CN108307397A - Network coverage evaluation method and system - Google Patents

Abstract

The invention discloses a kind of network coverage evaluation method and systems.This method includes：Obtain measured data of each grid point relative to each cell in network's coverage area；For each cell, processing is merged to measured data, to obtain processed measured data；Network coverage situation of each cell to grid point is predicted based on propagation model, to obtain prediction result of each grid point relative to each cell；Processed measured data and prediction result based on each grid point relative to each cell calculate error of each grid point relative to each cell；Calculate the angle between each cell and each grid point line direction and predetermined direction；By Fourier expansion, error is expressed as to the function of angle；Propagation model is modified using error, with the propagation model being corrected；And based on the propagation model being corrected, the network coverage situation in network's coverage area is predicted.

Description

Network coverage evaluation method and system

technical field

The present invention generally relates to the technical field of wireless mobile communication, and more specifically relates to a network coverage evaluation method and system.

Background technique

The methods currently used for coverage and interference assessment include: using empirical formulas or standard propagation models (SPM) calculated by large-scale fading, using frequency sweeps or measurement report (MR) data obtained from actual drive tests, or using ray tracing models Obtain received power data, and use this as a basis to evaluate cell coverage, coverage effects, and interference.

However, in practical applications, the above methods are often limited by many factors such as the completeness of the model, the accuracy of the transceiver antenna, the complexity of the test method, the error between simulation and actual measurement, and the cost of required resources. Therefore, a more accurate network coverage evaluation method and system are needed.

Contents of the invention

In view of one or more of the above problems, embodiments of the present invention provide a network coverage evaluation method and system.

According to one aspect of the present invention, a network coverage evaluation method is disclosed, including: obtaining the measured data of each grid point in the network coverage area relative to each cell; Processed measured data; based on the propagation model, predict the network coverage of each cell to the grid point to obtain the prediction result of each grid point relative to each cell; based on the processed measured data of each grid point relative to each cell and Prediction results, calculate the error of each grid point relative to each plot; calculate the angle between each plot and each grid point connection direction and the predetermined direction; through Fourier series expansion, the error is expressed as a function of the angle ; correcting the propagation model by using the error to obtain the corrected propagation model; and predicting the network coverage in the network coverage area based on the corrected propagation model.

According to another aspect of the present invention, a network coverage evaluation system is disclosed, including: a data acquisition module configured to acquire the measured data of each grid point in the network coverage area relative to each cell; a data processing module configured to For each cell, the measured data is merged to obtain the processed measured data; the predictive analysis module is configured to: predict the network coverage of each cell to the grid point based on the propagation model, so as to obtain the relative network coverage of each grid point The prediction result of each sub-district; and the model correction module, configured to: calculate the error of each grid point relative to each sub-district based on the processed actual measurement data and prediction results of each grid point relative to each sub-district; calculate each The angle between the direction of the connection line between the cell and each grid point and the predetermined direction; through Fourier series expansion, the error is expressed as a function of the angle; and the error is used to correct the propagation model to obtain the corrected propagation model; Wherein, the prediction analysis module is further configured to predict the network coverage in the network coverage area based on the corrected propagation model obtained by the model correction module.

Description of drawings

Other features and advantages of various embodiments of the invention will be apparent from the following detailed description and accompanying drawings, in which:

Fig. 1 shows a schematic diagram of a network coverage evaluation system according to an embodiment of the present invention.

Fig. 2 shows a flowchart of a network coverage evaluation method according to an embodiment of the present invention.

Fig. 3 shows a schematic diagram of grid points in a network coverage area and angles between a connection direction of cells and a predetermined direction according to an embodiment of the present invention.

Fig. 4 shows a scatter diagram of a fitted curve of error-angle for a cell and an actual error according to an embodiment of the present invention.

Fig. 5 shows a situation where one grid point receives signals of multiple cells according to an embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that the elements and components shown in the exemplary embodiments are illustrative only, and the scope of the present invention is not limited to these elements or components. In the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

Fig. 1 shows a network coverage evaluation system 100 according to an embodiment of the present invention. As shown in FIG. 1 , the system 100 includes a data acquisition module 110 , a data processing module 120 , a predictive analysis module 130 and a model correction module 140 . The data acquisition module 110 can obtain the actual drive test data (hereinafter referred to as measured data) of each grid point in the network coverage area relative to each cell through actual drive tests, for example, the reference signal received power (RSRP) of each grid point, etc. . The data processing module 120 includes a data analysis sub-module 122 and a processing sub-module 124 for analyzing and processing the measured data from the data acquisition module 110 . The predictive analysis module 130 may predict the wireless propagation environment in the network coverage area according to a known propagation model, so as to obtain a prediction result. The model correction module 140 corrects the propagation model based on the measured data processed by the data processing module 120 and the prediction result of the prediction analysis module 130 . Therefore, the predictive analysis module 130 can accurately evaluate the coverage, coverage effect and interference situation of the network coverage area based on the corrected propagation model. One or more of the modules described above may be combined, and they may be realized by one or a combination of software, hardware, and firmware. For example, some modules may include one or more microprocessors, DSPs, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Radio Frequency Integrated Circuits (RFICs) and various other components for performing at least the functions described herein. A combination of hardware and logic circuits. In some embodiments, a module may refer to one or more processes running on one or more processing elements.

