An indoor wireless signal path loss modeling method based on surface wave excitation

Abstract

The present invention discloses a method for modeling the path loss of indoor wireless channels based on surface wave excitation. First, a measurement scenario is set up, and according to the propagation characteristics of indoor high-frequency channels, an initial model of wireless channel path loss based on surface waves is constructed; the path loss exponent is extracted from the measurement data according to the initial model of wireless channel path loss, and the path loss exponent formula and the surface wave path loss attenuation formula are constructed by parameter extraction using the least squares method. Finally, an indoor high-frequency channel path loss model is constructed; the accuracy of the fitting curve and fitting parameters is verified by judging whether the coefficient of determination is greater than a preset threshold, and the mean and variance of the difference between the predicted value and the measured value are obtained using the statistical law of the cumulative distribution function of the shadow factor, and compared with the traditional path loss model to verify the accuracy of the model. Through this method, the characteristics of indoor channels can be understood more comprehensively and accurately, thereby improving the accuracy of channel measurement.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly relates to a method for modeling indoor wireless channel path loss based on surface wave excitation. Background Art

[0002] Industrial Internet of Things (IIoT) has been increasingly widely applied as a key technology in modern manufacturing. It realizes intelligent production and management by connecting sensors, intelligent terminal devices, and systems to the Internet. In IIoT, wireless communication networks play an important role in information transmission such as factory automation, logistics management, and equipment monitoring. However, wireless communication in industrial environments is affected by a more complex indoor environment, such as multiple scattering points and multiple interferences, resulting in more complex path loss characteristics of signals, which has an important impact on the performance and reliability of communication systems.

[0003] Currently, there are some industrial Internet of Things wireless channel modeling methods, such as ray tracing and geometric optics-based methods, for studying the path loss of indoor wireless channels. However, these methods still have some problems and challenges. First, the complex structures and object distributions in industrial environments lead to unconventional signal propagation characteristics. For example, metal structures, obstacles, and reflections have a significant impact on signal propagation. Second, different frequency bands and frequencies widely used in industrial Internet of Things applications have a significant impact on the propagation characteristics of channels. For example, the 2.4 GHz and 5.8 GHz ISM frequency bands are widely used in industrial Internet of Things, but their propagation characteristics are significantly different from those of other frequency bands. In addition, the current channel modeling methods still target wireless channel modeling, without changing the actual physical transmission characteristics of the channel, nor changing the fading effect on signals.

[0004] Surface Wave refers to the electromagnetic wave that propagates along the surface of the medium. Its electromagnetic field is mainly concentrated near the surface of the medium, and it can cause a strong electromagnetic field distribution on the surface of the medium. Surface waves have high sensitivity and selectivity and are commonly used in fields such as wireless communication, sensing, and detection. There are various ways to excite surface waves. Among them, the more common one is to utilize the coupling effect between the metal conductor and the medium. By radiating electromagnetic waves onto the metal conductor, energy transfer occurs between the metal conductor and the medium, thereby exciting the surface waves on the medium. In addition, the microstructures on the surface of the medium, such as groove structures, array structures, etc., can also be used. By adjusting the shape and size of the microstructures, the excitation and control of surface waves can be achieved. In practical applications, different excitation methods can have different effects on the characteristics of surface waves, such as the excitation position, polarization direction, wavelength, etc. Therefore, it is necessary to select the appropriate surface wave excitation method according to the specific application scenario and requirements, and conduct in-depth research and optimization on it to improve the system performance and effect. The surface wave excitation method used in the present invention is to utilize the coupling effect between the metal conductor and the medium.

[0005] In view of the above problems, the present invention proposes an indoor wireless channel path loss modeling method based on surface wave excitation. This method first generates a transmission channel with effectively reduced path loss through the surface wave excitation method, converting the pure wireless channel propagation into a quasi-random channel combining wireless and wired, which greatly reduces the path loss. Secondly, comprehensively considering the complex structures and object distributions in the industrial Internet of Things environment and combining the propagation characteristics of different frequency bands and frequencies, accurate modeling of the indoor wireless channel path loss combining wired and wireless in the context of the industrial Internet of Things is achieved. Specifically, this method utilizes the surface wave excitation effect, considers the influence of metal structures, obstacles, and reflections on signal propagation, and provides a more accurate path loss estimation in the industrial Internet of Things environment through the analysis of different distance characteristics.

