Indoor wireless channel measurement modeling method for Sub-6G multi-band

Abstract

The present invention provides a method for measuring and modeling indoor wireless channels in Sub‑6G multi-bands, comprising establishing a wireless channel measurement platform based on USRP; establishing a three-dimensional indoor scene model and setting the position of a transmitting module; performing actual measurement and ray tracing simulation on the Sub‑6G wireless channels of all carrier frequencies at each position of the receiving module by changing multiple positions and multiple carrier frequencies of the receiving module; calculating the error between the measured indoor wireless channel results and the simulated indoor wireless channel results; and calibrating the material parameters using a material parameter calibration method based on multipath level correction, thereby completing high-precision measurement and modeling of the indoor wireless channel; the measurement platform of the present invention is highly portable, low-cost, and has a wide frequency range; in addition, the present invention can alleviate the problem that deterministic modeling requires complex scene information and huge computing resources, effectively improving model accuracy and reducing computational complexity.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and more specifically, to a method for measuring and modeling indoor wireless channels for Sub-6G multi-bands. Background Art

[0002] With the rapid development of fifth-generation mobile communication technology (5G) networks, the performance of communication systems continues to improve. Among various network communication methods, wireless communication offers the advantages of high flexibility and good economic benefits. Indoor scenarios occupy a key position in wireless communication application scenarios. According to the "5G Indoor Coverage White Paper" released by ZTE, approximately 70% of services in the 4G era occurred indoors, and it is predicted that more than 85% of applications in the 5G era will occur indoors. However, wireless communication has disadvantages such as limited transmission distance and susceptibility to external electromagnetic interference. Especially in indoor scenarios with complex obstacle structures, problems such as poor electromagnetic wave penetration and severe multipath interference are more prominent. Therefore, the study of indoor wireless channel characteristics has become a research hotspot and difficulty in the field of wireless communications.

[0003] The 3rd Generation Partnership Project (3GPP) R16 version specification defines the frequency range of the new 5G antenna as the FR1 band and the FR2 band. The former is called Sub-6G and the latter is called millimeter wave (mmWave). mmWave has a larger bandwidth, higher transmission rate, and smaller component specifications, but the extremely high frequency causes the communication distance to drop sharply. Especially when there are a large number of obstacles indoors, mmWave has weak transmission performance and is difficult to adapt to complex indoor environments. Even though the frequency, bandwidth, and spectrum resources of Sub-6G are lower than those of mmWave, its signal coverage area has a greater advantage, stronger transmission performance, and lower deployment cost. For indoor communication scenarios with complex environments and many reflective objects, Sub-6G is more suitable for indoor applications.

[0004] Sub-6G channel modeling is primarily categorized into two types: deterministic and statistical. Deterministic modeling eliminates the need for field measurements but requires highly detailed communication scenario information and high computational complexity. Statistical modeling utilizes a measurement platform to conduct actual measurements in communication scenarios, but this carries high equipment costs and requires extensive repetitive testing.

[0005] Channel measurement is a primary method for studying wireless channel characteristics. Using the Universal Software Radio Peripheral (USRP) facilitates the construction of an efficient and accurate channel measurement platform. A USRP-based channel measurement platform consists of a transmitter and receiver, with one USRP at each end for signal processing. The USRP at the transmitter performs digital-to-analog conversion on the baseband digital signal generated by a laptop computer. This signal is then up-converted through carrier modulation to generate an analog RF signal suitable for transmission over the wireless channel. This signal is amplified by a power amplifier and transmitted via an RF antenna to the USRP at the receiver. The USRP at the receiver receives the high-frequency signal, down-converts it to a baseband signal, performs analog-to-digital conversion, and stores the processed digital signal in binary format on the laptop computer. This USRP-based channel measurement platform provides accurate channel measurement results for building high-precision indoor wireless propagation models.

[0006] The current prior art discloses a method and system for presetting and correcting electrical parameters. The method includes importing a 3D digital information format of a complex indoor environment required by a ray tracing model and fixing the initial input parameters of the ray tracing model; examining the impact of changes in the electrical parameters of various materials on radio wave propagation, and prioritizing the electrical parameters of specific materials based on actual prediction points of interest; formulating a measurement plan for an actual indoor scene; combining the ray tracing model with the measurement plan, and setting appropriate initial input parameters for the ray tracing model based on the measured conditions; optimizing the electrical parameters of the ray tracing model using the error function between simulation and measurement as the objective function of a genetic algorithm, and comparing the difference between the simulated power and the measured power before and after the electrical parameter optimization; the prior art method uses a genetic algorithm to calibrate the relative dielectric constant and conductivity of the material, and the standard deviation between the simulated value and the measured value after calibration is reduced to a certain extent. However, even though this method constructs an indoor propagation model based on the ray tracing algorithm, it does not fully utilize the advantages of the model, does not consider multipath level correction, and lacks detailed modeling in the indoor scene modeling process, ultimately resulting in low accuracy of the established indoor propagation model. In addition, the genetic algorithm used in this method has high complexity and consumes excessive computing resources. Summary of the Invention

[0007] In order to overcome the defects of the above-mentioned prior art in that the indoor wireless communication model established has low accuracy and high algorithm complexity, the present invention provides an indoor wireless channel measurement modeling method for Sub-6G multi-band, which can effectively improve the model accuracy and reduce the computational complexity.

[0008] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0009] A method for measuring and modeling indoor wireless channels in Sub-6G multi-bands includes the following steps:

[0010] S1: Establish a USRP-based wireless channel measurement platform in a real indoor scenario, including a transmitter module, a clock synchronization module, and a receiver module;

[0011] Set the position of the transmitter module and, by changing the position and carrier frequency of the receiver module, measure the Sub-6G wireless channel at all carrier frequencies at each receiver module position to obtain the actual indoor wireless channel measurement results.

[0012] S2: Build a 3D indoor scene model based on the layout of the real indoor scene. Set up transmitter modules at corresponding locations in the model, synchronously change the position and carrier frequency of the receiver modules, and perform ray tracing simulation on the Sub-6G wireless channel at all carrier frequencies at each location of the receiver modules to obtain indoor wireless channel simulation results.

[0013] S3: Calculate the error between the measured indoor wireless channel results and the simulated indoor wireless channel results, and use the material parameter calibration method based on multipath level correction to calibrate the material parameters in the indoor three-dimensional scene model to obtain a high-precision indoor three-dimensional scene model, completing high-precision measurement and modeling of the indoor wireless channel.

[0014] Preferably, the USRP-based wireless channel measurement platform established in step S1 is specifically:

[0015] The transmitter module includes a transmitter PC and a transmitter USRP. The transmitter PC generates a digital baseband signal. After setting the parameters of the transmitter USRP, the digital baseband signal is modulated into a high-frequency digital baseband signal, which is transmitted to the Sub-6G wireless channel through the transmitting antenna of the transmitter USRP.

