Wireless channel dynamic modeling method, device and electronic device

Abstract

The present application proposes a method, device, and electronic device for dynamic modeling of wireless channels, relating to the field of wireless communication technology. The method for dynamic modeling of wireless channels includes the following steps: first, channel parameters can be collected at a first sampling rate during the movement of a user device. Then, the collected channel parameters can be divided into multiple different parameter sets based on the range of movement of the user device. Furthermore, the channel parameters contained in each parameter set can be clustered based on the parameter characteristics of the channel parameters to obtain characteristic channel parameters corresponding to each parameter set. Finally, a wireless channel model can be established based on the characteristic channel parameters corresponding to each parameter set. Thus, while reducing computational complexity, the obtained wireless channel model can be made closer to the actual channel variation.

Description

Technical field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method, device and electronic device for dynamic modeling of wireless channels. [Background Technology]

[0002] In the field of wireless communications, establishing wireless channel models is fundamental to evaluating and improving the performance of wireless communication systems. Currently, dynamic modeling of wireless channels in mobile scenarios, such as highways and high-speed trains, presents a technical challenge due to the real-time changes in channel parameters.

[0003] Currently, one common approach to dynamic wireless channel modeling is to directly use channel parameters obtained from actual measurements. However, due to the large amount of data obtained from actual measurements, this approach suffers from high computational complexity, hindering computational efficiency. Furthermore, due to the presence of errors in the measured data, the resulting channel model may deviate significantly from reality. [Summary of the invention]

[0004] The embodiments of the present application provide a method, apparatus, and electronic device for dynamic modeling of wireless channels, which can reduce computational complexity while making the resulting wireless channel model closer to actual channel variations.

[0005] In the first aspect, an embodiment of the present application provides a method for dynamic modeling of a wireless channel, comprising: collecting channel parameters at a first sampling rate during the movement of a user device; dividing the collected channel parameters into multiple different parameter sets according to the movement range of the user device; clustering the channel parameters contained in each of the parameter sets according to the parameter characteristics of the channel parameters to obtain characteristic channel parameters corresponding to each of the parameter sets; and establishing a wireless channel model based on the characteristic channel parameters corresponding to each of the parameter sets.

[0006] In one possible implementation, the collected channel parameters are divided into multiple different parameter sets according to the mobile range of the user equipment, including: determining the starting and ending base stations corresponding to the user equipment during the movement according to the mobile range of the user equipment; and dividing the collected channel parameters into multiple different parameter sets according to the distance between the starting and ending base stations.

[0007] In one possible implementation, the collected channel parameters are divided into multiple different parameter sets according to the distance between the starting and ending base stations, including: dividing N distance intervals between the starting and ending base stations according to the distance between the starting and ending base stations; and dividing the channel parameters collected in each of the distance intervals into a parameter set; wherein N is a positive integer.

[0008] In one possible implementation, the signals collected at each sampling moment include at least one signal cluster, and the channel parameters corresponding to each signal cluster include signal transmission power, signal transmission delay, signal arrival angle, and signal departure angle.

[0009] In one possible implementation method, according to the parameter characteristics of the channel parameters, the channel parameters contained in each parameter set are clustered respectively to obtain the characteristic channel parameters corresponding to each parameter set, including: according to the parameter characteristics of the signal transmission power, the channel parameters contained in each parameter set are clustered respectively to obtain the characteristic channel parameters corresponding to each parameter set.

[0010] In one possible implementation manner, clustering the channel parameters included in each parameter set includes: clustering the channel parameters included in each parameter set using a K-means algorithm.

[0011] In one possible implementation, a wireless channel model is determined based on the characteristic channel parameters corresponding to each of the parameter sets, including: performing linear interpolation between the characteristic channel parameters corresponding to each of the parameter sets at a second sampling rate; and establishing a wireless channel model based on the interpolated channel parameters obtained after linear interpolation and the characteristic channel parameters.

[0012] In the second aspect, an embodiment of the present application provides a wireless channel dynamic modeling device, including: an acquisition module, used to collect channel parameters according to a first sampling rate during the movement of a user device; a grouping module, used to divide the collected channel parameters into multiple different parameter sets according to the movement range of the user device; a clustering module, used to cluster the channel parameters contained in each of the parameter sets according to the parameter characteristics of the channel parameters, and obtain characteristic channel parameters corresponding to each of the parameter sets; a determination module, used to establish a wireless channel model based on the characteristic channel parameters corresponding to each of the parameter sets.

