High-speed railway broadband wireless channel modeling method based on time slices

Abstract

The invention provides a high-speed railway broadband wireless channel modeling method based on time slices. The method is suitable for optimization of a vehicle-to-ground wireless communication system. The method comprises the following specific steps: setting three-dimensional geometric parameters of a transmitter, a receiver and an obstacle, and deducing a channel transmission matrix between a transmitting antenna and a receiving antenna in a vehicle-to-ground wireless channel; dividing the movement direction of the train into a plurality of static intervals, and updating dynamic parameters of power and time delay by using a time slicing method; according to a geometrical relationship among a transmitter, a receiver and an obstacle, wireless channel characteristics such as path loss, a Rice factor, a power delay spectrum, time delay spread and the like are deduced. According to the method, complex three-dimensional propagation characteristics and dynamic characteristics of the broadband vehicle-to-ground wireless channel in a fast moving scene can be accurately represented, and effective channel model support is provided for design and optimization of a high-speed railway wireless communication system.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a high-speed railway broadband wireless channel modeling method based on time slicing. Background Art

[0002] As the cornerstone of high-speed rail transportation efficiency and reliability, wireless communication systems provide core services such as train control, dispatching, and maintenance communications. In recent years, with the development of 5G technology and the introduction of 5G-R in the railway sector, high-speed rail vehicle-to-ground wireless channels have exhibited new characteristics. Traditional high-speed rail vehicle-to-ground wireless channel modeling methods primarily focus on tapped delay lines, two-dimensional geometry, or narrowband communication systems, failing to fully reflect the characteristics of existing multi-antenna arrays, three-dimensional geometry, and broadband communication systems. Given the fixed locations of base stations, directional movement of trains, and random distribution of obstacles in vehicle-to-ground wireless communication systems, a three-dimensional geometric stochastic model is well-suited for characterization. This type of channel model abstracts the transmitter and receiver positions into geometric coordinates in three-dimensional space, deploys a large-scale antenna array, and randomly generates obstacle coordinates based on a statistical distribution. By comprehensively considering both direct and indirect paths between the transmitter, receiver, and obstacles, the channel transmission matrix, or the channel model formula, is calculated.

[0003] The channel transfer matrix is ​​merely a formula for the channel model, a mathematical expression lacking practical geometric meaning. To analyze the established wireless channel model and guide the design of practical communication systems, this mathematical model requires further processing. In the field of wireless communications, to graphically analyze the physical meaning of the channel model, channel characteristics are often calculated based on the channel transfer matrix, including path loss, Ricean factor, power delay spectrum, and delay spread.

[0004] Currently, there are two major challenges in modeling high-speed railway vehicle-to-ground wireless channels using three-dimensional geometric stochastic models. First, when the transmitter or receiver moves, the channel transmission matrix exhibits dynamic characteristics in the time domain. Traditional three-dimensional geometric stochastic models are based on the assumption of generalized stationarity and cannot effectively model such dynamic characteristics. Second, in high-speed railway vehicle-to-ground wireless communications, the rapid movement of the train will cause large Doppler frequency shifts in the channel. Therefore, joint modeling based on information such as position, angle, and velocity is required to accurately describe the channel position distortion caused by Doppler frequency shifts. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a high-speed railway broadband wireless channel modeling method based on time slicing.

[0006] The main innovations include: 1. 3D geometric broadband channel modeling: Targeted issue: Traditional high-speed railway channel models mostly focus on narrowband systems, two-dimensional geometry, or simplified tapped delay line models, which are difficult to fully reflect the true propagation characteristics of modern broadband communication systems in complex three-dimensional environments (including base stations, fast-moving trains, and various scattering obstacles).

[0007] Innovation: This invention proposes a three-dimensional geometric random model. By specifying the three-dimensional geometric coordinates of the transmitter, receiver, and obstacles, as well as the motion direction and antenna orientation, it constructs a channel transmission matrix consisting of a direct-beam path (LOS) and multiple non-direct-beam paths (NLOS). This model not only considers angular parameters in three-dimensional space (such as AAOD, AAOA, ZAOD, and ZAOA) but also incorporates polarization formulas to characterize the polarization state variations of wireless channels under multipath effects, thereby enabling a more detailed characterization of broadband signal propagation details.

