A measurement and modeling method for surface wave excitation transmission mode channel with load

Abstract

The present invention provides a surface wave excitation transmission mode channel measurement and modeling method with load, including: step 1, establishing a wireless channel measurement system; step 2, obtaining measurement data; step 3, calculating the average path loss value; step 4, building an initial model; step 5, building a new model; step 6, fitting a model function of the path loss index based on different load distances; step 7, obtaining a path loss coefficient and a path loss attenuation value; step 8, calculating the path loss attenuation value; step 9, fitting a relationship expression between the path loss attenuation value and the coupling distance; step 10, building an indoor high-frequency band channel path loss model; step 11, verifying the accuracy of the indoor high-frequency band channel path loss model. The present invention can effectively reduce the path loss of the signal transmission process in the indoor environment of the industrial Internet of Things, and can effectively optimize the performance of the wireless communication system in the indoor environment of the industrial Internet of Things.

Description

Technical Field

[0001] The invention relates to the field of wireless communications, and in particular to a method for measuring and modeling a surface wave excitation transmission mode channel with a load. Background Art

[0002] As a key technology in modern manufacturing, the Industrial Internet of Things (IIoT) is being used more and more widely. By connecting sensors, smart terminal devices and systems to the Internet, IIoT realizes the intelligence of production and management, thereby improving production efficiency and management level.

[0003] In the IIoT system, wireless communication network is the key technology to realize the interconnection of equipment and systems. It plays an important role in the information transmission in factory automation, logistics management, equipment monitoring, etc. Through the wireless communication network, enterprises can realize the real-time transmission and processing of production data, thereby improving production efficiency, reducing downtime, and improving the overall production management level.

[0004] Wireless communication technology is widely used in the Industrial Internet of Things (IIoT) environment. However, this technology faces a series of unique challenges in industrial environments. First, there are a large number of electromagnetic interference sources in industrial environments, such as motors, welding equipment, and high-power transmission lines. These interference sources can have a significant impact on the transmission of wireless signals, resulting in a decrease in signal quality. In addition, phenomena such as multipath effects and signal attenuation can further reduce the reliability and efficiency of communication. Second, industrial applications have strict requirements on the reliability and real-time performance of communications. Many mission-critical and safety-related applications require high reliability and low latency in data transmission. Any form of communication delay and data loss may have a serious impact on the performance and safety of industrial automation systems. For example, in an automated control system, communication delays may cause the system to respond untimely, leading to equipment failure or safety accidents. In addition, in an industrial environment, a variety of components as signal loads will also affect signal propagation. These components include but are not limited to metal structures, mechanical equipment, and other large obstacles, which not only block the signal path, but also may cause signal reflection and scattering, further increasing the complexity and uncertainty of signal transmission. In large-scale industrial sites, ensuring the full coverage and stability of wireless networks has become a major challenge. Summary of the invention

[0005] Purpose of the invention: The technical problem to be solved by the present invention is to provide a method for measuring and modeling a surface wave excitation transmission mode channel with a load in view of the shortcomings of the prior art, comprising the following steps:

[0006] Step 1: Establish a wireless channel measurement system and set up a measurement scenario;

[0007] Step 2, obtaining measurement data;

[0008] Step 3, calculating the average path loss value of each measurement point;

[0009] Step 4, constructing an initial model based on wireless communication combined with surface waves;

[0010] Step 5, constructing a new model based on the path loss index values ​​extracted from the measurement that vary based on different load distances;

[0011] Step 6, calculate the path loss coefficient values ​​based on different load distances, and the path loss index values ​​based on different load distances (it should be emphasized here that due to the combination of wireless propagation and surface waves, when the load distance is appropriate, most of the signals are propagated in the form of surface waves, and most of the signals are absorbed by the connected loads. Therefore, the wireless propagation part is relatively small, so the path loss index is relatively large), and fit the model function of the path loss index based on different load distances by the least squares method;

[0012] Step 7, substitute the measured data into the initial model, and obtain the path loss coefficient N and the path loss attenuation value SW at different load distances;

[0013] Step 8: Adjust the distance between the receiving end and the signal source. d , and the corresponding distance between the receiving end and the signal source at this time d Load distance d l The path loss value at Substitute into the initial model and calculate the load distance as d l , the distance between the receiving end and the signal source is d The path loss attenuation of the surface wave is ;

[0014] Step 9: Use the least squares method to extract parameters and fit the surface wave path loss attenuation value caused by the load distance. Distance to load d l The relational expression of

[0015] Step 10, constructing an indoor high frequency channel path loss model based on a model function of a path loss index at different load distances and a path loss model;

[0016] Step 11, compare the mean and standard deviation of the shadow factor of the traditional logarithmic distance model and the indoor high-frequency band channel path loss model to verify the accuracy of the indoor high-frequency band channel path loss model.

