Cell association with user behaviour awareness in heterogeneous cellular networks

Sun, Y. , Feng, G., Qin, S. and Sun, S. (2018) Cell association with user behaviour awareness in heterogeneous cellular networks. IEEE Transactions on Vehicular Technology, 67(5), pp. 4589-4601. (doi: 10.1109/TVT.2018.2796135)

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In heterogeneous cellular networks (HetNets) with macro base station (BS) and multiple small BSs (SBSs), cell association of user equipment (UE) affects UE transmission rate and network throughput. Conventional cell association rules are usually based on UE received signal-to-interference-and-noise-ratio (SINR) without being aware of other UE statistical characteristics, such as user movement and distribution. User behaviors can indeed be exploited for improving long-term network performance. In this paper, we investigate UE cell association in HetNets by exploiting both individual and clustering user behaviors with the aim to maximize long-term system throughput. We model the problem as a stochastic optimization problem, and prove that it is PSPACE-hard. For mathematical tractability, we solve the problem in two steps. In the first step, we investigate UE association for a specific SBS. We use a restless multiarmed bandit model to derive an association priority index for the SBS. In the second step, we develop an index enabled association (IDEA) policy for making the cell association decisions in general HetNets based on the indices derived in the first step. IDEA determines a set of admissible BSs for a UE based on SINR, and then associates the UE with the BS that has the smallest index in the set. We conduct simulation experiments to compare IDEA with other three cell association policies. Numerical results demonstrate the significant advantages of IDEA in typical scenarios.

Item Type:Articles
Additional Information:This work was supported by the National Science Foundation of China under Grants 61631005 and 61471089, and the Fundamental Research Funds for the Central Universities under Grant ZYGX2015Z005.
Glasgow Author(s) Enlighten ID:Sun, Dr Yao and Feng, Professor Gang
Authors: Sun, Y., Feng, G., Qin, S., and Sun, S.
College/School:College of Science and Engineering > School of Engineering
Journal Name:IEEE Transactions on Vehicular Technology
ISSN (Online):1939-9359
Published Online:23 January 2018
Copyright Holders:Copyright © 2018 IEEE
First Published:First published in IEEE Transactions on Vehicular Technology 67(5): 4589-4601
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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