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一对多语义通信系统的6G信息技术

时间:2022-09-26 16:30

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作者:admin

标签: 通信系统  6G  深度学习 

导读:一对多语义通信系统的6G信息技术-面向6G时代,本文在全球首次设计“一对多”语义通信系统,具有开创性,所提出的“一对多”语义通信系统“MR DeepSC”可以为未来语义通信系统的发展...

面向6G时代,本文在全球首次设计“一对多”语义通信系统,具有开创性,所提出的“一对多”语义通信系统“MR DeepSC”可以为未来语义通信系统的发展打下基础。

这项研究工作得到了国家自然科学基金62222107、62071223、62031012、61871446和中国科协青年精英科学家资助计划的部分支持;部分由江苏省重点研发计划项目BE2020084-1资助;部分由国家自然科学基金项目92067201资助。

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本文参考文献:

[1] Z. Qin et al., “Deep learning in physical layer communications,” IEEE Wireless Commun., vol. 26, no. 2, pp. 93–99, 2019.

[2] Q. Lan et al., “What is semantic communication? A view on conveying meaning in the era of machine intelligence,” Journal of Communications and Information Networks., vol. 6, no. 4, pp. 336–371, 2021.

[3] N. Farsad et al., “Deep learning for joint sourcechannel coding of text,” IEEE Int’l. Conf. Acoustics Speech Signal Process. (ICASSP)., algary, AB, Canada, pp. 2326–2330, 2018.

[4] H. Xie et al., “Deep learning enabled semantic communication systems,” IEEE Trans. Signal Process., vol. 69, pp. 2663–2675, 2021.

[5] F. Zhou et al., “Cognitive semantic communication systems driven by knowledge graph,” IEEE ICC., to be published, 2022.

[6] Z. Weng et al., “Semantic communication systems for speech transmission,” IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2434–2444, 2021.

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[8] D. B. Kurka et al., “DeepJSCC-f: Deep joint source-channel coding of images with feedback,” IEEE J. Select. Areas Inf. Theory., vol. 1, no. 1, pp. 178—193, 2020.

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【附录】本文作者

H. Hu、X. Zhu、H. Zhu:南京邮电大学江苏省无线通信重点实验室,南京邮电大学泛在网络健康服务系统教育部工程研究中心

F. Zhou:南京航空航天大学电子与信息工程学院。

W. Wu:南京邮电大学通信与信息工程学院。

R. Q. Hu:就职于美国犹他州洛根市犹他州立大学电气与计算机工程系。

编辑:黄飞

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