[0008] 2. Time Slicing Method: Targeted problem: The rapid movement of trains causes the wireless channel to exhibit significant dynamic characteristics. Traditional geometric random models based on the broad sense stationary assumption are difficult to effectively capture this time-varying characteristic.

[0009] Innovation: This invention introduces a time-slicing method to update dynamic channel parameters (such as power and delay). Specifically, the train's direction of motion is divided into several static intervals based on quasi-static distances, and innovative shaded areas are placed between adjacent static intervals. By updating parameters such as path delay and channel power within these segments and shaded areas, the continuity of macroscopic and microscopic channel parameters is guaranteed, and the time slicing of dynamic parameters is effectively facilitated, more realistically reflecting the continuously changing channel environment experienced by rapidly moving trains.

[0010] 3. Refined multi-antenna channel characterization: Targeted problem: Modern wireless communication systems (such as 5G-R) widely adopt multi-antenna technology to improve system capacity and reliability, but traditional models do not provide sufficient support for this.

[0011] Innovations of this invention: This paper describes in detail the modeling method for a multi-antenna architecture, configuring antenna arrays at both the transmitter (base station) and receiver (train). By defining the precise three-dimensional geometric coordinates and relative position vectors of each antenna element, the channel transmission matrix between each transmitting antenna and the receiving antenna pair is derived. This refined multi-antenna channel characterization incorporates the influence of antenna patterns, array geometry, and receiver velocity on the Doppler effect, laying the foundation for analyzing and optimizing the performance of multi-antenna systems in high-speed rail scenarios.

[0012] Specifically, Step (1) sets the three-dimensional geometric parameters of the transmitter, receiver and obstacles, configures the antenna direction vector and the moving speed of the receiver, and derives the first vehicle-to-ground wireless channel according to the direct path (LOS) and non-direct path (NLOS). The transmitting antenna to the The channel transmission matrix between the receiving antennas and the change of polarization state in shadow fading; Step (2), under the assumption of wide-sense stationarity, the train's motion direction is divided into several static intervals and shadow areas based on the quasi-static distance, and the dynamic parameters of power and delay are updated using the time slicing method; Step (3) derives the wireless channel characteristics, including path loss, Ricean factor, power delay spectrum and delay spread, based on the geometric relationship between the transmitter, receiver and obstacles.

[0013] Furthermore, in step (1), the configuration of the antenna direction vector and the moving speed of the receiver includes the following steps: Configure the geometric coordinates of the antenna array and To characterize the multi-antenna structure of the vehicle-to-ground wireless channel: (1) (2) in is the reference point coordinate, Indicates the transmitting antennas, Indicates the receiving antennas, and are the antenna spacings of the transmitter and receiver, respectively, and are the antenna tilt angles of the transmitter and receiver, respectively.

[0014] Furthermore, in step (1), when the moving speed is calculated, Defined as the maximum speed of the receiver and the corresponding angle The product of can be expressed as (3).

[0015] Furthermore, in step (1), the channel transmission matrix is ​​derived as follows: In the geometric coordinate system, the spatial configuration of the transmitter, receiver and obstacles is established, including the three-dimensional geometric coordinates and the direction of movement. The geometric relationship between these elements helps to calculate the key angular parameters: departure azimuth, arrival azimuth, departure elevation and arrival elevation.

[0016] Furthermore, in step (1), a three-dimensional geometric representation of the transmitter, receiver, and obstacles is established, and solving the channel transmission matrix includes the following steps: Step (1-1): using three-dimensional geometric shapes to represent physical objects in the propagation space, the modeling method abstracts environmental obstacles into different scattered blocks, each of which contributes to the multipath effect of electromagnetic waves; Step (1-2), according to the Rice factor and channel impulse response Calculate the The transmitting antenna to the The channel transmission matrix of the receiving antennas (4) in, For the The transmitting antenna to the The channel transmission matrix of the receiving antennas is is the current time, For delay, The delay is The impact function of Steps (1-3), in the case of straight diameter, and is the index distance between the transmitter and receiver, expressed as a time-varying angle Calculated (5) (6) Steps (1-4), in the case of non-straight path, and The index spacing that represents the multipath effect is expressed by the time-varying angle It can be deduced that (7) (8) In formulas (5), (6), (7), and (8), sin is the sine function and cos is the cosine function. represents the transposed matrix, Corresponds to the current time.

