A decomposable and interpretable cellular network traffic prediction model based on graph convolutional neural network

ZHANG Zitian; WEN Zhixin; ZHUGE Bin; LYU Zhihao; DONG Ligang; JIANG Xian

doi:10.11959/j.issn.1000-0801.2025120

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A decomposable and interpretable cellular network traffic prediction model based on graph convolutional neural network

Research and Development | 更新时间：2025-11-16

- A decomposable and interpretable cellular network traffic prediction model based on graph convolutional neural network
- Telecommunications Science Vol. 41, Issue 9, Pages: 93-107(2025)
- 作者机构：
  
  浙江工商大学，浙江杭州 310018
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62301488;W2421086);The Basic Research Funds for Provincial Universities of Zhejiang Gongshang University(QRK23010)
- DOI：10.11959/j.issn.1000-0801.2025120
  CLC： TP301
- Received：13 December 2024，
  
  Revised：2025-01-06，
  
  Published：20 September 2025
- 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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Keywords

references

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