Determining method for small-scale base station deployment position

Abstract

The embodiment of the invention discloses a determining method for a small-scale base station deployment position, which is applied to a second-layer base station deployment scene in common-frequency heterogeneous networking. According to the technical scheme provided by the embodiment of the invention, a business distribution map is meshed, the meshed business is attributed to corresponding pixel points, the pixel points, i.e. intersection points of mesh lines are taken as candidate base station locations, the final precise degree of the station location is determined by the density degree of the mesh, given network element model parameters are taken as input, and a base station position deployment scheme which comprehensively considers the business volume and channel quality situation parameters of the network, and equivalently acquires optimum network energy efficiency by searching the location point of a maximum parameter value is provided. Compared with the existing random search methods, such as a genetic algorithm, the method has lower time complexity.

Description

technical field [0001] The present invention relates to the field of communication technology, in particular to a method for determining the deployment position of a small base station. Background technique [0002] With the development of mobile communication networks, flexible, low-cost, and large-capacity small base stations (Small cells) are regarded as another new force besides traditional cellular networks in the next stage of network deployment, and have been commercialized by many international operators. It is used to solve the problem of network coverage, especially network capacity bearing, in commercial areas and data-intensive areas. [0003] With the deepening impact of communication on people's lives, the application of small base stations in cities is a development trend. Its purpose is to absorb hotspot communication services and strengthen in-depth coverage. However, large-scale application will also bring problems such as various equipment forms and high ...

AI Technical Summary

Problems solved by technology

[0008] The genetic algorithm requires a high time complexity to achieve multi-generational screening, and the uncertainty of the random distribution itself greatly increases the complexity of the algorithm implementation process in the corresponding random algorithm, reducing the need for optimal small base station deployment. program efficiency

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