CN108199792A - A kind of WCDMA base stations electromagnetic radiation Forecasting Methodology - Google Patents

A kind of WCDMA base stations electromagnetic radiation Forecasting Methodology Download PDF

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Publication number
CN108199792A
CN108199792A CN201810104417.0A CN201810104417A CN108199792A CN 108199792 A CN108199792 A CN 108199792A CN 201810104417 A CN201810104417 A CN 201810104417A CN 108199792 A CN108199792 A CN 108199792A
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base station
distance
electromagnetic radiation
base stations
wcdma
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CN108199792B (en
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杨万春
吴涛
谭平安
张园
彭艳芬
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Xiangtan University
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Xiangtan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3913—Predictive models, e.g. based on neural network models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a kind of WCDMA base stations electromagnetic radiation Forecasting Methodologies, and its step are as follows：With reference to base station call amount and future position and the distance of base station, establish a WCDMA base stations electromagnetic radiation prediction model based on telephone traffic and with base station different distance point, employ telephone traffic variation coefficient and distance change coefficient, prediction model is trained by WCDMA base stations history traffic data and with the history electromagnetic radiation value corresponding to the distance of base station difference position and these point positions again, determine the variation coefficient and correction parameter of prediction model, again by the trained prediction model of distance input of the base station call amount and future position and base station of prediction period, the electromagnetic radiation value of WCDMA base stations different distance point is predicted.The present invention considers telephone traffic variation coefficient and distance change coefficient, and comparatively fast and accurately the electromagnetic radiation value of WCDMA base stations different distance point can be predicted by this method, has certain social benefit.

