Channel capacity evaluation method for determining channel MIMO system based on RT algorithm

Abstract

The invention discloses an evaluation method for determining channel capacity of a channel MIMO (Multiple Input Multiple Output) system based on an RT (Reverse Transcription) algorithm, which mainly solves the problem of deviation in channel capacity estimation caused by a multipath effect in an existing wireless communication system. The scheme comprises the following steps: 1) modeling a specified indoor scene; 2) constructing an MIMO system for determining a channel in the line-of-sight and non-line-of-sight environments; 3) acquiring a multipath information file by using a ray tracing method, calculating channel capacities of different systems, and simulating to acquire a cumulative distribution function diagram and an average channel capacity; 4) simulating and calculating an H matrix singular value, a channel matrix condition number and a correlation coefficient between receiving antennas for each system under different frequency points, and evaluating the fluctuation and stability of the system channel capacity along with the frequency; and 5) judging the influence of different indexes on the channel capacity in combination with environmental factors and high-frequency influence, and completing the evaluation process. According to the method, high-precision channel capacity prediction can be realized, and the method can be used for measuring the performance of the MIMO system.

Description

Technical Field

[0001] This invention belongs to the field of wireless communications and further relates to the field of radio wave propagation prediction modeling and optimization. Specifically, it provides a ray tracing-based method for evaluating the channel capacity of a MIMO system in an indoor environment. This method can be used in smart buildings and smart offices to optimize the deployment of Wi-Fi 6 / 7 and 5G small base stations, reduce signal blind spots, and increase the concurrent transmission rate for multiple users. Background Art

[0002] Channel capacity is a key metric for evaluating a channel's information transmission capabilities and is crucial for designing and evaluating the performance of communication systems. In wireless communications, studying channel capacity helps determine the maximum data transmission rate under specific environmental conditions. There are three methods for increasing channel capacity: adding more base stations, expanding existing frequency bands, and improving frequency band utilization efficiency. Improving frequency band utilization efficiency is the most ideal option. Diversity technologies, such as building multiple-input, multiple-output (MIMO) systems, are an effective way to improve frequency band utilization. Compared to single-input, single-output (SISO) and single-input, multiple-output (SIMO) systems, MIMO systems can significantly increase channel capacity, breaking through the limitations of traditional channel capacity and expanding the scope of application of Shannon's theorem. Therefore, indoor MIMO systems have become a core component of modern wireless communications technology. Their significant channel capacity improvements and enhanced signal reliability make them promising for broad application in 3G and later mobile communications.

[0003] Traditional models, such as the Gaussian white noise channel model, the Rayleigh fading channel model, and the Ricean fading channel model, have been used to calculate channel capacity. However, the Gaussian white noise channel model is overly idealized, ignoring factors such as multipath effects and frequency-selective fading encountered during actual signal transmission. This makes it unsuitable for dynamic environments such as mobile communications. The Rayleigh fading model assumes that all multipath components have the same average power, which is often not the case in reality. Furthermore, it cannot accurately simulate environments with complex components, such as suburban areas or open areas. The Ricean parameters in the Ricean model require accurate estimation, but this is often difficult to achieve in practical applications. The determination of these model parameters depends on the specific environment, limiting their universality and flexibility. In summary, these traditional channel models are valuable for understanding and designing communication systems, but they often rely on idealized setups or observational data from specific environments. In real-world applications, the complexity and dynamic nature of the environment make these models impractical and subject to numerous drawbacks.

[0004] In 2009, Zhang Huawei of Xidian University published his master's thesis, "Wireless MIMO Channel Modeling and Channel Capacity Research," in which he classified MIMO channel modeling methods, focusing on the correlation matrix method and the ray method. He also proposed an improved solution for the channel coefficient in the direct path scenario, supporting mutually polarized antenna configurations. He also analyzed the differences in channel characteristics between macrocell and microcell scenarios and explored the impact of channel correlation on MIMO capacity. Although this method was the first to fully analyze the impact of channel correlation on MIMO system capacity from three perspectives: angle expansion, antenna spacing, and diversity, it did not use ray tracing to extract multipath information and combine it with the channel capacity formula to calculate the precise MIMO system channel capacity. It also did not consider the combined effects of antenna transmission frequency, channel matrix correlation, matrix singular values, and the specific environment on the evaluation of MIMO system capacity. Summary of the Invention

[0005] The present invention aims to address the shortcomings of the aforementioned prior art by proposing a method for estimating channel capacity in a MIMO system using a RT algorithm. This method addresses key bottlenecks in existing wireless communication systems, including channel capacity estimation errors caused by multipath effects and the blindness of MIMO system configuration. It enables high-precision channel capacity prediction and optimizes MIMO system design.

[0006] The technical approach to implementing the present invention is as follows: first, a specific indoor environment is measured, and the environmental data is converted into environmental information and electrical parameter files such as .asc and .txt files using the triangulated facet method. These files are used as configuration files for the ray tracing method to obtain a digital three-dimensional model of the indoor environment. A MIMO system is constructed in a line-of-sight or non-line-of-sight environment, and information such as the height, coordinates, transmit frequency, transmit power, and ray weight of the transmitting and receiving antennas is given. A ray tracing program is used to obtain a multipath information file, and useful information is extracted to accurately calculate the channel capacity of the MIMO system. The cumulative distribution function graph and average channel capacity in line-of-sight and non-line-of-sight environments are simulated to detect the stability of the MIMO system channel capacity. The multipath information is used to calculate the H matrix singular values, channel matrix condition number, and correlation coefficient between receiving antennas of different MIMO systems at different frequencies to evaluate the performance of the MIMO system. Through simulation and comparative analysis, the influence of various factors on channel capacity is explored, aiming to provide a method for evaluating channel capacity that better meets practical needs.

[0007] To achieve the above objectives, the technical solutions of the present invention include the following:

[0008] (1) Obtaining building environment information and electrical parameters of a specified indoor scene as a configuration file for the ray tracing method, and constructing a digital three-dimensional model of the indoor scene;

[0009] (2) Construct a MIMO system in an indoor scenario to determine the channel in line-of-sight (LOS) and non-line-of-sight (NLOS) environments, set the three-dimensional coordinates, transmit power, and transmit frequency of the transmitting and receiving antennas, and use the ray tracing method to obtain the multipath information file and obtain the effective value;

[0010] (3) Using the effective value, the channel capacity of the MIMO system for different channels is calculated. Through simulation, a curve of the channel capacity changing with frequency is obtained. Based on the different channel capacities at different frequencies, a cumulative distribution function is obtained. The average channel capacity is then obtained by summing the differences.

[0011] (4) Obtain the channel capacity of the MIMO system at each frequency point under LOS and NLOS environments through simulation, and calculate the H matrix singular values, channel matrix condition number, correlation coefficient between receiving antennas, and total number of multipaths of the MIMO system at each frequency point;

[0012] (5) Plot the channel capacities arranged from largest to smallest as the horizontal axis, and the corresponding channel matrix condition numbers and correlation coefficients between receiving antennas as the vertical axis, and label each point in the graph with its frequency.

