Resource allocation strategy for ultra-dense Internet of things based on graph convolutional neural network

HUANG Jie; LI Xingxing; YANG Fan; DING Ruijie; CAI Jieliang; YAO Fenghang; ZHANG Xin

doi:10.11959/j.issn.1000-436x.2024178

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Resource allocation strategy for ultra-dense Internet of things based on graph convolutional neural network

Correspondences | 更新时间：2024-11-14

- Resource allocation strategy for ultra-dense Internet of things based on graph convolutional neural network
- Journal on Communications Vol. 45, Issue 10, Pages: 243-252(2024)
- 作者机构：
  
  重庆理工大学电气与电子工程学院，重庆 400054
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62301094);Science and Technology Research Program of Chongqing Education Commission(KJQN202201157;KJQN202301135);Cultivation Program of Scientific Research and Innovation Team of Chongqing University of Technology(2023TDZ003)
- DOI：10.11959/j.issn.1000-436x.2024178
  CLC： TN929.5
- Received：15 June 2024，
  
  Revised：2024-09-10，
  
  Published：25 October 2024
- 稿件说明：
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黄杰,李幸星,杨凡等.基于图卷积神经网络的超密集物联网资源分配策略[J].通信学报,2024,45(10):243-252.

HUANG Jie,LI Xingxing,YANG Fan,et al.Resource allocation strategy for ultra-dense Internet of things based on graph convolutional neural network[J].Journal on Communications,2024,45(10):243-252.
黄杰,李幸星,杨凡等.基于图卷积神经网络的超密集物联网资源分配策略[J].通信学报,2024,45(10):243-252. DOI： 10.11959/j.issn.1000-436x.2024178.

HUANG Jie,LI Xingxing,YANG Fan,et al.Resource allocation strategy for ultra-dense Internet of things based on graph convolutional neural network[J].Journal on Communications,2024,45(10):243-252. DOI： 10.11959/j.issn.1000-436x.2024178.

摘要

针对超密集物联网（UD-IoT）中存在大量隐藏终端干扰严重影响资源管理问题，提出了一种基于图卷积神经网络的深度确定性梯度的超密集物联网资源分配策略。通过矩阵变换构建冲突图模型，采用极大团和超图理论将冲突图模型转化为冲突超图模型，进而将无冲突资源分配问题转化为超图顶点着色问题，并提出了一种基于图卷积神经网络的深度确定性梯度的超密集物联网资源分配算法，采用图卷积强化学习实现无冲突资源分配和资源复用率最大化。仿真实验表明，所提算法具有更高的资源复用率和吞吐量，可以在超密集物联网中提供更好的性能。

Abstract

To address the significant issue of hidden terminal interference that severely impacted resource management in ultra-dense Internet of things (UD-IoT) environments

a deep deterministic gradient-based conflict-free resource allocation strategy using graph convolution neural network was proposed. The conflict graph model was constructed by employing matrix transformations to represent potential hidden terminal interference among devices. Then

using the concepts of maximal cliques and hypergraph theory

the conflict graph model was transformed into a conflict hypergraph model. This transformation allowed the conflict-free resource allocation problem to be formulated as a hypergraph vertex coloring problem. A deep deterministic gradient-based conflict-free resource allocation algorithm

leveraging graph convolutional neural network reinforcement learning

was developed to achieve conflict-free resource allocation and maximize resource reuse. Simulation results demonstrated that the proposed algorithm achieved higher resource reuse rates and throughput compared to existing methods

providing superior performance in ultra-dense IoT.

关键词

Keywords

references

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