Idle Time Window Prediction in Cellular Networks with Deep Spatiotemporal Modeling

Luoyang Fang, Xiang Cheng, Haonan Wang, Liuqing Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Idle time windows (ITWs) consist of one critical trigger for various functions in green intelligent network management and traffic scheduling in mobile networks. In this paper, we study the ITW prediction in mobile networks based on network subscribers' demand and mobility behaviors observed by network operators. We first innovatively formulate the ITW prediction into a regression problem with an ITW presence confidence index that facilitates direct ITW detection and estimation. Feature extraction on the demand and mobility history is then proposed to capture the current trends of subscribers' demand and mobility as well as to account for the periodicity underlying subscribers' demand and mobility patterns as exogenous inputs. In light of feature engineering, a deep learning-based ITW prediction model is proposed, which consists of two components, namely the representation learning network and the output network. The representation learning network is aimed to learn effective patterns, whereas the output network is designed to produce the desired ITW presence confidence index and the ITW estimate by integrating the learned representation and exogenous inputs. In this paper, a novel temporal graph convolutional network (TGCN) for the representation learning network is proposed to effectively capture the graph-based spatiotemporal input features. The experiment results validate the proposed direct ITW prediction formulation and demonstrate the superiority of the proposed TGCN in terms of both ITW detection and ITW estimation performance, which can achieve a significant intersection-over-union (IoU) improvement compared with baselines.

Original languageEnglish (US)
Article number8667305
Pages (from-to)1441-1454
Number of pages14
JournalIEEE Journal on Selected Areas in Communications
Volume37
Issue number6
DOIs
StatePublished - Jun 2019
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received July 21, 2018; revised December 20, 2018; accepted March 3, 2019. Date of publication March 14, 2019; date of current version May 15, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61622101 and Grant 61571020, in part by the Shenzhen Fundamental Research Fund under Grant JCYJ20170411102217994, in part by the Shenzhen Peacock Plan under Grant KQTD2015033114415450, in part by the Guangdong Province under Grant 2017ZT07X152, and in part by the National Science Foundation under Grant DMS-1521746 and Grant DMS-1737795. (Corresponding author: Xiang Cheng.) L. Fang is with the Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523 USA (e-mail: luoyang.fang@colostate.edu).

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61622101 and Grant 61571020, in part by the Shenzhen Fundamental Research Fund under Grant JCYJ20170411102217994, in part by the Shenzhen Peacock Plan under Grant KQTD2015033114415450, in part by the Guangdong Province under Grant 2017ZT07X152, and in part by the National Science Foundation under Grant DMS-1521746 and Grant DMS-1737795

Publisher Copyright:
© 1983-2012 IEEE.

Keywords

  • Machine learning
  • mobile communication
  • predictive models

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