A Novel Model-Assisted Decentralized Multi-Agent Reinforcement Learning for Joint Optimization of Hybrid Beamforming in Massive MIMO mmWave Systems-Reference-Cited by-同舟云学术

A Novel Model-Assisted Decentralized Multi-Agent Reinforcement Learning for Joint Optimization of Hybrid Beamforming in Massive MIMO mmWave Systems

Author:

Yan Yan¹,Zhang Baoxian¹^ORCID,Li Cheng²^ORCID,Bai Jianjun³,Yao Zheng¹

Affiliation:

1. Research Center of Ubiquitous Sensor Networks, University of Chinese Academy of Sciences, Beijing, China

2. Faculty of Engineering and Applied Science, Memorial University, St. John's, NL, Canada

3. Institute of Advanced Technology, University of Science and Technology of China, Hefei, China

Funder

National Key R&D Program of China

NSF of China

Fundamental Research Funds for the Central Universities

Natural Sciences and Engineering Research Council (NSERC) of Canada

InnovateNL SensorTECH

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

Reference46 articles.

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4. The graph neural network model;scarselli;IEEE Trans Neural Netw,2009

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