Multi-Task Driven User Association and Resource Allocation in In-vehicle Networks-Reference-Cited by-同舟云学术

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Multi-Task Driven User Association and Resource Allocation in In-vehicle Networks

Published:2023-12-04 Issue: Volume: Page:01-06
ISSN:
Container-title:GLOBECOM 2023 - 2023 IEEE Global Communications Conference
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Author:

Chen Ziqi¹,Du Jun¹,Yang Guowei¹,Jiang Chunxiao²,Han Zhu³

Affiliation:

1. Tsinghua University,Department of Electronic Engineering,Beijing,China,100084

2. Beijing National Research Center for Information Science and Technology, Tsinghua University,Beijing,China,100084

3. University of Houston,Department of Electrical and Computer Engineering,Houston,TX,USA,77004

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10436708/10436716/10437198.pdf?arnumber=10437198

Reference13 articles.

1. Computation Offloading in Heterogeneous Vehicular Edge Networks: On-line and Off-policy Bandit Solutions

2. SDN-Based Resource Allocation in Edge and Cloud Computing Systems: An Evolutionary Stackelberg Differential Game Approach

3. Machine Learning for 6G Wireless Networks: Carrying Forward Enhanced Bandwidth, Massive Access, and Ultrareliable/Low-Latency Service

4. Deep Reinforcement Learning for Dynamic Uplink/Downlink Resource Allocation in High Mobility 5G HetNet

5. Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems

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