Mean Field Game-Based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems-Reference-Cited by-同舟云学术

Mean Field Game-Based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems

Published:2024-08 Issue:8 Volume:23 Page:10430-10444
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Wang Dezhi¹^ORCID,Huang Chongwen¹^ORCID,He Jiguang²^ORCID,Chen Xiaoming³^ORCID,Wang Wei³^ORCID,Zhang Zhaoyang³^ORCID,Han Zhu⁴^ORCID,Debbah Mérouane⁵^ORCID

Affiliation:

1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

2. Technology Innovation Institute, Abu Dhabi, United Arab Emirates

3. College of Information Science and Electronic Engineering, Hangzhou, China

4. Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA

5. Department of Electrical Engineering and Computer Science and KU 6G Center, Khalifa University, Abu Dhabi, United Arab Emirates

Funder

China National Key Research and Development Program

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Zhejiang Provincial Science and Technology Plan Project

Zhejiang University Global Partnership Fund

China Post-Doctoral Science Foundation

Post-Doctoral Fellowship Program of China Postdoctoral Science Foundation

NSF

U.S. Department of Transportation

Toyota. Amazon

Japan Science and Technology Agency Advanced Science through Promoting Innovative Research Excellence

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/7693/10634844/10463760-aam.pdf

Reference52 articles.

1. Waveform Precoding Design for Mobile Crowd ISCC System Using Mean Field Game

2. Challenges, Applications, and Future of Wireless Sensors in Internet of Things: A Review

3. A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

4. A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility

5. Achieving Privacy-Preserving Multitask Allocation for Mobile Crowdsensing

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