ultra-high performance requirements, the sixth generation of mobile communications system (6G) needs to improve the network endogenous intelligent perception and computing adaptive ability, and break through the communication-sensing-computing fusion theory and key technologies. First, the typical 6G communication-sensing-computing integration application requirements are described. Then, the link level and system level key technologies are put forward, including communication-sensing-computing integration, multi-source information data processing, multi-dimensional resource management, etc. The principles, methods and performance are also described. Finally, the technical challenges and future development directions are discussed. © 2021, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
引用
收藏
页码：1 / 11
页数：10
相关论文
共 33 条
  • [11] Wang Zhangjing, Wu Yu, Niu Qingqing, Multi-sensor fusion in automated driving: a survey, IEEE Access, 8, pp. 2847-2868, (2019)
  • [12] White paper of the internet of vehicles (C-V2X volume), China Academy of Information and Communications Technology (CAICT), pp. 1-47, (2019)
  • [13] Zhang Jun, Letaief K B., Mobile edge intelligence and computing for the Internet of vehicles, Proceedings of the IEEE, 108, 2, pp. 246-261, (2020)
  • [14] Price E, Lawless G, Ludwig R, Et al., Deep neural network-based cooperative visual tracking through multiple micro aerial vehicles, IEEE Robotics and Automation Letters, 3, 4, pp. 3193-3200, (2018)
  • [15] 5G for connected industries and automation second edition, 5G Alliance for Connec-ted Industries and Automation (5G ACIA), pp. 1-28, (2019)
  • [16] Liu Fan, Masouros C, Petropulu A P, Et al., Joint radar and communication design: applications, state-of-the-art, and the road ahead, IEEE Transactions on Communications, 68, 6, pp. 3834-3862, (2020)
  • [17] Sturm C, Wiesbeck W., Waveform design and signal processing aspects for fusion of wireless communications and radar sensing, Proceedings of the IEEE, 99, 7, pp. 1236-1259, (2011)
  • [18] Feng Zhiyong, Fang Zixi, Wei Zhiqing, Et al., Joint radar and communication: a survey, China Communications, 17, 1, pp. 1-27, (2020)
  • [19] Chen Chengrui, Sun Ning, He Shibiao, Et al., Joint channel estimation and equalization method based on deep learning for C-V2X communication, Journal of Computer Applications, pp. 1-7, (2021)
  • [20] Tse D, Viswanath P., Fundamentals of wireless communication, (2005)
1234