[0013] In a third aspect, an embodiment of the present application provides an electronic device comprising: at least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method described in the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method described in the first aspect.

[0015] In the above technical solution, channel parameters can first be collected at a first sampling rate while the user device is moving. The collected channel parameters can then be divided into multiple parameter sets based on the user device's range of motion. Furthermore, the channel parameters contained in each parameter set can be clustered based on their characteristics to obtain characteristic channel parameters corresponding to each parameter set. Finally, a wireless channel model can be established based on the characteristic channel parameters corresponding to each parameter set. This reduces computational complexity while making the resulting wireless channel model more closely resemble actual channel variations.

Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 A flowchart of a method for dynamic modeling of wireless channels provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of a scenario of a wireless channel dynamic modeling method provided in an embodiment of the present application;

[0019] Figure 3 A schematic diagram of the structure of a wireless channel dynamic modeling device provided in an embodiment of the present application;

[0020] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. [Specific implementation method]

[0021] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] It should be clear that the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0023] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. The singular forms "a", "an", "the" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0024] The present application may provide a wireless channel dynamic modeling device, which may be used to execute the wireless channel dynamic modeling method provided in the present application.

[0025] Figure 1 This is a flow chart of a method for dynamic modeling of wireless channels provided in an embodiment of the present application. Figure 1 As shown, the above wireless channel dynamic modeling method may include:

[0026] Step 101: When a user equipment moves, channel parameters are collected at a first sampling rate.

[0027] In the embodiment of the present application, the parameter collection time period can be set, and the channel parameters can be collected according to the first sampling rate F1 within the set parameter collection time period.

[0028] It should be noted that the signals collected at each sampling moment can include at least one signal cluster. The channel parameters corresponding to each signal cluster can include signal transmission power, signal transmission delay, signal arrival angle, and signal departure angle. A signal cluster refers to the different transmission paths generated by signal refraction and reflection in a wireless channel.

[0029] It can be understood that the channel parameters of the i-th cluster collected at time t can be defined as: Power(i,t), Delay(i,t), AOD(i,t), ZOD(i,t), AOA(i,t), ZOA(i,t).

[0030] Where Power(i,t) represents the signal transmission power of the i-th cluster collected at time t; Delay(i,t) represents the signal transmission delay of the i-th cluster collected at time t; AOD(i,t) represents the vertical departure angle of the signal of the i-th cluster collected at time t; ZOD(i,t) represents the horizontal departure angle of the signal of the i-th cluster collected at time t; AOA(i,t) represents the vertical arrival angle of the signal of the i-th cluster collected at time t; ZOA(i,t) represents the horizontal arrival angle of the signal of the i-th cluster collected at time t.

[0031] Step 102: Divide the collected channel parameters into a plurality of different parameter sets according to the mobility range of the user equipment.

[0032] First, within the set parameter collection time, the starting and ending base stations corresponding to the user equipment during its movement can be determined based on the user equipment's range of movement. The starting and ending base stations here refer to the base station accessed by the user equipment at its starting location and the base station accessed by the user equipment at its ending location, respectively.

[0033] The collected channel parameters can then be divided into multiple different parameter sets based on the distance between the starting and ending base stations. Specifically, N distance intervals can be created based on the distance between the starting and ending base stations. Each distance interval can be of the same length. The collected channel parameters within each distance interval can then be divided into a parameter set, resulting in N parameter sets. N is a positive integer whose value is determined by the distance between the starting and ending base stations.

[0034] To facilitate understanding, an example is described.

[0035] like Figure 2 As shown, it is assumed that the starting and ending base stations corresponding to the user equipment during the movement are base station 1 and base station 2 respectively. Then, the distance between base station 1 and base station 2 can be determined. Then, according to the distance between base station 1 and base station 2, N distance intervals can be divided from the starting and ending base stations. Specifically, when the distance between base station 1 and base station 2 is large, the value of N can be increased; on the contrary, when the distance between base station 1 and base station 2 is small, the value of N can be reduced. In the embodiment of the present application, illustratively, 10 distance intervals can be divided between base station 1 and base station 2. Finally, the channel parameters collected in each distance interval can be divided into a parameter set, and then 10 parameter sets can be obtained.

