Mobile Network Traffic Prediction Based on Seasonal Adjacent Windows Sampling and Conditional Probability Estimation

Huang, Jin; Xiao, Ming

doi:10.1109/tbdata.2020.3014049

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…[ 10 ] used MSE and MAE to evaluate the performance of the proposed ARMA model, and proved that short‐term prediction has high prediction accuracy. The literature [12] proved that the prediction accuracy of conditional probability estimation model is higher, and the daily seasonal model error is 9.92%.…”

Section: User Demand and Traffic Modelingmentioning

confidence: 99%

“…After receiving the random sample data from the sensors, the IRS then locally decides the passive beamforming vector according to Eqs. (10) and (11), which, according to the field test in Ref. [112], takes merely a few seconds in total.…”

Section: Passive Beamforming For Irs Without Csimentioning

confidence: 99%

“…The utilization rate of resources also increased by more than 6%. Clemente et al [11] used the method of combining naive Bayesian classifier and Holt Winters to accurately predict cell traffic, and limited the prediction RMSE to 5%.…”

Section: Traffic Modelsmentioning

confidence: 99%

See 2 more Smart Citations

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

Yang

Zhang

Ren

et al. 2022

Chinese J of Electronics

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With the rapid development of the fifthgeneration wireless communication systems, a profound revolution in terms of transmission capacity, energy efficiency, reliability, latency, and connectivity is highly expected to support a new batch of industries and applications. To achieve this goal, wireless networks are becoming extremely dynamic, heterogeneous, and complex. The modeling and optimization for the performance of realworld 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 conventional network modeling and optimization approaches rely on drive test, trial‐and‐error, and engineering experience, which are labor intensive, error‐prone, and far from optimal. On the other hand, while the research community has spent significant efforts in understanding the fundamental 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 networks in five aspects, including channel modeling, user demand and traffic modeling, throughput modeling and prediction, network parameter optimization, and IRS empowered performance optimization, and also presents the corresponding notable performance gains.

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Section: User Demand and Traffic Modelingmentioning

confidence: 99%