[0017] In step 1, the wireless channel measurement system includes three wide-beam antennas, a stainless steel metal tube and a coupling device with adjustable distance;

[0018] Among them, two wide-beam antennas are used to transmit and receive signals respectively, and the other wide-beam antenna is connected to the load and installed in the middle of the transceiver. A wide-beam antenna is an antenna with a transmitting and receiving angle greater than 30 degrees. Its main feature is a wider main lobe and smaller side lobe. Wide-beam antennas are widely used in wireless communications because they can cover a wider area and increase the coverage area of ​​the signal, while also providing better multi-path channel transmission characteristics.

[0019] The stainless steel metal tube serves as a medium for signal transmission, connecting the transmitting end and the load;

[0020] The adjustable coupling device is used to control the distance between the stainless steel metal tube and the antenna;

[0021] The adjustable coupling device adopts an adjustable structure, including two layers of upper and lower cardboard, and three wide beam antennas are located between the two layers of acrylic plates and closely attached to the lower acrylic plate;

[0022] The stainless steel metal tube is suspended above the antenna using a bracket, and the bracket is used to adjust the height;

[0023] The propagation of surface waves along the conductor surface in the wireless channel measurement system is expressed by the following formula:

[0024] , (1)

[0025] The radial distance ; represents the wave number, represents the radial coordinate, z is the axial distance; is the integration variable; the function refers to the modified Bessel function of the second kind, is the integral symbol, c represents the spherical integral, ph represents the phase, and ; e is a natural constant, j is an imaginary unit. Formula (1) indicates that the spherical wave is extended into a cylindrical wave, and the transmission law of the surface wave follows this formula (1).

[0026] Signal transmission mechanism: In the wireless channel measurement system of the present invention, part of the signal is still transmitted through the wireless channel, but most of the signal is absorbed by the load along the metal tube in the form of surface waves. Since the metal tube has better conductivity, most of the signal is absorbed by the load, causing interference to the receiving end signal.

[0027] The system can be used to perform channel measurement and analysis at different load distances, and evaluate the signal transmission quality and channel stability at different distances.

[0028] In step 1, the setting of the measurement scene includes: fixing the transmitting end of the antenna at a preset location and keeping the antenna height unchanged; at the same time, presetting the distance between the load and the transmitting end; the transmitting end, the load, and the receiving end are on the same straight line;

[0029] The distance between the transmitter and the load is called the load distance;

[0030] Start with the load 0.5 meters away from the antenna and gradually move the load to 3 meters. Set a measurement point every 0.25 meters. At each measurement point, perform X times (usually 9 times) of measurement and calculate the average path loss.

[0031] In step 2, the measurement data includes each different load distance d l , the distance between the receiving end and the signal source at different load distances d , and the path loss value of each measurement point.

[0032] In step 4, the initial model based on wireless communication combined with surface waves is:

[0033] , (2)

[0034] , (3)

[0035] in, is the reference distance. Here, the starting lengths of the receiving and sending distances are set to 1m, 1.25m, 1.5m, 1.75m, and 2m respectively for different load distances. , Indicates the wavelength of the wireless carrier signal. d l Set to 0.5m, At the reference distance Distance from reference load PL(d) is the path loss value at the distance d between the receiving end and the signal source; Shadow fading, shadow fading It has a mean of zero and a standard deviation of Normal distribution of and They represent the distribution functions The variance and mean of , p is the distribution probability of path loss; For the load distance d l The path loss exponent is Load distance based for surface waves d l and the attenuation value of the distance between the receiving end and the signal source, exp is the natural exponential function. N is the path loss coefficient.

[0036] In step 5, the new model is:

[0037] ， (4)

[0038] Among them, a, b, and c are the parameters to be fitted.

[0039] In step 6, the model function is:

[0040] (5).

[0041] In step 8, the load distance is calculated using the following formula: d l , the distance between the receiving end and the signal source is d The path loss attenuation of the surface wave is :

[0042] , (6)

[0043] , (7)

[0044] Among them, the floating intercept The value is the reference distance Distance from reference load Reference path loss value between ;

[0045] Will Set to a floating intercept for the path loss fit curve , The value is based on the reference load distance Increased value of the floating intercept of the path loss fitting curve;

[0046] The attenuation value varies according to the load distance. d l Build a simple model of the changes:

[0047] , (8)

[0048] in, , , are the parameters to be fitted.