[0006] In the surface wave channel, the path loss of signal transmission is usually lower than that of the wireless channel. The main reason is that the reflection and refraction of the signal between the medium and the conductor will cause a part of the signal energy to focus on the conductor surface, thereby achieving a certain degree of loss reduction. Therefore, it is obvious that introducing surface waves to combine the wired channel and the wireless channel into a quasi-random channel can greatly reduce the path loss and improve the quality and reliability of signal transmission. However, it should be noted that due to the influence of various factors such as the shape of the conductor surface and the properties of the medium on the transmission of the surface wave channel, its transmission characteristics need to be accurately modeled and measured in order to achieve the best transmission effect in practical applications.

[0007] In summary, the emergence of the surface wave channel provides a new solution for the combination of wireless and wired transmissions, and can provide more choices for achieving high-quality and reliable communication transmissions. Summary of the Invention

[0008] The purpose of the present invention is to provide a wireless channel measurement method, device and modeling method for transmitting surface waves using a conductor in an indoor industrial Internet of Things environment. By artificially introducing the concept of surface waves, the loss during signal transmission is effectively reduced.

[0009] To achieve the above object, the present invention provides an indoor wireless channel path loss modeling method based on surface wave excitation, including the following steps:

[0010] Step 1: Set up the measurement scenario;

[0011] Step 2: Obtain measurement data, where the measurement data includes each different coupling distance , the distance d between the receiving end and the signal source at different coupling distances, and the path loss values at each position point;

[0012] Step 3: Obtain the average path loss value at each distance point;

[0013] Step 4: Construct an initial model of the wireless channel path loss based on wireless communication combined with surface waves;

[0014] Step 5: Construct a log-distance loss model according to the path loss exponent values that vary based on different coupling distances extracted from the measurement;

[0015] Step 6: Calculate the path loss exponent values based on different coupling distances based on the average path loss of different coupling distances between the antenna and the conductor and different transceiver distances and the log-distance loss model, and fit a model function of the path loss exponent based on different coupling distances through the least squares method according to the path loss exponent values based on different coupling distances and the coupling distances;

[0016] Step 7: Based on the initial model of the wireless channel path loss and the measurement data, through appropriate data processing and fitting analysis, solve to obtain the path loss exponent of the measurement scenario and the surface wave attenuation value at different coupling distances;

[0017] Step 8: Substitute the distance d between the receiving end and the signal source and the corresponding path loss value at the distance d and the coupling distance into the initial model of the wireless channel path loss, and calculate to obtain the path loss attenuation value of the surface wave at the coupling distance of and the transceiver distance d;

[0018] Step 9: Use the least squares method for parameter extraction to fit the surface wave path loss attenuation formula caused by the coupling distance;

[0019] Step 10: According to the above coupling distance , transceiver distance , construct an indoor high-frequency band signal path loss model with path loss exponents at different coupling distances, model functions based on path loss exponents at different coupling distances, and path loss models;

[0020] Step 11: Compare according to the traditional logarithmic distance model and the mean and standard deviation of the shadow factor of the indoor high-frequency band signal path loss model to verify the accuracy of the indoor high-frequency band signal path loss model.

[0021] As a further improvement of the present invention, the specific steps of the step 1 are as follows: First, fix the transmitting end of the antenna at a preset point and keep the height of the antenna transmitting end unchanged; Subsequently, the coupling distance ranges from 10 mm to 40 mm at an interval of 5 mm. For each coupling distance, the antenna moves linearly from an interval of 1 m to an interval of 5 m, and a measurement point is set every 0.5 m; At each measurement point, 9 measurements are made and the average path loss is calculated to improve the accuracy of the measurement results.

[0022] As a further improvement of the present invention, the formula for obtaining the average path loss value at each distance point in the step 3 is as follows:

[0023] ;

[0024] Where, is the average path loss value at the distance , is the number of the measurement, is the total number of measurements, is the number of frequency points measured within the measurement frequency band, n is the nth frequency point, is the th measurement frequency response, , and are the amplitude and phase obtained from the th measurement respectively.

[0025] As a further improvement of the present invention, the formula for constructing the initial model based on wireless communication combined with surface waves in the step 4 is as follows:

[0026] ;

[0027] ;

[0028] Where, is the reference distance, which is set to 1 m here, and , represents the wavelength of the wireless carrier signal, is the reference coupling distance, set to 0.01 m, d is the distance between the antenna transceiver ends, For the path loss values at the reference distance and the reference coupling distance , PL(d) is the path loss value at distance d; is the shadow fading, and the shadow fading obeys a normal distribution with a mean of zero and a standard deviation of , and are the variance and mean of the distribution function; is the path loss exponent value at different coupling distances, is the attenuation value of the surface wave based on different coupling distances and the transceiver distance.

