ADMM-SLPNet: A Model-Driven Deep Learning Framework for Symbol-Level Precoding-Reference-Cited by-同舟云学术

ADMM-SLPNet: A Model-Driven Deep Learning Framework for Symbol-Level Precoding

Published:2024-01 Issue:1 Volume:73 Page:1376-1381
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Yang Junwen¹^ORCID,Li Ang¹^ORCID,Liao Xuewen¹^ORCID,Masouros Christos²^ORCID

Affiliation:

1. School of Information and Communications Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China

2. Department of Electronic and Electrical Engineering, University College London, London, U.K.

Funder

Young Elite Scientists Sponsorship Program

National Natural Science Foundation of China

Key Science and Technology Program of Shaanxi Province

National Key Research and Development Project of China

Southeast University

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/ielx7/25/10400535/10202568.pdf?arnumber=10202568

Reference18 articles.

1. Exploiting Known Interference as Green Signal Power for Downlink Beamforming Optimization

2. Constructive Multiuser Interference in Symbol Level Precoding for the MISO Downlink Channel

3. A Tutorial on Interference Exploitation via Symbol-Level Precoding: Overview, State-of-the-Art and Future Directions

4. Linear precoding via conic optimization for fixed MIMO receivers

5. Power Minimizer Symbol-Level Precoding: A Closed-Form Sub-Optimal Solution

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1. Deep learning in wireless communications for physical layer;Physical Communication;2024-09

2. Hybrid SLPC using Enhanced Deep secure Symbol level Auto encoder for Massive MIMO-NOMA Communication;2024 2nd International Conference on Networking and Communications (ICNWC);2024-04-02

3. Deep Learning in Wireless Communications for Physical Layer;2024