[0049] In step 9, the relational expression is:

[0050] , (9)

[0051] In step 10, the indoor high frequency band channel path loss model is:

[0052] (10)

[0053] in, Set to 1m, Set to 1m.

[0054] Step 11 includes: verifying the indoor high frequency band channel path loss model in the following manner:

[0055] Step 11-1: Use the following formula to verify the indoor high-frequency channel path loss model:

[0056] (11)

[0057] The mean formula is:

[0058] (12)

[0059] in, is the shadow fading prediction value of the i-th data, is the measured value of shadow attenuation of i data; is the obtained mean; represents the shadow fading difference, ;

[0060] The variance formula is:

[0061] (13)

[0062] in, is the shadow fading difference of the i-th data, is the shadow fading variance; the smaller the mean and variance are, the more reliable the indoor high-frequency channel path loss model is;

[0063] Perform more than two measurements and more than two fittings, select the indoor high-frequency channel path loss model with the smallest mean and variance, and execute step 11-2;

[0064] Step 11-2, establish a judgment module, using the determination coefficient To determine, select the actual measurement value of the required point and the calculated value obtained by the indoor high-frequency channel path loss model , coefficient of determination The calculation formula is:

[0065] (14)

[0066] (15)

[0067] in is the average value of the M measured values; is the average of M calculated values;

[0068] The determination module determines the determination coefficient Is it greater than the preset threshold 0.6? If so, the current measurement value is fitted successfully, otherwise, it is refitted.

[0069] The present invention is based on measurement data, and uses the least square method to extract the formal parameters of the path loss index formula and the surface wave path loss attenuation formula, so as to obtain the path loss index formula and the surface wave path loss attenuation formula. The measurement data is obtained by using a VNA measurement device through a frequency sweep measurement method. According to the propagation characteristics of the indoor high-frequency channel, an initial model with a path loss index change and a surface wave path loss attenuation channel is constructed, and the parameters in the initial model are formal parameters, which are actual parameters after the parameters are extracted, and the model is established. The method for verifying the accuracy of the model includes: verifying whether the fitting curve and the fitting parameters are accurate by judging whether the determination coefficient is greater than a preset threshold; calculating and obtaining the error set of the high-frequency channel model based on the path loss index change and the surface wave path loss attenuation in the indoor environment of the industrial Internet of Things, and using the statistical law of the cumulative distribution function of the shadow factor of the model to obtain the mean and variance of the difference between the predicted value and the measured value, and comparing it with the traditional path loss model to verify the accuracy of the model.

[0070] The method of the present invention analyzes and mathematically models the influence of the load on the surface wave transmission mode through data measurement and fitting, thereby effectively predicting various interference problems in the signal transmission process.

[0071] Surface waves are electromagnetic waves that propagate along the surface of a medium and have significant application advantages. First, the propagation path loss of surface waves is small, which is about 30 dB less than that of free space propagation. This is because the signal mainly propagates along the surface of the conductor, avoiding the high loss in free space. Second, the multipath effect of surface waves is small, and the signal propagation path is clear, which is suitable for robust and low-loss wireless communication in the industrial Internet of Things. The attenuation of surface waves is a logarithmic function of the load distance. As the load distance increases, the transmission effect gradually weakens. Surface waves can be excited in many ways, and the most common one is to use the coupling effect between metal conductors and media. In addition, by adjusting the surface microstructure of the medium (such as groove shape and array structure), the surface waves can be excited and controlled, including adjusting the excitation position, polarization direction and wavelength of the wave. Surface waves are highly sensitive and selective and are widely used in wireless communication, sensing and detection. Its efficient and stable transmission characteristics make it particularly important in the industrial Internet of Things, and can meet the needs of low-loss and robust communication. Through in-depth research and optimization of surface wave excitation methods, the system performance and effect can be further improved. In general, the transmission characteristics of surface waves are significantly affected by the conductor material and geometric dimensions, and require detailed mathematical analysis and modeling to accurately describe them. This is of great significance for understanding and applying surface wave technology.

[0072] The present invention introduces the load mechanism into the surface wave transmission model. Part of the signal from the transmitting end is transmitted to the receiving end through free space, and part of the signal is transmitted through the stainless steel metal tube and absorbed by the load. This causes strong interference to the propagation of the signal. The proportion of signal energy absorbed in the form of surface waves is determined by the load distance between the stainless steel metal tube and the antenna. The greater the load distance, the stronger the surface wave excited, the greater the proportion of the signal absorbed by the load, and the stronger the interference.