[0017] Furthermore, in step (1), the change of polarization state of the wireless channel in shadow fading is calculated, and the polarization formula is introduced: To describe the transformation of the polarization state of each ray: (9) in Indicates the initial phase in the vertical and horizontal directions, and Evenly distributed within, is the base of natural logarithms.

[0018] Furthermore, in step (2), the static interval divided based on the quasi-static distance includes the following steps: In step (2-1), under the assumption of wide-sense stationarity, each segment maintains a consistent tensor, and these segments are further subdivided into intervals with different finite elements; In step (2-2), a shadow area is set between adjacent static areas. The establishment of the static interval and the shadow area ensures the continuity of the macroscopic and microscopic channel parameters, while promoting the time slicing of the dynamic parameters.

[0019] Furthermore, in step (2), when the dynamic parameters of power and delay are updated using the time slicing method, solving the dynamic parameters of power and delay includes the following steps: Step (a), split the transmitter path and calculate the path delay of each segment and the shaded area, corresponding to the normalized path delay of each multipath Can be obtained as (10) (11) in is the delay coefficient; is the distance index; is the calculated shadow fading, which follows a normal distribution; is the maximum path delay; Step (b), split the transmitter path and calculate the channel power of each segment and the shaded area, corresponding to the channel power of each multipath Can be obtained as (12) (13) in Indicates the power of each multipath; is the base of natural logarithms; It is The channel transfer function of the interval; It is The channel power of the interval, is the decay factor, is the distance index; is the calculated shadow fading.

[0020] Furthermore, the wireless channel characteristics are derived based on the channel transmission matrix: In step (3), the derivation of the wireless channel characteristics includes the following steps: Step (3-1) derives the path loss formula. Path loss refers to the reduction in power of electromagnetic waves as they propagate from a transmitter to a receiver, representing macroscopic fading in a wireless channel, and is obtained using the following formula: (14) in is the transmit power, is the received power, and are the energy increases at the transmitter and receiver, respectively. is the multipath fading, which describes the fluctuation of the channel envelope with respect to the path loss and can be modeled as a normal distribution; in addition, the empirical path loss in decibels is given in the statistical model.

[0021] In step (3-2), derive the Rice factor formula. The Rice factor is defined as the power ratio of the direct path to the non-direct path. It is a key parameter to characterize the fading environment of the communication link. The Rice factor is usually obtained using logarithms: (15) in is the number of scattered blocks; yes The power of the direct diameter at the moment, yes The power of the non-direct path at any moment.

[0022] Step (3-3) derives the formula for the power delay spectrum. The power delay spectrum describes how the power of the transmitted signal is distributed over time when it reaches the receiver, and explains the multipath effect in the wireless channel. It is a microscopic fading characteristic. In the interval, the delay Power delay spectrum at It can be obtained through the channel transmission matrix in the time domain: (16) (17) in represents the size of the sliding window, is the carrier wavelength, is the channel transmission matrix, is the current time, For delay, is the distance sampling interval value; Step (3-4), derive the delay spread formula. Mathematically, the root mean square delay spread is used to quantify the dispersion of delay. Delay spread in the interval Given by the following formula (18) (19) (20) in and are the expected value and mean square expected value of the power delay spectrum, It is in In the interval, the delay The delay spread is derived from the channel characteristics of the power delay spectrum and is also a key fading characteristic of wireless channels.

[0023] Due to the adoption of the above solution, the beneficial effects of the present invention are: The present invention provides a time-slicing-based high-speed railway broadband wireless channel modeling method, which aims to accurately characterize the complex propagation characteristics of high-speed railway wireless channels in fast-moving scenarios and provide effective channel model support for the design and optimization of high-speed railway wireless communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flowchart of broadband vehicle-to-ground wireless channel modeling according to an embodiment of the present invention.

[0025] Figure 2 Schematic diagram of a three-dimensional geometric random model according to an embodiment of the present invention.

[0026] Figure 3 Schematic diagram of time slicing segmentation according to an embodiment of the present invention.

