A wireless channel modeling method based on the relative amplitude probability of multipath signals measured in real time.

Abstract

A wireless channel modeling method based on the relative amplitude probability of multipath propagation of measured signals is proposed. This method uses channel sounding equipment to collect and record channel impulse response data of propagation links in specific scenarios. By analyzing and processing the data, parameters such as large-scale propagation loss, small-scale multipath number, relative delay, relative amplitude, and amplitude distribution are obtained. A wireless channel model matching the specific propagation scenario is established, thereby achieving high-precision modeling of complex and variable radio wave propagation effects and solving the problem of poor matching between simulation models and real scenarios.

Description

Technical fields:

[0001] This invention belongs to the field of electronic communication technology, and mainly relates to a wireless channel modeling method based on the relative amplitude probability of multipath signals measured in actual measurements. Background technology:

[0002] The performance of wireless communication systems is primarily constrained by the wireless channel. In mobile communication systems, the propagation path between the transmitter and receiver is highly complex, ranging from simple line-of-sight paths to various complex terrains, such as large buildings, mountains, and obstacles that block radio waves. Furthermore, multipath propagation caused by reflection, diffraction, and scattering all affect the propagation of electromagnetic waves, resulting in multipath propagation, fading, delay, and attenuation in the received signal. The received signal exhibits strong randomness in different physical spaces.

[0003] Channel modeling and simulation, as important tools in channel research, can quickly reproduce channel propagation characteristics and enable performance evaluation and verification of complex communication systems. Wireless channel simulators can reproduce wireless channel propagation effects in a controlled laboratory environment, simulating typical multipath, fading, delay, and attenuation effects. The accurate simulation of space radio waves by wireless channel simulators relies on channel simulation models that closely match the simulation scenario. The accuracy of channel characteristic simulation affects the reliability of test results; therefore, realistically building channel simulation environments under different scenarios requires estimating channel parameters under specific conditions.

[0004] Wireless channel propagation effect simulation models are divided into large-scale and small-scale fading propagation models. Currently, the small-scale simulation model used in wireless channel simulators is the tapped delay model. This recommended model has a coarse granularity in distinguishing application scenarios, differentiating propagation scenarios as land and water, and propagation areas as cities, villages, hills, and suburbs, but failing to distinguish the coverage of buildings and vegetation along the propagation path. Even at the same distance, the performance of the communication system varies due to different scatterers in the propagation scenario. Because the channel model has poor matching with the real propagation scenario, it affects the simulation accuracy under specific propagation scenarios and is not conducive to the performance evaluation and verification of the communication system. Therefore, it is necessary to carry out wireless channel modeling under specific scenarios. Summary of the Invention:

[0005] To establish a wireless channel model that matches specific propagation scenarios and achieve a realistic simulation of radio wave propagation effects, this invention provides a wireless channel modeling method based on the relative amplitude probability of multipath propagation of measured signals. This method can accurately estimate wireless channel parameters such as transmission loss, multipath number, multipath delay, relative amplitude, and amplitude distribution type under specific scenarios, thereby achieving high-precision modeling of complex and variable radio wave propagation effects and solving the problem of poor matching between simulation models and real scenarios.

[0006] The technical solution adopted by this invention to solve its technical problem is as follows:

[0007] A wireless channel modeling method based on the measured multipath relative amplitude probability of signals includes the following steps:

[0008] Step 1: Select the test area, determine the positions of the transmitting and receiving antennas, connect the standard antenna to the transmitter and receiver of the channel sounding device respectively, place it at the test position, and set and record the transmit power P of the channel sounding device. t The gain G of the transmitting antenna antenna_t The gain G of the receiving antenna antenna_r Channel impulse response data of the propagation link is collected and stored using channel sounding and recording equipment;

[0009] Step 2: Process the channel impulse response data to eliminate noise interference in the measurement;

[0010] Step 3: Further process the preprocessed multipath data to obtain large-scale propagation loss, small-scale multipath number, relative delay, relative amplitude, and amplitude distribution, thereby modeling the propagation effect of the test scenario; the preprocessed multipath data processing method is as follows:

[0011] (1) Preprocess the channel impulse response data to generate large-scale parameters;

[0012] (2) Extract small-scale propagation loss parameters from the channel impulse response data;

[0013] (3) By combining the large-scale parameters generated in (1) with the small-scale parameters generated in (2), the channel propagation loss, multipath number, relative amplitude, relative delay, and amplitude distribution type parameters of a specific propagation scenario can be estimated, thus completing channel modeling.

[0014] The method for preprocessing the channel impulse response data is as follows:

[0015] ① Extract the path with the largest multipath signal amplitude from each snapshot between the transmit and receive channels as the main path, and record the main path signal power P of each snapshot. rn ;

[0016] ②Based on the transmission power P t The gain G of the transmitting antenna antenna_t The gain G of the receiving antenna antenna_r Calculate the large-scale propagation loss Lt for each snapshot. n ;

[0017] Lt n =P rn -G antenna_t -G antenna_r -Pt

[0018] ③ Calculate Lt n The value when the cumulative probability is 50% is the large-scale propagation loss Lt of the propagation link.

