CN106231621A - A kind of many scene adaptives optimization method of propagation model in FDD LTE system - Google Patents

Abstract

The invention relates to a multi-scenario adaptive propagation model optimization method based on an FDD-LTE wireless network, which uses an optimized and corrected propagation model to predict field strength, so that the predicted signal can be closer to the actual measurement signal. The present invention relates to the correction of these two propagation models, COST231-Hata and SPM. Considering the multi-scenario of the propagation environment, the invention takes a single base station data as a group, takes each group of data as the research object, and finally forms a model based on each A library of corrected propagation models for base stations. An optimal corrected propagation is selected for each scene covered by the base station, thereby realizing multi-scenario adaptive adjustment of the propagation model. The present invention also links the propagation environments and propagation models of different scenarios through the propagation model library, which has a high guiding and reference value for subsequent network planning and network optimization, and improves the correction accuracy while also improving the application scope.

Description

A multi-scenario adaptive optimization method for propagation model in FDD-LTE system

technical field

The invention relates to the field of wireless communication networks, and in particular proposes a multi-scenario adaptive optimization method for a propagation model in an FDD-LTE system.

Background technique

In recent years, with the widespread use of LTE wireless 4G networks, the information society has entered the era of network-based big data. The rapid development and popularization of intelligent mobile terminal applications has promoted a substantial increase in global mobile data traffic. In the era of mobile big data, Massive data, data diversity and other characteristics bring opportunities and challenges to the development of wireless networks. Wireless FDD-LTE is one of the two major 4G standards in the world. It was developed earlier than TD-LTE, with more mature technology and more abundant terminals. It is now the most widely used wireless 4G network in the world. In contrast, FDD-LTE has the advantages of fast speed and suitable for wide area coverage.

With the increasing complexity of wireless networks and wireless propagation environments, the calibration of propagation models has become a key link in network planning, design and network optimization, which directly affects the performance of final network coverage, network capacity, and communication quality. Because radio waves are affected by the propagation environment during air propagation, there are multiple propagation modes and paths such as reflection, diffraction, scattering, and direct radiation, which will lead to multipath attenuation and signal delay of received signals. The wireless propagation model is mainly used to describe the propagation behavior of the radio signal between the transmitter and the receiver and the field strength of the received signal at various points around the base station and its change law, and the commonly used wireless propagation model is based on a large number of data measurements. Statistical empirical models obtained for radio waves in different frequency bands under different propagation environments. Therefore, the propagation characteristics of the propagation model are closely related to the propagation environment, so the matching between the propagation model and the propagation environment is particularly important. For this reason, the patent of the present invention proposes a multi-scenario adaptive optimization method for the propagation model in the FDD-LTE system .

At present, CW (Continuous Wave, continuous wave) test is mostly used in the propagation model correction method, and then the correction and optimization of the propagation model is carried out by analyzing and calculating the CW test data, and then network planning and design such as cell coverage prediction are carried out. The CW test is to obtain test data by building an omnidirectional transmitter before the network is built. It takes the correction of the wireless environment propagation model in a large area as the research unit, which makes the prediction of the wireless environment of a specific cell lack of pertinence. , which virtually increases the prediction error; at the same time, the wireless environment in a large area is complex, which will lead to an increase in the variable parameters that need to be considered when performing model correction, such as the distance from the test point to the transmitter, the frequency point, and the effectiveness of the transmitting antenna. height, the effective height of the receiving antenna, the type of propagation environment, etc.; moreover, the CW test does not treat mobile communication networks of different standards differently.

Contents of the invention

The present invention aims to overcome the above-mentioned defects in the prior art and provide a time-saving, labor-saving, economical and highly accurate multi-scenario adaptive optimization method for propagation models in FDD-LTE networks.

