A Comprehensive Evaluation Method for Wireless Channel Delay Spread Based on Dual Correlation Test

Abstract

This invention discloses a comprehensive evaluation method for wireless channel delay spread based on a dual correlation test in the field of wireless communication and channel modeling technology. This method includes the following steps: data acquisition and preprocessing, correlation calculation, data merging and trend analysis, and result presentation and decision-making basis. By combining Pearson and Spearman correlation coefficients, this method analyzes the impact of physical environment parameters on RMS_DS under LOS and NLOS conditions, improving the comprehensiveness and accuracy of correlation analysis and providing a reliable basis for wireless communication system design and optimization.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and channel modeling technology, and more specifically, to a comprehensive evaluation method for wireless channel delay spread based on dual correlation tests. Background Technology

[0002] In wireless communication, due to the influence of multipath effects, reflection, and scattering during signal transmission, channel characteristics (especially root mean square delay spread, RMS_DS) often vary with changes in physical environmental parameters (such as distance, building density, and the number of obstacles). Traditional channel analysis methods fail to fully consider the different impacts of environmental parameters on the signal propagation characteristics of wireless systems, especially the delay spread (RMS_DS), under different transmission conditions (such as line-of-sight (LOS) and non-line-of-sight (NLOS)). (Under LOS conditions, as distance increases, the direct path of the signal becomes dominant, and the differences caused by multipath reflection become more pronounced, thus significantly increasing the delay spread; while under NLOS conditions, because obstacles block the direct path, the signal relies more on diffraction and scattering, which also shows a positive correlation trend with the parameters.) This leads to limitations in the performance prediction and optimization of wireless systems in complex environments, thereby affecting key design factors such as wireless network capacity planning, coverage, and anti-interference capabilities.

[0003] Currently, commonly used correlation analysis methods include:

[0004] 1. Pearson correlation coefficient: It is mainly used to measure the linear correlation between two continuous random variables, but it requires the data to meet the normal distribution and is quite sensitive to outliers;

[0005] 2. Spearman correlation coefficient: As a nonparametric version of the Pearson correlation coefficient, it uses data ranking to measure the monotonic correlation between two variables. It has no strict requirements on data distribution and is not sensitive to outliers.

[0006] Currently, there is a lack of technical solutions that combine these two correlation analysis methods to comprehensively evaluate the impact of different physical environmental parameters on RMS_DS under different transmission states through significance tests (e.g., based on t-statistics and p-values). Therefore, there is an urgent need for a method that integrates the two correlation coefficients to comprehensively assess the impact of environmental parameters on RMS_DS. Summary of the Invention

[0007] The purpose of this invention is to provide a comprehensive evaluation method for wireless channel delay spread based on dual correlation tests, which solves the technical problems of incomplete and inaccurate correlation analysis in the prior art. By combining Pearson and Spearman correlation coefficients, the influence of physical environment parameters on RMS_DS is analyzed under LOS and NLOS conditions, which improves the comprehensiveness and accuracy of correlation analysis and provides a reliable basis for the design and optimization of wireless communication systems.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] The comprehensive evaluation method for wireless channel delay spread based on dual correlation test includes the following steps:

[0010] S1. Data Acquisition and Preprocessing: Using GPS data and signal packet data, LOS and NLOS areas are divided, and physical environment parameters are extracted;

[0011] S2. Correlation Calculation: Calculate the Pearson correlation coefficient and Spearman correlation coefficient between RMS_DS and each physical environment parameter, and verify whether the correlation is significant through a significance test;

[0012] S3. Data Merging and Trend Analysis: Merge data from multiple scenarios to improve statistical reliability, compare the differences between Pearson and Spearman correlation coefficients, and identify nonlinear monotonic relationships;

[0013] S4. Results Presentation and Decision Basis: The impact of parameters on RMS_DS is presented through charts and graphs, providing a basis for channel modeling and system optimization.

