{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:07:49Z","timestamp":1775228869295,"version":"3.50.1"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U20B2060"],"award-info":[{"award-number":["U20B2060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62171260"],"award-info":[{"award-number":["62171260"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Institute for Guo Qiang, Tsinghua University","award":["2023GQS0002"],"award-info":[{"award-number":["2023GQS0002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>Mobile user traffic facilitates diverse applications, including network planning and optimization, whereas large-scale mobile user traffic is hardly available due to privacy concerns. One alternative solution is to generate mobile user traffic data for downstream applications. However, existing generation models cannot simulate the multi-scale temporal dynamics in mobile user traffic on individual and aggregate levels. In this work, we propose a multi-scale hierarchical generative adversarial network (MSH-GAN) containing multiple generators and a multi-class discriminator. Specifically, the mobile traffic usage behavior exhibits a mixture of multiple behavior patterns, which are called micro-scale behavior patterns and are modeled by different pattern generators in our model. Moreover, the traffic usage behavior of different users exhibits strong clustering characteristics, with the co-existence of users with similar and different traffic usage behaviors. Thus, we model each cluster of users as a class in the discriminator\u2019s output, referred to as macro-scale user clusters. Then, the gap between micro-scale behavior patterns and macro-scale user clusters is bridged by introducing the switch mode generators, which describe the traffic usage behavior in switching between different patterns. All users share the pattern generators. In contrast, the switch mode generators are only shared by a specific cluster of users, which models the multi-scale hierarchical structure of the traffic usage behavior of massive users. Finally, we urge MSH-GAN to learn the multi-scale temporal dynamics via a combined loss function, including adversarial loss, clustering loss, aggregated loss, and regularity terms. Extensive experiment results demonstrate that MSH-GAN outperforms state-of-art baselines by at least 118.17% in critical data fidelity and usability metrics. Moreover, observations show that MSH-GAN can simulate traffic patterns and pattern switch behaviors.<\/jats:p>","DOI":"10.1145\/3664655","type":"journal-article","created":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T14:43:39Z","timestamp":1715352219000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Mobile User Traffic Generation Via Multi-Scale Hierarchical GAN"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4343-703X","authenticated-orcid":false,"given":"Tong","family":"Li","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7753-5140","authenticated-orcid":false,"given":"Shuodi","family":"Hui","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3216-2256","authenticated-orcid":false,"given":"Shiyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6382-0861","authenticated-orcid":false,"given":"Huandong","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7871-4547","authenticated-orcid":false,"given":"Yuheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6026-1083","authenticated-orcid":false,"given":"Pan","family":"Hui","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0419-5514","authenticated-orcid":false,"given":"Depeng","family":"Jin","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-1659","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Statista. 2023. Number of Smartphone Mobile Network Subscriptions Worldwide From 2016 To 2022. Retrieved from https:\/\/www.statista.com\/statistics\/330695\/number-of-smartphone-users-worldwide\/"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2015.2491361"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2843533"},{"key":"e_1_3_1_5_2","first-page":"1","volume-title":"Proceedings of the 13th International Conference on Telecommunications (ConTEL \u201915)","author":"Soikkeli Tapio","year":"2015","unstructured":"Tapio Soikkeli and Antti Riikonen. 2015. Session level network usage patterns of mobile handsets. In Proceedings of the 13th International Conference on Telecommunications (ConTEL \u201915). IEEE, 1\u20138."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2017.01.003"},{"issue":"3","key":"e_1_3_1_7_2","first-page":"2","article-title":"6G autonomous mobile network enabled by digital twin network","volume":"29","author":"Liu Guangyi","year":"2023","unstructured":"Guangyi Liu, Juan Deng, and Qingbi Zheng. 2023. 