Method, device, processor and storage medium for generating channel coefficients of dynamic velocity TDL model based on Lagrange interpolation

Abstract

The present invention relates to a method for generating and processing channel coefficients of a dynamic speed TDL model based on Lagrange interpolation, comprising the following steps: generating a channel coefficient of a reference point at a uniform speed at maximum velocity; calculating the movement distance of the required interpolation sampling point based on velocity data; performing Lagrange interpolation on n points under the original uniform speed condition to calculate a channel coefficient of length n. ′ The present invention also relates to a device, processor, and computer-readable storage medium for implementing the generation and processing of channel coefficients for a dynamic speed TDL model based on Lagrangian interpolation. The method, device, processor, and computer-readable storage medium for implementing the generation and processing of channel coefficients for a dynamic speed TDL model based on Lagrangian interpolation of the present invention solve the problem of inaccurate simulation data in an environment where the signal receiving device in the model moves at a variable speed. At the same time, the computational complexity of the filter method is much lower than that of the sine wave superposition method, effectively reducing the time spent on simulation modeling.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, in particular to the field of channel simulation, and specifically refers to a method, device, processor and computer-readable storage medium thereof for realizing dynamic velocity TDL model channel coefficient generation processing based on Lagrange interpolation. Background Art

[0002] With the continuous development of wireless communication technology, fields related to wireless signal transmission have been widely studied. In order to facilitate research and verification, it is necessary to use a channel simulator in a laboratory environment to simulate the channel environment and restore the real scene.

[0003] Existing systems implement fixed-speed TDL channel model simulation using a traditional filter method, which has low computational complexity and widespread application. However, the simulation results for fading channels are suboptimal, especially when the speed of the moving device changes. Simulating a variable-speed device moving channel is not straightforward. Another sine wave superposition method for model simulation, based on the sum of Ricean sines, requires a very narrow bandwidth relative to the sampling rate, resulting in high complexity and long computational time. Therefore, current systems use a filter method as the basis for TDL modeling, utilizing a low-pass filter to generate two or more correlated Gaussian random processes.

[0004] TDL simulation modeling generally uses different probability distribution combinations based on the shielding conditions encountered by the signal along the propagation path. Common Rayleigh and Rice distributions generate band-limited Gaussian colored noise by filtering two mutually orthogonal Gaussian random processes with mean 0 and variance 1 through a unit-energy Jacks-type low-pass filter. This Gaussian colored noise is then multiplied by the standard deviation of the multipath component to produce a Rayleigh random distribution process. Simulating the Rice distribution requires only superimposing the amplitude of the direct component after generating the Rayleigh random process. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, device, processor and computer-readable storage medium for generating and processing channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation, which has high accuracy, small error and a wide range of applications.

[0006] To achieve the above objectives, the present invention provides a method, device, processor, and computer-readable storage medium for generating channel coefficients of a dynamic velocity TDL model based on Lagrangian interpolation.

[0007] The method for generating and processing channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation is characterized in that the method comprises the following steps:

[0008] (1) Generate the channel coefficient of the reference point at the maximum uniform speed;

[0009] (2) Calculate the movement distance of the required interpolation sampling points based on the velocity data;

[0010] (3) Perform Lagrange interpolation on the n points under the original uniform speed condition and calculate the channel coefficient array with a length of n′.

[0011] Preferably, the step (1) is specifically as follows:

[0012] The maximum speed in the dynamic speed environment is selected, and a uniform motion model is generated at the maximum speed to obtain the channel coefficient h of n sampling points at the maximum uniform speed.

[0013] Preferably, the step (2) specifically includes the following steps:

[0014] (2.1) Calculate the moving distance of n′ sampling points;

[0015] (2.2) Calculate the running distance corresponding to the terminal at n′ moments under the variable speed environment.

[0016] Preferably, the step (2.2) is specifically as follows:

[0017] Calculate the product of the time interval and actual speed of each sampling point, and add up the products of all sampling points to get the movement distance d' of sampling point i' i .

[0018] Preferably, the step (3) specifically includes the following steps:

[0019] (3.1) Find d′ from the reference point array of the uniform velocity model i Two consecutive points before and after d j and d j+1 , with point d j and d j+1 Traverse k points from the starting point to the beginning and end of the reference point array, and take 2k points as reference points for Lagrange interpolation calculation;

[0020] (3.2) Use Lagrange interpolation method to calculate the channel coefficient obtained by interpolation of the i-th point;

[0021] (3.3) Repeat step (3.1) until the channel coefficients of the n′ sampling points after interpolation are calculated.

[0022] The main feature of the device for realizing the generation and processing of the channel coefficients of the dynamic velocity TDL model based on Lagrange interpolation is that the device comprises:

[0023] a processor configured to execute computer-executable instructions;

[0024] The memory stores one or more computer executable instructions. When the computer executable instructions are executed by the processor, the various steps of the method for generating channel coefficients of the dynamic speed TDL model based on Lagrange interpolation are implemented.