[0013] (6) Evaluate the fluctuation of the MIMO system channel capacity with frequency and the stability of the system performance at different frequencies based on the slope of the curve in the channel capacity cumulative distribution function diagram;

[0014] (7) Combined with specific environmental factors and high-frequency influences, evaluate the impact of different channel matrix condition numbers and correlation coefficients between receiving antennas on channel capacity;

[0015] (8) Complete the evaluation process for determining the channel capacity of the MIMO system.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] First, the present invention adopts a ray tracing method based on an accurate digital model of the environment, so that accurate and effective multipath information can be extracted from the running results of the RT algorithm, thereby being free from certain limitations of the traditional model environment and accurately calculating the channel capacity of the MIMO system.

[0018] Second, because the present invention combines specific environmental factors and the influence of high frequency, and comprehensively considers the channel capacity stability and performance of the MIMO system from several indicators such as the cumulative distribution function diagram, the channel matrix condition number, and the correlation coefficient between the receiving antennas, the process is simple and easy to implement, and the results obtained are more accurate and comprehensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is an overall implementation flow chart of the method of the present invention;

[0020] Figure 2 This is an environmental modeling diagram of an indoor scene (small conference room) provided by an embodiment of the present invention.

[0021] Figure 3 This is an environmental modeling diagram of indoor scene 2 (multi-room classroom scene) provided by an embodiment of the present invention;

[0022] Figure 4 Schematic diagram of a multipath information file provided by an embodiment of the present invention;

[0023] Figure 5 2 is a schematic diagram of a 2×2 MIMO system in an indoor scenario-LOS environment provided by an embodiment of the present invention;

[0024] Figure 6 2 is a schematic diagram of a 2×2 MIMO system in an indoor NLOS environment provided by an embodiment of the present invention;

[0025] Figure 7 The embodiment of the present invention provides Figure 5 、 Figure 6 Curve diagram of MIMO system channel capacity changing with frequency in different environments;

[0026] Figure 8 The embodiment of the present invention provides Figure 5 、 Figure 6 Cumulative distribution function curve graph under the environment;

[0027] Figure 9 The embodiment of the present invention provides Figure 5 The relationship between the channel matrix condition number, the correlation coefficient between the receiving antennas and the channel capacity in the environment;

[0028] Figure 10 The embodiment of the present invention provides Figure 6 The relationship between the channel matrix condition number, the correlation coefficient between the receiving antennas and the channel capacity in the environment;

[0029] Figure 11 2 is a schematic diagram of a 2×2 MIMO system in an indoor scenario 2 LOS environment provided by an embodiment of the present invention;

[0030] Figure 12 2 is a schematic diagram of a 2×2 MIMO system in an indoor scenario 2 NLOS environment provided by an embodiment of the present invention;

[0031] Figure 13 The embodiment of the present invention provides Figure 11 、 Figure 12 Curve diagram of MIMO system channel capacity changing with frequency in the environment;

[0032] Figure 14 The embodiment of the present invention provides Figure 11 、 Figure 12 Cumulative distribution function curve graph under the environment;

[0033] Figure 15 The embodiment of the present invention provides Figure 11 The relationship between the channel matrix condition number, the correlation coefficient between the receiving antennas and the channel capacity in the environment;

[0034] Figure 16 The embodiment of the present invention provides Figure 12 The relationship between the channel matrix condition number, the correlation coefficient between the receiving antennas and the channel capacity in the environment; DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described below with reference to the accompanying drawings.

[0036] Example 1, reference Figure 1 The present invention provides a method for evaluating the channel capacity of a MIMO system based on a RT algorithm, which specifically includes the following steps:

[0037] Step 1) Obtain building environment information and electrical parameters of a specified indoor scene as a configuration file for the ray tracing method to construct a digital three-dimensional model of the indoor scene; the configuration file in this embodiment includes .asc and .txt formats and is declared in the info.ini setting file.

[0038] Step 2) In an indoor scenario, a MIMO system for determining channels in line-of-sight (LOS) and non-line-of-sight (NLOS) environments is constructed. The three-dimensional coordinates, transmit power, and transmit frequency of the transmitting and receiving antennas are set. A multipath information file is obtained using ray tracing to obtain effective values. The indoor scenarios described in this embodiment include independent space scenarios and multi-room scenarios. A MIMO system for determining channels in line-of-sight (LOS) and non-line-of-sight (NLOS) environments is constructed. When the indoor scenario is constructed in an independent space scenario, the specific steps are as follows: a MIMO_2×2 system is established in both LOS and NLOS environments; the transmitting and receiving antennas are both 0.63m apart, the transmitting and receiving distances are the same, and the transmitting power is 0.05W. When the indoor scenario is constructed in a multi-room scenario, the specific steps are as follows: a corridor is selected as the LOS environment to establish the first 2×2 MIMO system, separated by at least one room, and a second 2×2 MIMO system is established in the NLOS environment. The transmitting and receiving antennas of the two systems are kept at the same distance and height of 2m, and the transmitting power is 0.05W.

[0039] Step 3) Calculate the channel capacity of the MIMO system for different determined channels using the effective value, obtain a curve diagram of the change of its channel capacity with frequency through simulation, obtain a cumulative distribution function diagram based on the different channel capacities at different frequencies, and then obtain the average channel capacity through differential summation. The change curve diagram and the cumulative distribution function diagram described in this embodiment are both obtained by simulation using the ecdf function in MATLAB software. For example, the MIMO system in the LOS environment has different channel capacities at different frequencies, which is reflected as an inclined curve on the cumulative distribution function diagram. The MIMO system in the NLOS environment has different channel capacities at different frequencies, which is reflected as another curve with a different degree of inclination on the cumulative distribution function diagram. With the cumulative distribution function, the average channel capacity under the LOS environment and the NLOS environment can be obtained through differential summation.

[0040] In this embodiment, effective values ​​are used to respectively calculate the MIMO system channel capacities of different determined channels in this step, which is specifically implemented as follows:

[0041] Let the number of transmitting antennas of the MIMO system be N t , the number of receiving antennas is N r , the calculation formula of channel capacity C is:

[0042] C = log2[det(I + SNR / N t HH * )]bit / s / Hz,

[0043] Where SNR is the indoor signal-to-noise ratio; I is the unit matrix, N t is the number of transmitting antennas, H is the channel matrix, H * is the conjugate transpose matrix of the H matrix, det(·) represents the determinant of the matrix, and bit / s / Hz represents the unit of channel capacity;

[0044] Each element in the H matrix is ​​determined according to the following formula:

[0045]

[0046] Among them, h nm represents the power impulse response generated from the mth transmitting antenna to the nth receiving antenna; E k ,θ k , τ k are the effective electric field amplitude, phase and time delay of the kth ray respectively; f is the frequency and i is an imaginary number.

