基于图卷积神经网络的分解可解释性的蜂窝网络流量预测模型

张子天; 温之馨; 诸葛斌; 吕智豪; 董黎刚; 蒋献

doi:10.11959/j.issn.1000-0801.2025120

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基于图卷积神经网络的分解可解释性的蜂窝网络流量预测模型

研究与开发 | 更新时间：2025-11-16

- 基于图卷积神经网络的分解可解释性的蜂窝网络流量预测模型
- A decomposable and interpretable cellular network traffic prediction model based on graph convolutional neural network
- 电信科学 2025年41卷第9期页码：93-107
- 作者机构：
  
  浙江工商大学，浙江杭州 310018
- 作者简介：
  
  [ "张子天（1988- ），男，博士，浙江工商大学信息与电子工程学院副研究员，主要研究方向为基于机器学习的网络流量预测与资源管理。" ]
  [ " 温之馨（1998- ），男，浙江工商大学信息与电子工程学院硕士生，主要研究方向为时间序列网络流量预测。" ]
  [ "诸葛斌（1976- ），男，博士，浙江工商大学信息与电子工程学院教授，主要研究方向为网络和通信技术、互联网技术和网络安全。" ]
  [ "吕智豪（1999- ），男，浙江工商大学信息与电子工程学院硕士生，主要研究方向为通信与网络。" ]
  [ "董黎刚（1972- ），男，博士，浙江工商大学信息与电子工程学院教授，主要研究方向为智能网络、在线教育。" ]
  [ "蒋献（1988- ），男，浙江工商大学信息与电子工程学院讲师、实验员，主要研究方向为在线教育。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62301488;W2421086);浙江工商大学省属高校基本科研业务费项目(QRK23010)
- DOI：10.11959/j.issn.1000-0801.2025120
  中图分类号： TP301
- 收稿：2024-12-13，
  
  修回：2025-01-06，
  
  纸质出版：2025-09-20
- 稿件说明：
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张子天,温之馨,诸葛斌等.基于图卷积神经网络的分解可解释性的蜂窝网络流量预测模型[J].电信科学,2025,41(09):93-107.

ZHANG Zitian,WEN Zhixin,ZHUGE Bin,et al.A decomposable and interpretable cellular network traffic prediction model based on graph convolutional neural network[J].Telecommunications Science,2025,41(09):93-107.
张子天,温之馨,诸葛斌等.基于图卷积神经网络的分解可解释性的蜂窝网络流量预测模型[J].电信科学,2025,41(09):93-107. DOI： 10.11959/j.issn.1000-0801.2025120.

ZHANG Zitian,WEN Zhixin,ZHUGE Bin,et al.A decomposable and interpretable cellular network traffic prediction model based on graph convolutional neural network[J].Telecommunications Science,2025,41(09):93-107. DOI： 10.11959/j.issn.1000-0801.2025120.

摘要

随着智能互联应用在城市场景中的普及，城市网络流量的激增带来了新挑战。基站蜂窝网络中的流量预测是资源分配与调度等关键应用的核心，准确预测蜂窝流量对于高效分配网络资源尤为重要。然而在蜂窝网络流量预测研究中，复杂的城市蜂窝流量往往有着深层次的时间和空间特征需要挖掘，为了解决这个问题，提出一个基于图卷积神经网络的分解可解释性时空图卷积神经网络（DISTGCN）。该神经网络利用蜂窝网络流量分解以及时空相关性，提高了流量预测的准确性，同时分解后的流量特征增强了预测结果的可解释性。在真实的经典数据集上的实验结果表明，DISTGCN的预测性能优于传统深度学习预测模型和图神经网络模型。

Abstract

With the rapid proliferation of intelligent connected applications in urban environments

the dramatic increase in urban network traffic presents significant challenges. Accurate traffic prediction in cellular base station networks is critical for optimizing resource allocation and scheduling in various applications. However

the complexity of urban cellular traff

ic necessitates

the extraction of deep temporal and spatial features. To address this issue

a novel deep learning framework based on graph convolutional neural networks (GCN) was proposed

named decomposable and interpretable spatiotemporal graph convolutional neural network (DISTGCN). The decomposability of cellular network traffic and its spatiotemporal correlations were leveraged by DISTGCN to enhance prediction accuracy. Furthermore

the decomposed traffic features were shown to provide improved interpretability for the prediction results. Extensive experiments conducted on real-world benchmark datasets demonstrate that DISTGCN significantly outperforms conventional deep learning models and state-of-the-art GNN-based models in terms of prediction performance.

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