[0073] In surface wave channels, the path loss of signal transmission is usually lower than that of wireless channels. The main reason is that the reflection and refraction of the signal between the medium and the conductor will cause part of the signal energy to focus on the surface of the conductor, thereby achieving a certain degree of loss reduction. Therefore, it is obvious that the quasi-random channel that combines the wired channel and the wireless channel after the introduction of surface waves can greatly reduce the path loss and improve the quality and reliability of signal transmission. However, it should be noted that since the transmission of the surface wave channel is affected by many factors such as the shape of the conductor surface and the properties of the medium, its transmission characteristics need to be accurately modeled and measured in order to achieve the best transmission effect in practical applications.

[0074] The present invention fully considers the influence of antenna load on surface wave transmission in the industrial Internet of Things environment, so as to better utilize the surface wave transmission mechanism to amplify the signal in a complex industrial environment, and to make advance estimates of various complex situations and take corresponding early warning measures.

[0075] Beneficial effect: The present invention combines wired channels with wireless channels by adding conductors between transmitting and receiving antennas to introduce surface waves, which can effectively reduce the path loss of signal transmission in the indoor environment of the Industrial Internet of Things, and can effectively optimize the performance of the wireless communication system in the indoor environment of the Industrial Internet of Things, thereby helping the design and optimization of the wireless communication system in the indoor environment of the Industrial Internet of Things.

[0076] The present invention can effectively prevent and avoid the load influence that may occur in the surface wave transmission mode in the industrial Internet of Things environment by considering the influence of the load in the traditional surface wave transmission model. It can effectively optimize the performance of the wireless communication system in the indoor environment of the industrial Internet of Things, thereby helping the design and optimization of the wireless communication system in the indoor environment of the industrial Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0078] Figure 1 Schematic diagram of indoor industrial Internet of Things environment based on surface wave excitation.

[0079] Figure 2 Schematic diagram of surface wave excitation mode under load.

[0080] Figure 3 Schematic diagram of the change of path loss with the receiving and transmitting distance under different load distances. DETAILED DESCRIPTION

[0081] The present invention provides a method for measuring and modeling a surface wave excitation transmission mode channel with a load, comprising the following steps:

[0082] Step 1: Establish a wireless channel measurement system and set up a measurement scenario;

[0083] Step 2, obtaining measurement data;

[0084] Step 3, calculating the average path loss value of each measurement point;

[0085] Step 4, constructing an initial model based on wireless communication combined with surface waves;

[0086] Step 5, constructing a new model based on the path loss index values ​​extracted from the measurement that vary based on different load distances;

[0087] Step 6, calculate the path loss coefficient values ​​based on different load distances, and the path loss index values ​​based on different load distances (it should be emphasized here that due to the combination of wireless propagation and surface waves, when the load distance is appropriate, most of the signals are propagated in the form of surface waves, and most of the signals are absorbed by the connected loads. Therefore, the wireless propagation part is relatively small, so the path loss index is relatively large), and fit the model function of the path loss index based on different load distances by the least squares method;

[0088] Step 7, substitute the measured data into the initial model, and obtain the path loss coefficient N and the path loss attenuation value SW at different load distances;

[0089] Step 8: Adjust the distance between the receiving end and the signal source. d , and the corresponding distance between the receiving end and the signal source at this time d Load distance d l The path loss value at Substitute into the initial model and calculate the load distance as d l , the distance between the receiving end and the signal source is d The path loss attenuation of the surface wave is ;

[0090] Step 9: Use the least squares method to extract parameters and fit the surface wave path loss attenuation value caused by the load distance and the load distance. d l The relational expression of

[0091] Step 10, constructing an indoor high frequency channel path loss model based on a model function of a path loss index at different load distances and a path loss model;

[0092] Step 11, compare the mean and standard deviation of the shadow factor of the traditional logarithmic distance model and the indoor high-frequency band channel path loss model to verify the accuracy of the indoor high-frequency band channel path loss model.

[0093] like Figure 1 As shown, in step 1, the wireless channel measurement system includes three wide-beam antennas, a stainless steel metal tube (i.e. Figure 1 The metal tube in the middle) and a coupling device with adjustable distance; wherein the two wide beam antennas are used to transmit and receive signals respectively (i.e. Figure 1 The other wide beam antenna is connected to the load and set up in the middle of the transceiver (i.e. Figure 1The wide beam antenna is an antenna with a transmitting and receiving angle greater than 30 degrees. Its main feature is a wider main lobe and smaller side lobe. Wide beam antennas are widely used in wireless communications because they can cover a wider area and increase the coverage area of ​​the signal, while also providing better multipath channel transmission characteristics.