[0014] Furthermore, S1 includes the following steps:

[0015] S11. Using GPS data and signal packet data, divide the signal data collected in the test scenario into LOS and NLOS regions, and classify the data according to the requirements of LOS and NLOS scenario division.

[0016] S12. Perform physical environment parameter data statistics for each test scenario. Calculate and extract physical environment parameter information based on the given TX and RX coordinate information and the corresponding DEM elevation map.

[0017] Furthermore, S11 includes the following steps:

[0018] S111. Data is collected in multiple test scenarios using the deployed wireless measurement equipment, and the time, location and signal transmission parameters of each data packet are recorded by the built-in GPS module;

[0019] S112. According to the predetermined test plan, the signal data in each test scenario is divided into LOS region and NLOS region based on the actual environmental conditions;

[0020] S113. Perform preliminary statistics on the data collected in each test scenario, calculate the number of data packets of each type, and ensure that the LOS and NLOS data reach the required sample size.

[0021] Furthermore, the physical environment parameters in S122 include at least one of distance, building density, building height, open space, and number of buildings.

[0022] Furthermore, S2 includes the following steps:

[0023] S21. For each test scenario, calculate the Pearson correlation coefficient and Spearman correlation coefficient between RMS_DS and each physical environment parameter under LOS and NLOS conditions respectively;

[0024] S22. When calculating the sample Pearson correlation coefficient r, further calculate the t-statistic and use the degrees of freedom to find or calculate the corresponding p-value to test whether the correlation is significant.

[0025] Furthermore, the Pearson correlation coefficient r and the Spearman correlation coefficient ρ in S21 are calculated as follows:

[0026] The formula for the Pearson correlation coefficient is as follows:

[0027]

[0028] Where, x i ,y i It is the value of the i-th sample point. These are the means of variables x and y, respectively, and n is the total number of samples;

[0029] The formula for the Spearman correlation coefficient is as follows:

[0030]

[0031] Where: d i =R(x) i )-R(y i R(x) is the rank difference of the i-th sample point. i ), R(y i ) corresponds to x i and y i The ranking is given by n, where n is the number of samples.

[0032] Furthermore, in S22, when calculating the t-statistic and p-value to test whether the correlation is significant, the confidence level is 95% or 99%.

[0033] Furthermore, S3 includes the following steps:

[0034] S31. Merge the LOS and NLOS data from all test scenarios separately, and then calculate the correlation between the overall RMS_DS and each physical environment parameter;

[0035] S32. Compare the differences between the Pearson correlation coefficient and the Spearman correlation coefficient to determine whether there is a nonlinear monotonic relationship. If the Spearman coefficient of a certain parameter is found to be significantly higher than the Pearson coefficient, it indicates that its correlation may contain a nonlinear trend.

[0036] Furthermore, S4 includes the following steps:

[0037] S41. Using chart tools, compare the Pearson and Spearman correlation coefficients calculated under different test scenarios, and create bar charts or scatter plots to show their changing trends;

[0038] S42. Analyze the differences in the impact of each parameter on RMS_DS, and generate a detailed analysis report to provide a basis for subsequent channel modeling and wireless system optimization.

[0039] By adopting the above technical solution, the present invention has the following advantages:

[0040] This invention provides a comprehensive evaluation method for wireless channel delay spread based on a dual correlation test. By comprehensively analyzing the correlation between RMS delay spread and various physical environment parameters in a wireless channel using Pearson and Spearman correlation coefficients, it not only improves the accuracy and comprehensiveness of the correlation analysis but also provides a reliable statistical basis for the design and optimization of wireless systems. This comprehensive evaluation method for wireless channel delay spread based on a dual correlation test is particularly suitable for evaluating channel characteristics in complex multipath transmission environments such as outdoor or urban areas, providing a basis for wireless communication system design, channel modeling, and subsequent transmission optimization. Attached Figure Description

[0041] Figure 1 The logical framework diagram of the comprehensive evaluation method for wireless channel delay spread based on dual correlation test is shown below.