[0029] As a further improvement of the present invention, the formula of the model constructed in step 5 is as follows:

[0030] ;

[0031] where a, b, and c are parameters to be fitted;

[0032] In step 6

[0033] As a further improvement of the present invention, the model function of the path loss exponent based on different coupling distances fitted by the least squares method in step 6 is as follows:

[0034] .

[0035] As a further improvement of the present invention, the specific steps for calculating the path loss attenuation value of the surface wave at the transceiver distance d when the coupling distance is in step 8 are as follows:

[0036] Set as the floating intercept of the path loss fitting curve , The value is the increase in the floating intercept of the path loss fitting curve based on the reference coupling distance . The following is the corresponding relationship:

[0037] ;

[0038] Therefore, the increase in the floating intercept of the path loss fitting curve based on the reference coupling distance is:

[0039] ;

[0040] where is the coupling distance, and the floating intercept is the reference distance ​Location and reference coupling distance Reference path loss value at the location , is the attenuation value of the surface wave based on different coupling distances and transceiver distances. A model is constructed according to the change of the attenuation value with different coupling distances:

[0041] ;

[0042] Among them, is the coupling distance, is the attenuation value of the surface wave path loss caused by the coupling distance, and A, B, and a are fitting parameters to be determined.

[0043] As a further improvement of the present invention, the relational expression of the attenuation value of the surface wave path loss caused by the coupling distance and the coupling distance fitted in step 9 is as follows:

[0044]

[0045] Among them, is the coupling distance, is the attenuation value of the surface wave path loss caused by the coupling distance.

[0046] As a further improvement of the present invention, the indoor high-frequency band channel path loss model constructed in step 10 is as follows:

[0047] ;

[0048] Among them, is the reference distance, which is set to 1m here, is the reference coupling distance, which is set to 0.01m, d is the distance between the antenna transceiver ends, is at the reference distance and the reference coupling distance at the location of the path loss value, is the shadow fading, and the shadow fading obeys a normal distribution with a mean of zero and a standard deviation of .

[0049] As a further improvement of the present invention, the steps for verifying the accuracy of the indoor high-frequency band channel path loss model in step 11 are as follows:

[0050] ;

[0051] The result on the right side of the equation is equal to the left side, that is, there is an error in the statistical law of the indoor high-frequency band channel path loss model for random signals. The right side of the equation is this error set, and the statistical law of the cumulative distribution function of the error set is used to judge;

[0052] The specific verification is as follows: By the difference between the predicted value and the measured value, the mean and variance of the indoor high-frequency band signal path loss model are obtained, and the mean and variance are compared with the traditional path loss model. If the difference between the two indicators is lower than the preset threshold, the accuracy of the indoor high-frequency band signal path loss model can be verified;

[0053] The formula for calculating the mean is:

[0054] ;

[0055] where N is the total number of data, is the shadow fading predicted value of the i-th data, is the shadow fading measured value of the i-th data, is the obtained mean, where , representing the shadow fading difference;

[0056] The formula for calculating the variance is:

[0057] ;

[0058] where N is the total number of data, is the shadow fading difference of the i-th data, is the shadow fading mean, is the shadow fading variance.

[0059] As a further improvement of the present invention, verifying the accuracy of the indoor high-frequency band signal path loss model in step 11 further includes the following steps:

[0060] Add a judgment module to evaluate the fitting parameters in steps 6 and 9, and use the coefficient of determination to determine. Select the measured value and the calculated value , and the formula for the coefficient of determination is as follows:

[0061] ;

[0062]

[0063] where N is the number of measured values obtained, is the measured value, is the average value of the N measured values taken, is the transceiver distance, is the coupling distance, is the calculated value, is the coefficient of determination;

[0064] The determination module determines whether the coefficient of determination is greater than a preset threshold. If it is greater than the preset threshold, the current measurement value fitting is successful; otherwise, refitting is performed.

