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Deep Transfer Learning Across Cities for Mobile Traffic Prediction

Authors:

Junshan ZhangAuthors Info & Claims

IEEE/ACM Transactions on Networking, Volume 30, Issue 3

Pages 1255 - 1267

https://doi.org/10.1109/TNET.2021.3136707

Published: 23 December 2021 Publication History

Abstract

Precise citywide mobile traffic prediction is of great significance for intelligent network planning and proactive service provisioning. Current traffic prediction approaches mainly focus on training a well-performed model for the cities with a large amount of mobile traffic data. However, for the cities with scarce data, the prediction performance will be greatly limited. To tackle this problem, in this paper we propose a novel cross-city deep transfer learning framework named CCTP for citywide mobile traffic prediction in cities with data scarcity. Specifically, we first present a novel spatial-temporal learning model and pre-train the model by abundant data of a source city to obtain prior knowledge of mobile traffic dynamics. We then devise an efficient generative adversarial network (GAN) based cross-domain adapter for distribution alignment between target data and source data. To deal with data scarcity issue in some clusters of target city, we further design an inter-cluster transfer learning strategy for performance enhancement. Extensive experiments conducted on real-world mobile traffic datasets demonstrate that our proposed CCTP framework can achieve superior performance in citywide mobile traffic prediction with data scarcity.

References

[1]

V. Cisco, “Cisco visual networking index: Forecast and trends, 2017–2022,” Cisco, San Jose, CA, USA, White Paper 1,1, 2018.

[2]

J. Wu, Y. Zhang, M. Zukerman, and E. K. N. Yung, “Energy-efficient base-stations sleep-mode techniques in green cellular networks: A survey,” IEEE Commun. Surveys Tuts., vol. 17, no. 2, pp. 803–826, 2nd Quart., 2015.

[3]

F. Xuet al., “Big data driven mobile traffic understanding and forecasting: A time series approach,” IEEE Trans. Services Comput., vol. 9, no. 5, pp. 796–805, Sep./Oct. 2016.

[4]

M. Elsayed and M. Erol-Kantarci, “AI-enabled future wireless networks: Challenges, opportunities, and open issues,” IEEE Veh. Technol. Mag., vol. 14, no. 3, pp. 70–77, Sep. 2019.

[5]

N. Bui, M. Cesana, S. A. Hosseini, Q. Liao, I. Malanchini, and J. Widmer, “A survey of anticipatory mobile networking: Context-based classification, prediction methodologies, and optimization techniques,” IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1790–1821, 3rd Quart., 2017.

[6]

S. Ntalampiras and M. Fiore, “Forecasting mobile service demands for anticipatory MEC,” in Proc. IEEE 19th Int. Symp. ‘World Wireless, Mobile Multimedia Netw.’ (WoWMoM), Jun. 2018, pp. 14–19.

[7]

D. Bega, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Perez, “DeepCog: Optimizing resource provisioning in network slicing with AI-based capacity forecasting,” IEEE J. Sel. Areas Commun., vol. 38, no. 2, pp. 361–376, Feb. 2020.

[8]

H.-W. Kim, J.-H. Lee, Y.-H. Choi, Y.-U. Chung, and H. Lee, “Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for mobile Wimax,” Comput. Commun., vol. 34, no. 1, pp. 99–106, Jan. 2011.

[9]

D. Tikunov and T. Nishimura, “Traffic prediction for mobile network using Holt-Winter’s exponential smoothing,” in Proc. 15th Int. Conf. Softw., Telecommun. Comput. Netw., Sep. 2007, pp. 1–5.

[10]

H. D. Trinh, L. Giupponi, and P. Dini, “Mobile traffic prediction from raw data using LSTM networks,” in Proc. IEEE 29th Annu. Int. Symp. Pers., Indoor Mobile Radio Commun. (PIMRC), Sep. 2018, pp. 1827–1832.

[11]

C.-W. Huang, C.-T. Chiang, and Q. Li, “A study of deep learning networks on mobile traffic forecasting,” in Proc. IEEE 28th Annu. Int. Symp. Pers., Indoor, Mobile Radio Commun. (PIMRC), Oct. 2017, pp. 1–6.

[12]

C. Zhang and P. Patras, “Long-term mobile traffic forecasting using deep spatio-temporal neural networks,” in Proc. 18th ACM Int. Symp. Mobile Ad Hoc Netw. Comput., Jun. 2018, pp. 231–240.