[0019] The method for extracting small-scale propagation loss parameters from channel impulse response data is as follows:

[0020] ①The main diameter signal power P of each snapshot rn Based on this, the multipath data of each snapshot is normalized. After normalization, the relative attenuation of the main path signal of the small-scale multipath parameter is 0dB. There is only one data with a relative attenuation of 0dB in each snapshot. Invalid data before the relative attenuation of 0dB are deleted.

[0021] ② After deleting invalid data, the data in each snapshot are aligned with 0dB relative attenuation as the first element, and the shortest length of each snapshot is used as the column element length of the multipath data matrix. Construct an M-row, N-column multipath information data matrix P starting with the main path sequence.

[0022] ③ Take the elements p in matrix P i,j The relative amplitude is converted into signal amplitude A. i,j And generate matrix A;

[0023]

[0024] ④ Sum the elements of each column in matrix A, calculate the average, and then calculate the power, resulting in an array of N elements in one row.

[0025]

[0026] ⑤ Search array At the first position L where the value is less than the principal diameter threshold, truncate the data thereafter to generate an M×L matrix.

[0027] ⑥ For the matrix The data in each row takes the maximum value p ij p ij It should meet the following requirements:

[0028]

[0029] ⑦ Statistical matrix The probability η of the column maximum element in each column of data j ;

[0030] ⑧ Set a threshold for determining the probability of a maximum value, and set the probability η of the element that is the maximum value. jThe column containing data with a probability not less than the determination threshold is the effective multipath, and its amplitude is the relative amplitude of the multipath.

[0031] ⑨ Calculate the relative time delay τ of the effective multipath based on the relative position l of the effective multipath and the main diameter, and the time delay resolution τ of the measuring equipment. l ;

[0032] τ l =l*τ

[0033] ⑩ Statistical matrix The envelope probability density curves of the data in the column containing the effective multipaths are obtained and compared with typical envelope distribution types to obtain the distribution characteristics of the effective paths.

[0034] Due to the adoption of the technical solution described above, the present invention has the following advantages:

[0035] This invention provides a wireless channel modeling method based on the relative amplitude probability of multipath propagation of measured signals. It uses channel sounding equipment to collect and record channel impulse response data of propagation links in specific scenarios. By analyzing and processing the data, parameters such as large-scale propagation loss, small-scale multipath number, relative delay, relative amplitude, and amplitude distribution are obtained. A wireless channel model matching the specific propagation scenario is established, thereby achieving high-precision modeling of complex and variable radio wave propagation effects and solving the problem of poor matching between simulation models and real scenarios. Attached image description:

[0036] Figure 1 A test diagram of the propagation effect of radio waves in a specific propagation scenario;

[0037] Figure 2 Flowchart for channel modeling of radio wave propagation effects in a specific propagation scenario;

[0038] Figure 3 Comparison of images before and after removing multipath noise from any snapshot measurement data; Detailed implementation method:

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0040] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0041] A wireless channel modeling method based on the relative amplitude probability of multipath signals of measured signals is described in conjunction with the accompanying drawings. The modeling process is described in [reference needed]. Figure 2 It includes the following steps:

[0042] Step 1: Select the test area, determine the positions of the transmitting and receiving antennas, connect the standard antenna to the transmitter and receiver of the channel sounding device respectively, place it at the test position, and set and record the transmit power P of the channel sounding device. t The gain G of the transmitting antenna antenna_t The gain G of the receiving antenna antenna_r Channel impulse response data of the propagation link is collected and stored using channel sounding and recording equipment, such as... Figure 1 As shown.

[0043] Step 2: Process the channel impulse response data to eliminate noise interference in the measurement.

[0044] Step 3: Further process the preprocessed multipath data to obtain large-scale propagation loss, small-scale multipath number, relative delay, relative amplitude, amplitude distribution, etc., to model the propagation effect of the test scenario. The processing method is as follows:

[0045] (1) Preprocess the channel impulse response data, the specific method is as follows:

[0046] ① Extract the path with the largest multipath signal amplitude from each snapshot between the transmit and receive channels as the main path, and record the main path signal power P of each snapshot. rn , m indicates any snapshot;

[0047] ②Based on the transmission power P t The gain G of the transmitting antenna antenna_t The gain G of the receiving antenna antenna_r Calculate the large-scale propagation loss Lt for each snapshot. n ;

[0048] Lt n =P rn -G antenna_t -G antenna_r -P t

[0049] ③ Calculate Lt n The value when the cumulative probability is 50% is the large-scale propagation loss Lt of the propagation link.

[0050] (2) Extract small-scale propagation loss parameters from the channel impulse response data. The specific method is as follows:

[0051] ①The main diameter signal power P of each snapshot rn Based on this, the multipath data of each snapshot is normalized. After normalization, the relative attenuation of the main path signal of the small-scale multipath parameter is 0dB. There is one and only one data point with a relative attenuation of 0dB in each snapshot. Invalid data before the relative attenuation of 0dB are deleted.