[0027] Figure 4 Schematic diagram of the simulation of the Rice factor according to an embodiment of the present invention.

[0028] Figure 5 Schematic diagram of simulation of delay spread according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0030] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0031] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "up", "down", "left", "right", "front", "back", etc. indicating directional position relationships, they are based on the directional position relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the position relationships in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0032] Example: like Figure 1 As shown, a high-speed railway broadband wireless channel modeling method based on time slicing in this embodiment includes the following steps in sequence: (1) Set the three-dimensional geometric parameters of the transmitter, receiver and obstacles, configure the antenna direction vector and the moving speed of the receiver, and derive the first The transmitting antenna to the The channel transmission matrix between the receiving antennas and the change of polarization state in shadow fading; (2) Under the assumption of wide-sense stationarity, the train's motion direction is divided into several static intervals and shadow areas based on the quasi-static distance, and the dynamic parameters of power and delay are updated using the time slicing method; (3) Based on the geometric relationship between the transmitter, receiver and obstacles, derive the wireless channel characteristics, including path loss, Ricean factor, power delay spectrum and delay spread.

[0033] Furthermore, in step (1), the configuration of the antenna direction vector and the moving speed of the receiver includes the following steps: Configure the geometric coordinates of the antenna array and To characterize the multi-antenna structure of the vehicle-to-ground wireless channel: (1) (2) in is the reference point coordinate, Indicates the transmitting antennas, Indicates the receiving antennas, and are the antenna spacings of the transmitter and receiver, respectively, and are the antenna tilt angles of the transmitter and receiver, respectively.

[0034] In step (1), in the high-speed railway scenario, the moving speed is also a key channel parameter, which significantly affects the dynamics and Doppler characteristics of the channel model. When calculating the moving speed, Defined as the maximum speed of the receiver and the corresponding angle The product of can be expressed as (3).

[0035] In step (1), the channel transmission matrix is ​​derived as follows: In the geometric coordinate system, the spatial configuration of the transmitter, receiver and obstacles is established, including the three-dimensional geometric coordinates and the direction of movement. The geometric relationship between these elements helps to calculate the key angular parameters: departure azimuth, arrival azimuth, departure elevation and arrival elevation.

[0036] In step (1), a three-dimensional geometric representation of the transmitter, receiver, and obstacles is established. Solving the channel transmission matrix includes the following steps: Step (1-1), the three-dimensional geometric random modeling method adopts a mathematical modeling method to intuitively represent the propagation environment, and uses three-dimensional geometric shapes to represent physical objects in the propagation space. The modeling method abstracts environmental obstacles into different scattered blocks, each of which contributes to the multipath effect of electromagnetic waves.

[0037] like Figure 2 As shown in Figure 1, a typical high-speed railway vehicle-to-ground wireless channel consists of a transmitter, a receiver, and several scattering blocks, where the receiver supports mobility. For simplicity, only two scattering blocks are depicted, where the electromagnetic waves emitted from the transmitter generate multipath rays with the scattering blocks.

[0038] Step (1-2), according to the Rice factor and channel impulse response Calculate the The transmitting antenna to the The channel transmission matrix of the receiving antennas is mathematically represented by the matrix To indicate that, including transmit antennas and receiving antennas (4) in, For the The transmitting antenna to the The channel transmission matrix of the receiving antennas is is the current time, For delay, The delay is The impact function.

[0039] Steps (1-3), in case of LOS, and is the index distance between the transmitter and the receiver, which can be expressed by the time-varying angle Calculated (5) (6) Steps (1-4), in the NLOS case, and The index spacing represents the multipath effect. Similarly, they are composed of the time-varying angle It can be deduced that (7) (8) In formulas (7), (8), (9), and (10), sin is the sine function and cos is the cosine function. represents the transposed matrix, Corresponds to the current time.

[0040] In step (1), the channel model also needs to consider the change of the polarization state of the radio wave during the shadow fading process. Introducing the polarization formula The concept of The polarization state of the ray changes. It is composed of complex gain coefficients representing the changes in orthogonal polarization waves, and its specific expression is as follows (9) in Indicates the initial phase in the vertical and horizontal directions, and Evenly distributed within, is the base of natural logarithms.