[0094] The stainless steel metal tube serves as a medium for signal transmission, connecting the transmitting end and the load;

[0095] The adjustable coupling device is used to control the distance between the stainless steel metal tube and the antenna;

[0096] The adjustable coupling device adopts an adjustable structure, including two layers of upper and lower cardboard, and three wide beam antennas are located between the two layers of acrylic plates and closely attached to the lower acrylic plate;

[0097] The stainless steel metal tube is suspended above the antenna using a bracket, and the bracket is used to adjust the height;

[0098] The propagation of surface waves along the conductor surface in the wireless channel measurement system is expressed by the following formula:

[0099] , (1)

[0100] The radial distance ; represents the wave number, represents the radial coordinate, z is the axial distance; is the integration variable; the function refers to the modified Bessel function of the second kind, is the integral symbol, c represents the spherical integral, ph represents the phase, and ; e is a natural constant. Formula (1) indicates that the spherical wave is extended into a cylindrical wave, and the propagation law of the surface wave follows this formula (1).

[0101] Signal transmission mechanism: In the wireless channel measurement system of the present invention, part of the signal is still transmitted through the wireless channel, but most of the signal is absorbed by the load along the metal tube in the form of surface waves. Since the metal tube has better conductivity, most of the signal is absorbed by the load, causing interference to the receiving end signal.

[0102] The system can be used to perform channel measurement and analysis at different load distances, and evaluate the signal transmission quality and channel stability at different distances.

[0103] In step 1, the setting of the measurement scene includes: fixing the transmitting end of the antenna at a preset location and keeping the antenna height unchanged; and presetting the distance between the load and the transmitting end; the transmitting end, the load, and the receiving end are in the same straight line;

[0104] The distance between the transmitter and the load is called the load distance;

[0105] like Figure 2 As shown, the load is moved gradually from 0.5 meters away from the antenna to 3 meters, and a measurement point is set every 0.25 meters. At each measurement point, X times (usually 9 times) of measurement are performed, and the average path loss is calculated.

[0106] In step 2, the measurement data includes each different load distance d l , the distance between the receiving end and the signal source at different load distances d , and the path loss value of each measurement point.

[0107] In step 3, the average path loss value of each measurement point is calculated using the following formula;

[0108] (2)

[0109] in, is the average path loss value at the distance d between the receiving end and the signal source, M is the total number of measurements, is the number of frequency points measured in the measurement frequency band, n is the frequency point number, is the frequency response of the mth measurement, , A and are the amplitude and phase obtained from the mth measurement respectively, and f refers to the signal frequency.

[0110] In step 4, the initial model based on wireless communication combined with surface waves is:

[0111] , (3)

[0112] , (4)

[0113] in, is the reference distance. Here, the starting lengths of the receiving and sending distances are set to 1m, 1.25m, 1.5m, 1.75m, and 2m respectively for different load distances. , Indicates the wavelength of the wireless carrier signal. d l Set to 0.5m, At the reference distance Distance from reference load PL(d) is the path loss value at the distance d between the receiving end and the signal source; Shadow fading, shadow fading It has a mean of zero and a standard deviation of Normal distribution of and They represent the distribution functions The variance and mean of , p is the distribution probability of path loss; For the load distance d l The path loss exponent is Load distance based for surface waves d l and the attenuation value of the distance between the receiving end and the signal source, exp is the natural exponential function. N is the path loss coefficient.

[0114] In step 5, the new model is:

[0115] ， (5)

[0116] Among them, a, b, and c are the parameters to be fitted.

[0117] In step 6, the model function is:

[0118] (6).

[0119] In step 8, the load distance is calculated using the following formula: d l , the distance between the receiving end and the signal source is d The path loss attenuation of the surface wave is :

[0120] , (7)

[0121] , (8)

[0122] Among them, the floating intercept The value is the reference distance Distance from reference load Reference path loss value between ;

[0123] Will Set to a floating intercept for the path loss fit curve , The value is based on the reference load distance Increased value of the floating intercept of the path loss fitting curve;

[0124] The attenuation value varies according to the load distance. d l Build a simple model of the changes:

[0125] , (9)

[0126] in, , , are the parameters to be fitted.

[0127] In step 9, the relational expression is:

[0128] , (10)

[0129] In step 10, the indoor high frequency band channel path loss model is:

[0130] (11)

[0131] in, Set to 1m, Set to 1m.