Concrete technical realization scheme of the present invention is as follows:

A kind of multi-scenario adaptive optimization method for propagation model in FDD-LTE system, it is characterized in that, the method comprises the following steps:

Step 1.1, by analyzing the distribution of base stations on the electronic map of the area to be tested, select the test site and the road test route;

The drive test route is selected according to the following conditions:

Condition 1: The test route includes base stations along the way;

Condition 2: The test route passes through at least two different electromagnetic propagation environments and covers all roads;

Condition 3: Avoid the road test route passing through the shadow area of high-rise buildings, and cover different distances and directions in the area to be tested;

Said site selection is based on the following criteria:

Condition 1: The selected site covers all types of ground objects in the area to be tested;

Condition 2: Add at least one overlapping area between each site;

Condition 3: The base station antenna height of the selected site is greater than 20 meters, the antenna is more than 5 meters higher than the nearest obstacle, and the building where the site is located should be higher than the average height of surrounding buildings;

Step 1.2, obtaining the engineering parameters of the site to be tested selected in step 1.1 mainly refers to the basic engineering parameters of the base station, including: base station number, base station latitude and longitude, base station antenna hanging height, azimuth, downtilt, and then download step 1.1 The drive test data obtained by measuring the drive test route;

Step 1.3, performing preprocessing, data screening and filtering on the drive test data measured based on the FDD-LTE network system;

Step 1.4, perform data discretization on the data processed in step 1.3; since the sampling rate of GPS is slower than that of the receiver, this makes multiple data arranged in time order on the same latitude and longitude, so these data need to be discretized Expand, assuming that the speed during the test is consistent, and the time interval between each data is also equal, then the data can be interpolated in time order, so that the data repeated at one point can be tiled in time order open;

Step 1.5, using the effective data processed by the above method, extract the base station signal strength of the main cell and the neighboring cell received at the test point, calculate the overall path loss of the test point at the FDD-LTE cell road test point, and use Adaptive optimization of the data propagation model;

Step 1.6. Complete the adaptive correction and optimization of the COST231-Hata and SPM propagation models for specific propagation environments, select a correction propagation model that is closer to the measured data according to the error mean and standard deviation, and apply the propagation model to various scenarios Documented to form applicable propagation model library classes.

In the above-mentioned method for the multi-scenario adaptive optimization of the propagation model in the FDD-LTE system, the drive test data in the step 1.2 includes the test time, the latitude and longitude of the test point, the cell number of the main serving base station, the adjacent area number, and the test frequency. point, the received signal strength of the primary serving base station cell and the received signal strength of neighboring cells.

In the above-mentioned method for multi-scenario adaptive optimization of the propagation model in the FDD-LTE system, the preprocessing described in step 1.3 is to filter out the latitude and longitude of the test point and the longitude and latitude of the main cell lost by the drive test device in the drive test process Information, signal reception strength of the main cell;

Data filtering mainly includes the filtering method based on the received signal level strength and the filtering method based on the distance between the test point and the main base station

Filtering condition 1: The filtering method based on the received signal level strength. This filtering condition is based on the received signal strength to filter data. In the actual drive test process, the received signal strength is too strong or too weak to affect the accuracy of data processing. large impact, which is not conducive to the optimization of the propagation model; for this reason, the level filtering threshold is usually set in the actual operation process;

Filtering condition 2: The data filtering method based on the distance between the test point and the main base station, the measurement point near the base station is affected by the black area under the base station tower, and the test point near the base station often has no direct path, LTE near the base station The coverage test point did not receive the signal level or the received signal level was too weak during the test, and the device could not be identified. Similarly, when the distance from the base station is far away, the base station signal passes through the reflection, scattering, and diffraction of the propagation environment to make the signal If the strength is too weak, the receiving device fails to receive effective information. Avoid test points near the base station and far away from the base station during path selection.

In the above-mentioned method for the multi-scenario adaptive optimization of the propagation model in the FDD-LTE system, in the step 1.5, the calculation method for the overall path loss of the FDD-LTE cell road measurement point is:

Step 4.1. When the drive test data shows that the test point only receives the signal of the main serving cell and does not receive the signal of the neighboring cell, the actual path loss of the test point is the transmit power of the base station minus the received signal strength of the main serving cell, that is, PL= P _BS -P _UE , where PL(PathLoss) is the path loss, P _BS is the transmit power of the base station, and P _UE is the received signal strength of the road test point;

Step 4.2. When the drive test data shows that there are two or three sectors receiving signals, eliminate the sectors corresponding to the empty longitude and latitude of the drive test point, and only calculate the remaining sectors. If there is only one sector left, follow the above steps 4.1 calculation;

Step 4.3. If the remaining sectors are two sectors, the path loss of the primary sector is PL ₀ =P _BS0 -P _UE0 , and the path loss of the other adjacent cell is PL ₁ =P _BS1 -P _UE1 ; in order to take into account the primary cell Improve the test accuracy with the information of neighboring cells, and calculate the received signal strength by weighting the signals of the main cell and neighboring cells; after many tests and experiments, it is finally found that the weight ratio of 7:3 can realize the optimization of the experimental results, Therefore, the path loss of the final test point after integrating the received signal of the neighboring cell is PL=0.7*PL ₀ +0.3*PL ₁ ;