[0016] The receiving end module includes a receiving end PC and a receiving end USRP. After the receiving end USRP parameters are set, the receiving end USRP is used to receive high-frequency digital baseband signals from the Sub-6G wireless channel and demodulate them into digital baseband signals, transmit them to the receiving end PC for real-time display, storage and processing, and output the actual measurement results of the indoor wireless channel;

[0017] The clock synchronization module is used to eliminate modulation and demodulation errors between the transmitting end module and the receiving end module.

[0018] Preferably, the transmitting end USRP parameters include carrier frequency, transmission gain, sampling rate, real-time bandwidth, antenna gain, antenna height, synchronization mode and detection signal type;

[0019] The receiving end USRP parameters include carrier frequency, receiving gain, sampling rate, real-time bandwidth, antenna gain, antenna height, synchronization mode and detection signal type;

[0020] The detection signal type is a Chirp signal.

[0021] Preferably, in step S2, the specific method of establishing the indoor three-dimensional scene model according to the real indoor scene layout is:

[0022] The real indoor scene layout includes information on the size, position, shape and constituent materials of indoor objects;

[0023] Using one or more of a laser rangefinder, a surveying instrument, and a camera, measure the indoor scene layout and obtain measurement data of indoor objects;

[0024] An indoor 3D scene model is established based on the measurement data of indoor objects, and material parameters of the corresponding objects are assigned.

[0025] Preferably, the accuracy of the established indoor three-dimensional scene model is not less than decimeter level.

[0026] Preferably, in step S2, the specific method of performing ray tracing simulation on the indoor three-dimensional scene model to obtain the indoor wireless channel simulation result is:

[0027] S2.1: Input the position of the transmitter module and the positions of multiple receiver modules into the indoor 3D scene model in the form of 3D coordinates, and initialize the ray tracing simulation parameters;

[0028] The ray tracing simulation parameters include: parameters of the transmitting module and the receiving module, including transmission power, carrier frequency, antenna gain, and antenna height; and material parameters of each object, including material thickness and electrical parameters, including relative dielectric constant, relative magnetic permeability, and electrical conductivity;

[0029] S2.2: Perform ray tracing simulation on the Sub-6G wireless channel for all carrier frequencies at each location of the receiving module. Record the channel impulse response and received detection signal power for all frequencies at each location to obtain indoor wireless channel simulation results.

[0030] Preferably, the specific method of step S3 is:

[0031] S3.1: Calculate the error between the measured indoor wireless channel results and the simulated indoor wireless channel results, set the calibration range, calibration step size, and electrical parameter weights for the material parameters of each object, and set the objective function J and its corresponding constraints, a first threshold ζ, and a second threshold η, where the first threshold ζ is greater than the second threshold η.

[0032] S3.2: Within the material thickness calibration range of each object, adjust the material thickness of each object in the indoor wireless channel simulation results with a calibration step size, re - conduct ray - tracing simulation, and calculate the objective function J1 after adjusting the material thickness. When J1 satisfies J1 ≤ ζ, complete the calibration of the material thickness of each object and execute step S3.3; otherwise, repeat step S3.2;

[0033] S3.3: When η < J1 ≤ ζ or J1 ≤ η and J1 does not satisfy the constraint conditions, use the tabu search algorithm to calibrate the electrical parameters of each object. Specifically: within the electrical parameter calibration range of each object, adjust the electrical parameters of each object in the indoor wireless channel simulation results with a calibration step size, re - conduct ray - tracing simulation again, and calculate the objective function J2 after adjusting the electrical parameters. Add all the objective functions J2 after adjusting the electrical parameters into the tabu list, search for the electrical parameters of each object corresponding to the minimum value of J2 in the tabu list, and take this electrical parameter and the material thickness as the optimal solution H of the tabu search algorithm. min Output;

[0034] When any of the following conditions is satisfied, complete the calibration of the material parameters in the indoor three - dimensional scene model and obtain a high - precision indoor three - dimensional scene model: J1 ≤ η and J1 satisfies the constraint conditions, J2 ≤ η and J2 satisfies the constraint conditions, and the tabu search algorithm outputs the optimal solution H. min 。

[0035] Preferably, in step S3.1, the specific method for setting the weight of the electrical parameter in the material parameters of each object is as follows:

[0036] When electromagnetic waves penetrate an object, the loss L T is as follows:

[0037]

[0038] where e is the natural constant, d is the material thickness of the object, μ0 is the vacuum permeability, ε0 is the vacuum permittivity, ε r1 is the relative permittivity of the first medium, and ε t2 is the relative permittivity of the second medium;

[0039] The wavelength λ of electromagnetic waves in a medium and the relative permittivity ε r and the relative permeability μ r are related as follows:

[0040]

[0041] where c is the speed of light in vacuum and f is the frequency of electromagnetic waves;

[0042] The weights of the electrical parameters in the material parameters of each object are set as follows: the weight of the relative dielectric constant is greater than the weights of the relative magnetic permeability and electrical conductivity.

[0043] Preferably, in step S3.1, the calibration step length of the material parameters of each object is specifically:

[0044] Set the material thickness calibration step size of each object to 1mm;

[0045] The three multipaths with the largest channel impulse response gain in the indoor wireless channel measurement results are denoted as T m1 、T m2 、T m3 , the corresponding gain is recorded as g m1 、g m2 、g m3 , the corresponding delay is recorded as τ m1 , τ m2 , τ m3 ;

[0046] The three multipaths with the largest channel impulse response gain in the indoor wireless channel simulation results are denoted as T s1 、T s2 、T s3 , the corresponding gain is recorded as g s1 、g s2 、g s3 , the corresponding delay is recorded as τ s1 , τ s2 , τ s3 ;

[0047] The gains of the three multipath signal powers are calculated according to the following formula, which are recorded as g1, g2, and g3 respectively. The average gain of the three multipath signal powers is recorded as

[0048]

[0049] Among them, g ml,dB g ml The decibel is expressed as;

[0050] The calibration step size of the relative permittivity of each object is set to The calibration step size of the relative magnetic permeability and electrical conductivity of each object is set to

[0051] Preferably, in step S3.1, the objective function J and its corresponding constraints are specifically set as follows:

[0052] The signal power P in the indoor wireless channel measurement results is m,i And the signal power P in the indoor wireless channel simulation results s,iThe mean square error is recorded as the objective function J. The objective function J and its corresponding constraints are as follows:

[0053]

[0054] |τ ml -τ sl |≤10ns,l∈{1,2,3}

[0055] Where i is the position of the i-th receiving end module, and N is the total number of receiving end module positions.

