Asynchronous Federated Learning-Based Resource Management in URLLC-IoV Networks-Reference-Cited by-同舟云学术

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Asynchronous Federated Learning-Based Resource Management in URLLC-IoV Networks

Published:2023-12-04 Issue: Volume: Page:6874-6879
ISSN:
Container-title:GLOBECOM 2023 - 2023 IEEE Global Communications Conference
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Author:

Hazarika Bishmita¹,Singh Keshav¹,Singh Sandeep Kumar¹,Pan Cunhua²,Duong Trung Q.³

Affiliation:

1. Institute of Communications Engineering, National Sun Yat-sen University,Kaohsiung,Taiwan,804

2. Southeast University,National Mobile Communications Research Laboratory,Nanjing,China,210096

3. Institute of Electronics Communications & Information Technology, Queen's University,Belfast,United Kingdom

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10436708/10436716/10437135.pdf?arnumber=10437135

Reference10 articles.

1. DRL-Based Resource Allocation for Computation Offloading in IoV Networks

2. SAC-Based Resource Allocation for Computation Offloading in IoV Networks

3. Multi-Agent DRL-Based Computation Offloading in Multiple RIS-Aided IoV Networks

4. Proximal Policy Optimization for RIS-assisted Full Duplex 6G-V2X Communications

5. Joint Optimization of URLLC Parameters and Beamforming Design for Multi-RIS-Aided MU-MISO URLLC System

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