Step 4.4. If there are three sectors remaining, refer to step 4.3 to calculate the path loss PL ₀ of the primary sector, the path loss PL ₁ of the second sector, and the path loss PL ₂ of the third sector. The calculation of the first sector is based on 0.2*PL ₁ , and for the third sector 0.1*PL ₂ , the path loss of the final test point after integrating the received signal of the neighboring cell is PL=0.7*PL ₀ +0.2*PL ₁ +0.1*PL ₂ ;

Step 4.5. If there are more than three remaining sectors, keep the main sector and the other two sectors with relatively strong signals, and calculate the path loss of the road test point according to the above step 4.4. Due to the complexity and diversity of the propagation environment, the receiving Signal strength is limited by many factors, so data with higher signal strength is more reliable.

In the above-mentioned method for the multi-scenario adaptive optimization of the propagation model in the FDD-LTE system, the optimization of the propagation model using the processed data in the step 1.5 mainly includes the following steps:

Step 5.1, based on the COST231-Hata propagation model, use the processed data, adopt the least squares fitting method, and verify that the standard deviation of the propagation model prediction data and the measured data after the propagation model is optimized is less than 8dB, and proceed to the next step;

Step 5.2, based on the SPM propagation model, using the initial value of the coefficients of the SPM model, the correction and optimization of the propagation model is performed, and in the process of data fitting, the standard deviation between the predicted data of the SPM model and the measured data is minimized with the setting of each coefficient, such as If it is less than 8dB, it will be included in the model library, otherwise end this step and proceed to the next step;

Step 5.3, according to steps 5.1 and 5.2, compare the optimized standard deviation and error mean of the above two propagation models, and select a correction result closest to the predicted data to adapt to the propagation environment of the scene; step 5.4, the output is adaptive to the scene The communication environment, and the communication environment and the optimization model applicable to the communication environment are entered into the communication model library class, and a communication model library class with practical, reference and auxiliary functions is established.

The present invention has the following advantages: the correction of the propagation model in the FDD-LTE wireless network can directly use the drive test data of the network, which avoids the cumbersome and inefficient CW test and is more targeted, and can realize different geographical propagation The self-adaptive adjustment of the propagation model of the environment enriches the classes of the propagation model library, providing reliable reference and assistance for subsequent network planning and optimization.

Description of drawings

Fig. 1 is a flowchart of the present invention;

Fig. 2 is a flow chart of the propagation model correction of the present invention.

detailed description

In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

The core idea of the present invention is to use the drive test data acquired by the existing actual mobile communication network, and use the multi-scenario of the propagation environment as matching objects to perform multi-scenario self-adaptive correction of the propagation model. Advantages of the present invention are as follows:

On the one hand, the drive test data is obtained based on the actual network, which not only can accurately reflect the actual wireless propagation channel to improve the accuracy of model correction, but also has high utilization value in the planning and design of the wireless network and system optimization. Provide reliable data basis and know the direction for the improvement of network communication quality, network capacity and network coverage;

Compared with the CW test, the drive test does not require a dedicated omnidirectional transmitter for measurement, and the overall implementation plan saves time and effort and is easy to implement, greatly reducing manpower and material resources;

When correcting the propagation model, the three propagation models are corrected at the same time, and finally the correction result of the propagation model that is closest to the actual measured data is selected according to the error mean and standard deviation between the predicted data and the measured data to match the current measurement scene, which realizes Multi-scenario adaptive correction, and at the same time establish a communication model library class that matches the specific communication environment.

Fig. 1 is a schematic flow sheet of the present invention, as shown in Fig. 1, this embodiment mainly comprises:

Step 1, select the appropriate site and road test route;

The test site conditions must be representative of typical base station conditions. The main conditions include the antenna height, the surrounding environment, and other conditions. The test route requires roads in all directions to be included. During the test, the positions of various distances should be covered, and the areas near various ground objects should be covered, so that the test data can be distributed as evenly as possible.

