Extreme Learning Machine-Based Channel Estimation in IRS-Assisted Multi-User ISAC System-Reference-Cited by-同舟云学术

Extreme Learning Machine-Based Channel Estimation in IRS-Assisted Multi-User ISAC System

Published:2023-12 Issue:12 Volume:71 Page:6993-7007
ISSN:0090-6778
Container-title:IEEE Transactions on Communications
language:
Short-container-title:IEEE Trans. Commun.

Author:

Liu Yu¹^ORCID,Al-Nahhal Ibrahim²^ORCID,Dobre Octavia A.²^ORCID,Wang Fanggang¹^ORCID,Shin Hyundong³^ORCID

Affiliation:

1. State Key Laboratory of Advanced Rail Autonomous Operation, School of Electronic and Information Engineering, Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing, China

2. Faculty of Engineering and Applied Science, Memorial University, St. Johns, Canada

3. Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin, Republic of Korea

Funder

Fundamental Research Funds for the Central Universities

State Key Laboratory of Rail Traffic Control and Safety

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Sciences and Engineering Research Council of Canada (NSERC), through its Discovery program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/26/10364892/10229204.pdf?arnumber=10229204

Reference44 articles.

1. Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

2. Reconfigurable Intelligent Surface Optimization for Uplink Sparse Code Multiple Access

3. Reconfigurable Intelligent Surface-Assisted Uplink Sparse Code Multiple Access

4. Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems

5. Practical RIS-Aided Coded Systems: Joint Precoding and Passive Beamforming

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