Semantic-Aware Sensing Information Transmission for Metaverse: A Contest Theoretic Approach-Reference-Cited by-同舟云学术

Semantic-Aware Sensing Information Transmission for Metaverse: A Contest Theoretic Approach

Published:2023-08 Issue:8 Volume:22 Page:5214-5228
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Wang Jiacheng¹^ORCID,Du Hongyang²^ORCID,Tian Zengshan¹^ORCID,Niyato Dusit²^ORCID,Kang Jiawen³^ORCID,Shen Xuemin⁴^ORCID

Affiliation:

1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China

2. School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore

3. School of Automation and the Key Laboratory of Intelligent Information Processing and System Integration of IoT, Guangdong University of Technology (GDUT), Ministry of Education, Guangzhou, China

4. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada

Funder

NSFC

Guangdong Provincial Pearl River Talents Program

National Research Foundation (NRF), Singapore and Infocomm Media Development Authority through the Future Communications Research Development Programme

DSO National Laboratories through the AI Singapore (AISG) Programme through the Energy Research Test-Bed and Industry Partnership Funding Initiative

Energy Grid (EG) 2.0 Programme, through DesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) Programme

Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7693/10214970/10007890.pdf?arnumber=10007890

Reference50 articles.

1. Analysis of Compressed CSI Feedback in MISO Systems

2. Structured Compressed Sensing: From Theory to Applications

3. Deep Learning-Based CSI Feedback Approach for Time-Varying Massive MIMO Channels

4. Deep Learning for Massive MIMO CSI Feedback

5. Sparse Channel Estimation with Zero Tap Detection

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