[13]

Q. Wu, X. Chen, Z. Zhou, L. Chen, and J. Zhang, “Deep reinforcement learning with spatio-temporal traffic forecasting for data-driven base station sleep control,” IEEE/ACM Trans. Netw., vol. 29, no. 2, pp. 935–948, Apr. 2021.

[14]

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, Oct. 2010.

[15]

E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko, “Simultaneous deep transfer across domains and tasks,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Dec. 2015, pp. 4068–4076.

[16]

G. Barlacchiet al., “A multi-source dataset of urban life in the city of Milan and the province of Trentino,” Sci. Data, vol. 2, no. 1, pp. 1–15, Dec. 2015.

[17]

K. He, Y. Huang, X. Chen, Z. Zhou, and S. Yu, “Graph attention spatial-temporal network for deep learning based mobile traffic prediction,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2019, pp. 1–6.

[18]

E. J. Keogh and M. J. Pazzani, “Derivative dynamic time warping,” in Proc. SIAM Int. Conf. Data Mining. Philadelphia, PA, USA: SIAM, 2001, pp. 1–11.

[19]

M. Brown and L. Rabiner, “Dynamic time warping for isolated word recognition based on ordered graph searching techniques,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), vol. 7, May 1982, pp. 1255–1258.

[20]

Y. Yuan and M. Raubal, “Extracting dynamic urban mobility patterns from mobile phone data,” in Proc. Int. Conf. Geographic Inf. Sci. Berlin, Germany: Springer, 2012, pp. 354–367.

[21]

A. Jain, A. R. Zamir, S. Savarese, and A. Saxena, “Structural-RNN: Deep learning on spatio-temporal graphs,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 5308–5317.

[22]

I. Goodfellowet al., “Generative adversarial nets,” in Proc. Adv. Neural Inf. Process. Syst., vol. 27, 2014, pp. 1–9.

[23]

A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” in Proc. 4th Int. Conf. Learn. Represent. (ICLR), San Juan, Puerto Rico, May 2016, pp. 1–16. [Online]. Available: http://arxiv.org/abs/1511.06434

[24]

D. Zhu, X. Cheng, F. Zhang, X. Yao, Y. Gao, and Y. Liu, “Spatial interpolation using conditional generative adversarial neural networks,” Int. J. Geographical Inf. Sci., vol. 34, no. 4, pp. 735–758, Apr. 2020.

[25]

Z. Wang, Q. She, and T. E. Ward, “Generative adversarial networks in computer vision: A survey and taxonomy,” 2019, arXiv:1906.01529.

[26]

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learn. Represent. (ICLR), San Diego, CA, USA, May 2015, pp. 1–15. [Online]. Available: https://arxiv.org/abs/1412.6980

[27]

R. J. Hyndman and G. Athanasopoulos. (2018). Forecasting: Principles and Practice. OTexts. [Online]. Available: https://www.otexts.org/fpp

[28]

N. Meade, “Neural network time series forecasting of financial markets,” Int. J. Forecasting, vol. 11, no. 4, pp. 601–602, Dec. 1995.

[29]

A. Azari, P. Papapetrou, S. Denic, and G. Peters, “Cellular traffic prediction and classification: A comparative evaluation of LSTM and ARIMA,” in Proc. Int. Conf. Discovery Sci. Cham, Switzerland: Springer, 2019, pp. 129–144.

[30]

A. Likas, N. Vlassis, and J. J. Verbeek, “The global K-means clustering algorithm,” Pattern Recognit., vol. 36, no. 2, pp. 451–461, Feb. 2003.

[31]

C. Zhang, H. Zhang, J. Qiao, D. Yuan, and M. Zhang, “Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data,” IEEE J. Sel. Areas Commun., vol. 37, no. 6, pp. 1389–1401, Jun. 2019.

[32]

L. Wang, X. Geng, X. Ma, F. Liu, and Q. Yang, “Cross-city transfer learning for deep spatio-temporal prediction,” in Proc. 28th Int. Joint Conf. Artif. Intell., S. Kraus, Ed. Macao, China: IJCAI, Aug. 2019, pp. 1893–1899.

[33]

N. I. Sapankevych and R. Sankar, “Time series prediction using support vector machines: A survey,” IEEE Comput. Intell. Mag., vol. 4, no. 2, pp. 24–38, May 2009.