The principles of road test route selection are as follows:

①Include as many base stations as possible along the test route;

②Test routes try to pass through different electromagnetic propagation environments and cover as many roads as possible;

③Try to avoid the road test route passing through the shadow area of high-rise buildings, and ensure that different distances and different directions are covered in the area to be tested.

The principles of site selection are:

① The selected site should cover all types of ground objects in the area to be tested as much as possible;

② Try to increase the overlapping area between each site;

③ The base station antenna height of the selected site should be greater than 20 meters, the antenna should be more than 5 meters higher than the nearest obstacle, and the building where the site is located should be higher than the average height of surrounding buildings.

Step 2, obtaining FDD-LTE network engineering parameters and drive test data;

FDD-LTE network parameters mainly include the latitude and longitude of the test site, antenna height, transmission power, cell number of the test site, and antenna feeder configuration; drive test data mainly include test time, latitude and longitude of the test point, the cell number of the main serving base station, and neighboring area numbers , Test frequency point, received signal strength of main serving base station cell and neighboring cell received signal strength.

Step 3, preprocessing the drive test data;

Because radio waves are affected by various fadings in the air, and the sensitivity of the receiving signal of the drive test equipment is limited, some important information may be lost during the drive test. In addition, the multi-scene switching of radio waves in various propagation environments is also an important reason for information loss. The more common missing information is the longitude and latitude information of the main cell, the signal reception strength of the main cell, etc. Such invalid data should be filtered out.

Table 1 shows the preprocessed FDD-LTE drive test data information table.

Table 1 Data information table after preprocessing:

Road test information information description LogTime testing time Lon Longitude of test point Lat Latitude of test point ServerCellPCI Primary cell number (physical layer cell identifier) ServerCellRSRP Received signal strength of main cell NBCellPCI Neighboring cell number (physical layer cell identity) NBCellRSRP Neighboring cell received signal strength DL Frequency Test frequency

After filtering out the invalid data in the drive test data, divide the remaining valid data into different groups according to different base stations.

Step 4, check whether all invalid data has been filtered out.

In step 5, in order to improve the accuracy of the data, the drive test data needs to be further processed. The specific flow chart is shown in FIG. 2 .

Filtering and filtering of data:

Data filtering should start from two aspects: filtering based on the distance between the test point and the main base station; filtering based on the received signal strength.

Level-based data filtering, considering that too strong or too many received signals have a great impact on the accuracy of data processing, set the level filtering threshold for this (such as filtering out data points less than -110dBm and greater than -50dBm) .

Based on distance data filtering, the measurement point near the base station is affected by the black area under the base station tower, and the test point near the base station often has no direct path, and the coverage test point near the base station of LTE does not receive the signal signal during the test. If the received signal level is too weak, the device fails to identify it. Similarly, when the base station is far away, the base station signal is reflected, scattered, and diffracted by the propagation environment, making the signal strength too weak, resulting in the receiving device failing to receive effective signals. Therefore, try to avoid test points near the base station and far away from the base station when selecting the path. Considering factors such as base station coverage and propagation environment, the test distance is generally 150m to 3000m.

Discretization of data, because the sampling rate of GPS is slower than that of the receiver, which makes multiple data arranged in time order on the same latitude and longitude, for this reason, it is necessary to carry out discrete expansion of these data, assuming that the speed during the test is consistent, And the time interval between each data is also equal, so the data can be interpolated in time order, so that the data repeated at one point can be spread out in time order.

Weighted calculation of cell received signal strength:

When the drive test data shows that the test point only receives the signal of the main serving cell and does not receive the signal of the neighboring cell, the actual path loss of the test point is the transmit power of the base station minus the received signal strength of the main serving cell, that is, PL=P _BS - P _UE , where PL (PathLoss) is the path loss, P _BS is the transmit power of the base station, and P _UE is the received signal strength of the road test point;

When the drive test data shows that there are two or three sectors to receive signals, the sector corresponding to the empty latitude and longitude of the drive test point is eliminated, and only the remaining sectors are calculated. If there is only one sector left, follow the above steps [0038] calculate;

If the remaining sectors are two sectors, the path loss of the primary sector is PL ₀ =P _BS0 -P _UE0 , and the path loss of the other neighboring cell is PL ₁ =P _BS1 -P _UE1 . In order to take into account the information of the main cell and neighboring cells to improve the test accuracy, the method of weighting the signals of the main cell and neighboring cells is used to calculate the received signal strength. After several test experiments, it is finally concluded that the optimization of the experimental results can be realized when the weight ratio is 7:3, so the path loss after the weighted calculation of the final test point is PL=0.7*PL ₀ +0.3*PL ₁ ;