[0025] The main feature of the processor for implementing the dynamic speed TDL model channel coefficient generation processing based on Lagrangian interpolation is that the processor is configured to execute computer-executable instructions. When the computer-executable instructions are executed by the processor, the various steps of the above-mentioned method for implementing the dynamic speed TDL model channel coefficient generation processing based on Lagrangian interpolation are implemented.

[0026] The main feature of the computer-readable storage medium is that a computer program is stored thereon, and the computer program can be executed by a processor to implement the various steps of the above-mentioned method for generating channel coefficients of a dynamic speed TDL model based on Lagrange interpolation.

[0027] The present invention employs a method, device, processor, and computer-readable storage medium for generating channel coefficients for a dynamic speed TDL model based on Lagrangian interpolation. These methods primarily address the problem of using a channel model generated by a filter method to simulate the motion of variable-speed equipment. This solves the issue of inaccurate simulation data when the signal receiving device in the model is moving at variable speeds. Furthermore, the computational complexity of the filter method is significantly lower than that of the sine wave superposition method, effectively reducing the time required for simulation modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The present invention is a flowchart of a method for realizing dynamic velocity TDL model channel coefficient generation processing based on Lagrange interpolation.

[0029] Figure 2 It is a schematic diagram of the speed change curve corresponding to the interpolation reference point and the required interpolation point of the present invention. DETAILED DESCRIPTION

[0030] In order to more clearly describe the technical content of the present invention, further description is given below in conjunction with specific embodiments.

[0031] The method for generating channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation of the present invention comprises the following steps:

[0032] (1) Generate the channel coefficient of the reference point at the maximum uniform speed;

[0033] (2) Calculate the movement distance of the required interpolation sampling points based on the velocity data;

[0034] (3) Perform Lagrange interpolation on the n points under the original uniform speed condition and calculate the channel coefficient array with a length of n′.

[0035] As a preferred embodiment of the present invention, the step (1) is specifically as follows:

[0036] The maximum speed in the dynamic speed environment is selected, and a uniform motion model is generated at the maximum speed to obtain the channel coefficient h of n sampling points at the maximum uniform speed.

[0037] As a preferred embodiment of the present invention, the step (2) specifically includes the following steps:

[0038] (2.1) Calculate the moving distance of n′ sampling points;

[0039] (2.2) Calculate the running distance corresponding to the terminal at n′ moments under the variable speed environment.

[0040] As a preferred embodiment of the present invention, the step (2.2) is specifically as follows:

[0041] Calculate the product of the time interval and actual speed of each sampling point, and add up the products of all sampling points to get the movement distance d' of sampling point i' i .

[0042] As a preferred embodiment of the present invention, the step (3) specifically includes the following steps:

[0043] (3.1) Find d′ from the reference point array of the uniform velocity model i Two consecutive points before and after d j and d j+1 , with point d j and d j+1 Traverse k points from the starting point to the beginning and end of the reference point array, and take 2k points as reference points for Lagrange interpolation calculation;

[0044] (3.2) Use Lagrange interpolation method to calculate the channel coefficient obtained by interpolation of the i-th point;

[0045] (3.3) Repeat step (3.1) until the channel coefficients of the n′ sampling points after interpolation are calculated.

[0046] The device for implementing the dynamic velocity TDL model channel coefficient generation process based on Lagrange interpolation of the present invention, wherein the device comprises:

[0047] a processor configured to execute computer-executable instructions;

[0048] The memory stores one or more computer executable instructions. When the computer executable instructions are executed by the processor, the various steps of the method for generating channel coefficients of the dynamic speed TDL model based on Lagrange interpolation are implemented.

[0049] The processor of the present invention is used to implement the dynamic speed TDL model channel coefficient generation processing based on Lagrangian interpolation, wherein the processor is configured to execute computer-executable instructions. When the computer-executable instructions are executed by the processor, the various steps of the above-mentioned method for implementing the dynamic speed TDL model channel coefficient generation processing based on Lagrangian interpolation are implemented.

[0050] The computer-readable storage medium of the present invention stores a computer program thereon, and the computer program can be executed by a processor to implement the various steps of the above-mentioned method for generating channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation.

[0051] Existing technologies use a fixed speed calculation, resulting in only a fixed value for speed-related data such as Doppler shift, making it impossible to simulate scenarios where the shift changes under variable speed conditions. This approach addresses the problem of the current simulation scenario being limited to a single scenario and unable to simulate more complex environments by employing an interpolation method.

[0052] In a specific embodiment of the present invention, it is mainly used to calculate the channel coefficient when the channel terminal moves at a non-uniform speed.