[0047] Step 4) The channel capacity of the MIMO system at each frequency point in LOS and NLOS environments is obtained through simulation, and the H matrix singular values, channel matrix condition number, correlation coefficient between receiving antennas, and total multipath count of the MIMO system at each frequency point are calculated. In this embodiment, the H matrix singular values, channel matrix condition number, and R normalized autocorrelation matrix algorithm are used to calculate and obtain the H matrix singular values, channel matrix condition number, and correlation coefficient between receiving antennas of the MIMO system at each frequency point. The H matrix singular values ​​are specifically obtained by performing singular value decomposition on the H matrix using the svd() function in MATLAB.

[0048] Step 5) Arrange the channel capacities from largest to smallest as the horizontal axis, and plot the corresponding channel matrix condition numbers and correlation coefficients between receiving antennas as the vertical axis, and label each point in the graph with a frequency.

[0049] Step 6) Evaluate the fluctuation of the MIMO system channel capacity with frequency and the stability of the system performance at different frequencies based on the slope of the curve in the channel capacity cumulative distribution function graph. In this embodiment, the evaluation is specifically based on the slope of the curve. The evaluation criteria are: the larger the slope, the more concentrated the system channel capacity is at a certain peak, the smaller the fluctuation with increasing frequency, and the more stable the performance.

[0050] Step 7) Evaluate the impact of different channel matrix condition numbers and correlation coefficients between receiving antennas on the channel capacity based on specific environmental factors and high-frequency influences. In this embodiment, the smaller the channel matrix condition number, the larger the channel capacity; and the smaller the correlation coefficient between receiving antennas, the larger the channel capacity.

[0051] Step 8) Complete the evaluation process of determining the channel capacity of the MIMO system.

[0052] Example 2: The overall implementation steps of this embodiment are the same as those of Example 1. Figure 2-16 Specific examples are given to further describe the implementation process of the present invention in detail:

[0053] Step 1: Import common .asc and .txt files, such as environmental information and electrical parameter files, as the ray tracing configuration files to generate a digital 3D model of the indoor environment. Two typical indoor environments are presented: a small conference room scenario and a multi-classroom, multi-room scenario. This step primarily provides accurate indoor environmental information, essential parameters required before using the ray tracing method. Before using ray tracing, the ray weight and maximum ray limit, transmit frequency, transmit power, transmit and receive antenna coordinates, height, simulation type, and other information must be specified in the info file as a prerequisite for using the ray tracing program.

[0054] 1) The information of the two indoor scenes is as follows:

[0055] A. Scenario 1: Small meeting room scenario

[0056] Scene 1 is a small conference room, which includes 5 wooden tables, 5 iron cabinets, 2 sofas, 1 air conditioner and other major indoor objects. In addition, the scene modeling only has one room, including 4 exterior walls (including 2 windows and 1 iron door on the exterior wall), 1 ceiling, 1 marble-covered floor and 2 columns. For the indoor environment, the main environmental elements that affect the propagation of radio waves, such as floors, ceilings, exterior walls, interior walls, doors, windows, beams and indoor objects (such as cabinets, tables, beds, etc.), are considered to model the small conference room environment. Related actual simulation environment modeling is as follows: Figure 2 The red objects in the picture are wooden products, such as wooden tables, coffee tables, and sofas; the green objects are metal products, such as iron doors, iron cabinets, and air conditioner units; the floor is made of marble, the ceiling is fiberboard, the brown walls are made of concrete, and the transparent windows are glass windows.

[0057] B. Scenario 2: Multi-room classroom scenario

[0058] Scene 2 is a classroom environment on the first floor of a teaching building. The overall environment is flip-symmetrically distributed, with a total of 8 basic classroom units and 1 small hall unit. The modeling scene includes 10 room units (corridors and small halls), 10 floors, 10 ceilings, 14 exterior walls (including 26 doors and windows on the exterior walls), and 28 interior walls (including 17 doors and windows on the interior walls). In addition, each classroom contains 4 concrete load-bearing columns and some tables. The large classroom has 27 wooden tables and the small classroom has 18 wooden tables. Related actual simulation environment modeling is as follows: Figure 3 shown.

[0059] 2) The electrical parameters of the materials constituting the indoor environment are as follows:

[0060] Table 1. Electrical parameters of indoor environment components

[0061]

[0062] 3) Description of ray weight and maximum number of rays:

[0063] The maximum number of reflections, transmissions, and diffractions allowed by the RT model is processed using a weighted ratio. For example, if the maximum limit is set to 7, the reflection weight and transmission weight are set to 1, and the diffraction weight is set to 6, then the combinations of reflections, transmissions, and diffractions allowed by RT are: a maximum of 7 reflections or 7 transmissions; a maximum of one diffraction plus one reflection or transmission. The corresponding relationship is described by the following formula:

[0064] W all ≥Wr ×N r +W t ×N t +W d ×N d

[0065] Where W all Indicates the maximum number of ray limits, W r 、W t and W d Represents the reflection, transmission and diffraction weights respectively, N r 、N t and N d They represent the number of reflection, transmission and diffraction respectively.

[0066] Step 2: Define the formula for calculating MIMO system channel capacity, the meaning of the cumulative distribution function plot, and the method for calculating average channel capacity. Describe the algorithms for calculating the singular values ​​of the H matrix, the channel matrix condition number, and the normalized autocorrelation matrix R, and their significance for evaluating channel capacity.

[0067] 1) Channel capacity calculation formula for MIMO system

[0068] For the number of transmitting antennas N t , the number of receiving antennas is N r For a MIMO system, the channel capacity formula is as follows:

[0069] C = log2[det(I + SNR / N t HH * )]bit / s / Hz

[0070] The expansion of each element in the H matrix is:

[0071]

[0072] The received signal h nm E is the power impulse response generated by the mth transmitting antenna to the nth receiving antenna. k ,θ k , τ k are the effective electric field amplitude, phase and time delay of each ray respectively. nm The H matrix represents the channel gain and phase change from the mth transmitting antenna to the nth receiving antenna and is a complex number. SNR is the indoor signal-to-noise ratio. Similarly, the H matrix is ​​also a complex matrix. Therefore, the H matrix comprehensively reflects the attenuation and phase characteristics of the channel and is fundamental to the analysis and design of MIMO systems.

[0073] For broadband internet connections, indoor signal-to-noise ratios are typically high, ranging from 20dB to 30dB or even higher, because limited connections experience relatively little interference. For wireless LAN environments (such as Wi-Fi), the signal-to-noise ratio can be even more variable because wireless signals are affected by multipath fading, interference from adjacent networks, and interference from other wireless devices. In this case, a good signal-to-noise ratio might be in the 20dB to 25dB range, but in very crowded or heavily interfered environments, the signal-to-noise ratio can drop below 10dB. This example uses a 25dB signal-to-noise ratio (SNR) for all simulations.