If there are three remaining sectors, refer to step [0040] to calculate the path loss PL ₀ of the main sector, the path loss PL ₁ of the second sector, and the path loss PL ₂ of the third sector. For the second sector with stronger signal The sector calculation is based on 0.2*PL ₁ , for the third sector 0.1*PL ₂ , the path loss after the weighted calculation of the last test point is PL=0.7*PL ₀ +0.2*PL ₁ +0.1*PL ₂ ;

If there are more than three remaining sectors, keep the main sector and the other two sectors with relatively strong signals, and calculate the path loss of the drive test point according to the above steps [0041]. Due to the complexity and diversity of the propagation environment, the received signal Strength is limited by many factors, so data with higher signal strengths are more reliable.

Step 6, performing adaptive correction on all propagation models under various scenarios.

This patent performs parameter correction based on COST231-Hata and SPM propagation model.

The COST231-Hata model formula is:

L _p (dB)＝46.3+33.9lgf-13.82lgh _b -α(h _m )+(44.9-6.55lgh _b )lgd+C _M

in,

f: signal carrier frequency (MHz) used by the base station;

h _m : mobile station antenna height (m);

h _b : base station antenna height (m);

d: distance (km) between base station and mobile station.

SPM propagation model formula:

L _p (dB)＝k ₁ +k ₂ lgd+k ₃ lgh _te +k ₄ lgh _re +k ₅ ×Diff+k ₆ lgd×lgh _te +C _clutter

k ₁ is the offset constant;

k ₂ is the distance attenuation factor, the default value is 44.9;

k ₃ is the effective height correlation factor of the base station transmitting antenna, the default value is 5.83, h _te is the effective height of the base station transmitting antenna;

k ₄ is the effective height correlation factor of the mobile station receiving antenna, the default value is 0, h _re is the effective height of the mobile station receiving antenna;

k ₅ is the correlation factor for diffraction calculation;

k ₆ is the correlation factor between the effective height of the transmitting antenna and the propagation distance, the default value is -6.55;

Diff is diffraction loss;

C _clutter is the terrain correction factor;

After the data used to correct the propagation model parameters is processed according to the aforementioned method, it is divided into different groups according to each base station, and the above-mentioned propagation model formula is observed. For the convenience of correction, the data is specially integrated into a data set:

Data={(x _ij ,y _ij ), i=1,2...,n; j=1,2...};

Among them, x _ij =log(d _ij ) is the logarithm of the distance between the receiver and the transmitter, d _ij is the actual distance obtained after coordinate conversion according to the latitude and longitude of the test point and the latitude and longitude of the main base station, and i is the drive test The number of data and the number of j base stations, so that each set of data constitutes a small data set, which is convenient for subsequent data processing.

Wherein, y _ij is the measured path loss under the condition of distance d _ij , which is the path loss PL calculated by weighting the received signal strength of the cell described in the above steps.

Let b=46.3+33.9lgf-13.82lgh _b -α(h _m )+C _M , a=44.9-6.55lgh _b , then the COST231-Hata model becomes:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; \&DoubleRightArrow;</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; .</span>$

In order to verify the correctness of the fitting results later, 30% of the data should be reserved before data fitting to verify the rationality of the fitting results, so only 70% of the overall data is used for model correction and fitting, because there are enough data , so don't worry about insufficient data, and it is more representative and convincing to use the same measurement data for the verification of the propagation model correction results.

A least squares fit is used to fit all data sets such that the sum of squared errors is minimized:

$\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

To judge whether the fitting degree of the corrected model and the measured data meets the requirements, the two indicators of error mean and standard deviation are generally used to measure.

$\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; error</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; ;</span>\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; =</span>\sqrt{\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; error</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}}$

At present, the industry generally believes that when the standard deviation is less than 8dB, it means that the calibration model is in line with the actual propagation environment, that is, the calibration result of the propagation model is accurate and can be used as the basis for network planning and network optimization. If this standard is not met, discard it. The group propagates the model correction results.

Similarly, for the SPM model calibration is a process of adjusting coefficient parameters, set k ₁ =-23.5, k ₂ =-44.9, k ₃ =5.83, k ₅ is set to 0.2 in the urban area, 0.4 in the suburbs, and 0.5 in the hills. k ₅ =0, k ₆ =-6.55, C _clutter =1, and use the drive test data to predict the field strength of the SPM propagation model.