[0056] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0057] The present invention provides a method for measuring and modeling indoor wireless channels for Sub-6G multi-bands. The method includes establishing a wireless channel measurement platform based on USRP in a real indoor scene; setting the position of a transmitting end module, and performing actual measurement of the Sub-6G wireless channels of all carrier frequencies at each position of the receiving end module by changing the position and carrier frequency of the receiving end module, thereby obtaining a measured result of the indoor wireless channel; establishing an indoor three-dimensional scene model according to the layout of the real indoor scene, setting the transmitting end module at a corresponding position in the model, synchronously changing the position and carrier frequency of the receiving end module, and performing ray tracing simulation on the Sub-6G wireless channels of all carrier frequencies at each position of the receiving end module, thereby obtaining a simulation result of the indoor wireless channel; calculating the error between the measured result of the indoor wireless channel and the simulation result of the indoor wireless channel, and calibrating the material parameters in the indoor three-dimensional scene model using a material parameter calibration method based on multipath level correction, thereby obtaining a high-precision indoor three-dimensional scene model, thereby completing high-precision measurement and modeling of the indoor wireless channel;

[0058] The USRP-based wireless channel measurement platform established in this invention has the advantages of high portability, high programmability, low cost, and a wide frequency range. It can effectively complete indoor channel measurement work. The data obtained by using this platform can provide important working premise and rigorous theoretical reference for indoor wireless communications.

[0059] In addition, a material parameter calibration method based on multipath level correction jointly analyzes channel measurement and simulation results, using the power and delay of multipath signals as the starting point to implement iterative multipath level correction of the material parameters of the indoor scene model. This ensures that wireless communication in the indoor three-dimensional scene model based on the ray tracing algorithm accurately reflects the radio propagation characteristics of the actual indoor communication environment. This overcomes the lack of physical significance of traditional statistical modeling methods and alleviates the problem that deterministic modeling requires complex scene information and huge computing resources.

[0060] In addition, the high-precision indoor three-dimensional scene model established by the present invention has high scalability and can be applied to other similar indoor environments. By reusing calibrated material parameters, the time and cost of repetitive work can be greatly reduced, and high accuracy can be maintained continuously. In practical applications, the model can be used to design and optimize wireless communication systems, helping users better understand and predict signal propagation, thereby improving system reliability and performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of a Sub-6G multi-band indoor wireless channel measurement modeling method provided in Example 1.

[0062] Figure 2 This is a flow chart of calibrating material parameters in an indoor three-dimensional scene model using the material parameter calibration method based on multipath level correction provided in Example 2.

[0063] Figure 3 This is a schematic diagram of the three-dimensional modeling and ray tracing simulation of the indoor scene provided in Example 2.

[0064] Figure 4 This is a schematic diagram of the ray tracing simulation provided in Example 2. DETAILED DESCRIPTION

[0065] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0066] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0067] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0068] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0069] Example 1

[0070] like Figure 1 As shown, this embodiment provides a Sub-6G multi-band indoor wireless channel measurement modeling method, including the following steps:

[0071] S1: Establish a USRP-based wireless channel measurement platform in a real indoor scenario, including a transmitter module, a clock synchronization module, and a receiver module;

[0072] Set the position of the transmitter module and, by changing the position and carrier frequency of the receiver module, measure the Sub-6G wireless channel at all carrier frequencies at each receiver module position to obtain the actual indoor wireless channel measurement results.

[0073] S2: Build a 3D indoor scene model based on the layout of the real indoor scene. Set up transmitter modules at corresponding locations in the model, synchronously change the position and carrier frequency of the receiver modules, and perform ray tracing simulation on the Sub-6G wireless channel at all carrier frequencies at each location of the receiver modules to obtain indoor wireless channel simulation results.

[0074] S3: Calculate the error between the measured indoor wireless channel results and the simulated indoor wireless channel results, and use the material parameter calibration method based on multipath level correction to calibrate the material parameters in the indoor three-dimensional scene model to obtain a high-precision indoor three-dimensional scene model, completing high-precision measurement and modeling of the indoor wireless channel.

[0075] During the specific implementation process, we first established a USRP-based wireless channel measurement platform in a real indoor scenario, including a transmitter module, a clock synchronization module, and a receiver module;

[0076] Set the position of the transmitter module and, by changing the position and carrier frequency of the receiver module, measure the Sub-6G wireless channel at all carrier frequencies at each receiver module position to obtain the actual indoor wireless channel measurement results.

[0077] Based on the layout of a real indoor scene, a 3D indoor scene model was established. Transmitter modules were placed at corresponding locations in the model. The position and carrier frequency of the receiver modules were simultaneously changed. Ray tracing simulation was performed on the Sub-6G wireless channel at all carrier frequencies at each location of the receiver modules to obtain indoor wireless channel simulation results.

[0078] Finally, the error between the measured and simulated indoor wireless channel results is calculated. The material parameters in the indoor 3D scene model are calibrated using a material parameter calibration method based on multipath level correction to obtain a high-precision indoor 3D scene model, completing high-precision measurement and modeling of the indoor wireless channel.

[0079] The wireless channel measurement platform based on USRP established by this method has the advantages of high portability, high programmability, low cost, and wide frequency range. It can effectively complete indoor channel measurement work. The data obtained by using this platform can provide important working premise and rigorous theoretical reference for indoor wireless communications.

[0080] In addition, a material parameter calibration method based on multipath level correction jointly analyzes channel measurement and simulation results, using the power and delay of multipath signals as the starting point to implement iterative multipath level correction of the material parameters of the indoor scene model. This ensures that wireless communication in the indoor three-dimensional scene model based on the ray tracing algorithm accurately reflects the radio propagation characteristics of the actual indoor communication environment. This overcomes the lack of physical significance of traditional statistical modeling methods and alleviates the problem that deterministic modeling requires complex scene information and huge computing resources.

[0081] In addition, the high-precision indoor three-dimensional scene model established by this method is highly scalable and can be applied to other similar indoor environments. By reusing calibrated material parameters, the time and cost of repetitive work can be greatly reduced while maintaining high accuracy. In practical applications, this model can be used to design and optimize wireless communication systems, helping users better understand and predict signal propagation, thereby improving system reliability and performance.

[0082] Example 2

[0083] This embodiment provides a method for measuring and modeling indoor wireless channels in Sub-6G multi-bands, including the following steps:

[0084] S1: Establish a USRP-based wireless channel measurement platform in a real indoor scenario, including a transmitter module, a clock synchronization module, and a receiver module;

[0085] Set the position of the transmitter module and, by changing the position and carrier frequency of the receiver module, measure the Sub-6G wireless channel at all carrier frequencies at each receiver module position to obtain the actual indoor wireless channel measurement results.

[0086] S2: Build a 3D indoor scene model based on the layout of the real indoor scene. Set up transmitter modules at corresponding locations in the model, synchronously change the position and carrier frequency of the receiver modules, and perform ray tracing simulation on the Sub-6G wireless channel at all carrier frequencies at each location of the receiver modules to obtain indoor wireless channel simulation results.

