Sum Rate Maximization in Multi-Cell Multi-User Networks: An Inverse Reinforcement Learning-Based Approach-Reference-Cited by-同舟云学术

Sum Rate Maximization in Multi-Cell Multi-User Networks: An Inverse Reinforcement Learning-Based Approach

Published:2024-01 Issue:1 Volume:13 Page:4-8
ISSN:2162-2337
Container-title:IEEE Wireless Communications Letters
language:
Short-container-title:IEEE Wireless Commun. Lett.

Author:

Tian Xingcong¹,Xiong Ke¹^ORCID,Zhang Ruichen¹^ORCID,Fan Pingyi²^ORCID,Niyato Dusit³^ORCID,Letaief Khaled Ben⁴^ORCID

Affiliation:

1. Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer and Information Technology, the Collaborative Innovation Center of Railway Traffic Safety, and the National Engineering Research Center of Advanced Network Technologies, Beijing Jiaotong University, Beijing, China

2. Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

3. School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore

4. Department of Electrical and Computer Engineering, Hong Kong University of Science and Technology, Sai Kung, Hong Kong

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

National Research Foundation, Singapore, and Infocomm Media Development Authority under its Future Communications Research & Development Programme, and MOE Tier 1

Research Grants Council under the Areas of Excellence Scheme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/5962382/10384452/10172220.pdf?arnumber=10172220

Reference14 articles.

1. Millimeter Wave Communications With Reconfigurable Intelligent Surfaces: Performance Analysis and Optimization

2. Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks

3. Energy Efficiency Maximization in RIS-Assisted SWIPT Networks With RSMA: A PPO-Based Approach

4. Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches

5. Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks