CN107750051B - Optimization method and device of wireless propagation model - Google Patents

Optimization method and device of wireless propagation model Download PDF

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Publication number
CN107750051B
CN107750051B CN201710912056.8A CN201710912056A CN107750051B CN 107750051 B CN107750051 B CN 107750051B CN 201710912056 A CN201710912056 A CN 201710912056A CN 107750051 B CN107750051 B CN 107750051B
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data
wireless propagation
block
value
target
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CN107750051A (en
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张添程
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Chongqing 9ebang Technology Co ltd
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Chongqing 9ebang Technology Co ltd
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W24/00Supervisory, monitoring or testing arrangements
    • H04W24/02Arrangements for optimising operational condition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W24/00Supervisory, monitoring or testing arrangements
    • H04W24/06Testing, supervising or monitoring using simulated traffic
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W28/00Network traffic or resource management
    • H04W28/02Traffic management, e.g. flow control or congestion control
    • H04W28/06Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information

Abstract

The embodiment of the invention discloses an optimization method and a device of a wireless propagation model, wherein the method comprises the following steps: acquiring a plane map according to GIS map data; carrying out area division and convergence processing on the plane map to obtain a plurality of blocks; establishing a corresponding relation between the MR data and the position of the user terminal to obtain target data, and attributing the base station user and the position of the user terminal to a block; if the data volume in the block reaches a preset data threshold value, performing regression fitting on each block by using a big data technology and target data based on a general wireless propagation model to obtain a plurality of block wireless propagation models; and carrying out test verification and iterative optimization on the wireless propagation models of the blocks. By implementing the embodiment of the invention, manual participation is not needed, the fitting precision is high, the model optimization effect is improved, and the efficiency and the time are saved.

Description

Optimization method and device of wireless propagation model
Technical Field
The invention relates to the technical field of mobile communication, in particular to a method and a device for optimizing a wireless propagation model.
Background
The wireless propagation model has important value for planning and optimizing a mobile communication network, including base station site selection, base station parameter configuration, accurate user positioning and the like. At present, the number of known wireless propagation models of various typical scenes is as many as ten, each model has a definite theoretical application range (scene), and parameters/coefficients of the model are clearly defined. However, since the actual use scene is absolutely not a theoretical typical scene, the influences of polymorphic interweaving and complex diversity of the actual scene exist, and the theoretical models can hardly achieve the theoretical expected effect in the actual application. This can lead to unreasonable site planning for mobile base stations or cross-dimensional base stations requiring secondary, tertiary or even multiple optimizations, or inaccurate user positioning based on radio base stations.
Further, the typical wireless propagation model has the characteristics of simplicity, time saving and the like. However, because there are many factors affecting the electromagnetic wave in the wireless propagation process, even if a model of a certain typical scene is applied, the individual difference of other scenes is large, and especially when the actual environment is complex, a typical wireless propagation model of any single scene is difficult to be directly applied, so the error of the typical wireless propagation model is large. Based on this, a typical wireless propagation model is generally optimized when it is practically applied. Specifically, the existing optimization methods are generally: firstly, a large amount of manually measured sampling data (such as altitude, electromagnetic interference, weather interference and the like) are used, and then the typical wireless propagation model is optimized through the sampling data. The optimization method is time-consuming and labor-consuming, and the model optimization effect is limited due to limited sampling points.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for optimizing a wireless propagation model, so that the model optimization effect is improved, and the method and the device are efficient and time-saving.
In order to achieve the above object, in a first aspect, an embodiment of the present invention provides a method for optimizing a wireless propagation model, including the following steps:
acquiring a plane map according to GIS map data;
carrying out area division and convergence processing on the plane map to obtain a plurality of blocks;
establishing a corresponding relation between MR data and the position of a user terminal to obtain target data, and attributing the position of a base station and the position of the user terminal to the block;
if the data volume in the blocks reaches a preset data threshold value, performing regression fitting on each block by using a big data technology and the target data based on a general wireless propagation model to obtain a plurality of block wireless propagation models;
and carrying out test verification and iterative optimization on the plurality of block wireless propagation models.
