ISAC-NET: Model-Driven Deep Learning for Integrated Passive Sensing and Communication-Reference-Cited by-同舟云学术

ISAC-NET: Model-Driven Deep Learning for Integrated Passive Sensing and Communication

Published:2024-08 Issue:8 Volume:72 Page:4692-4707
ISSN:0090-6778
Container-title:IEEE Transactions on Communications
language:
Short-container-title:IEEE Trans. Commun.

Author:

Jiang Wangjun¹^ORCID,Ma Dingyou¹^ORCID,Wei Zhiqing¹^ORCID,Feng Zhiyong¹^ORCID,Zhang Ping²^ORCID,Peng Jinlin³^ORCID

Affiliation:

1. School of Information and Communication Engineering and the Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China

2. School of Information and Communication Engineering and the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

3. Artificial Intelligence Research Center, National Innovation Institute of Defense Technology, Beijing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

BUPT Excellent Ph.D. Students Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/26/10637778/10474422.pdf?arnumber=10474422

Reference36 articles.

1. FRaC: FMCW-Based Joint Radar-Communications System Via Index Modulation

2. Joint radar and communication: A survey

3. Joint Radar-Communication Strategies for Autonomous Vehicles: Combining Two Key Automotive Technologies

4. Improve Sensing and Communication Performance of UAV via Integrated Sensing and Communication

5. Waveform Design and Signal Processing Aspects for Fusion of Wireless Communications and Radar Sensing

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