[0087] S3: Calculate the error between the measured and simulated indoor wireless channel results. Use a material parameter calibration method based on multipath level correction to calibrate the material parameters in the indoor 3D scene model to obtain a high-precision indoor 3D scene model, completing high-precision measurement and modeling of the indoor wireless channel.

[0088] The USRP-based wireless channel measurement platform established in step S1 is specifically as follows: the transmitter module includes a transmitter PC and a transmitter USRP, the transmitter PC generates a digital baseband signal, and after setting the transmitter USRP parameters, modulates the digital baseband signal into a high-frequency digital baseband signal, which is transmitted to the Sub-6G wireless channel through the transmitting antenna of the transmitter USRP;

[0089] The receiving end module includes a receiving end PC and a receiving end USRP. After the receiving end USRP parameters are set, the receiving end USRP is used to receive high-frequency digital baseband signals from the Sub-6G wireless channel and demodulate them into digital baseband signals, transmit them to the receiving end PC for real-time display, storage and processing, and output the actual measurement results of the indoor wireless channel;

[0090] The clock synchronization module is used to eliminate the modulation and demodulation errors between the transmitting end module and the receiving end module;

[0091] The transmitting end USRP parameters include carrier frequency, transmission gain, sampling rate, real-time bandwidth, antenna gain, antenna height, synchronization mode and detection signal type;

[0092] The receiving end USRP parameters include carrier frequency, receiving gain, sampling rate, real-time bandwidth, antenna gain, antenna height, synchronization mode and detection signal type;

[0093] The detection signal type is a Chirp signal;

[0094] In step S2, the specific method of establishing the indoor three-dimensional scene model according to the layout of the real indoor scene is:

[0095] The layout of the real indoor scene includes information on the size, position, shape and constituent materials of indoor objects;

[0096] Using one or more of a laser rangefinder, a surveying instrument, and a camera, measure the indoor scene layout and obtain measurement data of indoor objects;

[0097] Build an indoor 3D scene model based on the measurement data of indoor objects and assign material parameters to the corresponding objects;

[0098] The accuracy of the established indoor three-dimensional scene model is no less than decimeter level;

[0099] In step S2, the specific method for obtaining the indoor wireless channel simulation result is:

[0100] S2.1: Input the position of the transmitter module and the positions of multiple receiver modules into the indoor 3D scene model in the form of 3D coordinates, and initialize the ray tracing simulation parameters;

[0101] The ray tracing simulation parameters include: parameters of the transmitting end module and the receiving end module, including transmission power, carrier frequency, antenna gain, and antenna height; and material parameters of each object, including material thickness and electrical parameters, where the electrical parameters include relative permittivity, relative permeability, and conductivity;

[0102] S2.2: Perform ray tracing simulation on the Sub-6G wireless channels of all carrier frequencies at each position of the receiving end module, record the channel impulse response and the received probe signal power at all frequencies at each position, and obtain the indoor wireless channel simulation results;

[0103] As Figure 2 shown, the specific method of step S3 is as follows:

[0104] S3.1: Calculate the error between the measured results of the indoor wireless channel and the simulation results of the indoor wireless channel, set the calibration range, calibration step size of the material parameters of each object, and the weight of the electrical parameters, and set the objective function J and its corresponding constraint conditions, the first threshold ζ and the second threshold η, and the first threshold ζ is greater than the second threshold η;

[0105] S3.2: Within the calibration range of the material thickness of each object, adjust the material thickness of each object in the indoor wireless channel simulation results with the calibration step size, re-perform ray tracing simulation and calculate the objective function J1 after adjusting the material thickness. When J1 satisfies J1 ≤ ζ, complete the calibration of the material thickness of each object and execute step S3.3, otherwise repeat step S3.2;

[0106] S3.3: When η < J1 ≤ ζ or J1 ≤ η and J1 does not satisfy the constraint conditions, use the tabu search algorithm to calibrate the electrical parameters of each object. Specifically: within the calibration range of the electrical parameters of each object, adjust the electrical parameters of each object in the indoor wireless channel simulation results with the calibration step size, re-perform ray tracing simulation again and calculate the objective function J2 after adjusting the electrical parameters, add all the objective functions J2 after adjusting the electrical parameters into the tabu list, search for the electrical parameters of each object corresponding to the minimum value of J2 in the tabu list, and take this electrical parameter and the material thickness as the optimal solution H of the tabu search algorithm min Output;

[0107] When any of the following conditions is met, complete the calibration of the material parameters in the indoor three-dimensional scene model and obtain a high-precision indoor three-dimensional scene model: J1 ≤ η and J1 satisfies the constraint conditions, J2 ≤ η and J2 satisfies the constraint conditions, and the tabu search algorithm outputs the optimal solution H min ;

[0108] In step S3.1, the specific method for setting the weight of the electrical parameters in the material parameters of each object is:

[0109] When electromagnetic waves penetrate an object, the loss L T as follows:

[0110]

[0111] Among them, e is a natural constant, d is the material thickness of the object, μ0 is the vacuum permeability, ε0 ​​is the vacuum dielectric constant, ε r1 is the relative dielectric constant of the first medium, ε t2 is the relative permittivity of the second medium;

[0112] The wavelength λ and relative dielectric constant ε of electromagnetic waves in the medium r , relative magnetic permeability μ r The relationship is:

[0113]

[0114] Where c is the speed of light in a vacuum, and f is the frequency of the electromagnetic wave;

[0115] The weights of the electrical parameters in the material parameters of each object are set so that the weight of the relative dielectric constant is greater than the weights of the relative magnetic permeability and electrical conductivity;

[0116] In step S3.1, the calibration step length of the material parameters of each object is specifically:

[0117] Set the material thickness calibration step size of each object to 1mm;

[0118] The three multipaths with the largest channel impulse response gain in the indoor wireless channel measurement results are denoted as T m1 、T m2 、T m3 , the corresponding gain is recorded as g m1 、g m2 、g m3 , the corresponding delay is recorded as τ m1 , τ m2 , τ m3 ;

[0119] The three multipaths with the largest channel impulse response gain in the indoor wireless channel simulation results are denoted as T s1 、T s2 、T s3 , the corresponding gain is recorded as g s1 、g s2 、g s3 , the corresponding delay is recorded as τ s1 , τ s2 , τ s3 ;

[0120] The gains of the three multipath signal powers are calculated according to the following formula, which are recorded as g1, g2, and g3 respectively. The average gain of the three multipath signal powers is recorded as

[0121]

[0122] Among them, g ml,dB g ml The decibel is expressed as;

[0123] The calibration step size of the relative permittivity of each object is set to The calibration step size of the relative magnetic permeability and electrical conductivity of each object is set to

[0124] In step S3.1, the objective function J and its corresponding constraints are specifically set as follows:

[0125] The signal power P in the indoor wireless channel measurement results is m,i And the signal power P in the indoor wireless channel simulation results s,i The mean square error is recorded as the objective function J. The objective function J and its corresponding constraints are as follows:

[0126]

[0127] |τ ml -τ sl |≤10ns,l∈{1,2,3}

[0128] Where i is the position of the i-th receiving end module, and N is the total number of receiving end module positions.