[0042] Figure 2 This is a schematic diagram illustrating the division of signal data into LOS and NLOS based on GPS data and packet selection criteria.

[0043] Figure 3 GPS coordinate maps and DEM elevation information maps of the transmitter and receiver used to calculate physical environment parameters;

[0044] Figure 4 This is a flowchart of the correlation calculation in this invention;

[0045] Figure 5 A bar chart comparing the Pearson correlation coefficients of different physical environmental parameters under LOS conditions;

[0046] Figure 6 A bar chart comparing the significance of Pearson correlation coefficients for different physical environmental parameters under LOS conditions;

[0047] Figure 7 A bar chart comparing the Pearson correlation coefficients of different physical environmental parameters under NLOS conditions;

[0048] Figure 8 A bar chart comparing the significance of Pearson correlation coefficients for different physical environmental parameters under NLOS conditions;

[0049] Figure 9 A bar chart comparing the Spearman correlation coefficients of different physical environment parameters under LOS conditions;

[0050] Figure 10 A bar chart comparing the Spearman correlation coefficients of different physical environment parameters under NLOS conditions;

[0051] Figure 11 A comparison of Pearson correlation coefficients and Spearman correlation coefficients for different physical environment parameters under LOS conditions;

[0052] Figure 12 A comparison of Pearson correlation coefficients and Spearman correlation coefficients for different physical environmental parameters under NLOS conditions. Detailed Implementation

[0053] The technical solution of the present invention will be specifically described below with reference to the accompanying drawings. It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0054] Figure 1 The diagram illustrates the logical framework of a comprehensive evaluation method for wireless channel delay spread based on a dual correlation test. An apparatus corresponding to this method includes a data acquisition module, a preprocessing module, a correlation calculation module, and a result display module.

[0055] The comprehensive evaluation method for wireless channel delay spread based on dual correlation test is as follows: Figure 1 As shown, it includes the following steps:

[0056] S1. Data Acquisition and Preprocessing: The data acquisition module uses GPS data and signal packet data to divide the LOS and NLOS regions, and the preprocessing module extracts physical environment parameters;

[0057] S1 includes the following specific steps:

[0058] S11. Using GPS data and signal packet data, divide the signal data collected in the test scenarios (including Test1, Test2, Test3, etc.) into LOS and NLOS regions, and classify the data according to the requirements of LOS and NLOS scenario division. Specifically, as follows: Figure 2 As shown;

[0059] S11 includes the following specific steps:

[0060] S111. Data is collected in multiple test scenarios (e.g., Test1, Test2, Test3, etc.) using the deployed wireless measurement equipment, and the time, location and signal transmission parameters of each data packet are recorded by the built-in GPS module;

[0061] S112. According to the predetermined test plan, the signal data in each test scenario is divided into LOS and NLOS regions based on the actual environmental conditions, as follows: Figure 2 As shown;

[0062] S113. Perform preliminary statistics on the data collected in each test scenario, calculate the number of data packets of each type, and ensure that the LOS and NLOS data reach the required sample size respectively (e.g., 434 LOS data packets and 811 NLOS data packets). For example, in the Test1 scenario, in the GPS data packets, packet numbers 2-150 are marked as LOS, and 151-239 are NLOS area data.

[0063] S12. Environmental Feature Extraction: Physical environment parameter data are statistically analyzed for each test scenario. Based on the given transmitter (TX) and receiver (RX) coordinates, and the corresponding DEM elevation map, the specific details are as follows: Figure 3 As shown, physical environment parameter information is calculated and extracted.

[0064] The physical environment parameters in S122 include at least one of the following: distance, building density, building height, openness, and number of buildings. Among them, distance includes the distance between the transmitter and receiver, the distance to the nearest building at the transmitter, and the distance to the nearest building at the receiver; building height includes average building height, average height, the height of the nearest building at the transmitter, the height of the nearest building at the receiver, and the maximum building height; openness includes openness at the transmitter and openness at the receiver.

