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Parameter Setting of Load Forecasting Model for Adjacent Base Stations of Mobile Communication Based on Particle Swarm Optimization

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Abstract

In order to study the parameter setting of load forecasting model of mobile communication adjacent base station, the particle swarm optimization algorithm is used to make intelligent optimization of the parameter values in the support vector regression model, so as to obtain a prediction model with better parameters. Through the verification of base station load prediction scheme assisted by the nearest neighbor base station, we establish the support vector regression model only using the base station itself historical information that introducing adjacent base station historical information, and compare the performance of the two parts. It is proved that the base station assisted load prediction scheme with assisted adjacent base stations can achieve better performance.

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Acknowledgements

We thank the anonymous reviewers and the editors for the valuable feedback on earlier versions of this paper. This paper is supported by the National Statistical Science Research Project of China, under Grant Number 2015LY43.

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Authors and Affiliations

  1. School of Science, Hubei University for Nationalities, Enshi, Hubei, China

    Tao Wang

  2. College of Computer Science, Sichuan University, Chengdu, Sichuan, China

    Tao Wang & Minghui Wang

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  1. Tao Wang
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  2. Minghui Wang
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Correspondence to Tao Wang.

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Wang, T., Wang, M. Parameter Setting of Load Forecasting Model for Adjacent Base Stations of Mobile Communication Based on Particle Swarm Optimization. Wireless Pers Commun 102, 1057–1071 (2018). https://doi.org/10.1007/s11277-017-5139-6

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  • DOI: https://doi.org/10.1007/s11277-017-5139-6

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