Balancing Fairness and Energy Efficiency in SWIPT-Based D2D Networks: Deep Reinforcement Learning Based Approach-Reference-Cited by-同舟云学术

Balancing Fairness and Energy Efficiency in SWIPT-Based D2D Networks: Deep Reinforcement Learning Based Approach

Published:2022 Issue: Volume:10 Page:64495-64503
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Han Eun-Jeong¹^ORCID,Sengly Muy¹^ORCID,Lee Jung-Ryun¹^ORCID

Affiliation:

1. School of Intelligent Energy and Industry, Chung-Ang University, Seoul, South Korea

Funder

Ministry of Science and ICT (MSIT), South Korea, through the ITRC Support Program supervised by the Institute for Information & Communications Technology Planning & Evaluation

Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea

National Research Foundation of Korea (NRF) Grant funded by the Korea Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09795007.pdf?arnumber=9795007

Reference26 articles.

1. SWIPT-Enabled D2D Communication Underlaying NOMA-Based Cellular Networks in Imperfect CSI

2. Deep Reinforcement Learning: A Brief Survey

3. Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale

4. Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors

5. Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications

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