Quantitative prediction method and device for indicators of wireless network coverage

Abstract

The embodiment of the invention provides a quantitative prediction method and device for indicators of wireless network coverage. The method comprises the steps of: obtaining feature data of a wireless network, wherein the feature data includes network structure, wireless parameter data, topographical data, and service distribution data; and inputting the feature data into a trained deep learningmodel to obtain quantitative prediction data of the indicators of the wireless network coverage. According to the embodiment of the quantitative prediction method and the device, the quantitative prediction data of the indicators of the wireless network coverage can be obtained by analyzing and calculating the feature data of the wireless network using the trained deep learning model. The embodiment of the quantitative prediction method and the device includes all the details of the current scene as much as possible, which includes the network structure, the wireless parameter data, the topographical data, and the service distribution data, thereby maximizing the comprehensive analysis of the current scene, and enabling accurate, quantitative, customer-perceived, and timely prediction of the optimized indicators.

Description

Technical field [0001] The embodiments of the present invention relate to the field of communication technology, and more specifically, to a method and device for quantitatively predicting an indicator of wireless network coverage. Background technique [0002] Wireless network coverage optimization is an important work content to improve the quality of wireless networks. The current mainstream coverage optimization technology is mainly based on simulation optimization and manual experience optimization. [0003] The simulation optimization method is mainly based on network engineering parameters, three-dimensional maps, and propagation models to calculate the level and interference value of each sub-area in the area to be optimized, and adjust the network soft and hard parameters (antenna parameters) to achieve the coverage of the area to be optimized prediction. [0004] The simulation optimization goal is to increase the coverage of geographic areas, and does not fully consider t...

AI Technical Summary

Problems solved by technology

[0006] The artificial optimization method that relies purely on experience can only predict the optimization effect qualitatively or roughly, and often requires multiple actual adjustments on the live network to achieve the optimization goal, consuming a lot of manpower and material resources, and may lose the timeliness of optimization

The existing IT expert system based on artificial experience and knowledge also faces the problem of the accuracy of optimization prediction caused by the accuracy of the propagation model. In addition, the coverage optimization prediction based on prior knowledge, as the environment changes (such as climate , network structure, new technology introduction) need to re-learn knowledge and experience exploration

[0007] Therefore, in the quantitative prediction of coverage optimization indicators in the existing technology, there are one or several problems such as low prediction accuracy, failure to meet customer perception, poor timeliness, and excessive resource consumption.

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