Considering the correction efficiency and the influence of related parameters, only k ₁ , k ₂ , and C _clutter are corrected in the present invention.

According to the imported 70% drive test data, calculate the total error and mean square error in the whole process, and adjust k ₁ and C _clutter so that the overall total error and mean square error are 0;

Keep the above parameters unchanged, and adjust k ₂ up and down to minimize the mean square value.

After the above parameters are adjusted, the parameters are substituted into the SPM propagation model for prediction verification. Similarly, when the standard deviation between the corrected prediction result of the propagation model and the other 30% of the measured data is less than 8dB, the correction result is considered to be accurate. You can use It is used as the basis for network planning and network optimization. If this standard is not met, the correction results of this group of propagation models will be discarded.

Since the drive test data are used to correct the COST231-Hata and SPM propagation models respectively, the previous propagation model corrections are performed with each base station as the correction unit. Now it is necessary to select a model that is closer to the actual measurement by means of the error mean and standard deviation. The corrected propagation model of the data, in this way, the adaptive adjustment of the corrected propagation model is realized for the multi-coverage scenarios of different base stations.

To sum up, the present invention not only realizes the self-adaptive adjustment of the propagation model applicable to the multi-scenario propagation environment, but also links the propagation environment and the propagation model of different scenarios through the propagation model library class, so as to facilitate subsequent network planning and network optimization. It has a high guiding and reference value, and it not only improves the calibration accuracy, but also improves the application range.

The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which the present invention belongs can make various modifications or supplements to the described specific embodiments or adopt similar methods to replace them, but they will not deviate from the spirit of the present invention or go beyond the definition of the appended claims range.

Claims (5)

1. A multi-scenario adaptive optimization method for a propagation model in an FDD-LTE system is characterized by comprising the following steps:

step 1.1, selecting a test site and a drive test route by analyzing the distribution condition of base stations on an electronic map of a region to be tested;

the drive test route selection is based on the following conditions:

the first condition is as follows: the test route comprises base stations along the way;

and a second condition: the test route passes through at least two different electromagnetic propagation environments and covers all roads;

and (3) carrying out a third condition: the road test route is prevented from passing through a high-rise shadow area and covering in different distances and different directions in the area to be tested;

the site selection is based on the following conditions:

the first condition is as follows: the selected station covers all surface feature types in the area to be detected;

and a second condition: adding at least one overlapping area between each site;

and (3) carrying out a third condition: the height of the antenna of the base station of the selected station is more than 20 meters, the antenna is higher than the nearest barrier by more than 5 meters, and the building where the station is located is higher than the average height of the surrounding buildings;

step 1.2, acquiring the engineering parameters of the station to be tested selected in step 1.1, which mainly refers to the basic engineering parameters of the base station, and comprises the following steps: base station number, base station longitude and latitude, hanging height of base station antenna, azimuth angle and downtilt angle, and then downloading the drive test data obtained by the drive test route measurement in the step 1.1;

step 1.3, preprocessing, screening and filtering the drive test data measured based on the FDD-LTE network system;

step 1.4, carrying out data discretization on the data processed in the step 1.3; since the sampling rate of the GPS is slower than that of the receiver, a plurality of data are arranged on the same longitude and latitude in time sequence, and for this purpose, the data need to be spread discretely, and if the speed in the test process is consistent and the time interval between each datum is equal, the data can be interpolated in time sequence for a long time, so that the data repeated on one point are spread out in time sequence;

step 1.5, extracting the base station signal intensity of the main cell and the adjacent cell received at the test point by using the effective data processed by the method, calculating the overall path loss of the test point at the route test point of the FDD-LTE cell, and performing adaptive optimization of a propagation model by using the data;

and step 1.6, respectively completing self-adaptive correction and optimization of COST231-Hata and SPM propagation models to a specific propagation environment, selecting a correction propagation model closer to actually measured data according to the error mean value and the standard deviation, and recording the propagation models suitable for various scenes to form a suitable propagation model library.

2. The method of claim 1, wherein the routing test data in step 1.2 includes test time, longitude and latitude of the test point, cell number of the primary serving base station, neighbor cell number, test frequency point, cell received signal strength of the primary serving base station, and neighbor received signal strength.