[0074] Using the RT algorithm, changing the environment file, input parameters, and input file will generate the file Rayinformation.txt. Then open a Rayinformation file. The second column of data is the arrival time of each path at the receiving antenna (ns), the ninth column is the electric field amplitude (V / m), and the tenth column is the electric field phase (rad). Substitute the above formulas and use MATLAB to simulate the channel capacity of different MIMO systems. The following figure shows an example of a Rayinformation.txt file: Figure 4 shown.

[0075] 2) Parameters for measuring MIMO system channel capacity performance

[0076] Singular values ​​of the AH matrix

[0077] Singular values ​​can be obtained by directly performing singular value decomposition on the H matrix or by performing singular value decomposition HH*. In the first case, the decomposed singular values ​​provide information about the channel gain. These singular values ​​measure the signal strength across different spatial dimensions and are directly related to the spatial multiplexing capability of the MIMO system. In the second case, the decomposed eigenvalues ​​are actually the squares of the singular values, reflecting the energy of different channel modes and providing the energy distribution of the channel modes.

[0078] Singular values ​​can be obtained by directly performing singular value decomposition (SVD) on the H matrix or by using SVD HH*. In the first case, the singular values ​​provide information about channel gain. These singular values ​​measure the signal strength across different spatial dimensions and are directly related to the spatial multiplexing capability of the MIMO system. In the second case, the eigenvalues ​​are actually the squares of the singular values, reflecting the energy of different channel modes and providing the energy distribution of the channel modes.

[0079] A singular value greater than 1 means a large gain in the channel direction, reflecting good transmission capability; a singular value less than 1 indicates a small gain in the channel direction, possibly due to poor channel conditions leading to low transmission efficiency.

[0080] B. Channel Matrix Condition Number

[0081] In MIMO systems, the channel matrix condition number is a key indicator of its stability and reversibility. In communications systems, the condition number directly affects the channel capacity and overall system performance. The channel matrix condition number is defined by the ratio of the largest to the smallest singular value of the H matrix.

[0082] If the condition number of the channel is low, all the singular values ​​are close, so the channel capacity contribution in each signal transmission direction is close, the signal gain in each direction is relatively uniform, and each direction of the channel can be effectively utilized, which means that the channel matrix is ​​relatively stable, thereby improving the channel capacity of the system.

[0083] If the condition number of the channel is high, it means that there is strong correlation in the channel or the multipath component is insufficient, and the difference in singular values ​​is large, so that some signals are strengthened while other directions are weakened. Smaller singular values ​​may result in very low channel capacity contribution in the corresponding direction, thereby reducing the total channel capacity.

[0084] CR normalized autocorrelation matrix

[0085] The normalized correlation matrix R, where the element R[ij] represents the correlation between the transmitting antenna j and the receiving antenna i. The normalized autocorrelation matrix R can be calculated as follows:

[0086]

[0087] The normalized R matrix is ​​a complex matrix. Its real part typically represents the power correlation or coherence between antennas, while its imaginary part reflects the phase correlation between antennas. To obtain the autocorrelation coefficient and cross-correlation coefficient, perform absolute value processing on the R matrix.

[0088] The diagonal elements R[ii] represent the autocorrelation of each receive antenna, while the off-diagonal elements R[ij] represent the cross-correlation between different receive antennas. These correlation parameters range in value from 0 to 1. A high correlation value indicates strong correlation in signal transmission between antennas, which can affect the variation pattern of the multipath channel and reduce channel diversity. When the correlation is low, the system can fully utilize multiple independent channels to transmit information, thereby improving system capacity. In the following simulation analysis, we will focus only on the cross-correlation of the receive antennas, that is, the values ​​of the off-diagonal elements.

[0089] D. Cumulative distribution function curve of channel capacity

[0090] The Cumulative Distribution Function (CDF) of channel capacity is used to describe the capacity distribution of a channel. In a MIMO system, due to the presence of multiple channels, the CDF curve is used to represent the probability distribution of different channel capacities.

[0091] The CDF curve can be used to compare the channel capacity performance of different MIMO systems: the steeper the curve, the more concentrated the channel capacity is at a peak, which means the system performance is more stable. The CDF curve can also be used to calculate the average channel capacity of a MIMO system by differential summation.

[0092] Step 3: Reference Figure 5 、 Figure 6 In the indoor scenario, a MIMO_2×2 system in a LOS environment and a MIMO_2×2 system in a NLOS environment are established. The path of the ray in the simulation area is simulated using the reverse ray tracing method. The simulation parameters are set as follows:

[0093] Table 2. Simulation parameter settings for scenario 1

[0094]

[0095] The multipath information calculated by ray tracing is used to simulate the channel capacity of the MIMO system at each frequency point in LOS and NLOS environments, and the curve of channel capacity changing with frequency is obtained. Figure 7 As shown. At the same time, the H matrix singular values, matrix condition numbers, correlation coefficients between receiving antennas, and the total number of multipaths of the MIMO system at each frequency point are calculated. The total number of multipaths at each frequency point of the MIMO system under LOS and NLOS environments is shown in Table 3. The cumulative distribution function diagrams of the MIMO system under line-of-sight and non-line-of-sight environments are obtained by simulation, and the average channel capacity of the two MIMO systems is obtained by differential summation, as shown in Table 4. The channel capacities under 15 frequency points are arranged from large to small as the horizontal axis, and the matrix condition numbers and correlation coefficients between receiving antennas under each channel capacity are used as the vertical axis, and the frequency points corresponding to each point are marked. Draw a curve of the relationship between the channel matrix condition number, the correlation coefficient between receiving antennas and the channel capacity (since the correlation coefficients between receiving antennas are all between 0 and 1, it is difficult to see the trend in one graph, so the correlation coefficient is *10 processed). The relationship diagram under LOS and NLOS environments is shown as follows. Figure 9 、 Figure 10 shown.

[0096] Table 3. Figure 5 、 Figure 6The total number of multipaths at each frequency point of the MIMO system in the environment

[0097]

[0098] Table 4. Figure 5 、 Figure 6 Comparison of average channel capacity in different environments

[0099] MIMO systems in different environments LOS environment NLOS environment Average channel capacity / bit / s / Hz 9.1864 12.2463

[0100] from Figure 7 It can be seen from the figure that for a 2×2 MIMO system with the same physical structure, at each frequency point, the channel capacity of the MIMO system in a LOS environment is lower than that in an NLOS environment. This is because in a LOS environment, the main transmission path of the ray is directly from the transmitting antenna to the receiving antenna, and the reflection and scattering paths account for a low proportion, resulting in a small number of multipaths. In an NLOS environment, the propagation of the ray becomes more complicated, and the ray reaches the receiving antenna after more reflections, scattering, and diffraction. These paths increase the diversity and complexity of the signal. Figure 8 As can be seen, the total number of multipath paths in the NLOS environment is at most nearly 100 greater than that in the LOS environment, and at least 72 greater. Multipath effects cause signals to exhibit different phases and amplitudes at different frequencies, resulting in frequency-selective fading. This multipath combination can lead to constructive interference at some frequencies, increasing channel capacity, while at other frequencies, destructive interference can reduce channel capacity. Since the total number of multipath paths in the NLOS environment is far greater than that in the LOS environment, the receiving antenna in the NLOS environment must receive more energy, resulting in a higher channel capacity. This is consistent with the 33% higher average channel capacity in the NLOS environment compared to the LOS environment, as shown in Table 4. Multipath effects increase the independence between received signals, enabling MIMO systems to more effectively utilize their antenna arrays for spatial multiplexing, thereby increasing channel capacity. Even under frequency fluctuations, NLOS environments generally provide higher channel capacity because they allow for more efficient spatial separation and signal multiplexing.