As an optional implementation, the performing area division and aggregation processing on the planar map to obtain a plurality of blocks specifically includes:
carrying out region division on the plane map according to basic landform characteristics;
and carrying out convergence processing on the plane map after the area division according to the related landform characteristics, and dividing the same type area into a plurality of blocks according to the distribution characteristics.
As an optional implementation manner, the establishing of the correspondence between the MR data and the location of the user terminal specifically includes:
acquiring MR data, signaling data and a base station position;
associating the MR data with the signaling data through base station codes, MME identifications, UE identifications and sampling time to obtain a user IMEI;
acquiring user OTT data, and analyzing the user OTT data to obtain the position of a user terminal;
and establishing a corresponding relation between the MR data and the position of the user terminal through the IMEI of the user and the position of the user terminal.
As an alternative embodiment, performing regression fitting on each of the blocks by using big data technique and the target data to obtain a wireless block propagation model specifically includes:
for any block, calculating an initial predicted value and an actual value of any sampling point in a period T according to the target data;
calculating the deviation value of any sampling point according to the predicted value and the actual value;
calculating the total deviation value of all sampling points in each block according to the deviation values;
correcting each parameter in the universal wireless propagation model according to the total deviation value to obtain a target predicted value;
performing loop iteration calculation according to the target predicted value and the actual value to obtain a target parameter in the general wireless propagation model;
and substituting the target parameters into the general wireless propagation model to obtain a block wireless propagation model.
As an optional implementation manner, the performing test verification and iterative optimization on the block wireless propagation model specifically includes:
acquiring a plurality of new sampling points;
predicting any new sampling point through the block wireless propagation model to obtain a predicted value and an actual value of any new sampling point;
calculating the deviation value of the predicted value and the actual value of any new sampling point, and calculating the average error of the deviation values of a plurality of new sampling points;
and if the average error is larger than a preset error threshold value, performing iterative optimization on the block wireless propagation model.
In a second aspect, an embodiment of the present invention further provides an apparatus for optimizing a wireless propagation model, including:
the acquisition module is used for acquiring a plane map according to the GIS map data;
the first processing module is used for carrying out area division and convergence processing on the plane map to obtain a plurality of blocks;
the establishing module is used for establishing the corresponding relation between the MR data and the position of the user terminal to obtain target data;
the attribution module is used for attributing the position of the base station and the position of the user terminal to the block;
the second processing module is used for performing regression fitting on each block based on a general wireless propagation model and by using a big data technology and the target data to obtain a plurality of block wireless propagation models if the data amount in the block reaches a preset data threshold;
and the third processing module is used for carrying out test verification and iterative optimization on the plurality of block wireless propagation models.
As an optional implementation manner, the first processing module is specifically configured to:
carrying out region division on the plane map according to basic landform characteristics;
and carrying out convergence processing on the plane map after the area division according to the related landform characteristics, and dividing the same type area into a plurality of blocks according to the distribution characteristics.
As an optional implementation manner, the establishing module is specifically configured to:
acquiring MR data, signaling data and a base station position;
associating the MR data with the signaling data through base station codes, MME identifications, UE identifications and sampling time to obtain a user IMEI;
acquiring user OTT data, and analyzing the user OTT data to obtain the position of a user terminal;
and associating the IMEI with the OTT data to establish the corresponding relation between the MR data and the position of the user terminal.
As an optional implementation manner, the second processing module is specifically configured to:
for any block, calculating an initial predicted value and an actual value of any sampling point in a period T according to the target data;
calculating the deviation value of any sampling point according to the predicted value and the actual value;
calculating the total deviation value of all sampling points in each block according to the deviation values;
correcting each parameter in the universal wireless propagation model according to the total deviation value to obtain a target predicted value;
performing loop iteration calculation according to the target predicted value and the actual value to obtain a target parameter in the general wireless propagation model;
and substituting the target parameters into the general wireless propagation model to obtain a block wireless propagation model.