[0065] The physical environmental parameters involved in this invention include:

[0066]

[0067]

[0068] A brief explanation of the physical environment parameters shown in the table:

[0069] Building_Density, Mean_Building_height: Select a rectangular area on the elevation map with the line connecting TX and RX as the diagonal, generate a building mask within the rectangular area, and obtain the building density (Building_Density) by dividing the number of pixels occupied by the buildings by the total number of pixels in the rectangle. The average height of the buildings within the rectangular area is the average building height (Mean_Building_height).

[0070] Num_Obstacles, Max_Height, Mean_Height: The number of buildings, maximum building height, and average building height along the path connecting TX and RX.

[0071] Openness_Tx and Openness_Rx: Calculate the building density within a circular area with a radius of 200m (you can choose an appropriate value) centered at TX and RX, and take the complement of the obstacle density to get the openness.

[0072] Nearest_T_Height, Nearest_R_Height: The height of the building closest to the transmitter, the height of the building closest to the receiver.

[0073] Nearest_T_Dist, Nearest_R_Dist: The distance between the transmitter and the nearest building, and the distance between the receiver and the nearest building.

[0074] S2. Correlation Calculation: The correlation calculation module calculates the Pearson correlation coefficient and Spearman correlation coefficient between RMS_DS and each physical environment parameter, and verifies whether the correlation is significant through a significance test;

[0075] S2 includes the following specific steps, as detailed below: Figure 4 As shown:

[0076] S21. For each test scenario, calculate the Pearson correlation coefficient and Spearman correlation coefficient between RMS_DS and each physical environment parameter under LOS and NLOS conditions respectively;

[0077] S22. When calculating the sample Pearson correlation coefficient r, further calculate the t-statistic and use the degrees of freedom to find or calculate the corresponding p-value to test whether the correlation is significant.

[0078] In a specific embodiment, the calculation of the correlation coefficient and the significance test are performed as follows:

[0079] For the LOS data in each test scenario, calculate according to the following steps:

[0080] (1) Calculate the Pearson correlation coefficient between RMS_DS and a certain physical environment parameter using the raw data. The calculation formula is based on covariance and standard deviation.

[0081] (2) At the same time, the original data are converted into rank values ​​and the Spearman correlation coefficient is calculated;

[0082] (3) Calculate the degrees of freedom df = n-2 based on the sample size n, and use the r value to calculate the t-statistic;

[0083] (4) Obtain the corresponding p value based on the t-distribution and degrees of freedom, and test whether the null hypothesis (H0: ρ = 0) is true at a 99% or 95% confidence level.

[0084] 2. Repeat the above steps for NLOS data.

[0085] 3. If the p-value is less than the preset significance level (e.g., p<0.01 or p<0.05), the null hypothesis is rejected and the correlation is considered significant.

[0086] The formula for calculating the Pearson correlation coefficient (based on the ranking difference) is given below:

[0087]

[0088] Where: x i ,y i It is the value of the i-th sample point. y and y are the means of variables x and y, respectively, and n is the total number of samples.

[0089] The formula for calculating the Spearman correlation coefficient (based on the ranking calculation using the Pearson formula) is given below:

[0090]

[0091] Where: d i =R(x) i )-R(y i R(x) is the rank difference of the i-th sample point. i ), R(y i ) corresponds to x i and y i The ranking is given by n, where n is the number of samples.

[0092] S3. Data Merging and Trend Analysis: Merge data from multiple scenarios to improve statistical reliability, compare the differences between Pearson and Spearman correlation coefficients, and identify nonlinear monotonic relationships;

[0093] S3 includes the following steps:

[0094] S31. Merge the LOS and NLOS data from all test scenarios separately, and then calculate the correlation between the overall RMS_DS and each physical environment parameter;

[0095] S32. Compare the differences between the Pearson correlation coefficient and the Spearman correlation coefficient to determine whether there is a nonlinear monotonic relationship. If the Spearman coefficient of a certain parameter is found to be significantly higher than the Pearson coefficient, it indicates that its correlation may contain a nonlinear trend.

