无线电通信技术2024，Vol.50Issue(5)：921-931,11.DOI:10.3969/j.issn.1003-3114.2024.05.010
基于轻量化卷积神经网络的蜂窝流量低复杂度预测方法
Low-complexity Cellular Traffic Prediction Algorithm Based on Lightweight Convolutional Neural Network
郑淞之 1张兴 1张妍 2王兴瑜 3袁国翔1
作者信息
- 1. 北京邮电大学信息与通信工程学院,北京 100876
- 2. 中国电信股份有限公司北京分公司,北京 100032
- 3. 中国人民解放军93216 部队,北京 100085
- 折叠
摘要
Abstract
With the rapid growth of data traffic demand in cellular networks,accurate prediction of cellular traffic conditions at fu-ture moments can improve network resource allocation,achieve traffic load balancing,and deploy base station energy saving and sleeping strategies.Based on the lightweight linear bottleneck module,a spatiotemporal prediction model with multiple parallel branching modules is proposed to extract spatiotemporal features from recent historical data and periodic historical data,respectively.Meanwhile,for the spatial dependency in the gridded spatiotemporal data,high-dimensional grid features are additionally clustered by K-Means algo-rithm,and the grid base station density information is extracted and fed into the model as a cross-domain feature,thus realising accurate prediction of the cellular traffic in the whole area of the study range deploying a low-complexity and low-computing-power-demand model.关键词
空时流量预测/轻量化模型/卷积神经网络/深度学习/蜂窝网络Key words
spatiotemporal traffic prediction/lightweight model/convolutional neural network/deep learning/cellular network分类
信息技术与安全科学引用本文复制引用
郑淞之,张兴,张妍,王兴瑜,袁国翔..基于轻量化卷积神经网络的蜂窝流量低复杂度预测方法[J].无线电通信技术,2024,50(5):921-931,11.
