Optimisation of Cost 231-Hata model based on deep learning

摘要

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.