Description

A kind of WCDMA base stations electromagnetic radiation Forecasting Methodology
Technical field
The present invention relates to a kind of WCDMA base stations electromagnetic radiation Forecasting Methodologies.
Background technology
Electromagnetic radiation value around base station is to change with telephone traffic and with the variation of base station distance distance, and base station week It encloses the variation of electromagnetic radiation value and the size of base station call amount and has certain regularity with the distance of base station, distance can be established and become Change coefficient and telephone traffic variation coefficient to be predicted, but disclosed document and patent at present, be not based on this rule and provide To electromagnetic radiation value Forecasting Methodology around the electricity of base station, for assessing the electromagnetic radiation value of base station different distance point.
For the deficiencies in the prior art, this patent by combine base station call amount and future position and base station away from From establishing a WCDMA base stations electromagnetic radiation prediction model based on telephone traffic and with base station different distance point, employ traffic Measure variation coefficient and distance change coefficient, then by WCDMA base stations history traffic data and with the distance of base station difference position with And the history electromagnetic radiation value corresponding to these point positions is trained prediction model, determines variation coefficient and the school of prediction model Positive parameter, then the trained prediction model of distance input by the base station call amount and future position and base station of prediction period are right The electromagnetic radiation value of WCDMA base stations different distance point is predicted.It is shown experimentally that, the prediction model that this patent proposes can be compared with Electromagnetic radiation value that is fast and accurately predicting WCDMA base stations different distance point.
Invention content
In order to solve the above technical problem, the present invention provides a kind of WCDMA base stations electromagnetic radiation Forecasting Methodologies.
The present invention solves above-mentioned technical problem, and the technical scheme comprises the following steps：
1) the distance base station electromagnetic radiation as input for, establishing WCDMA base station calls amount and future position and base station is predicted Model, prediction model are as follows:
Eij=b+cHj+kYi (1)
Wherein, EijIn telephone traffic it is H for WCDMA base stationsjIt is Y with base station distanceiThe electromagnetic radiation predicted value of point, unit are V/m, HjFor telephone traffic of the base station in the j periods, unit Erl, YiFor i points and the distance of base station, unit m, b join for correction Number, c are telephone traffic variation coefficient, and k is distance change coefficient；
2), by WCDMA base stations history traffic data and with base station difference position distance and these point positions corresponding to History electromagnetic radiation value, the prediction model in step 1 is trained, passes through the determining prediction model correction parameter b of training, words The value of business amount variation coefficient c, distance change coefficient k；
3) telephone traffic of WCDMA base stations prediction period and the distance of future position and base station, are substituted into the determining prediction of step 2 Model predicts the electromagnetic radiation value of WCDMA base stations different distance point.
Above-mentioned a kind of WCDMA base stations electromagnetic radiation Forecasting Methodology, in the step 2), by WCDMA base stations history traffic Measure data and with the history electromagnetic radiation value corresponding to the distance of base station difference position and these point positions to the prediction in step 1 Model is trained, and the training mode of model is as follows：
Wherein, prediction model is trained by historical data in the training process, by the historical data m periods WCDMA base station call amounts Hm, the distance Y with base station difference positionl, l=1,2 ..., n input training pattern, take different ginsengs Array b, c, k obtain different trained valuesCalculate trained valuesWith electromagnetic radiation history value ElmError, error Calculation is：
As ε ＜ 0.01n, with regard to deconditioning, prediction model correction parameter b, telephone traffic variation coefficient c, distance change are determined The value of coefficient k.
The beneficial effects of the present invention are：By combining base station call amount and the distance of future position and base station, one is established WCDMA base stations electromagnetic radiation prediction model based on telephone traffic and with base station different distance point, distance change coefficient and telephone traffic Variation coefficient predicted, the Forecasting Methodology that this patent proposes can predict quickly and accurately WCDMA base stations difference away from Electromagnetic radiation value from point, this method have base station construction and environmental protection larger reference value, have certain society's effect Benefit.
Specific embodiment
The present embodiment is carried out lower premised on the content of present invention, gives detailed implementation steps, but the guarantor of the present invention Shield range is not limited to following embodiments.
This sample plot implemented is in spacious flat region, and objective for implementation is school teaching building roof base station, laboratory apparatus For spectrum analyzer AT6030D, measurement object is WCDMA base stations.
With reference to specific embodiment, the present invention is described further, the specific steps are：
Step 1：Establish the distance base station electromagnetic radiation as input of WCDMA base station calls amount and future position and base station Prediction model, prediction model are as follows:
Eij=b+cHj+kYi (4)
Wherein, EijIn telephone traffic it is H for WCDMA base stationsjIt is Yi with base station distanceiThe electromagnetic radiation predicted value of point, unit For v/m, HjFor telephone traffic of the base station in the j periods, unit Erl, YiFor i points and the distance of base station, unit m, b are correction Parameter, c are telephone traffic variation coefficient, and k is distance change coefficient.
Step 2：By WCDMA base stations history traffic data and with base station difference position distance and these point position institutes Corresponding history electromagnetic radiation value, is trained the prediction model in step 1, passes through the determining prediction model correction parameter of training B, telephone traffic variation coefficient c, distance change coefficient k value.
In this embodiment, by WCDMA base stations the m periods telephone traffic and with base station distance be 10m, 15m, The point of 20m ..., 55m distances and its corresponding history electromagnetic radiation value are trained prediction model, the training mould of model Formula is as follows：
Wherein, prediction model is trained by historical data in the training process, by the historical data m periods WCDMA base station call amounts Hm=6.316, the distance Y with base station difference positionl, l=1,2 ..., 10, wherein Y1=10m, Y2= 15m, Y3=20m ..., Y10=55m inputs training pattern, takes different parameter group b, c, k, obtain different trained valuesCalculate trained valuesWith electromagnetic radiation history value ElmError, wherein E1m=1.222, E2m=1.211 ..., E10m=0.854, unit v/m, error calculation mode are：
As ε ＜ 0.01 × 10, i.e., when ε ＜ 0.1 are with regard to deconditioning, determine prediction model parameters, correction parameter b=0.356, Telephone traffic variation coefficient c=0.1476, distance change coefficient k=- 0.006583.
Step 3：The telephone traffic of WCDMA base stations prediction period and the distance of future position and base station are substituted into what step 2 determined Prediction model predicts the electromagnetic radiation value of WCDMA base stations different distance point.
In this embodiment, by base station call amount Hj=7.216, unit Erl and the distance Y of base station differencei, Middle YiValue be 10,20,30 ..., 60, unit m input trained prediction model and are predicted, as follows：
Eij=0.356+0.1476Hj-0.006583Yi (7)
By input value H when predictingjAnd YiTrained prediction model formula (7) is substituted into, wherein predicted value and measured value is as follows Shown in table：
1 predicted value E of tableijWith the displaying of measured value
It as can be seen that can be compared with by base station call amount and future position and the distance input prediction model of base station from prediction result The electromagnetic radiation value by WCDMA base stations different distance point soon is predicted, while can be seen that WCDMA from experimental result The electromagnetic radiation predicted value and measured value of base station difference position are all more close, illustrates to utilize the method can quickly and accurately The electromagnetic radiation value of WCDMA base stations different distance point predicted, while experiment show method used herein Validity.