[0129] In the specific implementation process, we first establish a wireless channel measurement platform based on USRP, including a transmitter module, a clock synchronization module, and a receiver module;

[0130] The transmitter module includes a transmitter PC and a transmitter USRP, which are used to modulate and generate a detection signal and transmit it to the Sub-6G wireless channel. The transmitter PC is responsible for generating a digital baseband signal and setting the parameters of the transmitter USRP to modulate the digital baseband signal to a specified frequency. The transmitter USRP transmits the modulated signal through the transmitting antenna.

[0131] The receiving module includes a receiving PC and a receiving USRP, which are used to receive the detection signal from the Sub-6G wireless channel and demodulate it into a digital baseband signal. The receiving USRP demodulates the received high-frequency signal into a digital baseband signal and transmits it to the receiving PC. The receiving PC is responsible for real-time display, storage, and processing of the received signal and outputs accurate channel measurement results.

[0132] The clock synchronization module is used to eliminate the modulation and demodulation errors between the transmitter module and the receiver module. In indoor scenarios, clock synchronization can be achieved by connecting the same high-precision clock source via an RF feeder.

[0133] The transmitting end USRP parameters include carrier frequency, transmission gain, sampling rate, real-time bandwidth, antenna gain, antenna height, synchronization mode and detection signal type;

[0134] The receiving end USRP parameters include carrier frequency, receiving gain, sampling rate, real-time bandwidth, antenna gain, antenna height, synchronization mode and detection signal type;

[0135] The detection signal type is a Chirp signal;

[0136] Establishing an indoor three-dimensional scene model according to the indoor scene layout, wherein the indoor scene layout includes information on the size, position, shape, and constituent materials of indoor objects;

[0137] Using one or more of a laser rangefinder, a surveying instrument, and a camera, measure the indoor scene layout and obtain measurement data of indoor objects;

[0138] Build an indoor 3D scene model based on the measurement data of indoor objects and assign material parameters to the corresponding objects;

[0139] The accuracy of the established indoor three-dimensional scene model is no less than decimeter level;

[0140] The transmitter module position was then set. By varying the receiver module positions and carrier frequencies, the Sub-6G wireless channel at all carrier frequencies at each receiver module position was measured to obtain the actual indoor wireless channel measurement results.

[0141] Perform ray tracing simulation on the indoor 3D scene model to obtain indoor wireless channel simulation results. The specific method is as follows:

[0142] In an indoor scenario, select a point Tx as the location of the transmitter module and multiple points Rx(n) as the locations of the receiver modules. The principles for selecting the location of the receiver modules are as follows: the reflection, diffraction, and transmission characteristics of building materials such as walls, doors, windows, and furniture in the indoor scenario should be studied; the entire indoor scene should be covered to obtain comprehensive channel characteristics; unnecessary noise and interference should be avoided; and the location should be feasible.

[0143] We then adjusted the parameters of the transmitting and receiving USRPs, including carrier frequency, transmit / receive gain, sampling rate, real-time bandwidth, antenna gain, antenna height, synchronization method, and detection signal. We then measured the Sub-6G wireless channel at all carrier frequencies at each location of the receiving module to obtain actual indoor wireless channel measurement results.

[0144] Input the position of the transmitter module and the positions of multiple receiver modules into the indoor three-dimensional scene model in the form of three-dimensional coordinates, and initialize the ray tracing simulation parameters;

[0145] The ray tracing simulation parameters include: parameters of the transmitting module and the receiving module, including transmission power, carrier frequency, antenna gain, and antenna height; and material parameters of each object, including material thickness and electrical parameters, including relative dielectric constant, relative magnetic permeability, and electrical conductivity;

[0146] Perform ray tracing simulation on the Sub-6G wireless channel for all carrier frequencies at each location of the receiver module, record the channel impulse response and received detection signal power at all frequencies at each location, and obtain indoor wireless channel simulation results.

[0147] When a signal propagates across the boundary of different materials, reflection, diffraction, and transmission occur. During this process, differences in material parameters can alter the signal's phase, propagation path, and loss, affecting the signal's delay and power before it reaches the receiver. Due to modeling errors or material parameter deviations, errors are inevitable between the simulation results and the measured results.

[0148] Therefore, it is necessary to calculate the error between the measured indoor wireless channel results and the simulated indoor wireless channel results. The material parameters in the indoor 3D scene model are calibrated using a material parameter calibration method based on multipath level correction to obtain a high-precision indoor 3D scene model and complete high-precision measurement and modeling of the indoor wireless channel. The specific method is as follows:

[0149] S3.1: Calculate the error between the measured indoor wireless channel results and the simulated indoor wireless channel results, set the calibration range, calibration step size, and electrical parameter weights for the material parameters of each object, and set the objective function J and its corresponding constraints, a first threshold ζ, and a second threshold η, where the first threshold ζ is greater than the second threshold η.

[0150] The first threshold ζ is used to determine whether material thickness calibration is required, and the second threshold η is used to determine whether electrical parameter calibration based on the tabu search algorithm is completed;

[0151] S3.2: Within the material thickness calibration range of each object, adjust the material thickness of each object in the indoor wireless channel simulation results with a calibration step size, re - conduct ray - tracing simulation and calculate the objective function J1 after adjusting the material thickness. When J1 satisfies J1 ≤ ζ, complete the calibration of the material thickness of each object and execute step S3.3; otherwise, repeat step S3.2;

[0152] S3.3: When η < J1 ≤ ζ or J1 ≤ η and J1 does not satisfy the constraint conditions, use the tabu search algorithm to calibrate the electrical parameters of each object. Specifically: within the electrical parameter calibration range of each object, adjust the electrical parameters of each object in the indoor wireless channel simulation results with a calibration step size, re - conduct ray - tracing simulation again and calculate the objective function J2 after adjusting the electrical parameters. Add all the objective functions J2 after adjusting the electrical parameters into the tabu list, search for the electrical parameters of each object corresponding to the minimum value of J2 in the tabu list, and take this electrical parameter and the material thickness as the optimal solution H of the tabu search algorithm min Output;

[0153] When any of the following conditions is satisfied, complete the calibration of the material parameters in the indoor three - dimensional scene model and obtain a high - precision indoor three - dimensional scene model: J1 ≤ η and J1 satisfies the constraint conditions, J2 ≤ η and J2 satisfies the constraint conditions, and the tabu search algorithm outputs the optimal solution H min ;