[0101] And by Figure 8 It can be seen that the cumulative distribution function curve of the channel capacity in the LOS environment is steeper than that in the NLOS environment, indicating that the channel capacity in the LOS environment is more concentrated on a peak value than that in the NLOS environment, which means that the MIMO system performance in the LOS environment is more stable and the fluctuation of the channel capacity with frequency is smaller than that in the NLOS environment. This is consistent with the Figure 7 This is consistent with the fact that the channel capacity fluctuates less with frequency in the LOS environment than in the NLOS environment.

[0102] Observe again Figure 9The channel matrix condition number shows a clear downward trend as channel capacity increases, with only a few outliers observed. This result is relatively ideal. The correlation coefficient between receiving antennas also shows a downward trend, but it is less pronounced. According to the theoretical basis for measuring MIMO system performance parameters, low correlation between antennas allows the system to fully utilize multiple independent channels for information transmission, increasing system capacity. High correlation coefficients reduce system capacity. However, other factors influence channel determination in specific MIMO system scenarios.

[0103] In terms of environmental reflections and multipath effects, offices typically contain multiple sources of reflection and diffraction, such as walls, tables, chairs, and metal objects. These reflection paths vary with frequency. Electromagnetic waves of different frequencies experience varying degrees of reflection, scattering, and attenuation during propagation, resulting in inconsistent phase and amplitude variations in the received signal, which in turn affects the correlation between receiving antennas. Regarding frequency-selective fading, high-frequency signals are more susceptible to this effect during propagation. Due to the complex structures in office environments, signals at different frequencies experience different fading characteristics. Some frequencies may experience particularly severe fading, leading to increased correlation between antennas, while other frequencies may exhibit greater independence. Regarding the relationship between antenna spacing and frequency, the physical spacing between antennas, while fixed, varies relative to the wavelength at different frequencies. At lower frequencies, the antenna spacing may be larger relative to the wavelength, resulting in lower correlation; at higher frequencies, the antenna spacing may be smaller relative to the wavelength, resulting in increased correlation. The different wavelengths at different frequencies can result in the correlation coefficient between antennas not decreasing monotonically with changes in channel capacity.

[0104] For example, at high frequencies, high-frequency signals have shorter wavelengths and are more likely to interact with various structures in the environment, such as the walls and ceilings of a room. In addition, high-frequency signals are also more susceptible to attenuation caused by obstacles in the room. Therefore, when the frequency increases, the channel characteristics may become more complex and unstable, resulting in uneven signal gain in all directions, and inability to effectively utilize each direction of the channel, which increases the singular value gap of the H matrix and reduces the channel matrix condition number, resulting in fluctuations or decreases in channel capacity. In addition, the reflection angle of high-frequency signals is larger, which makes the multipath effect of the received signal more complicated, especially when encountering obstacles such as desks and walls. As the frequency increases, the relative time delay and phase difference of multipath signals become more significant, further exacerbating the mutual interference of signals. Figure 10Low frequencies are primarily concentrated after 8.9526 bits / s / Hz (corresponding to 2.8 GHz). At 3.0 GHz, 2.9 GHz, 3.1 GHz, and 3.5 GHz, the channel matrix condition number is large, and the corresponding channel capacity is also small. This is reasonable. However, anomalies occur at the low frequency of 2.3 GHz, where the channel matrix condition number and correlation coefficient are large. An office is a closed environment, which can be considered a resonant cavity. Signals in the 2.3 GHz band may resonate, resulting in large phase differences between reflection paths and a small channel capacity.

[0105] Let’s look at it again Figure 10 The following figure shows the relationship between the number of channel matrix antennas, the correlation coefficient between antennas, and the channel capacity of a MIMO system in an NLOS environment. The results in this figure are quite ideal. We can see that as the channel capacity increases, the channel matrix condition number and the correlation coefficient between receive antennas show a clear downward trend. Only a few points show anomalies, such as at 3.2 GHz, and the explanation for this is similar to the above.

[0106] Step 4: If Figure 11 、 Figure 12 In indoor scenario 2, a 2×2 MIMO system is established in LOS and NLOS environments. The reverse ray tracing method is used to simulate the path of the ray in the simulation area. The simulation parameters are set as follows:

[0107] Table 5. Simulation parameter settings for scenario 2

[0108]

[0109] Table 6. Figure 11 、 Figure 12 Comparison of the total number of multipaths in each frequency point of the MIMO system under different environments

[0110]

[0111]

[0112] The multipath information calculated by ray tracing is used to simulate the channel capacity of the MIMO system at each frequency point in LOS and NLOS environments, and the curve of channel capacity changing with frequency is obtained. Figure 13 As shown. At the same time, the H matrix singular values, matrix condition number, correlation coefficient between receiving antennas and total number of multipaths of the MIMO system at each frequency point are calculated. The comparison of the total number of multipaths of the MIMO system at each frequency point in LOS and NLOS environments is shown in Table 6 above. The cumulative distribution function diagram of the MIMO system in line-of-sight and non-line-of-sight environments is obtained by simulation, as shown in Figure 14As shown, the average channel capacity of the two MIMO systems is obtained by differential summation, as shown in Table 7. The channel capacities under 15 frequency points are arranged from large to small as the horizontal axis, and the matrix condition number corresponding to each channel capacity and the correlation coefficient between the receiving antennas are used as the vertical axis, and the frequency point corresponding to each point is marked. Draw a curve of the relationship between the channel matrix condition number and the correlation coefficient between the receiving antennas and the channel capacity (because the correlation coefficients between the receiving antennas are all between 0 and 1, it is difficult to see the trend in one graph, so the correlation coefficient is *10). The relationship diagram between LOS environment and NLOS environment is shown as follows: Figure 15 、 Figure 16 shown.

