Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning-Reference-Cited by-同舟云学术

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Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning

Published:2020 Issue:3 Volume:63 Page:1515-1532
ISSN:1546-2226
Container-title:Computers, Materials & Continua
language:en
Short-container-title:

Author:

Wei Yifei,Qu Yinxiang,Zhao Min,Zhang Lianping,Richard Yu F.

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanics of Materials,Modelling and Simulation,Biomaterials

Link

https://www.techscience.com/cmc/v63n3/38890/pdf

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1. An Autonomous Multi Agent Q-Learning Approach for Resource Allocation in D2D-Enabled Heterogeneous Networks;2023 31st International Conference on Electrical Engineering (ICEE);2023-05-09

2. An efficient resource optimization scheme for D2D communication;Digital Communications and Networks;2022-12

3. Internet of Things Based Communication Architecture for Switchport Security and Energy Management in Interoperable Smart Microgrids;Arabian Journal for Science and Engineering;2022-07-19

4. GANCSA: A novel approach for finding the best path for migration of mobile-agents;Pervasive and Mobile Computing;2022-07

5. MSRM-IoT: A Reliable Resource Management for Cloud, Fog, and Mist-Assisted IoT Networks;IEEE Internet of Things Journal;2022-02-15

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