Fig. 2 shows a flowchart of a network coverage evaluation method 200 according to an embodiment of the present invention. The network coverage evaluation method 200 will be described in detail below with reference to FIG. 1 and FIG. 2 . Method 200 may include the following steps:

In step 201, the data acquisition module 110 acquires measured data of each grid point in the network coverage area relative to each cell, including the RSRP value of each grid point (for example, latitude and longitude) in the network coverage area relative to each cell.

In step 202, the data processing module 120 merges the measured data to obtain processed measured data, where the measured data may include frequency sweep data obtained through actual drive tests or signal strength in MR data. For frequency sweep data, it can be directly read into the processing sub-module 124 for processing, while for MR data, it can be read into the processing sub-module 124 for processing after being analyzed by the data analysis sub-module 122 .

In the case of a TD-LTE system, the cell to which the reference signal received power (RSRP) value of each grid point (for example, latitude and longitude points) in the network coverage area belongs can be determined according to the cell frequency point and the physical cell ID (PCID). . In the case of the TD-SCDMA system, the cell to which the RSRP value of each grid point (eg, latitude and longitude point) in the network coverage area belongs can be determined according to the cell frequency point and the scrambling code number. According to this matching method, the same RSRP value may correspond to one or more cells. Among them, in the case of corresponding to multiple cells, the cell with the closest Euclidean distance to the grid point of the RSRP value on the map can be taken as The home cell of the RSRP value.

Then, the processing sub-module 124 performs combining processing on the RSRP values of the same cell received at the same latitude and longitude point. For example, assume that the measured RSRP values from the same cell at the same latitude and longitude point are RSRP[0], RSRP[1], ..., RSRP[N-1], where N is an integer greater than or equal to 2. When N is large (for example, N≥20), the arithmetic mean value of these RSRP values can be calculated to overcome the influence of small-scale fading, so as to obtain the result of large-scale fading, that is, large-scale RSRP=(RSRP[0]+...+ RSRP[N-1])/N. When N is small (for example, N<20), in order to avoid some of the RSRP values being too large or too small (ie, bad point values) to affect the final calculation result, it can be considered to arrange the RSRP values in ascending order, and find their median. For example, assume that the sorted RSRP values are RSRP[a ₀ ], RSRP[a ₁ ], . . . RSRP[a _N−1 ], where N is an integer greater than or equal to 2. When N is an odd number, large-scale RSRP=RSRP[a _N-1/2 ], when N is an even number, large-scale RSRP=(RSRP[a _N/2 ]+RSRP[a _N/2+1 ])/ 2.

Afterwards, store the processed RSRP data into the RSRP set {RSRP _measured [i, j]}, where i is the grid point number, j is the cell number (PCID), and record the position of the grid point (for example, latitude and longitude ) set {[x _i , y _i ]}.

In step 203, the predictive analysis module 130 uses a known empirical model or a SPM model calibrated through continuous wave (CW) testing to evaluate the network coverage of each cell in the network coverage area to the grid points, so as to obtain each grid Points relative to the predicted results of each plot. For example, the predictive analysis module 130 may sequentially traverse all grid points within the network coverage area. Then, according to the known transmitter position, base station antenna direction and other information, the received power from each transmitter to each grid point is calculated by using the large-scale fading calculation formula.

Assuming that the SPM model is adopted in one embodiment, it can be represented by the following formula (1):

L＝K ₁ +K ₂ log(d)+K ₃ log(HTx _eff )+K ₄ ×DiffractionLoss+K ₅ log(d)×log(HTx _eff )+K ₆ (HRx _eff )+K _clutter f(clutter ) (1)

where L represents the large-scale fading path loss in the transmission path (dB), K ₁ represents a constant (dB), d represents the distance between the transmit antenna and the receive antenna (m), and K ₂ represents the multiplier of log(d) Factor, HTx _eff represents the effective height of the transmitting antenna (m), K ₃ represents the multiplier factor of log(HTx _eff ), DiffractionLoss represents the diffraction loss (dB) caused by passing through an obstacle path, and K ₄ represents the multiplier of diffraction loss Factor (it must be a positive value), K ₅ represents the multiplier factor of log(HTx _eff )log(d), K ₆ represents the height correction factor of the mobile station, HRx _eff represents the effective height of the receiving antenna (m), K _clutter represents The ground object attenuation correction factor, and f(clutter) represent the average weighted loss caused by the ground object.