[34]

C. Qiu, Y. Zhang, Z. Feng, P. Zhang, and S. Cui, “Spatio-temporal wireless traffic prediction with recurrent neural network,” IEEE Wireless Commun. Lett., vol. 7, no. 4, pp. 554–557, Aug. 2018.

[35]

C. Zhang, H. Zhang, D. Yuan, and M. Zhang, “Citywide cellular traffic prediction based on densely connected convolutional neural networks,” IEEE Commun. Lett., vol. 22, no. 8, pp. 1656–1659, Aug. 2018.

[36]

M. Wang and W. Deng, “Deep visual domain adaptation: A survey,” Neurocomputing, vol. 312, pp. 135–153, Oct. 2018.

[37]

R. Caruana, “Multitask learning,” Mach. Learn., vol. 28, no. 1, pp. 41–75, 1997.

[38]

D. Yu, L. Deng, and G. Dahl, “Roles of pre-training and fine-tuning in context-dependent DBN-HMMs for real-world speech recognition,” in Proc. Workshop Deep Learn. Unsupervised Feature Learn. (NIPS), 2010, pp. 1–8.

[39]

B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, “Unsupervised visual domain adaptation using subspace alignment,” in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 2960–2967.

[40]

A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb, “Learning from simulated and unsupervised images through adversarial training,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 2107–2116.

[41]

J. Xue, J. Han, T. Zheng, X. Gao, and J. Guo, “A multi-task learning framework for overcoming the catastrophic forgetting in automatic speech recognition,” 2019, arXiv:1904.08039.

Cited By

Aouedi OLe VPiamrat KJi Y(2025)Deep Learning on Network Traffic Prediction: Recent Advances, Analysis, and Future DirectionsACM Computing Surveys10.1145/370344757:6(1-37)Online publication date: 10-Feb-2025
https://dl.acm.org/doi/10.1145/3703447
Lee JChiang Y(2024)CrossBag: A Bag of Tricks for Cross-City Mobility PredictionProceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge10.1145/3681771.3699935(55-59)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681771.3699935
Liao CChen CZhang WGuo SLiu C(2024)AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain AdaptationACM Transactions on Knowledge Discovery from Data10.1145/367702018:8(1-25)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3677020
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Deep Transfer Learning Across Cities for Mobile Traffic Prediction

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cover image IEEE/ACM Transactions on Networking

IEEE/ACM Transactions on Networking Volume 30, Issue 3

June 2022

485 pages

ISSN:1063-6692

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1558-2566 © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 23 December 2021

Published in TON Volume 30, Issue 3

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Cited By

Aouedi OLe VPiamrat KJi Y(2025)Deep Learning on Network Traffic Prediction: Recent Advances, Analysis, and Future DirectionsACM Computing Surveys10.1145/370344757:6(1-37)Online publication date: 10-Feb-2025
https://dl.acm.org/doi/10.1145/3703447
Lee JChiang Y(2024)CrossBag: A Bag of Tricks for Cross-City Mobility PredictionProceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge10.1145/3681771.3699935(55-59)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681771.3699935
Liao CChen CZhang WGuo SLiu C(2024)AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain AdaptationACM Transactions on Knowledge Discovery from Data10.1145/367702018:8(1-25)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3677020
Yang HHe HZhang WWang YJing L(2024)Multi-Source and Multi-modal Deep Network Embedding for Cross-Network Node ClassificationACM Transactions on Knowledge Discovery from Data10.1145/365330418:6(1-26)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653304
Chen YZheng YXu JLin HCheng PDing MWang XHu JZheng H(2024)Knowledge-Assisted Resource Allocation With Domain Adversarial Neural NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2024.344039521:6(6493-6504)Online publication date: 1-Dec-2024
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Ghosh SGhosh SDas SMitra P(2024)Mobilytics: Mobility Analytics Framework for Transferring Semantic KnowledgeIEEE Transactions on Mobile Computing10.1109/TMC.2024.341358923:12(11588-11603)Online publication date: 1-Dec-2024
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Liu YDu HNiyato DKang JXiong ZKim DJamalipour A(2024)Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case StudyIEEE Wireless Communications10.1109/MWC.009.230016531:4(199-207)Online publication date: 1-Aug-2024
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