[0065] The beneficial effects of the present invention are as follows. Compared with the prior art, the method proposed by the present invention is an improvement on the traditional indoor wireless channel measurement scheme. Previous measurement experiments only measured and modeled the wireless channel for free-space propagation between the transceiver ends, while the present invention performs artificial intervention on this basis, adding a conductor that does not contact the antenna between the transceiver ends and introducing the concept of surface waves. Surface waves are electromagnetic waves that propagate along the surface with the surface as the interface, and their propagation characteristics are different from those of traditional free-space propagation and direct propagation. After introducing surface waves, the characteristics of the indoor channel can be understood more comprehensively and accurately, thereby improving the accuracy of channel measurement. At the same time, while maintaining the measurement accuracy, the time and resources required for channel measurement in the indoor environment of industrial Internet of Things are reduced, and the efficiency and cost-effectiveness of the wireless communication system in the indoor environment of industrial Internet of Things are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 FIG. is an experimental scenario diagram for indoor channel measurement based on surface wave excitation.

[0067] Figure 2 FIG. is an experimental device diagram for indoor channel measurement based on surface wave excitation.

[0068] Figure 3 FIG. is a schematic flowchart of a method for modeling the path loss of an indoor wireless channel based on surface wave excitation.

[0069] Figure 4 FIG. is a schematic diagram of the principle of surface wave excitation.

[0070] Figure 5 FIG. is a comparison diagram of the path loss of the indoor channel before and after the introduction of surface waves in indoor channel measurement. EMBODIMENTS

[0071] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] The present invention provides a method for modeling the path loss of an indoor wireless channel based on surface wave excitation, including the following steps:

[0073] Step 1: Set up the measurement scenario: In the scenario setup, first fix the transmitting end of the antenna at a preset point while keeping the height of the transmitting end of the antenna unchanged. Subsequently, the coupling distance ranges from 10 mm to 40 mm with an interval of 5 mm. For each coupling distance, the antenna moves along a straight line from an interval of 1 m to an interval of 5 m, and a measurement point is set every 0.5 m. At each measurement point, 9 measurements are taken and the average path loss is calculated to improve the accuracy of the measurement results.

[0074] Step 2: Obtain the measurement data: The measurement data includes each different coupling distance , the distance d between the receiving end and the signal source at different coupling distances, and the path loss values at each position point;

[0075] Step 3: Obtain the average path loss value at each distance point. The formula is:

[0076]

[0077] where is the average path loss value at distance , is the number of the measurement, is the total number of measurements, is the number of frequency points measured within the measurement frequency band, n is the nth frequency point, is the th measurement frequency response, , and are the amplitude and phase obtained from the th measurement respectively.

[0078] Step 4: Construct an initial model of the wireless signal path loss based on wireless communication combined with surface waves:

[0079]

[0080]

[0081] where is the reference distance, which is set to 1 m here, and , represents the wavelength of the wireless carrier signal, is the reference coupling distance, set to 0.01 m, d is the distance between the transmitting and receiving ends of the antenna, is the path loss value at the reference distance and the reference coupling distance , PL(d) is the path loss value at distance d; is the shadow fading, and the shadow fading obeys a normal distribution with a mean of zero and a standard deviation of The normal distribution, and are the variance and mean of the distribution function; is the path loss exponent value at different coupling distances, is the attenuation value of the surface wave based on different coupling distances and the transceiver distance.

[0082] Step 5: Construct a log-distance loss model based on the path loss exponent values varying with different coupling distances extracted from the measurement:

[0083]

[0084] wherein, is the coupling distance between the conductor and the antenna, is the path loss exponent with respect to the coupling distance , and a, b, and c are parameters to be fitted.

[0085] Step 6: Calculate the path loss exponent values based on different coupling distances based on the average path loss of different coupling distances between the antenna and the conductor and different transceiver distances and the above log-distance loss model (it should be emphasized here that since wireless propagation is combined with surface waves, when the coupling distance is appropriate, most signals propagate in the form of surface waves, so the wireless propagation part is relatively small, and thus the path loss exponent is relatively small), and fit the model function of the path loss exponent based on different coupling distances through the least squares method according to the path loss exponent values based on different coupling distances and the coupling distance:

[0086]

[0087] wherein, is the coupling distance between the conductor and the antenna, is the path loss exponent with respect to the coupling distance .

[0088] Step 7: Based on the initial model of the wireless channel path loss and the measurement data, solve for the path loss exponent of the measurement scenario and the surface wave attenuation value at different coupling distances through appropriate data processing and fitting analysis.