[0053] Because the reference points have the same velocity, direct interpolation calculations are not possible. Therefore, further conversion is performed. Each reference point travels a different distance at the same velocity at different times. Therefore, the core of the present invention lies in calculating the distance traveled by each interpolation point from the starting moment. Channel coefficients are then interpolated based on this distance. As shown in Table 1, the first three rows of data are the parameters required for interpolation. The interpolation process uses the travel distance as a reference to calculate the polynomial coefficients, which are then multiplied by the channel coefficients and accumulated to obtain the interpolated channel coefficients h′.

[0054] Table 1

[0055] Reference point moving distance <![CDATA[d array {d0, d1,,......, d n ,}]]> Reference point channel coefficient <![CDATA[h array {h0, h 1, ,......, h n,}]]> Interpolation point moving distance <![CDATA[d' array {d'0, d'1,......, d' n′}]]> Interpolation point channel coefficient <![CDATA[Array of h'{h'0, h'1,......, h' n′}]]>

[0056] The process is as follows:

[0057] In a dynamic speed environment, the maximum speed is selected, and a uniform motion model is generated based on the filter method at this maximum speed to obtain n channel coefficients h.

[0058] Since each moving terminal has a unique distance value to the base station at any time, the n′ channel coefficients that need to be calculated under variable speed conditions can be converted into an interpolation process for solving the distance at n′ sampling points.

[0059] The distance traveled at n moments under the original uniform speed condition is known to be n values ​​spaced evenly from 0. Then, the distances traveled by n′ points need to be calculated. Since the speed value at each moment is known, the corresponding distances traveled by the terminal at n′ moments under the variable speed condition can be calculated. Next, we simply interpolate the original n points under the uniform speed condition, using the distance value as a reference point, to obtain the new n′ points.

[0060] The output data is a new channel coefficient array of length n' obtained through interpolation calculation. The interpolation here uses the Lagrange interpolation method. Assuming that the distance of the channel coefficient to be interpolated is d, it is necessary to take 16 points from the uniform channel coefficient array as reference points, including the largest 8 values ​​whose moving distance is less than d, and the smallest 8 values ​​whose moving distance is greater than d.

[0061] After obtaining the 16 reference points around the point to be interpolated, the channel coefficient at a distance of d can be calculated using the Lagrange interpolation method.

[0062] Figure 1 The speed of each sampling point is shown through a speed change curve, and the interpolation reference point is the speed v max The distance data of the point on the horizontal dotted line corresponding to the time is the maximum speed v max The actual distance data of the sampling point is the area between the velocity curve and the time axis from the initial moment to the point, for example, t i The actual speed at the sampling point at the moment is v i , and its movement distance is the area between the solid speed curve and the time axis.

[0063] Figure 1 The input data is the calculated channel coefficient under the uniform speed environment, and the value of the channel coefficient under the variable speed environment is generated by interpolation method.

[0064] Figure 2 A speed change curve is used to show the speeds corresponding to the interpolation reference point and the point to be interpolated. The distance data of the interpolation reference point is the product of the maximum speed and the time at a certain moment, while the distance data of the required interpolation is the area between the speed curve and the time axis from 0 to that point.

[0065] In a specific embodiment of the present invention, the system first uses a filter method to calculate the reference point data. In the actual calculation, a white noise generator is used to generate orthogonal components, and then a filter is used in the frequency domain to perform shaping filtering. The transfer function of the shaping filter is equal to the square root of the Doppler power density of the fading process. Finally, the output signal of the shaping filter is transformed into the time domain through the inverse fast Fourier transform.

[0066] In this way, the system will calculate the channel coefficients of n sampling points based on the channel fading model. In order to ensure the accuracy of subsequent interpolation and sampling rate conversion, the pre-generated uniform speed model takes the maximum value of the moving speed for calculation, and samples the complete motion trajectory of the model scene. The maximum speed calculation here is to ensure that the reference point distance data can cover the actual distance data and ensure the correctness of the interpolation data. In this way, the values ​​of n channel coefficients from 0 to the maximum distance are obtained, and each point position is {d0, d1, ..., d n} is used to indicate that the distance interval of each reference point is of equal length, so the value of the distance of n points can be obtained by multiplying the maximum speed by the time of the point, thus obtaining the data of the sampling reference point distance and the corresponding channel coefficient.

[0067] Next, we start to calculate the actual movement distance. For example, here we assume that the signal receiving end moves in a certain direction along a straight line. Figure 2 The original n points have the same time interval, and the distance of the trajectory of the points they represent increases in equal lengths during uniform motion. However, for the n′ points in the variable speed situation, since the speed does not necessarily remain unchanged, it is necessary to calculate the movement distance at each moment. Figure 2 The area between the speed curve and the horizontal axis represents the actual movement distance. Here, we can use the integration method to accumulate the product of the speed at each sampling point and the sampling time interval to get the value of the distance at each sampling point. Figure 2 The distance calculation of sampling point i at the moment requires obtaining the speed of all points up to point i {v0,v1,…,v i Since the time interval between sampling points is generally small enough, the value obtained by multiplying the velocity values ​​of all points by the sampling time interval can be approximately regarded as the actual distance moved at that moment.

