Wireless network signal propagation path loss prediction method and electronic device

Abstract

The present disclosure provides a wireless network signal propagation path loss prediction method and electronic device. The method includes: acquiring network environment data and original test data, generating initial feature data according to the network environment data and original test data, and extracting and determining high-order feature data therefrom; performing redundancy and correlation on the initial feature data and the high-order feature data. feature analysis, and screen out key feature data; build a multiple linear regression model, and solve the model coefficients according to the key feature data; use the multiple linear regression model to calculate the path loss prediction value corresponding to the key feature data to generate combined feature data; build deep neural network A network model, using the combined feature data to train and optimize the deep neural network model to obtain an optimization model, and using the optimization model to predict the path loss of the target wireless network. The electronic device is used to implement the method for predicting the propagation path loss of a wireless network signal.

Description

technical field [0001] The present disclosure relates to the technical field of wireless network communication, and in particular, to a method for predicting the propagation path loss of a wireless network signal and an electronic device. Background technique [0002] At present, the research on the key technologies of 5G mobile communication is gradually mature, but the network construction still faces two main challenges. First, the construction cost of 5G network is huge. In order to achieve seamless coverage of the network, the number of 5G base stations will increase by about 2 times compared to the number of base stations in traditional mobile communication networks; second, the energy consumption of base stations is huge. The energy consumption of a single 5G base station is about 3 times that of a 4G base station, and the total energy consumption will be more than 9 times that of a 4G base station. Therefore, the operating cost problem caused by energy consumption c...

AI Technical Summary

Problems solved by technology

However, the empirical model evaluation method cannot accurately describe the wireless environment in different cities and different areas. In engineering practice, operators need to select an appropriate empirical model according to the characteristics of the network, and correct the model through measured data to fit the local real wireless environment.

However, due to the complexity of 5G network signal propagation, it is difficult for the model correction method of related technologies to accurately predict the path loss

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