[0041] Furthermore, step (2) includes: Step (2-1), the implementation of time slicing requires accurate identification of the area where the generalized stationary assumption is valid. According to the concept of quasi-static distance, the direction of motion of the receiver is divided into several static intervals. Under the generalized stationary assumption, each segment maintains a consistent tensor. Step (2-1), under the generalized stationary assumption, each segment maintains a consistent tensor, and these segments are further subdivided into intervals with different finite elements; remove the distance sampling interval value Determined by the following formula (10) (11) in The bandwidth representing the Doppler shift; Indicates the carrier wavelength; is the velocity matrix calculated by formula (3), and .

[0042] In step (2-2), shadow regions are set between adjacent static intervals. Existing geometric stochastic models primarily consider independent static intervals without accounting for the transition phases between them. While this approach has proven effective in vehicle-to-vehicle and drone scenarios, it is insufficient for characterizing the rapid motion characteristics of high-speed rail. The establishment of static intervals and shadow regions ensures the continuity of macroscopic and microscopic channel parameters while facilitating time slicing of dynamic parameters. Figure 3 A schematic diagram of two adjacent segments and their corresponding shaded areas is provided to illustrate this conceptual framework.

[0043] Steps (2-3) segment the transmitter path and calculate the path delay for each segment and the shadow area. In the high-speed railway channel modeling process, path delay is a key dynamic parameter that characterizes large-scale fading and follows a logarithmic distribution. The normalized path delay corresponding to each multipath is Can be obtained as (12) (13) in is the delay coefficient; is the distance index; is the calculated shadow fading, which follows a normal distribution; is the maximum path delay; Steps (2-4) segment the transmitter path and calculate the channel power in the shadow area. Channel power is another common dynamic parameter that represents the fading of the wireless channel. Similar to delay spread, it focuses on each path and is calculated over the entire trajectory, which can be expressed as (14) (15) in Indicates the power of each multipath; is the base of natural logarithms; It is The channel transfer function of the interval; It is The channel power of the interval, is the decay factor, is the distance index; is the calculated shadow fading, which follows a normal distribution.

[0044] In addition, the power in the shaded area is calculated from the power of its adjacent segments and is expressed as (16) Furthermore, step (3) includes: Step (3-1), derive the path loss formula. Path loss refers to the reduction in power of electromagnetic waves as they propagate from a transmitter to a receiver. It represents macroscopic fading in a wireless channel and is obtained by the following formula: (17) in is the transmit power, is the received power. and Represents the energy increase at the transmitter and receiver respectively. is the multipath fading, which describes the fluctuation of the channel envelope relative to the path loss and can be modeled as a normal distribution. In addition, the empirical path loss in decibels is given in the statistical model.

[0045] Step (3-2) derives the Rice factor formula. The Rice factor is defined as the power ratio of the LOS path to the NLOS path and is a key parameter to characterize the fading environment of the communication link. The Rice factor is usually obtained using logarithms. (18) in is the number of scattered blocks; yes The power of the direct diameter at the moment, yes The power of the non-direct path at any moment.

[0046] Step (3-3) derives the formula for the power delay spectrum. The power delay spectrum describes how the power of the transmitted signal is distributed over time as it reaches the receiver. It describes the multipath effect in the wireless channel and is an important microscopic fading characteristic. In the interval, the delay Power delay spectrum at It can be obtained by the channel transmission matrix in the time domain (19) (20) in represents the size of the sliding window, is the carrier wavelength, is the channel transmission matrix, is the current time, For delay, is the distance sampling interval value.

[0047] Step (3-4), derive the delay spread formula. Mathematically, the root mean square delay spread is used to quantify the dispersion of delay. Delay spread in the interval Given by the following formula (twenty one) (twenty two) (twenty three) in and are the expected value and mean square expected value of the power delay spectrum, It is in In the interval, the delay The delay spread is derived from the channel characteristics of the power delay spectrum and is also a key fading characteristic of wireless channels.

[0048] Model performance comparison: Based on the measurement results, the modeling accuracy of the broadband vehicle-to-ground wireless channel model of the present invention, the general 5G channel model, and the traditional geometric model are compared to analyze the channel characteristics. The relevant parameters are set as follows: , , , , , , .

