Base station engineering parameter optimization method and apparatus

Abstract

The invention discloses a base station engineering parameter optimization method and apparatus. The method includes the following steps that: a plurality of groups of corresponding engineering parameters are allocated for each cell covered by a base station according to the range and accuracy of the engineering parameters of the base station; and one group of optimal engineering parameters is searched out for each cell covered by the base station through adopting a genetic algorithm and according to preset performance optimization indexes. The invention also discloses a base station engineering parameter optimization apparatus. With the base station engineering parameter optimization method and apparatus of the invention adopted, optimal engineering parameter combinations of the base station covering the cells can be searched out quickly and accurately, and the engineering parameters of the base station can be optimized in a targeted manner through the preset performance optimization indexes, and therefore, the resource allocation efficiency of the base station can be improved.

Description

technical field [0001] The invention relates to parameter optimization technology in the wireless communication field, in particular to a base station engineering parameter optimization method and device. Background technique [0002] The cellular mobile communication system, also known as the "cell system" system, divides all covered areas into several cells, and the radius of each cell can be set at about 1-10km depending on the distribution density of users; and, in each cell A base station is set up to serve user terminals within the cell. [0003] A base station is a form of a radio station, which refers to a radio transceiver station that transmits information between a mobile communication switching center and a mobile phone terminal in a certain radio coverage area. The construction of mobile communication base stations is generally carried out around factors such as coverage, call quality, investment benefits, construction difficulty, and maintenance convenience. ...

AI Technical Summary

Problems solved by technology

[0006] 1) The automation degree of engineering parameter optimization of the base station is low;

[0007] 2) The efficiency of engineering parameter optimization for base stations is low. If the entire network needs to adjust the engineering parameters of base stations on a large scale, it will take a lot of time;

[0008] 3) The engineering parameter optimization accuracy of the base station is low. Since the existing engineering parameter optimization scheme of the base station is based on network management, drive test and complaint data combined with certain subjective experience to optimize the engineering parameters of the base station, the engineering parameters of the base station The optimization accuracy of parameters is relatively low;

[0009] 4) After the engineering parameters of the base station are optimized, the user perception is low. Due to the existing engineering parameter optimization scheme of the base station, there is no way to evaluate the optimization dimension and the actual application effect. Therefore, the effect after optimization is also unpredictable.

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