Title: Optimisation of Cost 231-Hata model based on deep learning

Authors: Qinxia Huang; Cheng Zhang; Jing Liu; Shilin Wu

Addresses: School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan 430200, China ' School of Mathematics and Computer, Wuhan Textile University, Wuhan 430200, China ' School of Management, Wuhan Textile University, Wuhan 430200, China ' School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan 430200, China

Abstract: Based on the dataset of question A in the 16th 'Huawei Cup' mathematical modelling competition, this paper uses deep learning algorithm to optimise the Cost 231-Hata model of wireless communication. Firstly, the feature parameters of Cost 231-Hata model are analysed, and the corresponding features are found in the dataset. Secondly, two new reference features are extracted according to the geometric relationship between base station and cell location. Then, the principal component analysis is used to reduce the dimension of the dataset, and six features that are highly correlated with the target are extracted from multiple reference features. Finally, as these six features are taken as the input of neural network, and a wireless propagation model based on deep learning is constructed by using error back propagation algorithm. The results show that the prediction accuracy of this model is higher than that of the traditional Cost 231-Hata model.

Keywords: wireless communication; Cost 231-Hata model; feature engineering; deep learning.

DOI: 10.1504/IJICA.2022.128433

International Journal of Innovative Computing and Applications, 2022 Vol.13 No.5/6, pp.259 - 268

Received: 30 Apr 2020
Accepted: 23 Aug 2020
Published online: 23 Jan 2023 *

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