[0036] Step 103 : Cluster the channel parameters contained in each parameter set according to the parameter characteristics of the channel parameters to obtain characteristic channel parameters corresponding to each parameter set.

[0037] Since the amount of data actually collected is large, in order to reduce the complexity of the calculation, in an embodiment of the present application, characteristic analysis can be performed on the channel parameters in each parameter set respectively, and then characteristic channel parameters that can characterize the channel characteristics can be determined from each parameter set.

[0038] The implementation method corresponding to each parameter set is the same. For the convenience of description, the embodiment of the present application takes any parameter set n as an example for illustration.

[0039] Specifically, this embodiment of the present application uses signal transmission power as the feature analysis object. For any parameter set n, the signal transmission power of each channel parameter contained therein can be determined. Then, based on the parameter characteristics of signal transmission power, the K-means algorithm can be used to cluster the channel parameters to obtain the characteristic channel parameters corresponding to parameter set n.

[0040] In a specific implementation, the characteristic channel parameters corresponding to the parameter set n may be determined according to the following formula:

[0041]

[0042] Where J represents the number of signal clusters obtained by clustering; T n represents the number of sampling points in parameter set n; Power(j,n) represents the signal transmission power of the jth cluster obtained by clustering parameter set n; represents the signal transmission power of the i-th cluster collected at the t-th time in the parameter set n.

[0043] It can be understood that the characteristic channel parameters of parameter set n can be used to characterize the channel characteristics at the distance interval corresponding to parameter set n.

[0044] Through the embodiments of the present application, the channel parameters included in each distance interval can be significantly reduced, thereby reducing the computational complexity of establishing the channel model. Moreover, because the characteristic channel parameters in each distance interval are obtained based on parameter feature clustering, they can accurately characterize the channel characteristics.

[0045] Step 104: Establish a wireless channel model based on the characteristic channel parameters corresponding to each parameter set.

[0046] Since the above step 103 reduces the channel parameters contained in each distance interval through clustering, in a mobile scenario, when the real-time change rate of the channel parameters is large, there may be jumps between the various characteristic channel parameters obtained in the above step 103, resulting in an uneven parameter curve.

[0047] Based on the above description, in a specific implementation, linear interpolation can be performed between the characteristic channel parameters obtained in step 103 according to the second sampling rate F2 to obtain multiple interpolated channel parameters. Thus, the multiple interpolated channel parameters can make the characteristic channel parameters tend to be smooth.

[0048] Specifically, linear interpolation can be performed according to the following formula to obtain the interpolation channel parameters:

[0049]

[0050] Wherein, C(j,k) represents the interpolation channel parameter of the j-th cluster at the k-th interpolation position, and the second sampling rate corresponding to the linear interpolation process is F2=M′×F1.

[0051] Then, a wireless channel model may be established based on the interpolated channel parameters and the characteristic channel parameters.

[0052] The specific method of establishing a wireless channel model can refer to the existing technology. For example, the above-mentioned interpolated channel parameters and characteristic channel parameters can be substituted into the impulse response function of the wireless channel model to obtain the wireless channel model. The impulse response function of the wireless channel model is a function already known in the prior art and is not described in detail in the embodiments of this application.

[0053] In the above technical solution, channel parameters can first be collected at a first sampling rate while the user device is moving. The collected channel parameters can then be divided into multiple parameter sets based on the user device's range of motion. Furthermore, the channel parameters contained in each parameter set can be clustered based on their characteristics to obtain characteristic channel parameters corresponding to each parameter set. Finally, a wireless channel model can be established based on the characteristic channel parameters corresponding to each parameter set. This reduces computational complexity while making the resulting wireless channel model more closely resemble actual channel variations.

[0054] Figure 3 This is a schematic diagram of the structure of a wireless channel dynamic modeling device provided in an embodiment of the present application. The wireless channel dynamic modeling device in this embodiment can be used as a wireless channel dynamic modeling device to implement the wireless channel dynamic modeling method provided in an embodiment of the present application. Figure 3 As shown, the above-mentioned wireless channel dynamic modeling device may include: a collection module 31 , a grouping module 32 , a clustering module 33 and a determination module 34 .

[0055] The collection module 31 is configured to collect channel parameters at a first sampling rate during movement of the user equipment.

[0056] The grouping module 32 is configured to divide the collected channel parameters into a plurality of different parameter sets according to the mobility range of the user equipment.

