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Erschienen in: Neural Computing and Applications 1/2019

30.08.2018 | S.I. : Machine Learning Applications for Self-Organized Wireless Networks

A multi-objective location and channel model for ULS network

verfasst von: Hejun Liang, Guanghui Yuan, Jingti Han, Lily Sun

Erschienen in: Neural Computing and Applications | Sonderheft 1/2019

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Abstract

In this paper, we construct a multi-objective location and channel model for ULS network to alleviate the urban traffic congestion problem. First, we construct a multi-objective node selection model and obtain the position of the first-level nodes in the ULS network by using agglomerative hierarchical clustering method. Then, we obtain the position of the second-level nodes in the ULS network by using the greedy algorithm. We also calculate the service scope, actual traffic volume and transport rate of first-level node for each node based on the determined node group. After that, we select the optimal channel scheme to make nodes at different levels of the ULS and its load more balanced by using the plant growth simulation algorithm. We extract the key variables of the problem, quantify some indicators with reasonable quantification, construct a multi-objective location and channel model based on the actual logistics situation for the specific region and achieve reasonable results to meet multiple objectives. Therefore, the model could be used as a reference for the construction of urban ULS network.

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Metadaten
Titel
A multi-objective location and channel model for ULS network
verfasst von
Hejun Liang
Guanghui Yuan
Jingti Han
Lily Sun
Publikationsdatum
30.08.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3636-5

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