Deep-Unfolding Neural-Network Aided Hybrid Beamforming Based on Symbol-Error Probability Minimization-Reference-Cited by-同舟云学术

Deep-Unfolding Neural-Network Aided Hybrid Beamforming Based on Symbol-Error Probability Minimization

Published:2023-01 Issue:1 Volume:72 Page:529-545
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Shi Shuhan¹,Cai Yunlong¹^ORCID,Hu Qiyu¹^ORCID,Champagne Benoit²^ORCID,Hanzo Lajos³^ORCID

Affiliation:

1. Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking (IPCAN), College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China

2. Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada

3. Department of Electronics and Computer Science, University of Southampton, Southampton, Hants, U.K.

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars

Engineering and Physical Sciences Research Council

European Research Council's Advanced Fellow Grant QuantCom

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/10017140/09868110.pdf?arnumber=9868110

Reference42 articles.

1. Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems

2. Model-Driven Deep Learning for Massive MU-MIMO With Finite-Alphabet Precoding

3. Improved polar decoder based on deep learning

4. Learning to Detect

5. Multilevel MIMO Detection with Deep Learning

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