[0057] The clustering module 33 is configured to cluster the channel parameters contained in each parameter set according to parameter characteristics of the channel parameters, and obtain characteristic channel parameters corresponding to each parameter set.

[0058] The determination module 34 is configured to establish a wireless channel model according to the characteristic channel parameters corresponding to each parameter set.

[0059] During the specific execution process, the above-mentioned grouping module 32 is specifically used to determine the corresponding starting and ending base stations of the user equipment during the movement process according to the movement range of the user equipment; and divide the collected channel parameters into multiple different parameter sets according to the distance between the starting and ending base stations.

[0060] During the specific execution process, the grouping module 32 is specifically used to divide N distance intervals between the starting and ending base stations according to the distance between them; and to divide the channel parameters collected in each distance interval into a parameter set; wherein N is a positive integer.

[0061] During the specific implementation process, the signals collected at each sampling moment include at least one signal cluster, and the channel parameters corresponding to each signal cluster include signal transmission power, signal transmission delay, signal arrival angle, and signal departure angle.

[0062] In a specific execution process, the clustering module 33 is specifically configured to cluster the channel parameters contained in each parameter set according to the parameter characteristics of the signal transmission power, and obtain characteristic channel parameters corresponding to each parameter set.

[0063] In a specific execution process, the clustering module 33 is specifically used to cluster the channel parameters contained in each parameter set using the K-means algorithm.

[0064] During the specific execution process, the determination module 34 is specifically used to perform linear interpolation between the characteristic channel parameters corresponding to each parameter set according to the second sampling rate; and establish a wireless channel model based on the interpolated channel parameters and characteristic channel parameters obtained after linear interpolation.

[0065] In the above technical solution, first, the acquisition module 31 can collect channel parameters at a first sampling rate while the user device is moving. Then, the grouping module 32 can divide the collected channel parameters into multiple different parameter sets based on the user device's range of motion. Furthermore, the clustering module 33 can cluster the channel parameters contained in each parameter set based on their characteristics, thereby obtaining characteristic channel parameters corresponding to each parameter set. Finally, the determination module 34 can establish a wireless channel model based on the characteristic channel parameters corresponding to each parameter set. This reduces computational complexity while making the resulting wireless channel model more closely resemble actual channel variations.

[0066] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown, the above-mentioned electronic device may include at least one processor; and at least one memory communicatively connected to the above-mentioned processor, wherein: the memory stores program instructions that can be executed by the processor, and the above-mentioned processor calls the above-mentioned program instructions to execute the wireless channel dynamic modeling method provided in the embodiment of the present application.

[0067] The electronic device may be a wireless channel dynamic modeling device, and this embodiment does not limit the specific form of the electronic device.

[0068] Figure 4 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application is shown. Figure 4 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0069] like Figure 4 As shown, the electronic device is implemented as a general-purpose computing device. Components of the electronic device may include, but are not limited to, one or more processors 410, a memory 430, and a communication bus 440 connecting different system components (including the memory 430 and the processor 410).

[0070] Communication bus 440 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of such architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnection (PCI) bus.

[0071] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0072] The memory 430 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Figure 4Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a Compact Disc Read Only Memory (hereinafter referred to as: CD-ROM), a Digital Video Disc Read Only Memory (hereinafter referred to as: DVD-ROM), or other optical media) may be provided. In these cases, each drive can be connected to the communication bus 440 via one or more data medium interfaces. The memory 430 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0073] A program / utility having a set (at least one) of program modules may be stored in memory 430. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. The program modules generally perform the functions and / or methods of the embodiments described herein.

[0074] The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through the communication interface 420. In addition, the electronic device may also communicate with the network adapter ( Figure 4 The network adapter can communicate with other modules of the electronic device through the communication bus 440. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays of Independent Drives; hereinafter referred to as: RAID) systems, tape drives, and data backup storage systems.

[0075] The processor 410 executes various functional applications and data processing by running the programs stored in the memory 430, such as implementing the wireless channel dynamic modeling method provided in the embodiment of the present application.

[0076] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the wireless channel dynamic modeling method provided in the embodiment of the present application.

[0077] The above-mentioned computer-readable storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0078] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0079] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0080] The computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect via the Internet).

[0081] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0082] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0083] 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 custom logical function or process, and the scope of the preferred embodiments of the present application 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 application belong.