[0052] ② After deleting invalid data, the data in each snapshot are aligned with 0dB relative attenuation as the first element, and the shortest length of each snapshot is used as the column element length of the multipath data matrix. Construct an M-row, N-column multipath information data matrix P starting with the main path sequence.

[0053] ③ Take the elements p in matrix P i,j The relative amplitude is converted into signal amplitude A. i,j And generate matrix A;

[0054]

[0055] ④ Sum the elements of each column in matrix A, calculate the average, and then calculate the power, resulting in an array of N elements in one row.

[0056]

[0057] ⑤ Search array The data at the first position L where the diameter is less than -25dB of the principal diameter is truncated, and an M×L matrix is ​​generated.

[0058] ⑥ For the matrix The data in each row takes the maximum value p ij p ij It should meet the following requirements:

[0059]

[0060] ⑦ Statistical matrix The probability η of the column maximum element in each column of data j ;

[0061] ⑧ Set a threshold for determining the probability of a maximum value, and set the probability η of the element that is the maximum value. j The column containing data with a probability not less than the determination threshold is the effective multipath, and its amplitude is the relative amplitude of the multipath.

[0062] ⑨ Calculate the relative time delay τ of the effective multipath based on the relative position l of the effective multipath and the main diameter, and the time delay resolution τ of the measuring equipment. l;

[0063] τ l =l*τ

[0064] ⑩ Statistical matrix The envelope probability density curves of the data in the column containing the effective multipath are obtained and compared with typical envelope fading distribution types to obtain the distribution characteristics of the effective path.

[0065] (3) By combining the large-scale parameters generated in (1) with the small-scale parameters generated in (2), the channel propagation loss, multipath number, relative amplitude, relative delay, and amplitude distribution type parameters of a specific propagation scenario can be estimated, and the channel modeling can be completed.

[0066] The parts not detailed above are existing technologies and therefore have not been described in detail.

Claims

1. A wireless channel modeling method based on the relative amplitude probability of multipath propagation of measured signals, characterized in that: Includes the following steps: Step 1: Select the test area, determine the positions of the transmitting and receiving antennas, connect the standard antenna to the transmitter and receiver of the channel sounding device respectively, place it at the test position, and set and record the transmitting power of the channel sounding device. Gain of the transmitting antenna Gain of the receiving antenna Channel impulse response data of the propagation link is collected and stored using channel sounding and recording equipment; Step 2: Process the channel impulse response data to eliminate noise interference in the measurement; Step 3: Further process the preprocessed multipath data to obtain large-scale propagation loss, small-scale multipath number, relative delay, relative amplitude, and amplitude distribution, thereby modeling the propagation effect of the test scenario; the preprocessed multipath data processing method is as follows: (1) Preprocess the channel impulse response data to generate large-scale parameters; (2) Extract small-scale propagation loss parameters from the channel impulse response data; ①Based on the main diameter signal power of each snapshot Based on this, the multipath data of each snapshot is normalized. After normalization, the relative attenuation of the main path signal of the small-scale multipath parameter is 0dB. There is only one data with a relative attenuation of 0dB in each snapshot. Invalid data before the relative attenuation of 0dB are deleted. ② After deleting invalid data, the data in each snapshot are aligned with 0dB relative attenuation as the first element. The shortest length of each snapshot is used as the column element length of the multipath data matrix, and a matrix is ​​constructed starting with the main path sequence. OK, Multipath Information Data Matrix ; ③The matrix medium elements The relative amplitude is converted into signal amplitude. and generate a matrix ; ④ The matrix Add the elements of each column and take the average, then calculate the power of the sum to get a row. An array of elements ; ⑤ Search array The first position smaller than the main diameter threshold Truncate the subsequent data and generate matrix ( ); ⑥ For the matrix Take the maximum value of each row of data , It should meet the following requirements: ⑦ Statistical matrix The probability of the column maximum element in each column of data ; ⑧ Set a threshold for determining the probability of a maximum value, and list the probability of elements that have a maximum value. The column containing data with a probability not less than the determination threshold is the effective multipath, and its amplitude is the relative amplitude of the multipath. ⑨ Based on the relative position of the effective multipath and the main diameter and the time delay resolution of the measuring equipment Calculate the relative delay of effective multipath. ; ⑩ Statistical matrix The envelope probability density curves of the data in the column containing the effective multipath are obtained and compared with typical envelope distribution types to obtain the distribution characteristics of the effective multipath. (3) By combining the large-scale parameters generated in (1) with the small-scale parameters generated in (2), the channel propagation loss, multipath number, relative amplitude, relative delay, and amplitude distribution type parameters of a specific propagation scenario can be estimated, thus completing channel modeling.

2. The wireless channel modeling method based on the relative amplitude probability of multipath signals according to claim 1, characterized in that: The method for preprocessing the channel impulse response data is as follows: ① Extract the path with the largest multipath signal amplitude from each snapshot between the transmit and receive channels as the main path, and record the main path signal power of each snapshot. ; ②Based on transmission power Gain of the transmitting antenna Gain of the receiving antenna Calculate the large-scale propagation loss for each snapshot. ; ③Calculation The value at which the cumulative probability is 50% is the large-scale propagation loss of the propagation link. .

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