[0113] Table 7. Figure 12 、 Figure 13 Comparison of average channel capacity in different environments

[0114] MIMO systems in different environments LOS environment NLOS environment Average channel capacity / bit / s / Hz 4.8706 1.1300

[0115] Observe first Figure 13 , it was found that the channel capacity of the MIMO system in the LOS environment is higher than that in the NLOS environment at every frequency point. This result is easy to understand. When the antenna is placed in a long and narrow corridor, the waveguide effect will produce a rich multipath. In the NLOS environment, due to the numerous obstacles, many rays have their energy depleted before reaching the receiving antenna. Table 6 shows that the total number of rays at each frequency point in the NLOS environment is very small, with 56 paths in each case. In the LOS environment, the total number of rays is over 1000, and the signal energy received by the receiving antenna is naturally much greater than that in the NLOS environment. This explains why, at every frequency point, the channel capacity of the MIMO system in the LOS environment is greater than that in the NLOS environment.

[0116] And from Table 7, we can see that the average channel capacity in the LOS environment is 331% higher than that in the NLOS environment, and the cumulative distribution function curve of the channel capacity in the LOS environment is steeper than that in the NLOS environment, which means that the channel capacity in the LOS environment is more concentrated on a certain peak than that in the NLOS environment, which means that the MIMO system performance in the LOS environment is more stable, and the fluctuation of the channel capacity with frequency is smaller than that in the NLOS environment, which is consistent with the Figure 10 This is consistent with the fact that the channel capacity fluctuates less with frequency in the LOS environment than in the NLOS environment.

[0117] Observe again Figure 15The channel matrix condition number shows a clear downward trend as channel capacity increases. However, when the channel capacity is low, the channel matrix condition number experiences dramatic fluctuations with increasing channel capacity. These fluctuations are generally concentrated at high frequencies, such as 2.8 GHz, 2.9 GHz, 3.0 GHz, 3.4 GHz, and 3.5 GHz. Anomalies also occur at 3.3 GHz. The correlation coefficient between receive antennas does not show a clear downward trend as channel capacity increases; it only shows a low correlation coefficient at higher channel capacity.

[0118] First, a narrow corridor essentially acts like a waveguide, guiding electromagnetic waves along its path. Within this waveguide, wave reflections and multipath effects create specific propagation patterns that depend on frequency. When the frequency changes, the propagation pattern also changes, resulting in significant variations in the received signal strength and phase. At certain frequencies, standing waves or waveguide cutoff may form, causing a sudden increase or decrease in channel capacity. For example, at 3.3 GHz, this may be the cause of the anomaly.

[0119] At higher frequencies, however, fluctuations in channel characteristics and capacity are more pronounced. This is because high-frequency signals have shorter wavelengths, making them more susceptible to interaction with various structures in the environment, such as corridor walls and ceilings. Furthermore, high-frequency signals are more susceptible to attenuation and scattering caused by corridor obstacles. Therefore, as frequency increases, channel characteristics can become more complex and unstable, leading to dramatic changes in the channel matrix condition number as channel capacity increases in high-frequency bands. This explains why the channel matrix condition number fluctuates dramatically in frequency bands with lower channel capacity.

[0120] In a long and narrow corridor, the walls, ceiling, and floor of the corridor reflect electromagnetic waves, forming multipath propagation. Due to the waveguide effect of the corridor, these reflected waves propagate along specific paths, making the signal propagation pattern more stable and predictable.

[0121] When the signal frequency changes, different propagation modes appear or disappear, changing the strength and phase of the received signal. This stable propagation mode leads to increased correlation between the signals received by the receiving antenna. Specifically, when electromagnetic waves propagate in a long and narrow corridor, the multipath effect causes the signal to propagate along multiple paths and superimpose at the receiving antenna. This superposition effect causes the signals at the receiving antenna to exhibit highly correlated characteristics.

[0122] Therefore, in this waveguide environment, although the channel capacity may increase, the correlation coefficient between the receiving antennas will also increase accordingly. This is because the waveguide effect makes the signal propagation path more concentrated, reducing the effect of spatial diversity and thus increasing the correlation between the receiving antennas.

[0123] like Figure 16 As shown in the figure, in a 2×2 MIMO system in a one-story NLOS (non-line-of-sight) scenario, as channel capacity increases, the channel matrix condition number shows a downward trend, but with small fluctuations, while the correlation coefficient between receive antennas fluctuates, with no clear downward trend. Furthermore, as channel capacity increases, the correlation coefficient between receive antennas reaches a large peak at 3.5 GHz.

[0124] Regarding the channel matrix condition number curve, regarding multipath propagation effects, in NLOS scenarios, signals reach the receiving antenna via multiple propagation paths, including reflection, transmission, and diffraction. This multipath effect imparts rich spatial diversity to the channel matrix, thereby increasing channel capacity and reducing the channel matrix condition number. However, due to the complex reflective surfaces and random distribution of obstacles in the environment, multipath effects vary at different frequencies and locations, causing small fluctuations in the channel matrix condition number within the overall downward trend. Regarding frequency-selective fading, the fading characteristics of signal propagation paths vary at different frequencies. Certain frequencies may experience severe fading, causing a temporary increase in the channel matrix condition number, resulting in fluctuations. Furthermore, changes in frequency-selective fading can affect the structure of the channel matrix, causing temporary fluctuations in the condition number.

[0125] Regarding the fluctuations in the correlation coefficient curve between receiving antennas, at specific frequencies and in terms of multipath effects, in an NLOS environment, signals propagate through multiple paths, including reflection, transmission, and diffraction, to reach the receiving antennas. At certain frequencies, these multipath paths may combine in a unique manner, resulting in high correlation between the signals between the receiving antennas. Regarding the randomness of the environment, in an NLOS environment, signals propagate through multiple paths to reach the receiving antennas, and the propagation characteristics of each path are affected by reflective surfaces and obstacles in the environment. Due to the randomness of these influencing factors, the correlation coefficient between receiving antennas will fluctuate at different frequencies. For example, at a given frequency, reflective surfaces at certain locations may cause the signals received by two receiving antennas to be more correlated, while at other locations, the correlation may be lower. Because rays propagate differently through obstacles at different frequencies, and considering the complexity of multipath effects, the correlation coefficient between receiving antennas fluctuates at different frequencies and when rays pass through different obstacle locations, rather than decreasing monotonically.

[0126] At 3.5 GHz, the correlation coefficient of the receiving antennas reaches a very high peak. This phenomenon can be explained from the following perspectives: Regarding resonance and standing wave effects, at certain frequencies, reflection and diffraction paths may form a resonance effect, resulting in a high correlation of signals when superimposed at the receiving antenna. This resonance effect may lead to a peak in the correlation coefficient between the receiving antennas at 3.5 GHz. At certain frequencies, electromagnetic waves may form standing waves in the propagation environment. The nodes and antinodes of standing waves cause significant variations in signal strength at the receiving antennas, thereby increasing signal correlation. Regarding frequency-selective fading, the fading characteristics of signal propagation paths vary at different frequencies. At 3.5 GHz, signals may encounter unique fading conditions, significantly increasing signal correlation between the receiving antennas. In NLOS environments, high-frequency signals (such as 3.5 GHz) are more susceptible to multipath effects than low-frequency signals (such as 2.1 GHz), resulting in increased correlation of received signals. Regarding the arrangement and spacing of receiving antennas, the arrangement and spacing of receiving antennas have different effects on signal correlation at different frequencies. At 3.5 GHz, the antenna arrangement may cause the received signal correlation to peak. At higher frequencies, the effect of antenna spacing on signal correlation is more significant. At 3.5 GHz, antenna spacing may increase signal coherence, resulting in a peak in the correlation coefficient.