[0154] The specific method for determining the calibration range of material parameters in step S3.1 is as follows: for the material thickness, its value range should be reasonably set according to the actual material thickness. For example, the thickness range of wood is 30 - 50 mm, the thickness range of glass is 3 - 12 mm, and the thickness range of the wall is 50 - 150 mm; for the electrical parameters of the material, the value range of the electrical parameters should also be reasonably set according to the actual electrical characteristics of the material. For example, the relative permittivity of wood is about 1.5 - 5.0, the relative permeability is about 1.0, and the conductivity is about 0.003 - 0.110 S / m; the relative permittivity of glass is about 3.7 - 10.0, the relative permeability is about 1.0, and the conductivity is about 0.001 - 0.100 S / m; the relative permittivity of the wall is about 3.0 - 10.0, the relative permeability is about 1.0, and the conductivity is about 0.001 - 0.100 S / m. The method in this embodiment considers using the tabu search algorithm to calibrate the electrical parameters of the material. This algorithm will iteratively update the tabu list and record the solutions that have been searched. When a searched solution is encountered within T iterations, this solution will be ignored, thus avoiding falling into the local optimal solution during the iterative process of electrical parameter adjustment;

[0155] In step S3.1, the specific method for setting the weight of the electrical parameters in the material parameters of each object is as follows:

[0156] When electromagnetic waves penetrate an object, the loss L experiencedT as follows:

[0157]

[0158] Among them, e is a natural constant, d is the material thickness of the object, μ0 is the vacuum permeability, ε0 ​​is the vacuum dielectric constant, ε r1 is the relative dielectric constant of the first medium, ε r2 is the relative permittivity of the second medium;

[0159] The wavelength λ and relative dielectric constant ε of electromagnetic waves in the medium r , relative magnetic permeability μ r The relationship is:

[0160]

[0161] Where c is the speed of light in a vacuum, and f is the frequency of the electromagnetic wave;

[0162] The weights of the electrical parameters in the material parameters of each object are set so that the weight of the relative dielectric constant is greater than the weights of the relative magnetic permeability and electrical conductivity;

[0163] In step S3.1, the calibration step length of the material parameters of each object is specifically:

[0164] Set the material thickness calibration step size of each object to 1mm;

[0165] The three multipaths with the largest channel impulse response gain in the indoor wireless channel measurement results are denoted as T m1 、T m2 、T m3 , the corresponding gain is recorded as g m1 、g m2 、g m3 , the corresponding delay is recorded as τ m1 , τ m2 , τ m3 ;

[0166] The three multipaths with the largest channel impulse response gain in the indoor wireless channel simulation results are denoted as T s1 、T s2 、T s3 , the corresponding gain is recorded as g s1 、g s2 、g s3 , the corresponding delay is recorded as τ s1 , τ s2 , τ s3 ;

[0167] The gains of the three multipath signal powers are calculated according to the following formula, which are recorded as g1, g2, and g3 respectively. The average gain of the three multipath signal powers is recorded as

[0168]

[0169] Among them, g ml,dB g ml The decibel is expressed as;

[0170] The calibration step size of the relative permittivity of each object is set to The calibration step size of the relative magnetic permeability and electrical conductivity of each object is set to

[0171] when If it is a positive number, it means that the measured received signal power is greater than the simulated received signal power. In this case, the relative permittivity and relative permeability should be increased and the conductivity should be decreased. Otherwise, the relative permittivity and relative permeability should be decreased and the conductivity should be increased.

[0172] Regarding signal delay calibration, an increase in relative permittivity will reduce signal propagation speed, thereby increasing signal delay; an increase in conductivity will reduce signal attenuation, thereby reducing signal delay;

[0173] In step S3.1, the objective function J and its corresponding constraints are specifically set as follows:

[0174] The signal power P in the indoor wireless channel measurement results is m,i And the signal power P in the indoor wireless channel simulation results s,i The mean square error is recorded as the objective function J. The objective function J and its corresponding constraints are as follows:

[0175]

[0176] |τ ml -τ sl |≤10ns,l∈{1,2,3}

[0177] Where i is the position of the i-th receiver module, N is the total number of receiver module positions, and 10 ns is the minimum resolution delay achievable by the USRP-based channel measurement platform constructed by this method.

[0178] The feasibility of this method is verified by combining specific application examples:

[0179] This example uses two Ettus USRP N300s to build a channel measurement platform. The completed measurement platform supports a test frequency range of 10 MHz to 6 GHz, provides up to 100 MHz instantaneous bandwidth, and a maximum sampling rate of 153.6 Msps. Furthermore, each USRP measures 35.7 × 21.1 × 4.37 cm and weighs only 3.1 kg, making it portable and offering excellent performance. This platform meets the channel measurement hardware requirements for most scenarios and provides easy-to-install, mobile dynamic measurement capabilities. The channel measurement platform supports binary storage of collected RF information. Signal processing and analysis of the collected data using MATLAB can reveal signal information in the time, frequency, and energy domains.

[0180] Both the transmitting and receiving PCs are laptops running the Linux operating system. GNU Radio is open-source software for the Linux operating system that provides comprehensive signal processing and operation modules. With this software, developers can quickly build a software-defined radio simulation environment using the low-cost USRP and develop real-time, high-capacity wireless communication systems for experimental research. All signal processing modules in GNU Radio can be selected and used using a flowchart. Once the transmitter and receiver parameters are set in the software, such as sampling rate, bandwidth, center frequency, data format, transmit gain, and receive gain, signals can be sent, received, and recorded on the USRP.

[0181] The WinProp platform was then used to perform 3D modeling of the indoor scene. To demonstrate the feasibility of this method, in this example, channel measurement and modeling were performed on a typical residential building. This building contained a large amount of furniture, as well as building materials such as wooden doors, glass doors, glass windows, and cement walls. The carrier frequencies tested were 700MHz, 2000MHz, 3300MHz, and 4900MHz.

[0182] Arrange the transmitter module and receiver module positions in the top view of the indoor scene, such as Figure 3 As shown in the left figure, once the position of the transmitter module is determined, it will not change. The position of the receiver module is set as follows: Rx (1) and Rx (2) as well as Rx (7) and Rx (8) are distributed on both sides of the wooden door, Rx (2) and Rx (3) as well as Rx (6) and Rx (7) are distributed on both sides of the cement wall, Rx (3) and Rx (4) are distributed on both sides of the glass window, Rx (9) and Rx (10) are distributed on both sides of the glass door, and Rx (5) and Rx (9) are used to verify the direct path loss to study the indoor penetration ability of electromagnetic waves.

[0183] like Figure 3The figure shows the software's path loss simulation results at a frequency of 2000MHz. Specifically, the software performs a path loss simulation at a resolution of 0.1m for the entire indoor scene model. The simulated path loss values ​​at 10 receiving end locations at the current frequency are recorded. After recording is completed, the frequency in the simulation parameters is changed to the next set of frequencies. The path loss simulation test is continued and the simulation values ​​are recorded until the path loss simulation values ​​at all receiving end locations at four sets of frequencies are recorded, that is, the received signal power values.