3. The method of claim 1, wherein the preprocessing of step 1.3 is to filter out latitude and longitude of missing test point of the road test equipment, latitude and longitude information of the main cell, and signal receiving strength of the main cell during the road test;

the data filtering mainly includes a filtering mode based on the received signal level intensity and a filtering mode based on the distance between the test point and the main base station

The filtration condition one: the filtering method based on the received signal level intensity, the filtering condition is that the data is filtered based on the received signal intensity, and the received signal intensity is too strong or too weak in the actual drive test process, which greatly affects the accuracy of data processing and is not beneficial to the optimization processing of the propagation model; for this purpose, level filtering thresholds are usually set during actual operation;

and (2) filtering conditions II: based on the data filtering mode of the distance between the test point and the main base station, the test point at the position close to the base station is influenced by the black range under the base station tower, and the test point of the close base station usually has no direct path, the close base station coverage test point of the LTE does not receive the signal level or the received signal level is too weak when testing, the equipment cannot be identified, and similarly, when the distance from the base station is far, the signal intensity is too weak through the reflection, scattering and diffraction of the propagation environment of the base station signal, so that the receiving equipment cannot receive effective information, and the test point of the close base station and the test point far away from the base station are avoided when selecting the path.

4. The method for adaptive optimization of multiple scenarios applicable to a propagation model in an FDD-LTE system according to claim 1, wherein in step 1.5, the method for calculating the overall path loss of the FDD-LTE cell path measurement point comprises:

step 4.1, when the route test data shows that the test point only receives the signal of the main service cell and does not receive the signal of the adjacent cell, the actual path loss of the test point is the transmission power of the base station minus the strength of the received signal of the main service cell, that is, PL is P_BS-P_UEWhere PL (pathloss) is the path loss, P_BSFor base station transmitting power, P_UEReceiving signal strength for the drive test point;

step 4.2, when the road test data shows that two or three sectors receive signals, eliminating the sector corresponding to the road test point with the latitude and longitude being null, and only calculating the remaining sectors, if only one sector remains, calculating according to the step 4.1;

step 4.3, if the remaining sectors are two sectors, the path loss of the main sector is PL₀＝P_BS0-P_UE0The path loss of another neighbor cell is PL₁＝P_BS1-P_UE1(ii) a In order to improve the test accuracy by considering the information of the main cell and the adjacent cell, the received signal strength is calculated by a method of weighting the signals of the main cell and the adjacent cell; through multiple test experiments, the optimization of the experimental result can be realized when the weighting ratio is 7:3, so that the path loss after the signals are received by the adjacent cells and integrated at the last test point is

PL＝0.7*PL₀+0.3*PL₁；

Step 4.4, if three sectors remain, the primary sector path loss PL is calculated with reference to step 4.3₀Second sector path loss PL₁And third sector path loss PL₂For the second sector with stronger signal, the signal is calculated as 0.2 PL₁0.1 PL for the third sector₂And finally, the path loss after the test point integrates the adjacent region received signals is PL (0.7) PL₀+0.2*PL₁+0.1*PL₂；

And 4.5, if the number of the remaining sectors is more than three, reserving the main sector and the other two sectors with relatively strong signals, and calculating the path loss of the path measuring point according to the step 4.4.

5. The method according to claim 1, wherein the step 1.5 of optimizing the propagation model using the processed data mainly includes the following steps:

step 5.1, based on the COST231-Hata propagation model, using the processed data, adopting a least square fitting method, verifying that the standard deviation between the predicted data and the actually measured data of the propagation model is less than 8dB after the propagation model is optimized, and performing the next step;

step 5.2, based on the SPM propagation model, utilizing the initial value of the SPM model coefficient to correct and optimize the propagation model, and in the process of data fitting, setting each coefficient to ensure that the standard deviation of the predicted data and the actually measured data of the SPM model is minimum, if the standard deviation is less than 8dB, the data is recorded into a model class library, otherwise, the step is ended and the next step is carried out;

step 5.3, according to the steps 5.1 and 5.2, comparing the standard deviation and the error mean value after the two propagation models are optimized, and selecting a correction result closest to the predicted data to be adaptive to the scene propagation environment;

and 5.4, outputting a propagation environment adaptive to the scene, inputting the propagation environment and an optimization model suitable for the propagation environment into a propagation model library, and establishing the propagation model library with practical, reference and auxiliary functions.