[0132] Step 11 includes: verifying the indoor high frequency band channel path loss model in the following manner:

[0133] Step 11-1: Use the following formula to verify the indoor high-frequency channel path loss model:

[0134] (12)

[0135] The mean formula is:

[0136] (13)

[0137] in, is the shadow fading prediction value of the i-th data, is the measured value of shadow attenuation of i data; is the obtained mean; represents the shadow fading difference, ;

[0138] The variance formula is:

[0139] (14)

[0140] in, is the shadow fading difference of the i-th data, is the shadow fading variance; the smaller the mean and variance are, the more reliable the indoor high-frequency channel path loss model is;

[0141] Perform more than two measurements and more than two fittings, select the indoor high-frequency channel path loss model with the smallest mean and variance, and execute step 11-2;

[0142] Step 11-2, establish a judgment module, using the determination coefficient To determine, select the actual measurement value of the required point and the calculated value obtained by the indoor high-frequency channel path loss model , coefficient of determination The calculation formula is:

[0143] (15)

[0144] (16)

[0145] in is the average value of the M measured values; is the average of M calculated values;

[0146] The determination module determines the determination coefficient Is it greater than the preset threshold 0.6? If so, the current measurement value is fitted successfully, otherwise, it is refitted.

[0147] In a specific embodiment of the present invention, the method comprises the following steps:

[0148] Step 1: Establish a wireless channel measurement system and set up a measurement scenario;

[0149] Step 2, obtaining measurement data;

[0150] Step 3, calculating the average path loss value of each measurement point;

[0151] Step 4, constructing an initial model based on wireless communication combined with surface waves;

[0152] Step 5, constructing a new model based on the path loss index values ​​extracted from the measurement that vary based on different load distances;

[0153] Step 6, calculate the path loss coefficient values ​​based on different load distances, and the path loss index values ​​based on different load distances (it should be emphasized here that due to the combination of wireless propagation and surface waves, when the load distance is appropriate, most of the signals are propagated in the form of surface waves, and most of the signals are absorbed by the connected loads. Therefore, the wireless propagation part is relatively small, so the path loss index is relatively large), and fit the model function of the path loss index based on different load distances by the least squares method;

[0154] Step 7, substitute the measured data into the initial model, and obtain the path loss coefficient N and the path loss attenuation value SW at different load distances;

[0155] Step 8: Adjust the distance between the receiving end and the signal source.d , and the corresponding distance between the receiving end and the signal source at this time d Load distance d l The path loss value at Substitute into the initial model and calculate the load distance as d l , the distance between the receiving end and the signal source is d The path loss attenuation of the surface wave is ;

[0156] Step 9, using the least square method to extract parameters, and fitting the relationship expression between the surface wave path loss attenuation value caused by the load distance and the load distance;

[0157] Step 10, constructing an indoor high frequency channel path loss model based on a model function of a path loss index at different load distances and a path loss model;

[0158] Step 11, compare the mean and standard deviation of the shadow factor of the traditional logarithmic distance model and the indoor high-frequency band channel path loss model to verify the accuracy of the indoor high-frequency band channel path loss model.

[0159] like Figure 3 As shown, multiple measurements are performed at each measurement point and the average value is taken to obtain the following data (the unit of load distance and transceiver distance is m, the unit of path loss is dB; the transceiver distance is the distance d between the receiving end and the signal source):

[0160] When the load distance is 0.5 and the transceiver distance is 1, the path loss is 39.61052; when the transceiver distance is 1.25, the path loss is 43.84481; when the transceiver distance is 1.5, the path loss is 45.65442; when the transceiver distance is 1.75, the path loss is 47.80034; when the transceiver distance is 2, the path loss is 49.18272; when the transceiver distance is 2.25, the path loss is 49.89052; when the transceiver distance is 2.5, the path loss is 50.53304;

[0161] When the load distance is 0.75, the path loss is 41.61977 when the transceiver distance is 1.25; when the transceiver distance is 1.5, the path loss is 44.3543; when the transceiver distance is 1.75, the path loss is 46.47074; when the transceiver distance is 2, the path loss is 47.96273; when the transceiver distance is 2.25, the path loss is 49.13768; when the transceiver distance is 2.5, the path loss is 50.13087; when the transceiver distance is 2.75, the path loss is 51.401;

[0162] When the load distance is 1, the path loss is 42.57752 when the transceiver distance is 1.5; when the transceiver distance is 1.75, the path loss is 45.85224; when the transceiver distance is 2, the path loss is 47.35226; when the transceiver distance is 2.25, the path loss is 48.95736; when the transceiver distance is 2.5, the path loss is 49.99834; when the transceiver distance is 2.75, the path loss is 50.76473; when the transceiver distance is 3, the path loss is 51.16859;

