Network Function Placement in Virtualized Radio Access Network with Reinforcement Learning Based on Graph Neural Network

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Yi, Mengting;Lin, Mugang;Chen, Wenhui

通讯作者：

Lin, MG

作者机构：

[Yi, Mengting; Chen, Wenhui; Lin, Mugang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421008, Peoples R China.

[Chen, Wenhui; Lin, Mugang] Hengyang Normal Univ, Hunan Engn Res Ctr Cyberspace Secur Technol & Appl, Hengyang 421002, Peoples R China.

[Chen, Wenhui; Lin, Mugang] Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421008, Peoples R China.

通讯机构：

[Lin, MG ] H

Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421008, Peoples R China.

Hengyang Normal Univ, Hunan Engn Res Ctr Cyberspace Secur Technol & Appl, Hengyang 421002, Peoples R China.

Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421008, Peoples R China.

语种：

英文

关键词：

network function placement;functional split;radio access network;deep reinforcement learning;graph neural networks;proximal policy optimization

期刊：

Electronics

ISSN：

2079-9292

年：

2025

卷：

期：

页码：

1686-

DOI：

10.3390/electronics14081686

基金类别：

This work was supported in part by the Scientific Research Fund of Hunan Provincial Education Department (22A0502, 22B0728, 23C0228, and 23C0240), the National Natural Science Foundation of China (61772179), the Hunan Provincial Natural Science Foundation of China (2019JJ40005, 2023JJ50095), the 14th Five-Year Plan Key Disciplines and Application-oriented Special Disciplines of Hunan Province (Xiangjiaotong [2022] 351), and the Science and Technology Plan Project of Hunan Province (2016TP1020).

机构署名：

本校为第一且通讯机构

院系归属：

计算机科学与技术学院

摘要：

In 5G and beyond 5G networks, function placement is a crucial strategy for enhancing the flexibility and efficiency of the Radio Access Network (RAN). However, demonstrating optimal function splitting and placement to meet diverse user demands remains a significant challenge. The function placement problem is known to be NP-hard, and previous studies have attempted to address it using Deep Reinforcement Learning (DRL) approaches. Nevertheless, many existing methods fail to capture the network state in RANs with specific topologies, leading to suboptimal decision-making and resource allocation....

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

Network Function Placement in Virtualized Radio Access Network with Reinforcement Learning Based on Graph Neural Network

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问