6G autonomous mobile network enabled by digital twin network. ZTE Technology Journal 29, 3 (2023), 2\u20137.","journal-title":"ZTE Technology Journal"},{"issue":"3","key":"e_1_3_1_8_2","first-page":"32","article-title":"Digital twin technology for wireless access network oriented to 6G","volume":"29","author":"Duan Xiangyang","year":"2023","unstructured":"Xiangyang Duan, Honghui Kang, Xingzai Lyu, and Rui Hua. 2023. Digital twin technology for wireless access network oriented to 6G. ZTE Technology Journal 29, 3 (2023), 32\u201337.","journal-title":"ZTE Technology Journal"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3581791.3597297"},{"key":"e_1_3_1_10_2","first-page":"1","volume-title":"Proceedings of the IEEE\/ACM 24th International Symposium on Quality of Service (IWQoS \u201916)","author":"Wang Shanshan","year":"2016","unstructured":"Shanshan Wang, Zhenxiang Chen, Lei Zhang, Qiben Yan, Bo Yang, Lizhi Peng, and Zhongtian Jia. 2016. TrafficAV: An effective and explainable detection of mobile malware behavior using network traffic. In Proceedings of the IEEE\/ACM 24th International Symposium on Quality of Service (IWQoS \u201916). IEEE, 1\u20136."},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3007748.3007763"},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1145\/2594368.2594391","volume-title":"Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services","author":"Crussell Jonathan","year":"2014","unstructured":"Jonathan Crussell, Ryan Stevens, and Hao Chen. 2014. MAdFraud: Investigating ad fraud in android applications. In Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services. 123\u2013134."},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1145\/2504730.2504742","volume-title":"Proceedings of the 2013 Conference on Internet Measurement Conference","author":"Barbera Marco V.","year":"2013","unstructured":"Marco V. Barbera, Alessandro Epasto, Alessandro Mei, Vasile C. Perta, and Julinda Stefa. 2013. Signals from the crowd: Uncovering social relationships through smartphone probes. In Proceedings of the 2013 Conference on Internet Measurement Conference. 265\u2013276."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939918.2939922"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.013.2100087"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600911"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615043"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41893-023-01206-5"},{"key":"e_1_3_1_19_2","first-page":"1","volume-title":"Proceedings of the IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC \u201916)","author":"Xiao Zhu","year":"2016","unstructured":"Zhu Xiao, Jianzhi Yu, Tong Li, Zhiyang Xiang, Dong Wang, and Wenjie Chen. 2016. Resource allocation via hierarchical clustering in dense small cell networks: A correlated equilibrium approach. In Proceedings of the IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC \u201916). IEEE, 1\u20135."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3098664"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627995"},{"key":"e_1_3_1_22_2","first-page":"307","volume-title":"Handbook of Mobile Data Privacy","author":"Acs Gergely","year":"2018","unstructured":"Gergely Acs, Gergely Bicz\u00f3k, and Claude Castelluccia. 2018. Privacy-preserving release of spatio-temporal density. In Handbook of Mobile Data Privacy. A. Gkoulalas-Divanis and C. Bettini (Eds.), Springer, 307\u2013335."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2022.3163176"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370442"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109156"},{"key":"e_1_3_1_27_2","first-page":"0728","volume-title":"Proceedings of the IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON \u201919)","author":"Cheng Adriel","year":"2019","unstructured":"Adriel Cheng. 2019. PAC-GAN: Packet generation of network traffic using generative adversarial networks. In Proceedings of the IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON \u201919). IEEE, 0728\u20130734."},{"key":"e_1_3_1_28_2","first-page":"1149","volume-title":"Proceedings of the 18th IEEE International Conference On Machine Learning And Applications (ICMLA \u201919)","author":"Dowoo Baik","year":"2019","unstructured":"Baik Dowoo, Yujin Jung, and Changhee Choi. 2019. PcapGAN: Packet capture file generator by style-based generative adversarial networks. In Proceedings of the 18th IEEE International Conference On Machine Learning And Applications (ICMLA \u201919). IEEE, 1149\u20131154."},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544216.3544251"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2018.12.012"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3419394.3423643"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599853"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599801"},{"key":"e_1_3_1_34_2","article-title":"Finding spatiotemporal patterns of mobile application usage","author":"Li Tong","year":"2021","unstructured":"Tong Li, Yong Li, Tong Xia, and Pan Hui. 2021. Finding spatiotemporal patterns of mobile application usage. IEEE Transactions on Network Science and Engineering.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"e_1_3_1_36_2","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30, 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_1_38_2","unstructured":"Eric Jang Shixiang Gu and Ben Poole. 