As an optional implementation manner, the third processing module is specifically configured to:
acquiring a plurality of new sampling points;
predicting any new sampling point through the block wireless propagation model to obtain a predicted value and an actual value of any new sampling point;
calculating the deviation value of the predicted value and the actual value of any new sampling point, and calculating the average error of the deviation values of a plurality of new sampling points;
and if the average error is larger than a preset error threshold value, performing iterative optimization on the block wireless propagation model.
According to the optimization method and device of the wireless propagation model, provided by the embodiment of the invention, a plurality of blocks are obtained according to GIS map data, the corresponding relation between MR data and the position of a user terminal is established, then regression fitting is carried out on the blocks by adopting a big data technology based on a general wireless propagation model to obtain the wireless propagation model of the blocks, and finally test verification and iterative optimization are carried out on the wireless propagation model of the blocks.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a schematic flow chart of an optimization method of a wireless propagation model provided by a first embodiment of the present invention;
FIG. 2 is a sub-flowchart of step S103 of FIG. 1;
FIG. 3 is a sub-flowchart of step S104 in FIG. 1;
fig. 4 is a schematic structural diagram of an optimization apparatus for a wireless propagation model according to a first embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Referring to fig. 1, a flowchart of a method for optimizing a wireless propagation model according to a first embodiment of the present invention is shown, where the method includes the following steps:
and S101, acquiring a plane map according to the GIS map data.
Taking the Chongqing as an example, a planar map of the Chongqing can be obtained according to Geographic Information System (GIS) map data.
S102, carrying out area division and convergence processing on the plane map to obtain a plurality of blocks.
Specifically, the planar map is firstly subjected to region division according to basic geomorphic features. The basic landform features include plains, mountains, rivers, high-rise building areas (above 6 floors), medium-low building areas (below 6 floors), roads and the like. And then, carrying out convergence processing on the plane map after the area division according to the related landform characteristics, dividing the similar area into a plurality of convex polygonal blocks according to the distribution characteristics, and further coding each block. Wherein, the high building area and the medium and low building area can be defined as related topographic features.
For example, the Chongqing planar map is divided into areas according to the basic landform features to form an area map, wherein the area map comprises an area A, an area B, an area C, an area D, an area E and the like. And then, carrying out convergence processing on the area graphs according to the related topographic features to obtain similar areas, wherein the areas A and C belong to a similar area 1, and the areas B and D belong to a similar area 2, and dividing the similar areas into a plurality of convex polygon blocks according to the distribution features, for example, forming the similar areas 1 and the similar areas 2 into the convex polygon blocks 1. Understandably, according to the above method, a plurality of convex polygonal blocks can be formed on the plane map, and each block is encoded, for example, the coding of the Chongqing block is 023.
S103, establishing a corresponding relation between the MR data and the position of the user terminal to obtain target data, and attributing the position of the base station and the position of the user terminal to a block.
Further, as shown in fig. 2, step S103 specifically includes:
s1031, obtaining MR data, signaling data and base station positions;
specifically, MR (Measurement Report) data is acquired from a base station, signaling data is acquired from a Mobility Management Entity (MME), and base station engineering parameters including a base station location are acquired from a database;
s1032, the MR data is associated with the signaling data through the base station code, the MME identifier, the UE identifier and the sampling time to obtain the IMEI of the user;
specifically, the MR data is associated with the signaling data through a base station code (eci), an MME identifier, a UE (user equipment) identifier, and a sampling time to obtain an International Mobile Equipment Identity (IMEI) of the user;
s1033, obtaining OTT data of the user, and analyzing the OTT data of the user to obtain the position of the user terminal;
the OTT data is Over The Top, which means that various application services are provided for users through The internet, and The map APP is one of The application services. When a user uses a map APP and the like, the gateway acquires OTT data of the user and analyzes the OTT data of the user to obtain the position of a user terminal;
s1034, establishing the corresponding relation between the MR data and the user terminal position through the user IMEI user terminal position.
