AI-Driven Traffic-Aware Dynamic TDD Configuration in B5G Networks-Reference-Cited by-同舟云学术

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AI-Driven Traffic-Aware Dynamic TDD Configuration in B5G Networks

Published:2024-05-06 Issue: Volume: Page:1-4
ISSN:
Container-title:NOMS 2024-2024 IEEE Network Operations and Management Symposium
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Author:

Jeong Sanguk¹,Mok Dahyun²,Byun Gyurin³,Mwasinga Lusungu J.⁴,Choo Hyunseung²

Affiliation:

1. R&D Team, Networks, Samsung Electronics,Suwon,S. Korea

2. Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,South Korea

3. Sungkyunkwan University,Dept. of AI Systems Engineering,Suwon,South Korea

4. Sungkyunkwan University,Dept. of Computer Science and Engineering,Suwon,South Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10574855/10574897/10575144.pdf?arnumber=10575144

Reference14 articles.

1. Multi-Agent Deep Reinforcement Learning to Enable Dynamic TDD in a Multi-Cell Environment

2. Potential Enabling Technologies for 6G Mobile Communication Networks: A Recent Review

3. EdgeMatrix: A Resource-Redefined Scheduling Framework for SLA-Guaranteed Multi-Tier Edge-Cloud Computing Systems

4. Multi-Agent Deep Reinforcement Learning to Enable Dynamic TDD in a Multi-Cell Environment

5. Dynamic TDD and interference management towards 5G

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