[0049] refer to Figure 4The comparison results of the Ricean factor cumulative distribution function (CDF) demonstrate the superiority of the channel modeling method proposed in this paper in characterizing the channel tensor. The figure shows the Ricean factor (in decibels) on the X-axis and the CDF value on the Y-axis, comparing the three modeling methods with the baseline. In modeling the Ricean factor CDF, the proposed method demonstrates high consistency with measured data, achieving an average improvement of approximately 32.03% in modeling accuracy compared to the common 5G channel model and approximately 20.74% in modeling accuracy compared to the traditional geometric model.

[0050] refer to Figure 5 The comparison results of the cumulative distribution function of the root mean square delay spread shown in the figure verify the superiority of the channel modeling method proposed in this paper in characterizing the channel finite element. The X-axis of the figure represents the root mean square delay spread (unit: nanoseconds) and the Y-axis represents the cumulative distribution function value. The figure shows the comparison of the three modeling methods with the benchmark. In the modeling of the root mean square delay spread cumulative distribution function, the proposed method shows a high degree of consistency with the measured data. Compared with the general 5G channel model, its modeling accuracy is improved by an average of approximately 23.51%; compared with the traditional geometric model, its modeling accuracy is improved by an average of approximately 22.18%.

[0051] In summary, the method of the present invention can effectively simulate the impact of time slicing on channel characteristics in a broadband vehicle-to-ground wireless channel, which has reference value for the design and evaluation of high-speed railway communication systems.

[0052] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art will appreciate that the technical solutions of the present invention may be modified or adjusted using equivalent substitutions, without departing from the spirit and scope of the present technical solutions. All modifications or substitutions are intended to be encompassed by the claims of the present invention.

Claims

1. A time-slicing-based high-speed railway broadband wireless channel modeling method, characterized by: It includes the following steps: (1) Set the three-dimensional geometric parameters of the transmitter, receiver and obstacles, configure the antenna direction vector and the moving speed of the receiver, and derive the first The transmitting antenna to the The channel transmission matrix between the receiving antennas and the change of polarization state in shadow fading; (2) Under the assumption of wide-sense stationarity, the train's motion direction is divided into several static intervals and shadow areas based on the quasi-static distance, and the dynamic parameters of power and delay are updated using the time slicing method; (3) Based on the geometric relationship between the transmitter, receiver and obstacles, derive the wireless channel characteristics, including path loss, Ricean factor, power delay spectrum and delay spread.

2. The time-slicing-based high-speed railway broadband wireless channel modeling method according to claim 1 is characterized in that: In step (1), the configuration of the antenna direction vector and the moving speed of the receiver includes the following steps: Configure the geometric coordinates of the antenna array and To characterize the multi-antenna structure of the vehicle-to-ground wireless channel: （1） （2） in is the reference point coordinate, Indicates the transmitting antennas, Indicates the receiving antennas, and are the antenna spacings of the transmitter and receiver, respectively, and are the antenna tilt angles of the transmitter and receiver, respectively.

3. The time-slicing-based high-speed railway broadband wireless channel modeling method according to claim 1 is characterized in that: In step (1), when calculating the moving speed, Defined as the maximum speed of the receiver and the corresponding angle The product of can be expressed as （3）。 4. The time-slicing-based high-speed railway broadband wireless channel modeling method according to claim 1, characterized in that: In step (1), the channel transmission matrix is ​​derived as follows: In the geometric coordinate system, the spatial configuration of the transmitter, receiver and obstacles is established, including the three-dimensional geometric coordinates and the direction of movement. The geometric relationship between these elements helps to calculate the key angular parameters: departure azimuth, arrival azimuth, departure elevation and arrival elevation.