It should be noted that after the corresponding relationship between the MR data and the position of the user terminal is established, target data can be obtained, and the target data is used for subsequent model parameter fitting, and the specific process will be described in detail in the subsequent content and will not be described herein again. Furthermore, the corresponding relation between the MR data and the position of the user terminal is established, and the base station position and the position of the user terminal are attributed to the divided blocks.
And S104, if the data volume in the blocks reaches a preset data threshold, performing regression fitting on each block based on the general wireless propagation model by using a big data technology and the regression fitting parameters to obtain a plurality of block wireless propagation models.
S1041, for any block, calculating an initial predicted value and an actual value of any sampling point in the period T according to the regression fitting parameters;
s1042, calculating the deviation value of any sampling point according to the predicted value and the actual value;
s1043, calculating a total deviation value of all sampling points in each block according to the deviation value;
s1044, correcting each parameter in the general wireless propagation model according to the total deviation value to obtain a target predicted value;
s1045, performing loop iteration calculation according to the target predicted value and the actual value to obtain a target parameter in the general wireless propagation model;
and S1046, substituting the target parameters into the general wireless propagation model to obtain a block wireless propagation model.
To better explain steps S1041 to S1046, the following description is made:
as users use mobile networks, the amount of data within each block is increasing. For a single block, when the data amount reaches a predetermined threshold (10 thousands), a regression fitting may be performed on each block based on the common wireless propagation model using the big data technique and the regression fitting parameters to obtain a plurality of block wireless propagation models.
It should be noted that, for the blocks, due to the people stream density, the data accumulation speed of some blocks is fast, the data accumulation speed of some blocks is slow, and it takes about one month for the Chongqing blocks to reach 10 ten thousand of data as a whole, where the slowest time is the block with the minimum people stream density, and about one month is needed for accumulating 10 ten thousand of records. In practice, the blocks may be classified by the administrative areas to which the blocks belong, because the administrative areas may represent the density of people streams to some extent, for example, the number of main urban areas is greater than that of suburban areas, so that the number of main urban areas is greater, the threshold value may be set to a larger value (e.g., 10 ten thousand), the number of suburban areas is less, and the threshold value is set to a smaller value (e.g., 5 ten thousand).
On the other hand, since the administrative area is too large, the internal environment is still too complex, and fitting is not facilitated, one administrative area often includes a plurality of GIS blocks.
The general wireless propagation model is as follows:
Lmodel=K1+K2*log(d)+K3*log(HTexff)+K4*DiffractionLoss+K5*log(d)*
log(HTexff)+K6*HRxeff+Kclutter*f(clutter)
the parameters involved in the model are: k1Constant, in dB; k2A constant value; d, the distance from the receiver to the transmitter (the relative location distance of the user and the base station) is measured in meters; k3A constant value; hTexffThe equivalent height of the transmitting antenna (equivalent height of the base station antenna) is in meters; k4A constant value; diffraction loss, in dB; k5A constant value; k6A constant value; hRxeffThe equivalent height of the receiving antenna (the equivalent height of the terminal antenna) is in meters; kclutterA constant factor; f (clutter), the average loss of terrain is related to the landform. The above parameters in the model can be manually set to the following initial values: k1:160.93;K2:44.90;K3:-13.82;K4:0.20;K5:-6.55;K6:0.00;Kcluter:1.00。
For any block, the following prediction function is used:
Lmodel=F(d,HTexff,Diffraction Loss,HRxeff,f(clutter))
(1) for any one block, an unequal number n (n) may be obtained in each calculation period T>=105) The corresponding relation between the position of the sampling point and the evaluation loss is obtained by substituting the relevant parameters into the prediction function to obtain the initial prediction value L of each sampling point ipre=F(di,Htxeffi,Diffraction Lossi,HRexffiF (clutter) i) and the actual value Ltrue(ii) a Wherein, the related parameters refer to d and HTexff、Diffraction loss、HRxeffAnd f (clutter), i.e., the target data obtained in step S103. The relevant parameters are obtained after the correlation of the MR data and the position of the user terminal.
