Intelligent wireless network coverage prediction method based on multi-source heterogeneous data fusion and deep learning

Through multi-source heterogeneous data fusion and deep learning technology, wireless network coverage features are extracted and prediction models are constructed, which solves the problem of large prediction errors in traditional methods, and achieves high-accurate wireless network coverage prediction and base station layout optimization.

CN119402881AInactive Publication Date: 2025-02-07DETSERWEI TECH CO LTD
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Patent Information

Application Number
CN202411959803.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-30
Publication Date
2025-02-07
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The traditional wireless network coverage prediction method has problems such as idealized environmental assumptions, high computational complexity and weak generalization capabilities, which leads to large errors in the prediction results and is difficult to fully reflect the network coverage situation in the actual environment.

Method used

Using an intelligent wireless network coverage prediction method based on multi-source heterogeneous data fusion and deep learning, the height features, distance features, and angles are extracted by collecting multiple data sources such as mobile terminal test data, industrial parameter data and building elevation raster data. Features and occlusion features are used to build deep learning models such as CNN for training, and predict the average coverage field strength of the cell-wide area.

Benefits of technology

It improves the accuracy and reliability of wireless network coverage prediction, realizes refined prediction of wireless network coverage, helps operators discover blind spots in coverage, optimize base station layout, and improve network performance and service quality.

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Abstract

The invention relates to an intelligent wireless network coverage prediction method based on multi-source heterogeneous data fusion and deep learning. Comprising the following steps: data acquisition: collecting various engineering data and communication data of a cell; constructing a model: generating features for modeling according to the collected data; taking the average coverage field intensity of the grids as a label, and constructing a prediction model for outputting the average coverage field intensity of the cell range; and predicting the average coverage field intensity of the coverage area of the planning station: carrying out grid division on the prediction area, inputting the features in each grid unit into the prediction model, and outputting the predicted average coverage field intensity of each grid unit and the coverage condition of the wireless network. According to the invention, through multiple data sources, the data quality and consistency are ensured. And the deep learning model improves the prediction accuracy. Therefore, refined prediction of the coverage area of the wireless network is realized.
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Citation Information

Patent Citations

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