[0089] Step 8: Substitute the distance d between the receiver and the signal source and the corresponding path loss value at the distance d and the coupling distance into the initial model of the wireless channel path loss, and calculate and obtain the path loss attenuation value of the surface wave when the transceiver distance is d at the coupling distance according to the following formula. Here, is set as the floating intercept of the path loss fitting curve , The value is the increased value of the floating intercept of the path loss fitting curve based on the reference coupling distance . The following is the corresponding relationship formula:

[0090]

[0091] Therefore, based on the reference coupling distance , the increased value of the floating intercept of the path loss fitting curve is:

[0092]

[0093] Among them, is the coupling distance, and the floating intercept is the reference distance at and the reference coupling distance at the reference path loss value , is the attenuation value of the surface wave based on different coupling distances and the transceiver distance. A simple model is constructed according to the change of the attenuation value of different coupling distances:

[0094]

[0095] Among them, is the coupling distance, is the attenuation value of the surface wave path loss caused by the coupling distance, and A, B, and a are parameters to be fitted.

[0096] Step 9: Use the least squares method to extract parameters and fit the relationship expression between the attenuation value of the surface wave path loss caused by the coupling distance and the coupling distance :

[0097]

[0098] Among them, is the coupling distance, is the attenuation value of the surface wave path loss caused by the coupling distance.

[0099] Step 10: According to the above coupling distance , the transceiver distance , the path loss exponent of different coupling distances, the model function of the path loss exponent based on different coupling distances, and the path loss model, construct an indoor high-frequency band channel path loss model:

[0100]

[0101] Among them, is the reference distance, which is set to 1m here, is the reference coupling distance, set to 0.01m, and d is the distance between the antenna transceiver ends. At the reference distance and the reference coupling distance are the path loss values. is the shadow fading. The shadow fading obeys a normal distribution with a mean of zero and a standard deviation of .

[0102] Step 11: Compare the means and standard deviations of the shadow factors of the traditional log-distance model and the indoor high-frequency band channel path loss model to verify the accuracy of the established indoor high-frequency band channel path loss model. This indoor high-frequency band channel path loss model is a statistical law of random signals, and there are certain errors. The right side of the following formula is the error set of this model. Generally, the statistical law of the cumulative distribution function of this set is used to judge. The following formula is the calculation method through the right side of the equation to verify the modeling model:

[0103]

[0104] The result on the right side of the equation is equal to the left side. It can be understood that there are errors in the statistical law of the model for random signals. The right side of the equation is this error set, and the statistical law of the cumulative distribution function of this set is used to judge; the specific verification is: by Find the difference between the predicted value and the measured value, calculate its mean and variance, make the mean and variance as small as possible and compare with the traditional path loss model. If the two indicators are not very different or more accurate, the accuracy of the model can be verified. The mean formula is:

[0105]

[0106] Among them, N is the total number of data, is the predicted value of the shadow fading for the i-th data, is the measured value of the shadow fading for the i-th data, is the obtained mean. Among them , represents the shadow fading difference.

[0107] The variance formula is:

[0108]

[0109] Among them, N is the total number of data, is the shadow fading difference for the i-th data. is the shadow fading mean, is the shadow fading variance.

[0110] Further, a judgment module needs to be added to evaluate the fitting parameters in Steps 6 and 9, and the coefficient of determination is used for determination to select the measured values of the required points and the calculated values . The calculation formula of the coefficient of determination is as follows:

[0111]

[0112]

[0113] where N is the number of measured values obtained, is the measured value, is the average value of the N measured values taken, is the distance between the transceiver, is the coupling distance, is the calculated value, is the coefficient of determination.

[0114] This judgment module determines whether the coefficient of determination is greater than the preset threshold. If it is greater, the current measured value is successfully fitted; otherwise, refitting is performed.

[0115] The indoor wireless signal path loss modeling method based on surface wave excitation of the present invention has the following advantages: First, by introducing a conductor into the signal transmission path, part of the wireless signal can be converted into a surface wave signal, that is, part of the signal is transmitted in the form of a surface wave, and the other part of the signal is still transmitted through the wireless channel. The proportion of the signal transmitted in the form of a surface wave is determined by the coupling distance between the conductor and the transceiver antenna; by using the transmission mode of the surface waveguide, the combination of the wireless channel and the wired channel (surface wave channel) is realized; specifically, the surface wave channel is a transmission mode achieved by placing an insulating medium near the surface of the conductor, and this medium is called the medium on the conductor. In the surface wave channel, the signal propagates in the medium near the surface of the conductor and also along the surface of the conductor. This propagation mode has both the advantages of the wireless channel, such as long transmission distance and no need for a physical medium, and the advantages of the wired channel, such as stable transmission and anti-interference.