(2) Calculating the deviation value d of any sampling pointL=Ltrue–Lpre+ lambda; λ is a regular term that prevents overfitting.
(3) Calculating the total deviation ∑ d for all the sampling points in each block by the method of steps (1) and (2)L=∑(wt*dL)/∑wtWherein, according to the time distance current time (model training time) of each sampling point, each sampling point is given different weight wi
It should be noted that, a time period T (T is generally 3 months) is preset, the weight of the sampling point between 0.5 times T and T is 1, the weight of the sampling point between 0.5 times T and T is increased with decreasing time, the weight of the sampling point between T and 2 times T is decreased with increasing time, and the sampling point above 2 times T does not participate in calculation when the number of available sampling points is sufficient.
(4) The total deviation value is a function H (K) of the parameters in the generic radio propagation model1,K2,K3,K4,K5,K6,Kclutter) The function H is the partial derivative for each parameter.
(5) For each parameter, correction Ki 'Ki + dH' Ki dki is performed by partial derivation.
(6) For each parameter corrected last time, a new predicted value (i.e., a target predicted value) can be obtained.
Repeating the steps (1) to (6) to obtain the optimal parameter K1、K2、K3、K4、K5、K6And KclutterAnd obtaining a wireless propagation formula of the block, namely performing loop iterative computation according to the target predicted value and the actual value to obtain a target parameter in the general wireless propagation model, and substituting the target parameter into the general wireless propagation model to obtain the block wireless propagation model.
It should be noted that when steps (1) to (6) are repeatedly executed, there are two end conditions, i.e., the number of cycles is greater than the preset number N, and the average deviation ∑ dLAnd n is less than a preset value and is the number of sampling points.
It should be noted that the process described in step S104 may be regarded as a model training. Parameter K in general wireless propagation model during first model training1、K2、K3、K4、K5、K6And KcluterThe initial values of the parameters are set artificially, and at the next model training, the initial values of the parameters are the result of the previous model training.
And S105, testing, verifying and iteratively optimizing the wireless propagation models of the blocks.
Step S105 includes two processes of test validation and iterative optimization. Wherein, the test verification process is as follows:
because the system automatically acquires data, a plurality of new sampling points are obtained by utilizing the time sampling points obtained after the last model training. Then, any new sampling point is predicted through a block wireless propagation model formula to obtain a predicted value and an actual value of any new sampling point, a deviation value of the predicted value and the actual value of any new sampling point is calculated, and further, an average error of the deviation values of the plurality of new sampling points is calculated. According to the average error and the number of new sampling points, the accuracy of the block wireless propagation model can be represented.
The iterative optimization process is as follows:
and when the average error of the test verification is larger than the preset error threshold value, restarting the model training program, and retraining the model.
According to the optimization method of the wireless propagation model, provided by the embodiment of the invention, a plurality of blocks are obtained according to GIS map data, the corresponding relation between the MR data and the position of the user terminal is established, then regression fitting is carried out on the blocks by adopting a big data technology based on a general wireless propagation model to obtain the wireless propagation model of the blocks, and finally test verification and iterative optimization are carried out on the wireless propagation model of the blocks.