2016. Categorical reparameterization with gumbel-softmax. arXiv:1611.01144. Retrieved from https:\/\/arxiv.org\/abs\/1611.01144"},{"key":"e_1_3_1_39_2","unstructured":"Chris J. Maddison Andriy Mnih and Yee W. Teh. 2016. The concrete distribution: A continuous relaxation of discrete random variables. arXiv:1611.00712. Retrieved from https:\/\/arxiv.org\/abs\/1611.00712"},{"key":"e_1_3_1_40_2","volume-title":"Statistical Theory of Extreme Values and Some Practical Applications: A Series of Lectures","author":"Gumbel Emil J.","year":"1954","unstructured":"Emil J. Gumbel. 1954. Statistical Theory of Extreme Values and Some Practical Applications: A Series of Lectures, Vol. 33. US Government Printing Office."},{"key":"e_1_3_1_41_2","unstructured":"Shaojie Bai Jeremy Z. Kolter and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271. Retrieved from https:\/\/arxiv.org\/abs\/1803.01271"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","unstructured":"Crist\u00f3bal Esteban Stephanie L. Hyland and Gunnar R\u00e4tsch. 2017. Real-valued (medical) time series generation with recurrent conditional GANs. arXiv:1706.02633. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.1706.02633","DOI":"10.48550\/arXiv.1706.02633"},{"key":"e_1_3_1_43_2","unstructured":"Olof Mogren. 2016. C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv:1611.09904. Retrieved from https:\/\/arxiv.org\/abs\/1706.02633"},{"key":"e_1_3_1_44_2","first-page":"1","article-title":"Time-series generative adversarial networks","volume":"32","author":"Yoon Jinsung","year":"2019","unstructured":"Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar. 2019. Time-series generative adversarial networks. Advances in Neural Information Processing Systems 32, 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0016-0032(96)00063-4"},{"key":"e_1_3_1_46_2","first-page":"1","volume-title":"Proceedings of the 3rd International Conference on Cyberspace Technology (CCT \u201915)","author":"Zhang Junhui","year":"2015","unstructured":"Junhui Zhang, Jiqiang Tang, Xu Zhang, Wen Ouyang, and Dongbin Wang. 2015. A survey of network traffic generation. In Proceedings of the 3rd International Conference on Cyberspace Technology (CCT \u201915). IET, 1\u20136."},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/1140103.1140309"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2009.2020830"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/1140086.1140094"},{"key":"e_1_3_1_50_2","first-page":"119","volume-title":"Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom \u201922)","author":"Xu Kai","year":"2022","unstructured":"Kai Xu, Rajkarn Singh, Hakan Bilen, Marco Fiore, Mahesh K. Marina, and Yue Wang. 2022. Cartagenie: Context-driven synthesis of city-scale mobile network traffic snapshots. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom \u201922). IEEE, 119\u2013129."},{"key":"e_1_3_1_51_2","first-page":"243","volume-title":"Proceedings of the 17th International Conference on Emerging Networking EXperiments and Technologies","author":"Xu Kai","year":"2021","unstructured":"Kai Xu, Rajkarn Singh, Marco Fiore, Mahesh K. Marina, Hakan Bilen, Muhammad Usama, Howard Benn, and Cezary Ziemlicki. 2021. SpectraGAN: Spectrum based generation of city scale spatiotemporal mobile network traffic data. In Proceedings of the 17th International Conference on Emerging Networking EXperiments and Technologies. 243\u2013258."},{"key":"e_1_3_1_52_2","unstructured":"Kay G. Hartmann Robin T. Schirrmeister and Tonio Ball. 2018. EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv:1806.01875. https:\/\/arxiv.org\/abs\/1806.01875"},{"key":"e_1_3_1_53_2","first-page":"3597","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Golany Tomer","year":"2020","unstructured":"Tomer Golany, Kira Radinsky, and Daniel Freedman. 2020. SimGANs: Simulator-based generative adversarial networks for ECG synthesis to improve deep ECG classification. In Proceedings of the International Conference on Machine Learning. PMLR, 3597\u20133606."},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.93"},{"key":"e_1_3_1_55_2","first-page":"1","article-title":"On GANs and GMMs","volume":"31","author":"Richardson Eitan","year":"2018","unstructured":"Eitan Richardson and Yair Weiss. 2018. On GANs and GMMs. Advances in Neural Information Processing Systems 31, 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_56_2","unstructured":"Nat Dilokthanakul Pedro A. M. Mediano Marta Garnelo Matthew C. H. Lee Hugh Salimbeni Kai Arulkumaran and Murray Shanahan. 2016. Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv:1611.02648. https:\/\/arxiv.org\/abs\/1611.02648"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664655","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664655","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:29Z","timestamp":1750295849000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664655"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,26]]},"references-count":55,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3664655"],"URL":"https:\/\/doi.org\/10.1145\/3664655","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,26]]},"assertion":[{"value":"2023-01-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}