[0084] It should be noted that the user devices involved in the embodiments of the present application may include but are not limited to personal computers (Personal Computer; hereinafter referred to as: PC), personal digital assistants (Personal Digital Assistant; hereinafter referred to as: PDA), wireless handheld devices, tablet computers (Tablet Computer), mobile phones, MP3 players, MP4 players, etc.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interface, indirect coupling or communication connection of the device or unit, which may be electrical, mechanical or other forms.

[0086] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0087] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for dynamic modeling of wireless channels, characterized in that: include: During the movement of the user equipment, channel parameters are collected at a first sampling rate, wherein the signals collected at each sampling moment include at least one signal cluster, and the channel parameters corresponding to each signal cluster include: signal transmission power, signal transmission delay, signal arrival angle, and signal departure angle; dividing the collected channel parameters into a plurality of different parameter sets according to a mobility range of the user equipment; Clustering the channel parameters contained in each of the parameter sets according to parameter characteristics of the channel parameters to obtain characteristic channel parameters corresponding to each of the parameter sets; Establishing a wireless channel model according to characteristic channel parameters corresponding to each of the parameter sets; Dividing the collected channel parameters into a plurality of different parameter sets according to the mobility range of the user equipment, including: Determining, according to the mobile range of the user equipment, the starting and ending base stations corresponding to the user equipment during the movement, the starting and ending base stations including the base station accessed by the user equipment at the starting position and the base station accessed by the user equipment at the ending position; Dividing the collected channel parameters into a plurality of different parameter sets according to the distance between the starting and ending base stations; The clustering of the channel parameters contained in each of the parameter sets according to the parameter characteristics of the channel parameters to obtain characteristic channel parameters corresponding to each of the parameter sets includes: According to the formula Determine the characteristic channel parameters corresponding to the parameter set, where J represents the number of signal clusters obtained by clustering, T n represents the number of sampling points in parameter set n, Power(j,n) represents the signal transmission power of the jth cluster obtained by clustering parameter set n, represents the signal transmission power of the i-th cluster collected at the t-th time in the parameter set n.

2. The method according to claim 1, characterized in that Dividing the collected channel parameters into a plurality of different parameter sets according to the distance between the starting and ending base stations, including: According to the distance between the starting and ending base stations, N distance intervals are divided between the starting and ending base stations; Dividing the channel parameters collected in each distance interval into a parameter set; Wherein, N is a positive integer.

3. The method according to claim 1, characterized in that Establishing a wireless channel model according to characteristic channel parameters corresponding to each parameter set includes: Performing linear interpolation between characteristic channel parameters corresponding to each of the parameter sets according to the second sampling rate; A wireless channel model is established according to each interpolation channel parameter obtained after linear interpolation and the characteristic channel parameter.

4. A wireless channel dynamic modeling device, characterized in that: include: a collection module, configured to collect channel parameters at a first sampling rate during movement of the user equipment, wherein the signals collected at each sampling moment include at least one signal cluster, and the channel parameters corresponding to each signal cluster include: signal transmission power, signal transmission delay, signal arrival angle, and signal departure angle; a grouping module, configured to divide the collected channel parameters into a plurality of different parameter sets according to a mobility range of the user equipment; a clustering module, configured to cluster the channel parameters contained in each of the parameter sets according to parameter characteristics of the channel parameters, to obtain characteristic channel parameters corresponding to each of the parameter sets; A determination module, configured to establish a wireless channel model according to characteristic channel parameters corresponding to each of the parameter sets; The grouping module is specifically configured to determine, based on a mobile range of the user equipment, a starting and ending base station corresponding to the user equipment during movement, the starting and ending base stations including a base station accessed by the user equipment at a starting position and a base station accessed by the user equipment at a terminating position; and to divide the collected channel parameters into a plurality of different parameter sets based on a distance between the starting and ending base stations; The clustering module is specifically used to calculate the Determine the characteristic channel parameters corresponding to the parameter set, where J represents the number of signal clusters obtained by clustering, T n represents the number of sampling points in parameter set n, Power(j,n) represents the signal transmission power of the jth cluster obtained by clustering parameter set n, represents the signal transmission power of the i-th cluster collected at the t-th time in the parameter set n.

5. An electronic device, characterized in that: include: at least one processor; as well as at least one memory in communication with the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can execute the method according to any one of claims 1 to 3 by calling the program instructions.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method according to any one of claims 1 to 3.

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