[0163] When the load distance is 1.25, the path loss is 43.05403 when the transceiver distance is 1.75; when the transceiver distance is 2, the path loss is 46.05906; when the transceiver distance is 2.25, the path loss is 48.25562; when the transceiver distance is 2.5, the path loss is 49.44079; when the transceiver distance is 2.75, the path loss is 50.3995; when the transceiver distance is 3, the path loss is 51.01546;

[0164] When the load distance is 1.5, the path loss is 37.90017 when the transceiver distance is 2; when the transceiver distance is 2.25, the path loss is 40.66308; when the transceiver distance is 2.5, the path loss is 42.39591; when the transceiver distance is 2.75, the path loss is 43.19688; when the transceiver distance is 3, the path loss is 44.10696;

[0165] Substituting the data in Table 1 into the initial surface wave model, we get the formula for how the path loss changes with the distance between the receiver and the signal source at different load distances. Fitting with the path loss model, we get the path loss coefficient and attenuation value at different load distances, as shown in Table 1 below (SW and N are parameters in the fitting formula and have no units).

[0166] Table 1

[0167] Load distance 0.5 0.75 1 1.25 1.5 SW 40.59 42.02 43.48 43.82 38.66 N 1.175 1.207 1.216 1.462 1.496

[0168] The functions of the attenuation value SW and the path loss coefficient N are fitted as follows:

[0169] ,

[0170] ,

[0171] The final formula for the overall path loss with respect to the load distance and the transmit / receive distance is:

[0172] .

[0173] The resulting model mainly solves the following two problems:

[0174] 1. Impact of load absorption of surface wave signals: By modeling the signal absorption by the load, the signal transmission process is optimized, which is particularly important in the industrial IoT environment because load changes can have a significant impact on signal transmission;

[0175] 2. Enhancement of surface wave transmission mechanism: Standardized surface wave transmission mechanism improves signal transmission effect and anti-interference performance. This is especially important for wireless communication environments that require high reliability and stability.

