首页|Real-World Wireless Network Modeling and Optimization:From Model/Data-Driven Perspective

Real-World Wireless Network Modeling and Optimization:From Model/Data-Driven Perspective

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With the rapid development of the fifth-generation wireless communication systems,a profound revolution in terms of transmission capacity,energy effi-ciency,reliability,latency,and connectivity is highly ex-pected to support a new batch of industries and applica-tions.To achieve this goal,wireless networks are becom-ing extremely dynamic,heterogeneous,and complex.The modeling and optimization for the performance of real-world wireless networks are extremely challenging due to the difficulty to predict the network performance as a function of network parameters,and the prohibitively huge number of parameters to optimize.The convention-al network modeling and optimization approaches rely on drive test,trial-and-error,and engineering experience,which are labor intensive,error-prone,and far from op-timal.On the other hand,while the research community has spent significant efforts in understanding the funda-mental limits of radio channels and developing physical layer techniques to operate close to it,very little is known about the performance limits of wireless networks,where millions of radio channels interact with one another in complex manners.This paper reviews the very recent mathematical and learning based techniques for modeling and optimizing the performance of real-world wireless net-works in five aspects,including channel modeling,user demand and traffic modeling,throughput modeling and prediction,network parameter optimization,and IRS em-powered performance optimization,and also presents the corresponding notable performance gains.

Mathematical methodsLearning based methodsNetwork performance modelingNet-work performance optimization

LI Yang、ZHANG Shutao、REN Xiaohui、ZHU Jianhang、HUANG Jiajie、HE Pengcheng、SHEN Kaiming、YAO Zhiqiang、GONG Jie、CHANG Tsunghui、SHI Qingjiang、LUO Zhiquan

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Shenzhen Research Institute of Big Data,Shenzhen 518172,China

Pengcheng Lab,Shenzhen 518055,China

School of Science and Engineering,The Chinese University of Hong Kong(Shenzhen),Shenzhen 518172,China

College of Mathematics and Computational Sciences,Xiangtan University,Xiangtan 411105,China

School of Computer Science and Engineering,Sun Yat-Sen University,Guangzhou 510275,China

School of Software Engineering,Tongji University,Shanghai 201804,China

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National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaSpecial Support Program of GuangdongNatural Science Foundation of Guangdong Province

2022YFA10039006210134962001411621714812019TQ05X1502021A1515011124

2022

10.1049/cje.2022.00.191

电子学报(英文)

电子学报(英文)

CSTPCDSCIEI
ISSN：1022-4653
年,卷(期)：2022.31(6)
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