The predictive analysis module 130 can use the following formula (2) to calculate the predicted received power P _r (d) of each grid point relative to each cell:

P _r (d) = P _t (d) - L (2)

Among them, P _t (d) represents the transmission power of the transmitter.

Store the predicted received power calculated based on formula (2) into the set {RSRP _prediction [X _i , Y _i , j]}, where X and Y respectively represent the position information (for example, latitude and longitude) of the current grid point, and j The base station number for the power received at the current grid point.

However, a large number of system-level simulation results show that although the CW model parameters can be calibrated based on the measured data and using the least squares method, the prediction results still have large errors with the measured data. The reason is that, on the one hand, the formulas of the existing propagation models are inaccurate (specifically, Maxwell’s equations are nonlinear partial differential equations, the general solution is not an elementary function, and the propagation model is basically expressed by elementary functions), on the other hand, the model considerations are incomplete.

Therefore, in step 204, the model correction module 140 calculates the error of each grid point relative to each cell based on the processed measured data and prediction results. The propagation model is corrected using the error. In an embodiment, in order to further reduce the error between the prediction result and the measured data, it may be considered to compensate this part of the error into the path loss.

For example, the model correction module 140 calculates the measured RSRP value (that is, _{the measured} RSRP) from the cell [m] (0≤m≤J-1) to the i-th grid point A[x, y] and the predicted RSRP predicted by the propagation model The difference between the values (that is, the RSRP _prediction ), that is, the error Error between the RSRP _measurement and the RSRP _prediction , where Error=RSRP _measurement −RSRP _prediction .

Then, in step 205, the model correction module 140 calculates the angle between the direction of the line connecting each cell and each grid point and a predetermined direction. For example, as shown in Figure 3, the line connecting the position of the grid point A (for example, the longitude and latitude point) to the position of the cell [m] (for example, the longitude and latitude point) and the predetermined direction (for example, the due east direction of the map) The included angle is recorded as Alpha. In addition, when calculating the included angle Alpha, a coordinate conversion method (for example, Mercator coordinates or Gauss-Kruger coordinates are used according to the type of electronic map and the accuracy required by the project) can be used to convert the latitude and longitude coordinates into a plane right angle coordinate. Since the coordinate transformation method belongs to the common knowledge of those skilled in the art, it will not be repeated here.

Based on the above method, the model correction module 140 can calculate the angle between the vector of each cell pointing to all the grid points covered by it and the predetermined direction (ie, Alpha), and each grid point relative to each The error Error between the measured data of the cell and the predicted result. Thus, for a cell (assuming that the cell corresponds to J latitude and longitude points and covers i grid points), the vector (Error ₁ , Error ₂ ,..., Error _J ) composed of these error values and its corresponding angle ( Alpha ₁ , Alpha ₂ ,..., Alpha _J ) form a set of functional correspondences, where Alpha _i (1≤i≤J)∈[0, 2π]. In this way, the error Error can be expressed as a function Error=f(Alpha) of the angle Alpha, and its period is 2π, as shown in the following formula (3):

Error=f(Alpha)=f(Alpha+2π) (3)

In step 206, the model correction module 140 can use the trigonometric function with a period of 2π and its higher harmonic terms to obtain the trigonometric function expansion of the Fourier series based on the formula (3). For example, assuming y=Error, x=Alpha, the following formula (4) can be obtained:

In practical applications, for example, the 3rd to 5th harmonic terms can be used. Then, calculate c ₀ , a ₀ ... a _i , b ₀ ... b _i (assuming that the i-th harmonic item is taken) using the least square method, and fit the error-angle function for the cell as shown in Figure 4 The fitting curve of , which represents the error value of the cell in the 360° (ie, 2π) propagation direction. It should be understood that each cell may have such an error-angle curve fitted using a Fourier series function.

Then, in step 207, the model correction module 140 uses the error to correct the propagation model to obtain a corrected propagation model. For example, the error value based on the error-angle function can be subtracted from the original propagation model to obtain the corrected propagation model, as shown in the following equation (5):

P _r (d) _correction = P _t (d) – L-Error (5)

Where P _t (d) represents the transmit power of the transmitter, and P _r (d) _correction represents the received power predicted by the revised propagation model.

