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HMM-based User Behavior Prediction Method in Heterogeneous Cellular Networks

Volume 14, Number 9, September 2018, pp. 2163-2174
DOI: 10.23940/ijpe.18.09.p25.21632174

Shanshan Tua,b, Xinyi Huanga, Yaqin Zhanga, Mingyang Ana, Lei Liuc, and Yao Huangd

aFaculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
bBeijing Key Laboratory of Trusted Computing, Beijing, 100124, China
cBeijing Electro-Mechanical Engineering Institute, Beijing, 100074, China
dChengdu University of Information Technology, Chengdu, 610225, China

(Submitted on June 17, 2018; Revised on July 8, 2018; Accepted on August 16, 2018)


In the heterogeneous cellular networks (HCN) environment, users travel between different cells to ensure that the network connection will not be interrupted, and there is a need for network handover management for users. In HCN, adopting the same handover strategy for different cells will reduce handover performance. Therefore, a reasonable handover management strategy needs to consider the mobile preference and mobility characteristics of users in hot spots. Aiming at the existing problems, in this study, the self-similar least-action human walk (SLAW) is analyzed and a method based on the hidden Markov model (HMM) for perceiving user behavior in hot spots is proposed. First, users’ mobile paths in hot spots are simulated based on SLAW, and user behaviors are modeled using HMM. Then, the corresponding moving time is predicted by the mobile sequence of the user. Finally, the effects of different sampling times and different base station densities on the behavior prediction are analyzed through simulation experiments, providing specific parameter settings for designing a reasonable handover management plan. Meanwhile, the prediction of user movement time can ensure that the base stations in hot spots will make effective preparations for the upcoming handover requests.


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