Offloading Cellular Traffic Through Opportunistic Communications: Analysis and Optimization

Vincenzo Sciancalepore; Domenico Giustiniano; Albert Banchs; Andreea Hossmann-Picu

doi:10.1109/jsac.2015.2452472

Offloading Cellular Traffic Through Opportunistic Communications: Analysis and Optimization

IEEE Journal on Selected Areas in Communications ◽

10.1109/jsac.2015.2452472 ◽

2016 ◽

Vol 34 (1) ◽

pp. 122-137 ◽

Cited By ~ 46

Author(s):

Vincenzo Sciancalepore ◽

Domenico Giustiniano ◽

Albert Banchs ◽

Andreea Hossmann-Picu

Keyword(s):

Opportunistic Communications ◽

Communications Analysis ◽

Cellular Traffic

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Cellular Traffic Offloading through Opportunistic Communications Based on Human Mobility

KSII Transactions on Internet and Information Systems ◽

10.3837/tiis.2015.03.002 ◽

2015 ◽

Vol 9 (3) ◽

Keyword(s):

Human Mobility ◽

Traffic Offloading ◽

Opportunistic Communications ◽

Cellular Traffic

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Cellular traffic offloading through opportunistic communications

Proceedings of the 5th ACM workshop on Challenged networks - CHANTS '10 ◽

10.1145/1859934.1859943 ◽

2010 ◽

Cited By ~ 170

Author(s):

Bo Han ◽

Pan Hui ◽

V.S. Anil Kumar ◽

Madhav V. Marathe ◽

Guanhong Pei ◽

...

Keyword(s):

Traffic Offloading ◽

Opportunistic Communications ◽

Cellular Traffic

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Attention based multi-component spatiotemporal cross-domain neural network model for wireless cellular network traffic prediction

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00756-0 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Qingtian Zeng ◽

Qiang Sun ◽

Geng Chen ◽

Hua Duan

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Network Traffic ◽

Cellular Network ◽

Critical Issue ◽

Traffic Prediction ◽

Weekly Cycle ◽

Cross Domain ◽

Cellular Traffic

AbstractWireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually shows high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods lack the abilities of modeling the dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) is proposed, which uses Conv-LSTM or Conv-GRU for neighbor data, daily cycle data, and weekly cycle data modeling, and then assigns different weights to the three kinds of feature data through the attention layer, improves their feature extraction ability, and suppresses the feature information that interferes with the prediction time. Finally, the model is combined with timestamp feature embedding, multiple cross-domain data fusion, and jointly with other models to assist the model in traffic prediction. Experimental results show that compared with the existing models, the prediction performance of the proposed model is better. Among them, the RMSE performance of the att-MCSTCNet (Conv-LSTM) model on Sms, Call, and Internet datasets is improved by 13.70 ~ 54.96%, 10.50 ~ 28.15%, and 35.85 ~ 100.23%, respectively, compared with other existing models. The RMSE performance of the att-MCSTCNet (Conv-GRU) model on Sms, Call, and Internet datasets is about 14.56 ~ 55.82%, 12.24 ~ 29.89%, and 38.79 ~ 103.17% higher than other existing models, respectively.

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Cellular Traffic Prediction using Recurrent Neural Networks

2020 IEEE 5th International Symposium on Telecommunication Technologies (ISTT) ◽

10.1109/istt50966.2020.9279373 ◽

2020 ◽

Author(s):

Shan Jaffry ◽

Syed Faraz Hasan

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Traffic Prediction ◽

Cellular Traffic

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DeepTP: An End-to-End Neural Network for Mobile Cellular Traffic Prediction

IEEE Network ◽

10.1109/mnet.2018.1800127 ◽

2018 ◽

Vol 32 (6) ◽

pp. 108-115 ◽

Cited By ~ 24

Author(s):

Jie Feng ◽

Xinlei Chen ◽

Rundong Gao ◽

Ming Zeng ◽

Yong Li

Keyword(s):

Neural Network ◽

Traffic Prediction ◽

Mobile Cellular ◽

End To End ◽

Cellular Traffic

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Science in magazines, and its readers

Public Understanding of Science ◽

10.1088/0963-6625/2/1/001 ◽

1993 ◽

Vol 2 (1) ◽

pp. 3-20 ◽

Cited By ~ 2

Author(s):

Daniel Jacobi ◽

Bernard Schiele

Keyword(s):

Daily Newspaper ◽

General Series ◽

Communications Analysis

How do magazines make science accessible and appealing to a broad readership? To answer this question, we studied an article which was published in Le Figaro magazine, the weekly magazine supplement of a large French daily newspaper. The article, which presents information on cancer and immunology, is illustrated with three large and spectacular colour photographs of microscopic corpuscles. A semio-linguistic and communications analysis revealed that a general series of elements, made up of headlines, photos and captions, forms a kind of narrative that can be read like a short melodrama. The text of the interview with the researcher proposes reformulations, directed to conscientious readers who take the trouble to understand the specialized terms. Finally, in the infratext, experts and specialists are able to discern references and allusions to different sorts of issues at play. In short, we show how an article can simultaneously attract different categories of readers.

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