Optimal Resource Allocation Considering Non-Uniform Spatial Traffic Distribution in Ultra-Dense Networks: A Multi-Agent Reinforcement Learning Approach-Reference-Cited by-同舟云学术

Optimal Resource Allocation Considering Non-Uniform Spatial Traffic Distribution in Ultra-Dense Networks: A Multi-Agent Reinforcement Learning Approach

Published:2022 Issue: Volume:10 Page:20455-20464
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kim Eunjin¹^ORCID,Choi Hyun-Ho²^ORCID,Kim Hyungsub³,Na Jeehyeon³,Lee Howon¹^ORCID

Affiliation:

1. School of Electronic and Electrical Engineering and IITC, Hankyong National University, Anseong, Republic of Korea

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

3. Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea

Funder

Institute of Information & Communications Technology Planning & Evaluation

Korean Government (MSIT) through the 5G Open Intelligence-Defined RAN (ID-RAN) Technique Based on 5G New Radio

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09715076.pdf?arnumber=9715076

Reference25 articles.

1. Towards 1 Gbps/UE in Cellular Systems: Understanding Ultra-Dense Small Cell Deployments

2. Energy-Efficient Switching ON/OFF Strategies Analysis for Dense Cellular Networks With Partial Conventional Base-Stations

3. Energy Efficient User Association and Power Allocation in Millimeter-Wave-Based Ultra Dense Networks With Energy Harvesting Base Stations

4. Reinforcement Learning Based Cooperative Coded Caching Under Dynamic Popularities in Ultra-Dense Networks

5. Dynamic Load Adjustments for Small Cells in Heterogeneous Ultra-dense Networks

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