[0127] Step 5: Summarize the example simulation analysis of steps 3 and 4, and provide a method for analyzing and evaluating the channel capacity performance of the MIMO system.

[0128] In this paper, a deterministic channel model based on reverse ray tracing is used to obtain multipath information for different deterministic channels in a typical indoor environment. A 2×2 MIMO system was established in two typical indoor scenarios under LOS and NLOS conditions. The channel matrix was established using the effective multipath information output by the algorithm. Fifteen frequency points, ranging from 2.1 GHz to 3.5 GHz, were selected. Based on the MIMO system channel capacity formula, the channel capacity of the MIMO system was calculated for different line-of-sight environments in the two typical indoor scenarios. The cumulative distribution function (CDF) was plotted, and the average capacity of the different systems was calculated. Simulations obtained the correlation coefficient between the receiving antennas and the channel matrix condition number corresponding to each channel capacity. The channel capacity was arranged from largest to smallest on the horizontal axis, and the correlation coefficient between the receiving antennas and the channel matrix condition number on the vertical axis. The corresponding frequency points were labeled and plotted. The relationship between the three was explored. Results show that the cumulative distribution function plot can predict the fluctuation of the MIMO system channel capacity. Overall, as the channel capacity increases, the channel matrix condition number curve and the correlation coefficient curve between the receiving antennas show a downward trend, and vice versa. The number of channel matrix entries and the correlation coefficient between receive antennas can generally be used to assess the increasing trend of MIMO system channel capacity and the fading frequency, which typically occurs at high frequencies. Specific scenarios require detailed analysis.

[0129] The effects of the present invention will be further described below in conjunction with simulation.

[0130] 1. Simulation conditions:

[0131] The simulation was carried out in Windows 10 hardware environment and Matlab software environment.

[0132] 2. Simulation content

[0133] Use multipath information files such as Figure 4 The effective electric field amplitude, phase and delay data sets of each ray extracted are used to calculate the channel capacity of different MIMO systems at each frequency point using the channel capacity calculation formula of the MIMO system. Figure 7 、 Figure 13 As shown. Using the ray path data set, the total number of multipaths of the MIMO system at different frequencies is simulated using matlab, as shown in Table 3 and Table 6. Based on the above-mentioned channel capacity data set of the MIMO system at different frequencies, the edcf function in matlab is used to simulate the cumulative distribution function diagram of the channel capacity of the MIMO system in the LOS environment and the NLOS environment, as well as the average channel capacity of the MIMO system in the LOS environment and the NLOS environment, as shown in Table 4 and Table 7. Using the multipath information file as Figure 4The effective electric field amplitude, phase and delay data sets of each ray extracted are used. The calculation method of the singular value of the H matrix, the channel matrix condition number and the R normalized channel matrix is ​​used to simulate the channel matrix condition number and the correlation coefficient between the receiving antennas of the MIMO system under different environments and different frequencies using matlab software. Figure 9 、 Figure 10 、 Figure 15 、 Figure 16 .

[0134] 3. Simulation results:

[0135] Figure 7 for Figure 5 、 Figure 6 The curve of MIMO system channel capacity changing with frequency in LOS environment shows that for a 2×2 MIMO system with the same physical structure, the channel capacity of the MIMO system in LOS environment is lower than that in NLOS environment at each frequency point. Table 3 gives the channel capacity of the MIMO system in LOS environment. Figure 5 、 Figure 6 The total number of multipath paths at each frequency point in the MIMO system is shown in Figure 1. The NLOS environment has the largest number of multipath paths, while the LOS environment has nearly 100 multipath paths and the minimum is 72. Figure 8 Table 4 shows the embodiment of the present invention. Figure 5 、 Figure 6 The comparison of the cumulative distribution function curve and the average channel capacity in the NLOS environment shows that the average channel capacity in the NLOS environment is 33% higher than that in the LOS environment. The cumulative distribution function curve of the channel capacity in the LOS environment is steeper than that in the NLOS environment, indicating that the channel capacity in the LOS environment is more concentrated on a peak value than that in the NLOS environment. This means that the MIMO system performance in the LOS environment is more stable, and the fluctuation of the channel capacity with frequency is smaller than that in the NLOS environment. This is consistent with the Figure 7 This is consistent with the fact that the channel capacity fluctuates less with frequency in the LOS environment than in the NLOS environment. Figure 9 yes Figure 5 The relationship between the channel matrix condition number, the correlation coefficient between receive antennas, and the channel capacity in this environment is shown. The channel matrix condition number shows a clear downward trend as the channel capacity increases, with only a few exceptions. This result is relatively ideal. The correlation coefficient between receive antennas also shows a downward trend, but it is not very significant. Low frequencies are mainly concentrated after 8.9526 bits / s / Hz (corresponding to 2.8 GHz). At 3.0 GHz, 2.9 GHz, 3.1 GHz, and 3.5 GHz, the channel matrix condition number is large, and the corresponding channel capacity is also small, which is reasonable. However, at the low frequency of 2.3 GHz, anomalies occur, with the channel matrix condition number and correlation coefficient being large. Figure 10yes Figure 6 The relationship between the channel matrix condition number, the correlation coefficient between receive antennas, and the channel capacity in the given environment is shown in the figure. As the channel capacity increases, the channel matrix condition number and the correlation coefficient between receive antennas show a clear downward trend. Only a few points show abnormalities, such as the high frequency point of 3.2 GHz.

[0136] Figure 13 yes Figure 11 、 Figure 12 The curve of the MIMO system channel capacity changing with frequency in the LOS environment shows that the channel capacity of the MIMO system in the LOS environment is higher than that in the NLOS environment at every frequency point. Table 6 gives Figure 11 、 Figure 12 The comparison of the total number of multipaths of the MIMO system at each frequency point in the NLOS environment shows that the total number of rays at each frequency point in the NLOS environment is very small, all with 56 paths, while in the LOS environment the total number of rays is over 1000. The signal energy received by the receiving antenna is naturally much greater than that received by the receiving antenna in the NLOS environment. Figure 14 The results shown match. Figure 14 and Table 7 is Figure 11 、 Figure 12 The comparison of the cumulative distribution function curve and the average channel capacity in the LOS environment shows that the average channel capacity in the LOS environment is 331% higher than that in the NLOS environment, and the cumulative distribution function curve of the channel capacity in the LOS environment is steeper than that in the NLOS environment, indicating that the channel capacity in the LOS environment is more concentrated on a peak value than that in the NLOS environment, which means that the MIMO system performance in the LOS environment is more stable, and the fluctuation of the channel capacity with frequency is smaller than that in the NLOS environment. Figure 10 This is consistent with the fact that the channel capacity fluctuates less with frequency in the LOS environment than in the NLOS environment.