[0068] d i =v0Δt+v1Δt}…+v i Δt; finally calculate the n′ channel coefficients after interpolation. Here we take the i′th sampling point to be interpolated as an example. There are a total of i′ sampling points from 0 to this point. Calculate the product of the time interval and actual speed of each point and get the movement distance d′ of point i′ i Then, from the reference point {d0, d1, ..., d n} to find d′ i Two consecutive points d in between j and d j+1 Starting from these two points, we traverse eight points toward the beginning and end of the reference point array, selecting 16 points as reference points for calculating the Lagrange polynomial. We then use Lagrange interpolation to calculate the channel coefficient for the i-th point. This process is repeated to calculate the channel coefficients for the n′ interpolated points.

[0069] In this way, the system completes the interpolation calculation of channel coefficients under variable speed environment.Through the combination of filter method and Lagrange interpolation method, the generation of TDL channel coefficients under variable speed environment is completed.

[0070] The specific implementation scheme of this embodiment can be found in the relevant descriptions in the above embodiments and will not be repeated here.

[0071] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0072] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0073] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0074] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0075] Those skilled in the art will understand that all or part of the steps in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the corresponding program can be stored in a computer-readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0076] Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing module, each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0077] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0078] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0079] The present invention employs a method, device, processor, and computer-readable storage medium for generating channel coefficients for a dynamic speed TDL model based on Lagrangian interpolation. These methods primarily address the problem of using a channel model generated by a filter method to simulate the motion of variable-speed equipment. This solves the issue of inaccurate simulation data when the signal receiving device in the model is moving at variable speeds. Furthermore, the computational complexity of the filter method is significantly lower than that of the sine wave superposition method, effectively reducing the time required for simulation modeling.

[0080] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it will be apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Accordingly, the specification and drawings are to be regarded as illustrative rather than restrictive.

Claims

1. A method for generating channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation, characterized in that: The method comprises the following steps: (1) Generate the channel coefficient h of the reference point under the condition of maximum uniform speed; (2) According to the velocity data, calculate the movement distance d′ of the required interpolation sampling point i ; (3) Perform Lagrange interpolation on the n points under the original uniform velocity condition to calculate the channel coefficient array with a length of n′; The step (3) specifically includes the following steps: (3.1) Find the movement distance d′ from the reference point array of the uniform velocity model i Two consecutive points before and after d j and d j+1 , with point d j and d j+1 Traverse k points from the starting point to the beginning and end of the reference point array, and take 2k points as reference points for Lagrange interpolation calculation; (3.2) Using the Lagrange interpolation method, the channel coefficient h obtained by interpolation of the i-th point is calculated; (3.3) Repeat step (3.1) until the channel coefficient h of the n′ interpolated sampling points is calculated.

2. The method for generating channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation according to claim 1, characterized in that: The step (1) is specifically as follows: The maximum speed in the dynamic speed environment is selected, and a uniform motion model is generated at the maximum speed to obtain the channel coefficient h of n sampling points under the maximum uniform speed condition.

3. The method for generating channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation according to claim 1, characterized in that: The step (2) specifically includes the following steps: (2.1) Calculate the moving distance of n′ sampling points; (2.2) Calculate the movement distance d′ corresponding to the terminal at n′ moments in the variable speed environment i .

4. The method for generating channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation according to claim 3, characterized in that: The step (2.2) is specifically: Calculate the product of the time interval and actual speed of each sampling point, and add up the products of all sampling points to obtain the movement distance d' of sampling point i' i .

5. A device for realizing the generation and processing of channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation, characterized in that: The device comprises: a processor configured to execute computer-executable instructions; A memory storing one or more computer executable instructions, wherein when the computer executable instructions are executed by the processor, the steps of the method for generating channel coefficients of a dynamic velocity TDL model based on Lagrange interpolation according to any one of claims 1 to 4 are implemented.

6. A processor for implementing a dynamic velocity TDL model channel coefficient generation process based on Lagrange interpolation, characterized in that: The processor is configured to execute computer-executable instructions. When the computer-executable instructions are executed by the processor, the various steps of the method for generating dynamic speed TDL model channel coefficients based on Lagrange interpolation as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that A computer program is stored thereon, and the computer program can be executed by a processor to implement the various steps of the method for generating channel coefficients of a dynamic speed TDL model based on Lagrange interpolation according to any one of claims 1 to 4.

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