Low-Complexity Q-Learning for Energy-Aware Small-Cell Networks With Integrated Access and Backhaul-Reference-Cited by-同舟云学术

Low-Complexity Q-Learning for Energy-Aware Small-Cell Networks With Integrated Access and Backhaul

Published:2023 Issue: Volume:11 Page:121529-121538
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lee Junseung¹,Choi Hyun-Ho²^ORCID,Lim Seung-Chan²^ORCID,Kim Hyungsub³,Na Jeehyeon³,Lee Howon¹^ORCID

Affiliation:

1. School of Electronic and Electrical Engineering and Research Center for Hyper-Connected Convergence Technology, Hankyong National University, Anseong, South Korea

2. School of ICT, Robotic, and Mechanical Engineering and IITC, Hankyong National University, Anseong, South Korea

3. Electronics and Telecommunications Research Institute, Daejeon, South Korea

Funder

Institute of Information and Communications Technology Planning and Evaluation

Korean Government through MSIT (5G Open Intelligence-Defined RAN

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/ielx7/6287639/10005208/10302270.pdf?arnumber=10302270

Reference23 articles.

1. Packet-Level Throughput Analysis and Energy Efficiency Optimization for UAV-Assisted IAB Heterogeneous Cellular Networks

2. Integrated Access and Backhaul in 5G mmWave Networks: Potential and Challenges

3. A survey of mobility models;bai;Wireless Adhoc Networks,2004

4. Deep Reinforcement Learning-based Spectrum Allocation and Power Management for IAB Networks

5. High Bandwidth Green Communication With Vehicles by Decentralized Resource Optimization in Integrated Access Backhaul 5G Networks

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1. Distributed DQN-based network-wide energy efficiency maximization for 5G NR-U and Wi-Fi coexistence environments;ICT Express;2024-08

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