[0096] In a specific embodiment, when the Spearman correlation coefficient of a certain parameter (e.g., 0.88 for Distance) is significantly higher than the Pearson correlation coefficient (e.g., 0.76), it is determined that there is a significant nonlinear monotonic relationship between the parameter and RMS_DS.

[0097] S4. Results Presentation and Decision Basis: The results presentation module uses charts to show the differences in the impact of parameters on RMS_DS, providing a basis for channel modeling and system optimization.

[0098] S4 includes the following specific steps:

[0099] S41. Using charting tools, compare the Pearson and Spearman correlation coefficients calculated under different test scenarios, and create bar charts or scatter plots to show their changing trends, as shown in the following examples. Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 , Figure 12 As shown.

[0100] S42. Analyze the differences in the impact of each parameter on RMS_DS, and generate a detailed analysis report to provide a basis for subsequent channel modeling and wireless system optimization.

[0101] 1. Correlation analysis between RMS_DS and physical environment parameters under LOS conditions

[0102] Its core parameters are affected as follows:

[0103] (1) Distance: The Pearson correlation coefficient is 0.76. (Specific details are as follows...) Figure 5 As shown in Figure 9, the Spearman correlation coefficient is 0.88, indicating a strong positive correlation between the root mean square delay spread and distance, meaning that increasing distance significantly increases the delay spread. In the LOS scenario, the direct signal path dominates, and increasing distance leads to more pronounced differences in multipath reflection paths, resulting in increased delay spread. Furthermore, the root mean square delay spread exhibits a significant nonlinear monotonic relationship with distance.

[0104] (2) Building Density: The Pearson correlation coefficient is 0.57. (Specific details are as follows...) Figure 5 As shown in Figure 9, the Spearman correlation coefficient is 0.59, indicating that the root mean square time delay spread is moderately positively correlated with building density. Densely built areas increase signal reflection and scattering, multipath components increase, and time delay spread is enhanced. Furthermore, the root mean square time delay spread has a certain nonlinear monotonic relationship with distance.

[0105] 2. Correlation analysis between RMS_DS and physical environment parameters under NLOS conditions

[0106] Impact of core parameters:

[0107] (1) Distance: Pearson correlation coefficient is 0.59, Spearman correlation coefficient is 0.50. (Specific details are as follows...) Figure 12 As shown, the root mean square delay spread is strongly positively correlated with distance, similar to LOS, indicating that increasing distance significantly increases delay spread. In NLOS scenarios, increased distance leads to more pronounced differences in multipath reflection paths, resulting in increased delay spread.

[0108] (2) Num_Obstacles: Pearson correlation coefficient is 0.47, Spearman correlation coefficient is 0.49. (Details are as follows...) Figure 12 As shown, the root mean square time delay spread is moderately positively correlated with the number of buildings. Increased obstacles block the direct path, leading to signal dependence on diffraction and scattering, increasing path differences, and thus increasing the time delay spread. Furthermore, the root mean square time delay spread exhibits a certain degree of nonlinear monotonicity with the number of buildings.

[0109] (3) Max_Height: Pearson correlation coefficient is 0.43, Spearman correlation coefficient is 0.45. (Details are as follows...) Figure 12As shown, the root mean square delay spread is moderately positively correlated with the maximum building height of the transceiver connection. Extremely tall buildings may form the dominant reflection path, increasing the diversity of multipath components and thus increasing the delay spread. Furthermore, there is a certain nonlinear monotonic relationship between the root mean square delay spread and the maximum building height of the transceiver connection.

[0110] Finally, it should be noted that although the present invention has been described with reference to specific embodiments, those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Various equivalent changes or substitutions can be made without departing from the concept of the present invention. Therefore, any changes or modifications to the above embodiments within the essential spirit of the present invention will fall within the scope of the claims of the present invention.