[0184] The process and results of calibrating the material parameters of the indoor wireless propagation model based on the measured results of the channel measurement platform are as follows:

[0185] First, because the indoor wireless propagation model has an accuracy of 0.1m, traversing every pixel to perform material parameter calibration would undoubtedly increase the algorithm complexity. Therefore, before implementing the material parameter calibration method based on multipath level correction, we first screened the current channel measurement results and simulation results, selected the results with the largest error for analysis, and determined the main cause of the error.

[0186] Table 1 shows the comparison between the received signal power obtained by actual measurement and the received signal power obtained by ray tracing simulation at a frequency of 2 GHz:

[0187] Rx point 1 2 3 4 5 6 7 8 9 10 Measured value -40.1 -43.5 -49.5 -55.6 -47.5 -48.1 -59.4 -59.3 -52.5 -54 Simulation value -43.68 -46.34 -54.22 -64.58 -50.36 -50.73 -65.68 -65.37 -56.61 -64.59

[0188] Table 1 Comparison of measured and simulated received signal power at 2 GHz (unit: dBm)

[0189] From Table 1, it can be seen that the errors between the simulation values ​​and the measured values ​​of Rx1, Rx5, Rx6, and Rx9 are small, there are certain errors between the simulation values ​​and the measured values ​​of Rx2, Rx7, and Rx8, and there are large errors between the simulation values ​​and the measured values ​​of Rx3, Rx4, and Rx10. Figure 3 From the receiver positions, it can be seen that the receiver positions with smaller errors are all in direct-path relationship with the transmitter positions, and they mainly experience multipath effects. The transmission of indoor objects does not have a major impact on the received signal power of these four positions. Therefore, it can be determined that the size design of the indoor scene model basically conforms to the actual scene scale; among the receiver positions with certain errors, Rx2 is separated from the transmitter by a wooden door, and Rx7 and Rx8 are separated from the transmitter by a wall. Therefore, the reason for the error at this position must be that the material parameters have not been adjusted. These three receiver positions are used as the main targets for subsequent parameter calibration. Among the positions with larger errors, it can be found that the common point of the three receiver positions is that they are also separated from the transmitter by one or more glass doors / windows. Therefore, it is determined that the abnormal glass material parameters have caused a large range of errors. The default glass material should be directly replaced in the simulation software for a second simulation test.

[0190] Secondly, call the ray display function of the ray tracing model, which can intuitively show which rays affect the received signal reaching the receiving end, and what materials these rays pass through to reach the receiving end, such as Figure 4 As shown in the figure, the rays reaching Rx7 are mainly transmitted by the cement wall and wooden door, and reflected by the wooden door and glass. Therefore, the thickness, relative dielectric constant, relative magnetic permeability, and electrical conductivity of these three materials are used as parameters to be adjusted. The objective function is defined as the mean square error between the simulated received signal power and the measured received signal power, until the mean square error between the measured received signal power and the simulated received signal power is less than the set threshold and the constraint condition is satisfied:

[0191] Finally, as shown in Table 2, the simulated received signal power after calibration is very close to the measured received signal power. The reliability of the simulation model at this frequency has been improved, and the simulation results of the ray tracing model can already reflect the wireless signal propagation characteristics in the actual environment:

[0192] Rx point 1 2 3 4 5 6 7 8 9 10 Measured value -40.1 -43.5 -49.5 -55.6 -47.5 -48.1 -59.4 -59.3 -52.5 -54 Simulation value -43.68 -46.34 -54.22 -64.58 -50.36 -50.73 -65.68 -65.37 -56.61 -64.59 After calibration -41.06 -44.43 -50.82 -57.15 -49.79 -50.11 -59.39 -60.48 -53.39 -56.4

[0193] Table 2 Comparison of measured received signal power and simulated received signal power after calibration at 2 GHz (unit: dBm)

[0194] The wireless channel measurement platform based on USRP established by this method has the advantages of high portability, high programmability, low cost, and wide frequency range. It can effectively complete indoor channel measurement work. The data obtained by using this platform can provide important working premise and rigorous theoretical reference for indoor wireless communications.

[0195] In addition, a material parameter calibration method based on multipath level correction jointly analyzes channel measurement and simulation results, using the power and delay of multipath signals as the starting point to implement iterative multipath level correction of the material parameters of the indoor scene model. This ensures that wireless communication in the indoor three-dimensional scene model based on the ray tracing algorithm accurately reflects the radio propagation characteristics of the actual indoor communication environment. This overcomes the lack of physical significance of traditional statistical modeling methods and alleviates the problem that deterministic modeling requires complex scene information and huge computing resources.

[0196] In addition, the high-precision indoor three-dimensional scene model established by this method is highly scalable and can be applied to other similar indoor environments. By reusing calibrated material parameters, the time and cost of repetitive work can be greatly reduced while maintaining high accuracy. In practical applications, this model can be used to design and optimize wireless communication systems, helping users better understand and predict signal propagation, thereby improving system reliability and performance.

[0197] The same or similar reference numerals correspond to the same or similar components;

[0198] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0199] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for measuring and modeling indoor wireless channels in Sub-6G multi-band, characterized in that: The following steps are involved: S1: Establish a USRP-based wireless channel measurement platform in a real indoor scenario, including a transmitter module, a clock synchronization module, and a receiver module; Set the position of the transmitter module and, by changing the position and carrier frequency of the receiver module, measure the Sub-6G wireless channel at all carrier frequencies at each receiver module position to obtain the actual indoor wireless channel measurement results. S2: Build a 3D indoor scene model based on the layout of the real indoor scene. Set up transmitter modules at corresponding locations in the model, synchronously change the position and carrier frequency of the receiver modules, and perform ray tracing simulation on the Sub-6G wireless channel at all carrier frequencies at each location of the receiver modules to obtain indoor wireless channel simulation results. S3: Calculate the error between the measured indoor wireless channel results and the simulated indoor wireless channel results, and use the material parameter calibration method based on multipath level correction to calibrate the material parameters in the indoor three-dimensional scene model to obtain a high-precision indoor three-dimensional scene model, completing high-precision measurement and modeling of the indoor wireless channel.

