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Field intensity prediction method

A field strength prediction and consistent technology, applied in the field of communication, can solve problems such as low usability, inapplicability, and large errors, and achieve high-precision results

Active Publication Date: 2020-06-30
ZTE CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The first method can make full use of environmental information, and can achieve high accuracy in the modeling of field strength prediction. However, the measurement of environmental information requires a lot of manpower and material resources, as well as a very high time cost. In the rapid modeling demand not applicable
Although the second method can meet the needs of rapid modeling, since all known data sets are used as training samples for modeling, the model produced by it has low accuracy and large error, and is not usable in solving the field strength coverage problem. high

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0013] figure 1 It is the field strength prediction process in Embodiment 1 of the present invention. The field strength prediction method includes:

[0014] S13: Select a first data combination from the data set in the non-target area, the signal corresponding to the first data combination is consistent with the fluctuation law of the signal corresponding to the second data combination in the target area conforms to the preset rule;

[0015] S14: Perform training of the field strength prediction model of the target area, where the training samples of the field strength prediction model include the first data set.

[0016] In the field strength prediction method provided by the embodiment of the present invention, compared with the prior art where all the data sets of the non-target area are used for modeling the target area, the present invention selects the first data combination from all the data sets of the non-target area as The training samples of the field strength pr...

Embodiment 2

[0038] In the embodiment of the present invention, a full-link neural network is used as a basic model for field strength prediction in a full data set composed of 67 cell data that does not contain any topographic information. The size of the model performance improvement of the verification scheme method.

[0039] The specific implementation steps are as follows:

[0040] S21: Divide the data of each of the 67 sub-districts into multiple groups of single-operation parameter data according to the variation of the industrial parameter value contained in the data of the sub-district. Wherein, the industrial parameter may include transmit power, antenna direction angle, antenna downtilt angle, antenna height, etc. In S21, the change of the industrial parameter data may specifically be the change of the antenna direction angle.

[0041] S22: Sorting the signal data in each set of single parameter data in S21 in descending order according to the distance from the base station to ...

Embodiment 3

[0055] The difference between this embodiment and Embodiment 2 is that the model is converted from a fully-linked neural network to an Xgboost model as the basic model for field strength prediction. The steps from S31 to S34 in the specific steps of this example are completely consistent with the steps from S21 to S24 in Embodiment 2, and the difference between S35 and S37 lies in the change of the basic model.

[0056] The specific implementation steps are as follows:

[0057] S31: Divide the data of each of the 67 sub-districts into multiple groups of single-operation parameter data according to the variation of the industrial parameter value included in the data of the sub-district. Wherein, the industrial parameter may include transmit power, antenna direction angle, antenna downtilt angle, antenna height, etc. In S31, the change of the industrial parameter data may specifically be the change of the antenna direction angle.

[0058] S32: Sorting the signal data in each se...

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Abstract

The invention discloses a field intensity prediction method, which comprises the steps of selecting a first data combination from a data set of a non-target area, and enabling the consistency degree of fluctuation rules of a signal corresponding to the first data combination and a signal corresponding to a second data combination of a target area to meet a preset rule; training a field intensity prediction model of the target area, wherein a training sample of the field intensity prediction model comprises a first data set. Comparing with the prior art, in the method, a first data combinationselected from all data sets of a non-target area is used as a training sample of a field intensity prediction module, and the first data combination and the second data combination have a certain degree of correlation, so that the first data combination is used as a training sample of the field intensity prediction model, the field intensity prediction model can have high precision, and the prediction method does not need to perform on-site measurement on various factors of the environment.

Description

technical field [0001] The invention relates to the technical field of communication, in particular to a method for predicting field strength of a signal coverage area. Background technique [0002] In field strength prediction and other related technical fields, environmental factors are an important factor to determine the accuracy of the model. At present, there are mainly two ways to use environmental information: first, conduct detailed manual measurements of various environmental factors on the spot; second, ignore environmental information and use all data sets (full data) in non-target areas as training samples for modeling. The first method can make full use of environmental information, and can achieve high accuracy in the modeling of field strength prediction. However, the measurement of environmental information requires a lot of manpower and material resources, as well as a very high time cost. In the rapid modeling demand does not apply. Although the second ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B17/391H04L12/24G01R29/12
CPCH04B17/3913H04L41/145G01R29/12H04W24/06H04W24/04H04B17/318
Inventor 武继龙
Owner ZTE CORP