5. The time-slicing-based high-speed railway broadband wireless channel modeling method according to claim 1 is characterized in that: In step (1), a three-dimensional geometric representation of the transmitter, receiver, and obstacles is established. Solving the channel transmission matrix includes the following steps: Step (1-1): using three-dimensional geometric shapes to represent physical objects in the propagation space, the modeling method abstracts environmental obstacles into different scattered blocks, each of which contributes to the multipath effect of electromagnetic waves; Step (1-2), according to the Rice factor and channel impulse response Calculate the The transmitting antenna to the The channel transmission matrix of receiving antennas is: （4） in, For the The transmitting antenna to the The channel transmission matrix of the receiving antennas is is the current time, For delay, The delay is The impact function of Steps (1-3), in the case of straight diameter, and is the index distance between the transmitter and receiver, expressed as a time-varying angle Calculated （5） （6） Steps (1-4), in the case of non-straight path, and The index spacing that represents the multipath effect is expressed by the time-varying angle It can be deduced that （7） （8） In formulas (5), (6), (7), and (8), sin is the sine function and cos is the cosine function. represents the transposed matrix, Corresponds to the current time.

6. The time-slicing-based high-speed railway broadband wireless channel modeling method according to claim 1, characterized in that: In step (1), the change of polarization state of wireless channel in shadow fading is calculated, and the polarization formula is introduced: To describe the transformation of the polarization state of each ray: （9） in Indicates the initial phase in the vertical and horizontal directions, and Evenly distributed within, is the base of natural logarithms.

7. The time-slicing-based high-speed railway broadband wireless channel modeling method according to claim 1, characterized in that: In step (2), the static interval division based on the quasi-static distance includes the following steps: In step (2-1), under the assumption of wide-sense stationarity, each segment maintains a consistent tensor, and these segments are further subdivided into intervals with different finite elements; In step (2-2), a shadow area is set between adjacent static areas. The establishment of the static interval and the shadow area ensures the continuity of the macroscopic and microscopic channel parameters, while promoting the time slicing of the dynamic parameters.

8. The time-slicing-based high-speed railway broadband wireless channel modeling method according to claim 1, characterized in that: In step (2), when the dynamic parameters of power and delay are updated using the time slicing method, solving the dynamic parameters of power and delay includes the following steps: Step (a), split the transmitter path and calculate the path delay of each segment and the shaded area, corresponding to the normalized path delay of each multipath Can be obtained as （10） （11） in is the delay coefficient; is the distance index; is the calculated shadow fading, which follows a normal distribution; is the maximum path delay; Step (b), split the transmitter path and calculate the channel power of each segment and the shaded area, corresponding to the channel power of each multipath Can be obtained as （12） （13） in Indicates the power of each multipath; is the base of natural logarithms; It is The channel transfer function of the interval; It is The channel power of the interval, is the decay factor, is the distance index; is the calculated shadow fading.

9. The time-slicing-based high-speed railway broadband wireless channel modeling method according to claim 1, characterized in that: Derivation of wireless channel characteristics based on the channel transmission matrix: In step (3), the derivation of the wireless channel characteristics includes the following steps: Step (3-1) derives the path loss formula. Path loss refers to the reduction in power of electromagnetic waves as they propagate from a transmitter to a receiver, representing macroscopic fading in a wireless channel, and is obtained using the following formula: （14） in is the transmit power, is the received power, and are the energy increases at the transmitter and receiver, respectively. is the multipath fading, which describes the fluctuation of the channel envelope with respect to the path loss and can be modeled as a normal distribution; in addition, the empirical path loss in decibels is given in the statistical model; In step (3-2), derive the Rice factor formula. The Rice factor is defined as the power ratio of the direct path to the non-direct path. It is a key parameter to characterize the fading environment of the communication link. The Rice factor is usually obtained using logarithms: （15） in is the number of scattered blocks; yes The power of the direct diameter at the moment, yes The power of the non-direct path at any moment; Step (3-3) derives the formula for the power delay spectrum. The power delay spectrum describes how the power of the transmitted signal is distributed over time when it reaches the receiver, and explains the multipath effect in the wireless channel. It is a microscopic fading characteristic. In the interval, the delay Power delay spectrum at It can be obtained through the channel transmission matrix in the time domain: （16） （17） in represents the size of the sliding window, is the carrier wavelength, is the channel transmission matrix, is the current time, For delay, is the distance sampling interval value; Step (3-4), derive the delay spread formula. Mathematically, the root mean square delay spread is used to quantify the dispersion of delay. Delay spread in the interval Given by the following formula （18） （19） （20） in and are the expected value and mean square expected value of the power delay spectrum, It is in In the interval, the delay The delay spread is derived from the channel characteristics of the power delay spectrum and is also a key wireless channel fading characteristic.

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