Referring to fig. 4, a schematic structural diagram of an optimization apparatus for a wireless propagation model according to a first embodiment of the present invention is shown, where the optimization apparatus includes:
the acquisition module 10 is used for acquiring a planar map according to the GIS map data;
the first processing module 11 is configured to perform area division and convergence processing on the planar map to obtain a plurality of blocks;
the establishing module 12 is configured to establish a corresponding relationship between the MR data and the position of the user terminal to obtain target data;
an attribution module 13, configured to attributing a base station location and a user terminal location to a block;
a second processing module 14, configured to perform regression fitting on each block based on the general wireless propagation model and by using a big data technology and target data to obtain a plurality of block wireless propagation models if the data amount in the block reaches a preset threshold;
and the third processing module 15 is configured to perform test verification and iterative optimization on the multiple block wireless propagation models.
Further, the first processing module 11 is specifically configured to:
carrying out region division on the plane map according to the basic landform characteristics;
and carrying out convergence processing on the plane map after the area division according to the related landform characteristics, and dividing the same type area into a plurality of blocks according to the distribution characteristics.
Further, the establishing module 12 is specifically configured to:
acquiring MR data, signaling data and a base station position;
the MR data is associated with the signaling data through base station codes, MME identifications, UE identifications and sampling time to obtain a user IMEI;
acquiring user OTT data, and analyzing the user OTT data to obtain the position of a user terminal;
and establishing a corresponding relation between the MR data and the position of the user terminal through the IMEI of the user and the position of the user terminal.
Further, the second processing module 14 is specifically configured to:
for any block, calculating an initial predicted value and an actual value of any sampling point in a period T according to target data;
calculating the deviation value of any sampling point according to the predicted value and the actual value;
calculating the total deviation value of all sampling points in each block according to the deviation value;
correcting each parameter in the universal wireless propagation model according to the total deviation value to obtain a target predicted value;
performing loop iteration calculation according to the target predicted value and the actual value to obtain a target parameter in the general wireless propagation model;
and substituting the target parameters into the general wireless propagation model to obtain a block wireless propagation model.
Further, the third processing module 15 is specifically configured to:
acquiring a plurality of new sampling points;
predicting any new sampling point through a block wireless propagation model to obtain a predicted value and an actual value of any new sampling point;
calculating the deviation value of the predicted value and the actual value of any new sampling point, and calculating the average error of the deviation values of a plurality of new sampling points;
and if the average error is larger than a preset threshold value, performing iterative optimization on the block wireless propagation model.
According to the optimization device of the wireless propagation model, provided by the embodiment of the invention, a plurality of blocks are obtained according to GIS map data, the corresponding relation between the MR data and the position of the user terminal is established, then regression fitting is carried out on the blocks by adopting a big data technology based on a general wireless propagation model to obtain the wireless propagation model of the blocks, and finally test verification and iterative optimization are carried out on the wireless propagation model of the blocks.
It should be noted that, in this embodiment, please refer to the description of the method portion for the specific work flow of the optimization apparatus for a wireless propagation model, which is not described herein again.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.

Claims (6)

1. A method for optimizing a wireless propagation model is characterized by comprising the following steps:
acquiring a plane map according to GIS map data;
carrying out area division and convergence processing on the plane map to obtain a plurality of blocks;
establishing a corresponding relation between MR data and the position of a user terminal to obtain target data, and attributing the position of a base station and the position of the user terminal to the block;
if the data volume in the blocks reaches a preset data threshold value, performing regression fitting on each block by using a big data technology and the target data based on a general wireless propagation model to obtain a plurality of block wireless propagation models;
testing, verifying and iteratively optimizing the wireless propagation models of the blocks;
the establishing of the corresponding relationship between the MR data and the position of the user terminal specifically includes:
acquiring MR data, signaling data and a base station position; associating the MR data with the signaling data through base station codes, MME identifications, UE identifications and sampling time to obtain a user IMEI;
acquiring user OTT data, and analyzing the user OTT data to obtain the position of a user terminal;
establishing a corresponding relation between MR data and the position of the user terminal through the IMEI of the user and the position of the user terminal;
performing regression fitting on each block by using a big data technique and the target data to obtain a block wireless propagation model specifically comprises:
for any block, calculating an initial predicted value and an actual value of any sampling point in a period T according to the target data;
calculating the deviation value of any sampling point according to the predicted value and the actual value;
calculating the total deviation value of all sampling points in each block according to the deviation values;
correcting each parameter in the universal wireless propagation model according to the total deviation value to obtain a target predicted value;
performing loop iteration calculation according to the target predicted value and the actual value to obtain a target parameter in the general wireless propagation model;
and substituting the target parameters into the general wireless propagation model to obtain a block wireless propagation model.