2. The method for measuring and modeling indoor wireless channels for Sub-6G multi-band according to claim 1, characterized in that: The USRP-based wireless channel measurement platform established in step S1 is specifically: The transmitter module includes a transmitter PC and a transmitter USRP. The transmitter PC generates a digital baseband signal. After setting the parameters of the transmitter USRP, the digital baseband signal is modulated into a high-frequency digital baseband signal, which is transmitted to the Sub-6G wireless channel through the transmitting antenna of the transmitter USRP. The receiving end module includes a receiving end PC and a receiving end USRP. After the receiving end USRP parameters are set, the receiving end USRP is used to receive high-frequency digital baseband signals from the Sub-6G wireless channel and demodulate them into digital baseband signals, transmit them to the receiving end PC for real-time display, storage and processing, and output the actual measurement results of the indoor wireless channel; The clock synchronization module is used to eliminate modulation and demodulation errors between the transmitting end module and the receiving end module.

3. The method for measuring and modeling indoor wireless channels for Sub-6G multi-band according to claim 2, wherein: The transmitting end USRP parameters include carrier frequency, transmission gain, sampling rate, real-time bandwidth, antenna gain, antenna height, synchronization mode and detection signal type; The receiving end USRP parameters include carrier frequency, receiving gain, sampling rate, real-time bandwidth, antenna gain, antenna height, synchronization mode and detection signal type; The detection signal type is a Chirp signal.

4. The method for measuring and modeling indoor wireless channels for Sub-6G multi-band according to claim 1, wherein: In step S2, the specific method for establishing the indoor three-dimensional scene model according to the real indoor scene layout is: The real indoor scene layout includes information on the size, position, shape and constituent materials of indoor objects; Using one or more of a laser rangefinder, a surveying instrument, and a camera, measure the indoor scene layout and obtain measurement data of indoor objects; An indoor 3D scene model is established based on the measurement data of indoor objects, and material parameters of the corresponding objects are assigned.

5. The method for measuring and modeling indoor wireless channels for Sub-6G multi-band according to claim 4, characterized in that: The accuracy of the established indoor three-dimensional scene model is no less than decimeter level.

6. A method for measuring and modeling indoor wireless channels for Sub-6G multi-band according to claim 1 or 5, characterized in that: In step S2, the specific method for obtaining the indoor wireless channel simulation result is: S2.1: Input the position of the transmitter module and the positions of multiple receiver modules into the indoor 3D scene model in the form of 3D coordinates, and initialize the ray tracing simulation parameters; The ray tracing simulation parameters include: parameters of the transmitting module and the receiving module, including transmission power, carrier frequency, antenna gain, and antenna height; and material parameters of each object, including material thickness and electrical parameters, including relative dielectric constant, relative magnetic permeability, and electrical conductivity; S2.2: Perform ray tracing simulation on the Sub-6G wireless channel for all carrier frequencies at each location of the receiving module. Record the channel impulse response and received detection signal power for all frequencies at each location to obtain indoor wireless channel simulation results.

7. The method for measuring and modeling indoor wireless channels for Sub-6G multi-band according to claim 6, characterized in that: The specific method of step S3 is: S3.1: Calculate the error between the measured indoor wireless channel results and the simulated indoor wireless channel results, set the calibration range, calibration step size, and electrical parameter weights for the material parameters of each object, and set the objective function J and its corresponding constraints, a first threshold ζ, and a second threshold η, where the first threshold ζ is greater than the second threshold η. S3.2: Within the material thickness calibration range of each object, adjust the material thickness of each object in the indoor wireless channel simulation results using the calibration step size. Re-perform the ray tracing simulation and calculate the objective function J1 after adjusting the material thickness. When J1 satisfies J1 ≤ ζ, complete the material thickness calibration for each object and execute step S3.

3. Otherwise, repeat step S3.

2. S3.3: When η < J1 ≤ ζ or J1 ≤ η and J1 does not satisfy the constraint conditions, use the tabu search algorithm to calibrate the electrical parameters of each object. Specifically: within the calibration range of the electrical parameters of each object, adjust the electrical parameters of each object in the indoor wireless channel simulation results with a calibration step size, re - conduct the ray - tracing simulation again and calculate the objective function J2 after adjusting the electrical parameters. Add all the objective functions J2 after adjusting the electrical parameters into the tabu list, search for the electrical parameters of each object corresponding to the minimum value of J2 in the tabu list, and take this electrical parameter and the material thickness together as the optimal solution H of the tabu search algorithm min Output; When any one of the following conditions is met, the material parameters in the indoor 3D scene model are calibrated to obtain a high-precision indoor 3D scene model: J1≤η and J1 satisfies the constraint condition, J2≤η and J2 satisfies the constraint condition and the taboo search algorithm outputs the optimal solution H min .

8. The method for measuring and modeling indoor wireless channels for Sub-6G multi-band according to claim 7, wherein: In step S3.1, the specific method for setting the weight of the electrical parameters in the material parameters of each object is: When electromagnetic waves penetrate an object, the loss L T as follows: Among them, e is a natural constant, d is the material thickness of the object, μ0 is the vacuum permeability, ε0 ​​is the vacuum dielectric constant, ε r1 is the relative dielectric constant of the first medium, ε r2 is the relative permittivity of the second medium; The wavelength λ and relative dielectric constant ε of electromagnetic waves in the medium r , relative magnetic permeability μ r The relationship is: Where c is the speed of light in a vacuum, and f is the frequency of the electromagnetic wave; The weights of the electrical parameters in the material parameters of each object are set as follows: the weight of the relative dielectric constant is greater than the weights of the relative magnetic permeability and electrical conductivity.

9. The method for measuring and modeling indoor wireless channels for Sub-6G multi-band according to claim 7, wherein: In step S3.1, the calibration step length of the material parameters of each object is specifically: Set the material thickness calibration step size of each object to 1mm; The three multipaths with the largest channel impulse response gain in the indoor wireless channel measurement results are denoted as T m1 、T m2 、T m3 , the corresponding gain is recorded as g m1 、g m2 、g m3 , the corresponding delay is recorded as τ m1 , τ m2 , τ m3 ; The three multipaths with the largest channel impulse response gain in the indoor wireless channel simulation results are denoted as T s1 、T s2 、T s3 , the corresponding gain is recorded as g s1 、g s2 、g s3 , the corresponding delay is recorded as τ s1 , τ s2 , τ s3 ; The gains of the three multipath signal powers are calculated according to the following formula, which are recorded as g1, g2, and g3 respectively. The average gain of the three multipath signal powers is recorded as Among them, g ml,dB g ml The decibel is expressed as; The calibration step size of the relative permittivity of each object is set to The calibration step size of the relative magnetic permeability and electrical conductivity of each object is set to 10. The method for measuring and modeling indoor wireless channels for Sub-6G multi-band according to claim 7, wherein: In step S3.1, the objective function J and its corresponding constraints are specifically set as follows: The signal power P in the indoor wireless channel measurement results is m,i And the signal power P in the indoor wireless channel simulation results s,i The mean square error is recorded as the objective function J. The objective function J and its corresponding constraints are as follows: |t ml -t sl |≤10ns,l∈{1,2,3} Where i is the position of the i-th receiving end module, and N is the total number of receiving end module positions.

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