Individual traffic prediction in cellular networks based on tensor completion

Liu, Chunsheng; Wu, Tao; Li, Zhifei; Wang, Bin

doi:10.1002/dac.4952

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…The decomposition technique can be used in either the frequency or spatial domain. The authors of [26] employed discrete wavelet transform, in [27] tensor completion was used, while the authors of [28] applied Fourier analysis. In [26], a traffic trace was decomposed into two components using a discrete wavelet transform: one with high frequencies and another with low frequencies.…”

Section: Related Workmentioning

confidence: 99%

Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video

Biernacki

2024

Applied Sciences

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Predicting throughput is essential to reduce latency in time-critical services like video streaming, which constitutes a significant portion of mobile network traffic. The video player continuously monitors network throughput during playback and adjusts the video quality according to the network conditions. This means that the quality of the video depends on the player’s ability to predict network throughput accurately, which can be challenging in the unpredictable environment of mobile networks. To improve the prediction accuracy, we grouped the throughput trace into clusters taking into account the similarity of their mean and variance. Once we distinguished the similar trace fragments, we built a separate LSTM predictive model for each cluster. For the experiment, we used traffic captured from 5G networks generated by individual user equipment (UE) in fixed and mobile scenarios. Our results show that the prior grouping of the network traces improved the prediction compared to the global model operating on the whole trace.

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Section: Related Workmentioning

confidence: 99%

Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video

Biernacki

2024

Applied Sciences

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“…Expanding upon these foundational models, subsequent research has delved into dynamic tensor flow models that aim to capture the spatiotemporal dynamics of traffic flow. These models are sophisticated, as they are designed to analyze data across both macroscales, which consider long-term trends and cyclic patterns, and microscales, which focus on the minute-by-minute fluctuations of traffic flow [ 26 , 27 , 28 ]. By doing so, they provide a granular view of traffic dynamics, offering insight into the temporal progression and spatial distribution of traffic congestion, vehicle speeds, and density.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems

Rui,

Zhao,

et al. 2023

Sensors

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The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field.

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“…In the Ref. [17], Prophet model and GPR model are combined to predict single cell traffic, and RMSE, MAE and MAPE are used to verify the superiority of prediction accuracy and explain the inherent space‐time correlation of traffic data. In order to realize ultra reliable low delay communication (URLLC) in large‐scale machine communication networks, the Tree based ML model was proposed by Weerasinghe et al .…”

Section: User Demand and Traffic Modelingmentioning

confidence: 99%

“…mainly tree based, such as random forest (RF) [15] and LightGBM [16] , and others are based on Gaussian process or other mechanisms. Decision trees are the most basic concepts in tree based models, which can be used to solve classification or regression problems.…”

Section: Traffic Modelsmentioning

confidence: 99%

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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Individual traffic prediction in cellular networks based on tensor completion

Cited by 11 publications

References 35 publications

Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video

Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video

Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems

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

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