[0137] Figure 15 yes Figure 11 The relationship between the channel matrix condition number (CMCN), the correlation coefficient between receive antennas (*10), and channel capacity in this environment shows a clear downward trend in the CMCN as channel capacity increases. However, at lower channel capacity, the CMCN experiences dramatic fluctuations with increasing channel capacity. These fluctuations are generally concentrated at high frequencies, such as 2.8 GHz, 2.9 GHz, 3.0 GHz, 3.4 GHz, and 3.5 GHz. Anomalies also occur at 3.3 GHz. The correlation coefficient between receive antennas does not show a clear downward trend with increasing channel capacity; it only shows a lower correlation coefficient at higher channel capacity. Figure 16 yes Figure 12The relationship between the channel matrix condition number, the correlation coefficient between receive antennas, and the channel capacity in this environment is shown in the graph. In a 2×2 MIMO system under NLOS conditions, the channel matrix condition number decreases with increasing channel capacity, but with small fluctuations, while the correlation coefficient between receive antennas fluctuates with no clear downward trend. Furthermore, the correlation coefficient between receive antennas reaches a significant peak at 3.5 GHz as channel capacity increases.

[0138] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0139] The above simulation analysis proves the correctness and effectiveness of the method proposed in the present invention.

[0140] Parts of the present invention that are not described in detail belong to common knowledge among those skilled in the art.

[0141] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Obviously, for professionals in this field, after understanding the content and principles of the present invention, they may make various modifications and changes in form and details without departing from the principles and structure of the present invention. However, these modifications and changes based on the ideas of the present invention are still within the scope of protection of the claims of the present invention.

Claims

1. A method for estimating channel capacity of a MIMO system based on a RT algorithm, characterized in that: The steps include: (1) Obtaining building environment information and electrical parameters of a specified indoor scene as a configuration file for the ray tracing method, and constructing a digital three-dimensional model of the indoor scene; (2) Construct a MIMO system in an indoor scenario to determine the channel in line-of-sight (LOS) and non-line-of-sight (NLOS) environments, set the three-dimensional coordinates, transmit power, and transmit frequency of the transmitting and receiving antennas, and use the ray tracing method to obtain the multipath information file and obtain the effective value; (3) Using the effective value, the channel capacity of the MIMO system for different channels is calculated. Through simulation, a curve of the channel capacity changing with frequency is obtained. Based on the different channel capacities at different frequencies, a cumulative distribution function is obtained. The average channel capacity is then obtained by summing the differences. (4) Obtain the channel capacity of the MIMO system at each frequency point under LOS and NLOS environments through simulation, and calculate the H matrix singular values, channel matrix condition number, correlation coefficient between receiving antennas, and total number of multipaths of the MIMO system at each frequency point; (5) Plot the channel capacities arranged from largest to smallest as the horizontal axis, and the corresponding channel matrix condition numbers and correlation coefficients between receiving antennas as the vertical axis, and label each point in the graph with its frequency. (6) Evaluate the fluctuation of the MIMO system channel capacity with frequency and the stability of the system performance at different frequencies based on the slope of the curve in the channel capacity cumulative distribution function diagram; (7) Combined with specific environmental factors and high-frequency influences, evaluate the impact of different channel matrix condition numbers and correlation coefficients between receiving antennas on channel capacity; (8) Complete the evaluation process for determining the channel capacity of the MIMO system.

2. The method according to claim 1, wherein: The configuration file described in step (1), including .asc and .txt formats, is declared in the info.ini settings file.

3. The method according to claim 1, wherein: The indoor scene described in step (2) includes an independent space scene and a multi-room scene.

4. The method according to claim 3, wherein: In step (2), a MIMO system for determining channels in line-of-sight (LOS) and non-line-of-sight (NLOS) environments is constructed. When the indoor scene is constructed in an independent space scene, specifically: a MIMO_2×2 system is established in LOS and NLOS environments; the transmitting and receiving antennas are both 0.63m, and the transmitting and receiving distances are the same, and the transmission power is 0.05W; when the indoor scene is constructed in a multi-room scene, specifically: the corridor is selected as the LOS environment to establish the first 2×2 MIMO system, and at least one room is separated, and a second 2×2 MIMO system is established in the NLOS environment, and the transmitting and receiving antenna distances of the two systems are kept consistent, the heights are both 2m, and the transmission power is 0.05W.

5. The method according to claim 1, wherein: The calculation of the MIMO system channel capacity of different determined channels using the effective value in step (3) is implemented as follows: Let the number of transmitting antennas of the MIMO system be N t , the number of receiving antennas is N r , the calculation formula of channel capacity C is: C=log2[det(I+SNR / N t HH * )]bit / s / Hz, Where SNR is the indoor signal-to-noise ratio; I is the unit matrix, N t is the number of transmitting antennas, H is the channel matrix, H * is the conjugate transpose matrix of the H matrix, det(·) represents the determinant of the matrix, and bit / s / Hz represents the unit of channel capacity; Each element in the H matrix is ​​determined according to the following formula: Among them, h nm represents the power impulse response generated from the mth transmitting antenna to the nth receiving antenna; E k ,θ k , τ k are the effective electric field amplitude, phase and time delay of the kth ray respectively; f is the frequency and i is an imaginary number.

6. The method according to claim 1, wherein: Step (3) obtains the curve diagram of the change of the MIMO system channel capacity with frequency under different determined channels through simulation, and obtains the cumulative distribution function diagram according to the different channel capacities under different frequencies, all of which are obtained by simulation using the ecdf function in the matlab software.

7. The method according to claim 1, wherein: In step (4), the H matrix singular values, channel matrix condition number and correlation coefficient between receiving antennas of the MIMO system at each frequency point are calculated using the H matrix singular value, channel matrix condition number and R normalized autocorrelation matrix algorithm; the H matrix singular values ​​are specifically obtained by performing singular value decomposition on the H matrix using the svd() function in Matlab.

8. The method according to claim 1, wherein: In step (6), the fluctuation of the channel capacity of the MIMO system with frequency and the stability of the system performance at different frequencies are evaluated according to the slope of the curve in the channel capacity cumulative distribution function diagram. Specifically, the evaluation is performed according to the slope of the curve. The larger the slope, the more concentrated the channel capacity of the system is at a certain peak, the smaller the fluctuation with increasing frequency, and the more stable the performance.

9. The method according to claim 1, wherein: The influence of different channel matrix condition numbers and correlation coefficients between receiving antennas on the channel capacity in step (7) is specifically as follows: the smaller the channel matrix condition number, the larger the channel capacity; the smaller the correlation coefficient between receiving antennas, the larger the channel capacity.

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