Mobile demand forecasting via deep graph-sequence spatiotemporal modeling in cellular networks

Luoyang Fang, Xiang Cheng, Haonan Wang, Liuqing Yang

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


The demand forecasting plays a crucial role in the predictive physical and virtualized network management in cellular networks, which can effectively reduce both the capital and operational expenditures by fully exploiting the network infrastructure. In this paper, we study the per-cell demand forecasting in cellular networks. The success of demand forecasting relies on the effective modeling of both the spatial and temporal aspects of the per-cell demand time series. However, the main challenge of the spatial relevancy modeling in the per-cell demand forecasting is the irregular spatial distribution of cells in a network, where applying grid-based models (e.g., convolutional neural networks) would lead to degradation of spatial granularity. In this paper, we propose to model the spatial relevancy among cells by a dependency graph based on spatial distances among cells without the loss of spatial granularity. Such spatial distance-based graph modeling is confirmed by the spatiotemporal analysis via semivariogram, which suggests that the relevancy between any two cells declines as their spatial distance increases. Hence, the graph convolutional networks and long short-term memory (LSTM) from deep learning are employed to model the spatial and temporal aspects, respectively. In addition, the deep graph-sequence model, graph convolutional LSTM, is further employed to simultaneously characterize both the spatial and temporal aspects of mobile demand forecasting. Experiments demonstrate that our proposed graph-sequence demand forecasting model could achieve a superior forecasting performance compared with the other two proposed models as well as the traditional auto regression integrated moving average time series model.

Original languageEnglish (US)
Pages (from-to)3091-3101
Number of pages11
JournalIEEE Internet of Things Journal
Issue number4
StatePublished - Aug 2018
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received February 10, 2018; revised April 3, 2018; accepted April 19, 2018. Date of publication May 1, 2018; date of current version August 9, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 61622101 and Grant 61571020, in part by the National Science and Technology Major Project under Grant 2018ZX03001031, and in part by the National Science Foundation under Grant DMS-1521746 and Grant DMS-1737795. (Corresponding author: Xiang Cheng.) L. Fang and L. Yang are with the Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523 USA (e-mail:;

Publisher Copyright:
© 2018 IEEE.


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