Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control-Reference-Cited by-同舟云学术

Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

Published:2023-01 Issue:1 Volume:17 Page:40-53
ISSN:1932-4553
Container-title:IEEE Journal of Selected Topics in Signal Processing
language:
Short-container-title:IEEE J. Sel. Top. Signal Process.

Author:

Yin Ziyan¹^ORCID,Wang Zhe²^ORCID,Li Jun¹^ORCID,Ding Ming³^ORCID,Chen Wen⁴^ORCID,Jin Shi⁵^ORCID

Affiliation:

1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China

2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

3. Data61, CSIRO, Sydney, NSW, Australia

4. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

5. National Mobile Communications Research Laboratory, Southeast University, Nanjing, China

Funder

National Key Project

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Future Network Grant of Provincial Education Board in Jiangsu

Natural Science Foundation of Jiangsu Province

Shanghai Kewei

Pudong

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/4200690/10050192/09946372.pdf?arnumber=9946372

Reference41 articles.

1. Joint Optimization of Fractional Frequency Reuse and Cell Clustering for Dynamic TDD Small Cell Networks

2. Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

3. Dynamic TDD Systems for 5G and Beyond: A Survey of Cross-Link Interference Mitigation

4. Multi-Hop RIS-Empowered Terahertz Communications: A DRL-Based Hybrid Beamforming Design

5. Analysis of Packet Throughput in Small Cell Networks Under Clustered Dynamic TDD

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