[0116] In summary, the method proposed by the present invention is an improvement on the traditional indoor wireless channel measurement scheme. Previous measurement experiments only measured and modeled the wireless channels for free space propagation between the transceiver ends. On this basis, the present invention conducts artificial intervention by adding a conductor that does not contact the antenna between the transceiver ends, introducing the concept of surface waves. Surface waves are electromagnetic waves that propagate along the surface with the surface as the interface, and their propagation characteristics are different from those of traditional free space propagation and direct propagation. After introducing surface waves, the characteristics of indoor channels can be understood more comprehensively and accurately, thereby improving the accuracy of channel measurement. At the same time, while maintaining the measurement accuracy, the time and resources required for channel measurement in the indoor environment of industrial Internet of Things are reduced, and the efficiency and cost-effectiveness of wireless communication systems in the indoor environment of industrial Internet of Things are improved.

Claims

1. An indoor wireless signal path loss modeling method based on surface wave excitation, characterized in that Including the following steps: Step 1: Set up the measurement scenario; Step 2: Obtain measurement data, where the measurement data includes each different coupling distance , the distance d between the receiving end and the signal source at different coupling distances, and the path loss values at each position point; Step 3: Obtain the average path loss value for each distance point; Step 4: Construct an initial model of the wireless signal path loss based on wireless communication combined with surface waves; Step 5: Construct a log-distance loss model according to the path loss exponent values varying with different coupling distances extracted from the measurement; Step 6: Calculate the path loss exponent values based on different coupling distances based on the average path loss at different coupling distances between the antenna and the conductor and different transceiver distances and the log-distance loss model, and fit the model function of the path loss exponent based on different coupling distances by the least squares method according to the path loss exponent values based on different coupling distances and the coupling distances; Step 7: Based on the initial model of the wireless signal path loss and the measurement data, through appropriate data processing and fitting analysis, solve to obtain the path loss exponent of the measurement scenario and the surface wave attenuation value at different coupling distances; Step 8: Substitute the distance d between the receiving end and the signal source and the corresponding path loss value at the distance d and the coupling distance into the initial model of the wireless signal path loss, and calculate the path loss attenuation value of the surface wave when the coupling distance is and the distance between the transceiver is d; Step 9: Use the least squares method for parameter extraction to fit the surface wave path loss attenuation formula caused by the coupling distance; Step 10: According to the above coupling distance , the transceiver distance , construct an indoor high-frequency band signal path loss model based on the path loss exponent of different coupling distances, the model function of the path loss exponent based on different coupling distances, and the path loss model Step 11: Compare the mean and standard deviation of the shadow factors of the traditional log-distance model and the indoor high-frequency band signal path loss model to verify the accuracy of the indoor high-frequency band signal path loss model.

2. The indoor wireless signal path loss modeling method based on surface wave excitation according to claim 1, characterized in that The specific steps of Step 1 are as follows: First, fix the transmitting end of the antenna at a preset point and keep the height of the antenna transmitting end unchanged; subsequently, the coupling distance ranges from 10 mm to 40 mm with an interval of 5 mm. For each coupling distance, the antenna moves linearly from an interval of 1 m to an interval of 5 m, and a measurement point is set every 0.5 m; at each measurement point, 9 measurements are taken and the average path loss is calculated to improve the accuracy of the measurement results.

3. The indoor wireless signal path loss modeling method based on surface wave excitation according to claim 2, characterized in that, The formula for obtaining the average path loss value for each distance point in Step 3 is as follows: ; where, is the average path loss value at a distance of , is the number of the measurement, is the total number of measurements, is the number of frequency points measured within the measurement frequency band, n is the nth frequency point, is the th measurement frequency response, , and are the amplitude and phase obtained from the th measurement, respectively.

