Deep Learning Based Joint Hybrid Precoding and Combining Design for mmWave MIMO Systems-Reference-Cited by-同舟云学术

Deep Learning Based Joint Hybrid Precoding and Combining Design for mmWave MIMO Systems

Published:2024-03 Issue:1 Volume:18 Page:560-567
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Liu Fulai¹^ORCID,Li Xiaoyu²^ORCID,Yang Xianghuan²^ORCID,Shi Huiyang²^ORCID,Shi Baozhu²^ORCID,Du Ruiyan¹^ORCID

Affiliation:

1. Lab of Key Technology of Millimeter-wave Large-scale MIMO System, Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao, China

2. School of Computer Science and Engineering, Northeastern University, Shenyang, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4267003/10473604/10430125.pdf?arnumber=10430125

Reference23 articles.

1. Sparse Hybrid Precoding for Power Minimization With an Adaptive Antenna Structure in Massive MIMO Systems

2. A Family of Hybrid Precoding Schemes for Millimeter-Wave Massive MIMO Systems

3. Spatially Sparse Precoding in Millimeter Wave MIMO Systems

4. Hybrid Block Diagonalization for Massive Multiuser MIMO Systems

5. Over-Sampling Codebook-Based Hybrid Minimum Sum-Mean-Square-Error Precoding for Millimeter-Wave 3D-MIMO

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