Claims (2)

1. a kind of WCDMA base stations electromagnetic radiation Forecasting Methodology, which is characterized in that include the following steps：
1) the distance electromagnetic radiation prediction model in base station as input of WCDMA base station calls amount and future position and base station, is established, Prediction model is as follows:
Eij=b+cHj+kYi (1)
Wherein, EijIn telephone traffic it is H for WCDMA base stationsjIt is Y with base station distanceiThe electromagnetic radiation predicted value of point, unit v/m, HjFor telephone traffic of the base station in the j periods, unit Erl, YiFor i points and the distance of base station, unit m, b are correction parameter, c For telephone traffic variation coefficient, k is distance change coefficient；
2), by WCDMA base stations history traffic data and with base station difference position distance and these point positions corresponding to going through History electromagnetic radiation value, is trained the prediction model in step 1, passes through the determining prediction model correction parameter b of training, telephone traffic The value of variation coefficient c, distance change coefficient k；
3) telephone traffic of WCDMA base stations prediction period and the distance of future position and base station, are substituted into the determining prediction mould of step 2 Type predicts the electromagnetic radiation value of WCDMA base stations different distance point.
2. a kind of WCDMA base stations electromagnetic radiation Forecasting Methodology as described in claim 1, in the step 2), which is characterized in that History electromagnetism spoke by WCDMA base stations history traffic data and corresponding to the distance of base station difference position and these point positions It penetrates value to be trained the prediction model in step 1, the training mode of model is as follows：
Wherein, prediction model is trained by historical data in the training process, by the WCDMA of historical data m periods Base station call amount Hm, the distance Y with base station difference positionl, l=1,2 ..., n, input training pattern, take different parameter group b, C, k obtain different trained valuesCalculate trained valuesWith electromagnetic radiation history value ElmError, error calculation side Formula is：
As ε ＜ 0.01n, with regard to deconditioning, prediction model correction parameter b, telephone traffic variation coefficient c, distance change coefficient k are determined Value.
CN201810104417.0A 2018-02-02 2018-02-02 WCDMA base station electromagnetic radiation prediction method Active CN108199792B (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111065109A (en) * 2020-01-16 2020-04-24 湘潭大学 Method for predicting electromagnetic radiation of base station of heterogeneous cellular network in rural area
CN115243271A (en) * 2022-07-14 2022-10-25 中国联合网络通信集团有限公司 Radiation evaluation method, device and storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103440428A (en) * 2013-09-12 2013-12-11 重庆大学 Method for determining self-adaption dynamic weight of combined prediction model for wind electricity power
CN103874090A (en) * 2014-03-31 2014-06-18 湘潭大学 GSM communication base station electromagnetic radiation prediction method
CN105184421A (en) * 2015-09-28 2015-12-23 南方电网科学研究院有限责任公司 Electromagnetic environment parameter prediction method based on data segmentation and model calibration

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103440428A (en) * 2013-09-12 2013-12-11 重庆大学 Method for determining self-adaption dynamic weight of combined prediction model for wind electricity power
CN103874090A (en) * 2014-03-31 2014-06-18 湘潭大学 GSM communication base station electromagnetic radiation prediction method
CN105184421A (en) * 2015-09-28 2015-12-23 南方电网科学研究院有限责任公司 Electromagnetic environment parameter prediction method based on data segmentation and model calibration

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
何晴晴: "移动通信基站电磁辐射预测方法研究", 《湘潭大学硕士学位论文》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111065109A (en) * 2020-01-16 2020-04-24 湘潭大学 Method for predicting electromagnetic radiation of base station of heterogeneous cellular network in rural area
CN111065109B (en) * 2020-01-16 2023-08-22 湘潭大学 Rural area heterogeneous cellular network base station electromagnetic radiation prediction method
CN115243271A (en) * 2022-07-14 2022-10-25 中国联合网络通信集团有限公司 Radiation evaluation method, device and storage medium
CN115243271B (en) * 2022-07-14 2023-09-05 中国联合网络通信集团有限公司 Radiation evaluation method, device and storage medium

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