Adaptive method for classifying communication environments in network planning

Abstract

The invention relates to an adaptive method for classifying communication environments in network planning, and relates to wireless network planning and optimizing technology. The method comprises the following steps of: selecting a sample point to acquire an attribute vector of the sample point; calculating the absolute similarity of a target cell and a cell where the sample point is positioned; calculating the relative similarity according to the percentage pij of the geo-information and building information of the cell oi, in which the sample point is positioned, in a grid; and performing cluster selection of the target cell according to the relative similarity. When the communication environment is changed, the method has good adaptivity and transportability, has good classification effect and precision although the execution speed is in a tolerable range, and can provide important reference for wireless network optimizing and planning.

Description

Technical field [0001] The present invention relates to network planning and optimization technology, in particular to an adaptive propagation environment classification method. Background technique [0002] The accurate description of the wireless propagation environment is used in the planning and design of the wireless cellular system, providing more accurate coverage prediction when used in simulation, and providing an accurate radio wave propagation model, thereby helping the planning, deployment and expansion of the wireless network. [0003] At present, there are few methods to classify the communication environment. One part mainly relies on on-site measurement, which will inevitably consume human resources; another part of the communication environment classification method is based on the idea of ​​clustering. For example, in the document "A Method to Correct the Communication Model According to the Classification of the Communication Environment In 》, first randomly sele...

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Problems solved by technology

[0003] At present, there are few methods for the classification of communication environments, some of which mainly rely on on-site measurements, which will inevitably consume human resources; In ", first randomly select k objects from the sample set as the initial centroid; and for the remaining sample points, assign them to the category with the smallest distance to them according to their Euclidean distances from these centroids; then Recalculate the centroids of the obtained clusters and repeat this process until the iteration converges

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