Claims

1. A comprehensive evaluation method for wireless channel delay spread based on dual correlation test, characterized in that, Includes the following steps: S1. Data Acquisition and Preprocessing: Using GPS data and signal packet data, LOS and NLOS areas are divided, and physical environment parameters are extracted; S2. Correlation Calculation: Calculate the Pearson correlation coefficient and Spearman correlation coefficient between RMS_DS and each physical environment parameter, and verify whether the correlation is significant through a significance test; S2 includes the following steps: S21. For each test scenario, calculate the Pearson correlation coefficient between RMS_DS and each physical environment parameter under LOS and NLOS conditions. Correlation coefficient with Spearman ; S22. When calculating the sample Pearson correlation coefficient r, further calculate the t-statistic and use the degrees of freedom to find or calculate the corresponding p-value to test whether the correlation is significant. S3. Data Merging and Trend Analysis: Merge data from multiple scenarios to improve statistical reliability, compare the differences between Pearson and Spearman correlation coefficients, and identify nonlinear monotonic relationships; S3 includes the following steps: S31. Merge the LOS and NLOS data from all test scenarios separately, and then calculate the correlation between the overall RMS_DS and each physical environment parameter; S32. Compare the differences between Pearson correlation coefficient and Spearman correlation coefficient to determine whether there is a nonlinear monotonic relationship. If the Spearman coefficient of a certain parameter is found to be significantly higher than the Pearson coefficient, it indicates that its correlation may contain a nonlinear trend. S4. Results Presentation and Decision Basis: The impact of parameters on RMS_DS is presented through charts and graphs, providing a basis for channel modeling and system optimization.

2. The comprehensive evaluation method for wireless channel delay spread based on dual correlation test according to claim 1, characterized in that, S1 includes the following steps: S11. Using GPS data and signal packet data, divide the signal data collected in the test scenario into LOS and NLOS regions, and classify the data according to the requirements of LOS and NLOS scenario division. S12. Perform physical environment parameter data statistics for each test scenario. Calculate and extract physical environment parameter information based on the given TX and RX coordinate information and the corresponding DEM elevation map.

3. The comprehensive evaluation method for wireless channel delay spread based on dual correlation test according to claim 2, characterized in that, S11 includes the following steps: S111. Data is collected in multiple test scenarios using deployed wireless measurement equipment, and the time, location, and signal transmission parameters of each data packet are recorded using the built-in GPS module; S112. According to the predetermined test plan, the signal data in each test scenario are divided into LOS region and NLOS region based on the actual environmental conditions; S113. Perform preliminary statistics on the data collected in each test scenario, calculate the number of data packets of each type, and ensure that the LOS and NLOS data reach the required sample size.

4. The comprehensive evaluation method for wireless channel delay spread based on dual correlation test according to claim 2, characterized in that, The physical environment parameters in S12 include at least one of distance, building density, building height, open space, and number of buildings.

5. The comprehensive evaluation method for wireless channel delay spread based on dual correlation test according to claim 1, characterized in that, The Pearson correlation coefficient in S21 Correlation coefficient with Spearman The calculation is as follows: The formula for the Pearson correlation coefficient is as follows: ， in, , It is the first The values ​​of each sample point, , They are variables and The mean, It is the total number of samples; The formula for the Spearman correlation coefficient is as follows: ， in: = It is the first The ranking difference of each sample point , It corresponds to and The ranking It refers to the number of samples.

6. The wireless channel delay spread comprehensive evaluation method based on dual correlation test according to claim 1, characterized in that, When calculating the t-statistic and p-value in S22 to test whether the correlation is significant, the confidence level is 95% or 99%.

7. The comprehensive evaluation method for wireless channel delay spread based on dual correlation test according to claim 1, characterized in that, S4 includes the following steps: S41. Using chart tools, compare the Pearson and Spearman correlation coefficients calculated under different test scenarios, and create bar charts or scatter plots to show their changing trends; S42. Analyze the differences in the impact of each parameter on RMS_DS, and generate a detailed analysis report to provide a basis for subsequent channel modeling and wireless system optimization.

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