[0176] The present invention provides a method for measuring and modeling a surface wave excitation transmission mode channel with a load. There are many methods and ways to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A method for measuring and modeling a surface wave excitation transmission mode channel with a load, characterized in that: The following steps are involved: Step 1: Establish a wireless channel measurement system and set up a measurement scenario; Step 2, obtaining measurement data; Step 3, calculating the average path loss value of each measurement point; Step 4, constructing an initial model based on wireless communication combined with surface waves; Step 5, constructing a new model based on the path loss index values ​​extracted from the measurement that vary based on different load distances; Step 6, calculating the path loss coefficient values ​​based on different load distances, the path loss index values ​​based on different load distances, and fitting the model function of the path loss index based on different load distances by the least square method; Step 7, substitute the measured data into the initial model, and obtain the path loss coefficient N and the path loss attenuation value SW at different load distances; Step 8: The distance d between the receiving end and the signal source, and the corresponding distance d between the receiving end and the signal source, the load distance d l The path loss value PL(d, d l ) into the initial model and calculate the load distance d l , the path loss attenuation value SW of the surface wave when the distance between the receiving end and the signal source is d (d l ); Step 9: Use the least squares method to extract parameters and fit the surface wave path loss attenuation value SW (d l ) and load distance d l The relational expression of Step 10, constructing an indoor high frequency channel path loss model based on a model function of a path loss index at different load distances and a path loss model; Step 11, comparing the mean and standard deviation of the shadow factor of the traditional logarithmic distance model and the indoor high-frequency band channel path loss model to verify the accuracy of the indoor high-frequency band channel path loss model; In step 1, the wireless channel measurement system includes three wide beam antennas, a stainless steel metal tube and a coupling device with adjustable distance; Among them, two wide-beam antennas are used to transmit and receive signals respectively, and another wide-beam antenna is connected to the load and set up in the middle of the transceiver. The wide-beam antenna is an antenna with a transmitting and receiving angle greater than 30 degrees. The stainless steel metal tube serves as a medium for signal transmission, connecting the transmitting end and the load; The adjustable coupling device is used to control the distance between the stainless steel metal tube and the antenna; The adjustable coupling device adopts an adjustable structure, including two layers of upper and lower cardboard, and three wide beam antennas are located between the two layers of acrylic plates and closely attached to the lower acrylic plate; The stainless steel metal tube is suspended above the antenna using a bracket, and the bracket is used to adjust the height; The propagation of surface waves along the conductor surface in the wireless channel measurement system is expressed by the following formula: The radial distance ψ represents the wave number, ρ represents the radial coordinate, z is the axial distance; ξ is the integral variable; the function K0 refers to the second kind of modified Bessel function, is the integral symbol, c represents the spherical surface integral, ph represents the phase, and 0≤ph(ξ 2 -ψ 2 )≤π; e is a natural constant, j is an imaginary unit; In step 1, the setting of the measurement scene includes: fixing the transmitting end of the antenna at a preset location and keeping the antenna height unchanged; at the same time, presetting the distance between the load and the transmitting end; the transmitting end, the load, and the receiving end are on the same straight line; The distance between the transmitter and the load is called the load distance; Start from a position where the load is 0.5 meters away from the antenna, and gradually move the load until it is 3 meters away. Set a measurement point every 0.25 meters. At each measurement point, perform X measurements and calculate the average path loss. In step 2, the measurement data includes each different load distance d l , the distance d between the receiving end and the signal source at different load distances, and the path loss value of each measurement point; In step 4, the initial model based on wireless communication combined with surface waves is: Where d0 is the reference distance, and d0>10λ, λ represents the wavelength of the wireless carrier signal, PL(d0, d l0 ) is the distance between the reference distance d0 and the reference load distance d l0 PL(d) is the path loss value at the distance d between the receiving end and the signal source; Shadow fading, shadow fading It follows a normal distribution with mean zero and standard deviation σ; σ and μ p They represent the distribution functions The variance and mean of the path loss, p is the distribution probability; N(d l ) is the load distance d l The path loss index, SW(d l ) is the surface wave based on the load distance d l and the attenuation value of the distance between the receiving end and the signal source, exp is the natural exponential function; In step 5, the new model is: N(d l )=aarctan(bd l -c)+d (4) Among them, a, b, and c are the parameters to be fitted; In step 6, the model function is: N(d l )=0.107 silver(17.7d l -19.95)+1.345 (5) In step 8, the load distance is calculated as d l , the path loss attenuation value SW of the surface wave when the distance between the receiving end and the signal source is d (d l ): <h2 style=";text-align:left;direction:ltr">PL(d,d<h2 style=";text-align:left;direction:ltr"> l <h2 style=";text-align:left;direction:ltr"> )=SW(d<h2 style=";text-align:left;direction:ltr"> l <h2 style=";text-align:left;direction:ltr"> )+β (6) <h2 style=";text-align:left;direction:ltr">SW(d<h2 style=";text-align:left;direction:ltr"> l <h2 style=";text-align:left;direction:ltr"> )=PL(d,d<h2 style=";text-align:left;direction:ltr"> l <h2 style=";text-align:left;direction:ltr"> )-β (7) Wherein, the floating intercept β is the reference distance d0 and the reference load distance d l0 The reference path loss value PL(d0, d l0 ); PL(d0,d l0 ) is set as the floating intercept β of the path loss fitting curve, SW(d l ) value is based on the reference load distance d l0 Increased value of the floating intercept of the path loss fitting curve; According to the attenuation value of different load distances, d l Build a simple model of the changes: Among them, a1, b1, c1 are the parameters to be fitted; In step 9, the relational expression is: <h2 style=";text-align:left;direction:ltr">SW(d<h2 style=";text-align:left;direction:ltr"> l <h2 style=";text-align:left;direction:ltr"> )=-1.504d<h2 style=";text-align:left;direction:ltr"> l <h2 style=";text-align:left;direction:ltr"> 5.126 <h2 style=";text-align:left;direction:ltr"> +40.17 (9) In step 10, the indoor high frequency band channel path loss model is: Step 11 includes: verifying the indoor high frequency band channel path loss model in the following manner: Step 11-1: Use the following formula to verify the indoor high-frequency channel path loss model: The mean formula is: in, is the shadow fading prediction value of the i-th data, is the measured value of shadow attenuation of the i-th data; μ σ is the mean value obtained; X i represents the shadow fading difference, The variance formula is: Among them, X i is the shadow fading difference of the i-th data, σ 2 is the shadow fading variance; Perform more than two measurements and more than two fittings, select the indoor high-frequency channel path loss model with the smallest mean and variance, and execute step 11-2; Step 11-2, establish a judgment module, using the determination coefficient R 2 To determine, select the actual measured value f(d, d l ) and the calculated value PL(d, d l ), coefficient of determination R 2 The calculation formula is: in is the average value of the M measured values; is the average of M calculated values; The determination module determines the determination coefficient R 2 Is it greater than the preset threshold? If so, the current measurement value is fitted successfully, otherwise it is refitted.

Images