Intelligent Load Balancing Relying on Load Prediction with MGCN-GRU

In cellular networks, the unbalanced load among base stations always results in the disparity of spectrum efficiency and bad user experience in heavy-loaded cells. In this paper, an intelligent cell individual offset (CIO) adjustment strategy is proposed to control the number of users served in the...

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Bibliographic Details
Published in2022 IEEE/CIC International Conference on Communications in China (ICCC) pp. 884 - 889
Main Authors Luo, Jingwei, Chen, Youjia, Hu, Jinsong, Chen, Pingping, Zheng, Haifeng
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.08.2022
Subjects
Base stations
cell individual offsets
Cellular networks
gated recurrent unit
graph convolutional network
Load balancing
Load management
Logic gates
Multilayer perceptrons
physical resource block
Predictive models
Simulation
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DOI10.1109/ICCC55456.2022.9880833

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Summary:In cellular networks, the unbalanced load among base stations always results in the disparity of spectrum efficiency and bad user experience in heavy-loaded cells. In this paper, an intelligent cell individual offset (CIO) adjustment strategy is proposed to control the number of users served in the cell aiming of minimizing the variance of the physical resource block (PRB) usage ratio among the cells. Firstly, a multi-graph convolutional and gated recurrent unit network (MGCN-GRU) is proposed to predict the active users, where an MGCN is developed to aggregate spatial features, and a GRU is used to capture temporal features. Secondly, a multilayer perceptron is used to model the relationship between the PRB usage ratio, active users number and cellular resources. Finally, the optimal CIO is obtained by the genetic algorithm to achieve load balancing. Simulation results show a good load balancing performance, and a better prediction performance achieved by the proposed MGCN-GRU.
DOI:10.1109/ICCC55456.2022.9880833