Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G-Reference-Cited by-同舟云学术

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Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G

Published:2024-03 Issue:2 Volume:38 Page:70-78
ISSN:0890-8044
Container-title:IEEE Network
language:
Short-container-title:IEEE Network

Author:

Sun Ruijin¹^ORCID,Cheng Nan¹^ORCID,Li Changle¹^ORCID,Chen Fangjiong²^ORCID,Chen Wen³^ORCID

Affiliation:

1. State Key Laboratory of ISN and the School of Telecommunications Engineering, Xidian University, Xi’an, China

2. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

3. Department of Electronics Engineering, Shanghai Jiao Tong University, Shanghai, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/65/10527424/10387423.pdf?arnumber=10387423

Reference15 articles.

1. Model-Driven Deep Learning for Physical Layer Communications

2. Model-Based Deep Learning: Key Approaches and Design Guidelines

3. Learning to Transmit Fresh Information in Energy Harvesting Networks

4. Deep Learning with Long Short-Term Memory for Time Series Prediction

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1. Uplink Performance Analysis of RIS-Assisted UAV Communication Systems With Random 3-D Mobile Pattern;IEEE Internet of Things Journal;2024-09-01

2. Information Timeliness Driven Statistical QoS Guarantee in RIS-Enabled Wireless Networks via Deep Reinforcement Learning;IEEE Internet of Things Journal;2024-07-15

3. Energy-Efficient Task Offloading in UAV-RIS-Assisted Mobile Edge Computing with NOMA;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2024-05-20

4. Knowledge-Driven Resource Allocation for Wireless Networks: A WMMSE Unrolled Graph Neural Network Approach;IEEE Internet of Things Journal;2024-05-15

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