2. The method of claim 1, wherein the area division and aggregation processing of the planar map to obtain a plurality of blocks specifically comprises:
carrying out region division on the plane map according to basic landform characteristics;
and carrying out convergence processing on the plane map after the area division according to the related landform characteristics, and dividing the same type area into a plurality of blocks according to the distribution characteristics.
3. The method of claim 2, wherein the performing test validation and iterative optimization on the block model specifically comprises:
acquiring a plurality of new sampling points;
predicting any new sampling point through the block wireless propagation model to obtain a predicted value and an actual value of any new sampling point;
calculating the deviation value of the predicted value and the actual value of any new sampling point, and calculating the average error of the deviation values of a plurality of new sampling points;
and if the average error is larger than a preset error threshold value, performing iterative optimization on the block wireless propagation model.
4. An apparatus for optimizing a wireless propagation model, comprising:
the acquisition module is used for acquiring a plane map according to the GIS map data;
the first processing module is used for carrying out area division and convergence processing on the plane map to obtain a plurality of blocks;
the establishing module is used for establishing the corresponding relation between the MR data and the position of the user terminal to obtain target data;
the attribution module is used for attributing the position of the base station and the position of the user terminal to the block;
the second processing module is used for performing regression fitting on each block based on a general wireless propagation model and by using a big data technology and the target data to obtain a plurality of block wireless propagation models if the data amount in the block reaches a preset data threshold;
the third processing module is used for carrying out test verification and iterative optimization on the plurality of block wireless propagation models;
the establishing module is specifically configured to:
acquiring MR data, signaling data and a base station position; associating the MR data with the signaling data through base station codes, MME identifications, UE identifications and sampling time to obtain a user IMEI;
acquiring user OTT data, and analyzing the user OTT data to obtain the position of a user terminal;
establishing a corresponding relation between MR data and the position of the user terminal through the IMEI of the user and the position of the user terminal;
the second processing module is specifically configured to:
for any block, calculating an initial predicted value and an actual value of any sampling point in a period T according to the target data;
calculating the deviation value of any sampling point according to the predicted value and the actual value;
calculating the total deviation value of all sampling points in each block according to the deviation values;
correcting each parameter in the universal wireless propagation model according to the total deviation value to obtain a target predicted value;
performing loop iteration calculation according to the target predicted value and the actual value to obtain a target parameter in the general wireless propagation model;
and substituting the target parameters into the general wireless propagation model to obtain a block wireless propagation model.
5. The apparatus for optimizing a wireless propagation model of claim 4, wherein the first processing module is specifically configured to:
carrying out region division on the plane map according to basic landform characteristics;
and carrying out convergence processing on the plane map after the area division according to the related landform characteristics, and dividing the same type area into a plurality of blocks according to the distribution characteristics.
6. The apparatus for optimizing a wireless propagation model of claim 5, wherein the third processing module is specifically configured to:
acquiring a plurality of new sampling points;
predicting any new sampling point through the block wireless propagation model to obtain a predicted value and an actual value of any new sampling point;
calculating the deviation value of the predicted value and the actual value of any new sampling point, and calculating the average error of the deviation values of a plurality of new sampling points;
and if the average error is larger than a preset error threshold value, performing iterative optimization on the block wireless propagation model.
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