In one embodiment, as shown in Figure 5, for grid point A, there are RSRP values from cell[i], cell[j], cell[k] and cell[n] respectively. When calculating the RSRP value from each cell to grid point A, based on the error-angle function curve fitted for each cell in step 204, the corresponding Error value in the direction of the transmission path from each cell to grid point A can be obtained , and perform error compensation on the propagation model in the direction of the corresponding transmission path through the above formula (5), so that the corrected propagation model can accurately predict the network coverage of each cell to the grid point A.

In step 208, the predictive analysis module 130 predicts the network coverage in the network coverage area based on the revised propagation model. For example, all grid points in the network coverage area (for example, all latitude and longitude points on an electronic map with 5-meter or 10-meter precision) may be traversed to obtain an overall assessment of the network coverage area.

Embodiments described herein may be implemented in one or a combination of hardware, firmware, and software. Embodiments can also be implemented as instructions stored on a computer-readable storage device, which can be read and executed by at least one processor to perform the operations described herein. A computer-readable storage device may include any non-transitory mechanism for storing information in a form readable by a machine (eg, a computer). For example, a computer readable storage device may include read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and other storage devices and media. Some embodiments may include one or more processors and may be configured with instructions stored on a computer-readable storage device.

The method and system disclosed in the present invention are based on the error between the measured data and the predicted results of each point in the network coverage area relative to each cell, and use the Fourier series to fit the error-angle function, so that the error compensation of the propagation model can be performed . When evaluating the overall coverage of the network coverage area, the error between the predicted results and the measured data can be reduced by using the revised propagation model, and the accuracy of the network coverage evaluation is improved.

Although embodiments of the invention have been described in detail for purposes of clarity, those skilled in the art will appreciate that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention. Accordingly, the scope of the invention should be determined only by the appended claims.

Claims (13)

1. A network coverage evaluation method, characterized in that the method comprises: Obtain the measured data of each grid point relative to each cell in the network coverage area; For each cell, the measured data are merged to obtain processed measured data; Predicting the network coverage of each cell to the grid point based on a propagation model, so as to obtain a prediction result of each grid point relative to each cell; calculating an error of each grid point relative to each cell based on the processed measured data and the prediction result of each grid point relative to each cell; Calculate the angle between each cell and each grid point connection direction and the predetermined direction; expressing said error as a function of said angle by Fourier series expansion; correcting the propagation model using the error to obtain a corrected propagation model; and Based on the modified propagation model, the network coverage situation in the network coverage area is predicted. 2. The method according to claim 1, wherein the measured data includes the measured reference signal received power at each grid point in the network coverage area. 3. The method according to claim 1, wherein the prediction result includes the predicted reference signal received power at each grid point in the network coverage area. 4. The method according to claim 1, characterized in that, the merging process comprises calculating an arithmetic mean or calculating an intermediate value. 5. The method according to claim 4, wherein, when the number of measured data received from the same cell for the same grid point within the network coverage area is not less than a threshold value, arithmetic calculation is performed on these measured data average value; When the number of measured data from the same cell received for the same grid point within the network coverage area is less than the threshold, an intermediate value is calculated for these measured data. 6. The method of claim 1, wherein the propagation model comprises a Standard Propagation Model (SPM). 7. The method according to any one of claims 1-6, wherein the step of using the error to correct the propagation model comprises: subtracting the error from an original propagation model. 8. A network coverage evaluation system, characterized in that the system comprises: The data acquisition module is configured to acquire the measured data of each grid point relative to each cell in the network coverage area; The data processing module is configured to combine the measured data for each cell to obtain processed measured data; The predictive analytics module, configured to: Predicting the network coverage of each cell to the grid point based on a propagation model, so as to obtain a prediction result of each grid point relative to each cell; and A model correction module configured to: calculating an error of each grid point relative to each cell based on the processed measured data and the prediction result of each grid point relative to each cell; Calculate the angle between each cell and each grid point connection direction and the predetermined direction; expressing said error as a function of said angle by Fourier series expansion; and correcting the propagation model by using the error to obtain a corrected propagation model; Wherein, the predictive analysis module is further configured to predict the network coverage in the network coverage area based on the corrected propagation model obtained by the model correction module. 9. The system according to claim 8, wherein the measured data includes the measured reference signal received power at each grid point in the network coverage area. 10. The system according to claim 8, wherein the prediction result comprises the predicted reference signal received power at each grid point within the network coverage area. 11. The system according to claim 8, characterized in that, the merging process comprises calculating an arithmetic mean or calculating an intermediate value. 12. The system according to claim 11 , wherein when the number of measured data received from the same cell for the same grid point within the network coverage area is not less than a threshold value, arithmetic calculation is performed on these measured data average value; When the number of measured data from the same cell received for the same grid point within the network coverage area is less than the threshold, an intermediate value is calculated for these measured data. 13. The system of claim 8, wherein the propagation model comprises a standard propagation model (SPM).