4. The indoor wireless channel path loss modeling method based on surface wave excitation according to claim 3, wherein, The formula for constructing the initial model based on wireless communication combined with surface waves in Step 4 is as follows: ； ; wherein, is the reference distance, which is set to 1 m here, and , represents the wavelength of the wireless carrier signal, is the reference coupling distance, set to 0.01 m, and d is the distance between the transmitting and receiving ends of the antenna, is at the reference distance and the reference coupling distance at the path loss value, and PL(d) is the path loss value at the distance d; is the shadow fading, and the shadow fading obeys a normal distribution with a mean of zero and a standard deviation of , and are the variance and mean of the distribution function; is the path loss exponent value under different coupling distances, is the attenuation value of the surface wave based on different coupling distances and the distance between the transmitting and receiving ends.

5. The indoor wireless channel path loss modeling method based on surface wave excitation according to claim 4, wherein The formula for the log-distance loss model constructed in Step 5 is as follows: ; where a, b, and c are parameters to be fitted; the model function of the path loss exponent based on different coupling distances fitted by the least squares method in step 6 is as follows: 。 6. The indoor wireless signal path loss modeling method based on surface wave excitation according to claim 5, wherein In the said step 8, the calculated coupling distance is When the distance between the transceiver is d, the path loss attenuation value of the surface wave at that distance specifically includes the following steps: Set as the floating intercept of the path loss fitting curve , The value is the increased value of the floating intercept of the path loss fitting curve based on the reference coupling distance . The following is the corresponding relational expression: ; Therefore, based on the reference coupling distance The increased value of the floating intercept of the path loss fitting curve is: ; wherein, is the coupling distance, the floating intercept is the reference distance at and the reference coupling distance at the reference path loss value , is the attenuation value of the surface wave based on different coupling distances and the transceiver distance. A model is constructed according to the change of the attenuation value with different coupling distances: ; wherein, is the coupling distance, is the surface wave path loss attenuation value caused by the coupling distance, and A, B, and a are parameters to be fitted.

7. The indoor wireless signal path loss modeling method based on surface wave excitation according to claim 6, wherein The relationship expression between the surface wave path loss attenuation value caused by the coupling distance and the coupling distance obtained by fitting in step 9 is as follows: is as follows: ; wherein, is the coupling distance, is the surface wave path loss attenuation value caused by the coupling distance.

8. The indoor wireless signal path loss modeling method based on surface wave excitation according to claim 7, wherein The indoor high-frequency band signal path loss model constructed in Step 10 is as follows: ; where is the reference distance, which is set to 1 m here, is the reference coupling distance, which is set to 0.01 m, and d is the distance between the antenna transmitting and receiving ends, is at the reference distance and the reference coupling distance at the path loss value, is the shadow fading, and the shadow fading obeys a normal distribution with a mean of zero and a standard deviation of .

9. The indoor wireless signal path loss modeling method based on surface wave excitation according to claim 8, wherein The steps for verifying the accuracy of the indoor high-frequency band signal path loss model in Step 11 are as follows: ； The result on the right side of the equation is equal to the left side, that is, there is an error in the statistical law of the indoor high-frequency band signal path loss model for random signals. The right side of the equation is this error set, and it is judged by the statistical law of the cumulative distribution function of the error set; Specific verification is as follows: By the difference between the predicted value and the measured value, the mean and variance of the indoor high-frequency band signal path loss model are obtained, and the mean and variance are compared with the traditional path loss model. If the difference between the two indicators is lower than the preset threshold, the accuracy of the indoor high-frequency band signal path loss model can be verified; The formula for calculating the mean is: ; where N is the total number of data, is the predicted value of shadow fading for the i-th data, is the measured value of shadow fading for the i-th data, is the obtained mean value, where , representing the shadow fading difference; the formula for calculating the variance is: ; where N is the total number of data, is the shadow fading difference of the i-th data, is the shadow fading mean value, is the shadow fading variance.

10. The indoor wireless signal path loss modeling method based on surface wave excitation according to claim 9, characterized in that, Verifying the accuracy of the indoor high-frequency band signal path loss model in Step 11 also includes the following steps: Add a judgment module to evaluate the fitting parameters of the said step 6 and the said step 9, and use the coefficient of determination to judge, and select the measured values of the required points and the calculated values . The calculation formula of the coefficient of determination is as follows: ； where N is the number of acquired measurement values, is the measurement value, is the average value of the N acquired measurement values, is the transceiver distance, is the coupling distance, is the calculated value, is the coefficient of determination; the determination module determines whether the coefficient of determination is greater than a preset threshold value. If it is greater than the preset threshold value, the fitting of the current measurement value is successful; otherwise, refitting is performed.

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