Cell clustering method and system based on wireless network traffic features

Abstract

The invention provides a cell clustering method based on wireless network traffic features. The method comprises the following five steps: S1, selecting to-be-processed data; S2, extracting feature parameters of all to-be-processed data; S3, carrying out clustering optimal K value selection on the to-be-processed data; S4, carrying out clustering integration on the to-be-processed data with the selected optimal K value by means of five kinds of clustering algorithms; and S5, combining a clustering result with geographic information in a geographic information system and displaying a combined clustering result. According to the invention, a cell with similar traffic data is obtained based on clustering. And thus an auxiliary scheme for evaluating and planning a network capacity can be provided for a mobile operator.

Description

technical field [0001] The invention relates to the field of mobile communication, in particular to a cell clustering method based on wireless network traffic characteristics. Background technique [0002] Mobile communication technology is a process of continuous evolution. With the large-scale construction of 4G networks, the complexity of the network is further increasing in the current 2 / 3 / 4G hybrid network. Therefore, in order to adapt to such changes, mobile When considering the potential 4G demand, operators should analyze the current wireless network traffic characteristics of different cells, and sort out the cells with similar wireless network traffic characteristics by using an efficient and low-energy clustering algorithm for network traffic characteristics, so as to provide It is possible for operators to provide a new network optimization method based on batch processing. [0003] In data processing, the process of dividing a collection of physical or abstract...

AI Technical Summary

Problems solved by technology

[0004] Existing clustering algorithms have some limitations and defects in terms of scope of application and clustering effect

For example, the classic k-means clustering algorithm, the result depends on the selection of the initial clustering center, and there are problems such as being sensitive to abnormal data, only dealing with data with numerical attributes, and the clustering structure may be unbalanced.

Hierarchical clustering also has problems such as too high computational complexity, singular values ​​can also have a great impact, and the algorithm is likely to cluster into a chain.

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