東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Ai-Based Traffic Forecasting in 5G Network.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Ai-Based Traffic Forecasting in 5G Network./
Author:	Mohseni, Maryam.
Description:	1 online resource (111 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-05.
Contained By:	Masters Abstracts International84-05.
Subject:	Kurtosis. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29943763click for full text (PQDT)
ISBN:	9798352979655

Ai-Based Traffic Forecasting in 5G Network.
Mohseni, Maryam.

Ai-Based Traffic Forecasting in 5G Network. - 1 online resource (111 pages)

Source: Masters Abstracts International, Volume: 84-05.

Thesis (M.E.)--The University of Western Ontario (Canada), 2022.

Includes bibliographical references

Forecasting of the telecommunication traffic is the foundation for enabling intelligent management features as cellular technologies evolve toward fifth-generation (5G) technology. Since a significant number of network slices are deployed over a 5G network, it is crucial to evaluate the resource requirements of each network slice and how they evolve over time. Mobile network carriers should investigate strategies for network optimization and resource allocation due to the steadily increasing mobile traffic. Network management and optimization strategies will be improved if mobile operators know the cellular traffic demand at a specific time and location beforehand. The most effective techniques nowadays devote computing resources in a dynamic manner based on mobile traffic prediction by machine learning techniques. However, the accuracy of the predictive models is critically important. In this work, we concentrate on forecasting the cellular traffic for the following 24 hours by employing temporal and spatiotemporal techniques, with the goal of improving the efficiency and accuracy of mobile traffic prediction. In fact, a set of real-world mobile traffic data is used to assess the efficacy of multiple neural network models in predicting cellular traffic in this study. The fully connected sequential network (FCSN), one-dimensional convolutional neural network (1D-CNN), single-shot learning LSTM (SS-LSTM), and autoregressive LSTM (AR-LSTM) are proposed in the temporal analysis. A 2-dimensional convolutional LSTM (2D-ConvLSTM) model is also proposed in the spatiotemporal framework to forecast cellular traffic over the next 24 hours. The 2D-ConvLSTM model, which can capture spatial relations via convolution operations and temporal dynamics through the LSTM network, is used after creating geographic grids. The results reveal that FCSN and 1D-CNN have comparable performance in univariate temporal analysis. However, 1D-CNN is a smaller network with less number of parameters. One of the other benefits of the proposed 1D-CNN is having less complexity and execution time for predicting traffic. Also, 2D-ConvLSTM outperforms temporal models. The 2D-ConvLSTM model can predict the next 24-hour traffic of internet, sms, and call with root mean square error (RMSE) values of 75.73, 26.60, and 15.02 and mean absolute error (MAE) values of 52.73, 14.42, and 8.98, respectively, which shows better performance compared to the state of the art methods due to capturing variables dependencies. It can be argued that this network has the capability to be utilized in network management and resource allocation in practical applications.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352979655Subjects--Topical Terms:

3697311
Kurtosis.
Index Terms--Genre/Form:

542853
Electronic books.

Ai-Based Traffic Forecasting in 5G Network.
LDR:03909nmm a2200361K 4500 001 2356806
005 20230619080118.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798352979655
035 $a (MiAaPQ)AAI29943763
035 $a (MiAaPQ)oaiirlibuwocaetd11545
035 $a AAI29943763
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Mohseni, Maryam. $3 3697310
245 1 0 $a Ai-Based Traffic Forecasting in 5G Network.
264 0 $c 2022
300 $a 1 online resource (111 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-05.
500 $a Advisor: Nikan, Soodeh ; Shami, Abdallah.
502 $a Thesis (M.E.)--The University of Western Ontario (Canada), 2022.
504 $a Includes bibliographical references
520 $a Forecasting of the telecommunication traffic is the foundation for enabling intelligent management features as cellular technologies evolve toward fifth-generation (5G) technology. Since a significant number of network slices are deployed over a 5G network, it is crucial to evaluate the resource requirements of each network slice and how they evolve over time. Mobile network carriers should investigate strategies for network optimization and resource allocation due to the steadily increasing mobile traffic. Network management and optimization strategies will be improved if mobile operators know the cellular traffic demand at a specific time and location beforehand. The most effective techniques nowadays devote computing resources in a dynamic manner based on mobile traffic prediction by machine learning techniques. However, the accuracy of the predictive models is critically important. In this work, we concentrate on forecasting the cellular traffic for the following 24 hours by employing temporal and spatiotemporal techniques, with the goal of improving the efficiency and accuracy of mobile traffic prediction. In fact, a set of real-world mobile traffic data is used to assess the efficacy of multiple neural network models in predicting cellular traffic in this study. The fully connected sequential network (FCSN), one-dimensional convolutional neural network (1D-CNN), single-shot learning LSTM (SS-LSTM), and autoregressive LSTM (AR-LSTM) are proposed in the temporal analysis. A 2-dimensional convolutional LSTM (2D-ConvLSTM) model is also proposed in the spatiotemporal framework to forecast cellular traffic over the next 24 hours. The 2D-ConvLSTM model, which can capture spatial relations via convolution operations and temporal dynamics through the LSTM network, is used after creating geographic grids. The results reveal that FCSN and 1D-CNN have comparable performance in univariate temporal analysis. However, 1D-CNN is a smaller network with less number of parameters. One of the other benefits of the proposed 1D-CNN is having less complexity and execution time for predicting traffic. Also, 2D-ConvLSTM outperforms temporal models. The 2D-ConvLSTM model can predict the next 24-hour traffic of internet, sms, and call with root mean square error (RMSE) values of 75.73, 26.60, and 15.02 and mean absolute error (MAE) values of 52.73, 14.42, and 8.98, respectively, which shows better performance compared to the state of the art methods due to capturing variables dependencies. It can be argued that this network has the capability to be utilized in network management and resource allocation in practical applications.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Kurtosis. $3 3697311
650 4 $a Time series. $3 3561811
650 4 $a Visualization. $3 586179
650 4 $a Neural networks. $3 677449
650 4 $a Computer science. $3 523869
650 4 $a Information technology. $3 532993
650 4 $a Web studies. $3 2122754
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0800
690 $a 0984
690 $a 0489
690 $a 0646
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The University of Western Ontario (Canada). $3 1017622
773 0 $t Masters Abstracts International $g 84-05.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29943763 $z click for full text (PQDT)