Deep Reinforcement Learning for Mobile 5G and Beyond: Fundamentals, Applications, and Challenges-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning for Mobile 5G and Beyond: Fundamentals, Applications, and Challenges

Published:2019-06 Issue:2 Volume:14 Page:44-52
ISSN:1556-6072
Container-title:IEEE Vehicular Technology Magazine
language:
Short-container-title:IEEE Veh. Technol. Mag.

Author:

Xiong Zehui^ORCID,Zhang Yang^ORCID,Niyato Dusit^ORCID,Deng Ruilong^ORCID,Wang Ping^ORCID,Wang Li-Chun^ORCID

Funder

National Natural Science Foundation of China

A STAR-NTU-SUTD Joint Research

Singapore MOE Tier 1

MOE Tier 2

Singapore

Singapore EMA Energy Resilience

NTU internal funding SUG-CoE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/10209/8721247/08683970.pdf?arnumber=8683970

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1. Deep Reinforcement Learning based Mobility Management in a MEC-Enabled Cellular IoT Network;Pervasive and Mobile Computing;2024-09

2. Topology Learning for Heterogeneous Decentralized Federated Learning Over Unreliable D2D Networks;IEEE Transactions on Vehicular Technology;2024-08

3. Joint Sensing and Communication Optimization in Target-Mounted STARS-Assisted Vehicular Networks: A MADRL Approach;IEEE Transactions on Vehicular Technology;2024-07

4. Optimizing the Deployment of an Aerial Base Station and the Phase-Shift of a Ground Reconfigurable Intelligent Surface for Wireless Communication Systems Using Deep Reinforcement Learning;Information;2024-07-01

5. Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey;International Journal of Communication Systems;2024-06-03

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