A method, device, medium and product for converting low-speed to high-speed channel model

Abstract

This invention discloses a method, device, medium, and product for converting low-speed to high-speed channel models, relating to the field of telecommunications technology. This invention overcomes the limitation of difficult testing in high-speed scenarios by conducting channel measurements in a controllable low-speed environment and obtaining a channel model through statistical channel modeling. Based on this, a channel model for high-speed scenarios is theoretically derived. Furthermore, the process of obtaining the channel model only involves traditional statistical model processing, eliminating the need for high-performance hardware simulations used in deterministic channel modeling, the need for obtaining map information of the actual scenario, and the absence of theoretical simplification of the model, thus representing a reasonable extension to the actual channel. Finally, it possesses the high-precision statistical characteristics of the channel model established through low-speed testing and is applicable to any low-speed scenario extension, exhibiting the advantages of semi-deterministic channel modeling.

Description

Technical Field

[0001] This invention relates to the field of telecommunications technology, and in particular to a method, device, medium, and product for converting low-speed to high-speed channel models. Background Technology

[0002] As the physical medium between the transmitter and receiver in a wireless communication system, the wireless channel is inherently susceptible to noise, interference, obstruction, and user movement. However, the actual condition of the wireless channel directly determines many important communication performance indicators, such as the bit error rate, system throughput, and transmission capacity limits. Therefore, establishing relevant mathematical models to accurately characterize the wireless propagation channel is crucial for the formulation of communication standards, the manufacturing of equipment and instruments, and the simulation and deployment planning of wireless communication systems.

[0003] High-speed transportation is an inevitable trend, and the actual condition of wireless channels in high-speed scenarios directly determines many important communication performance indicators, such as the bit error rate. Establishing relevant mathematical models is crucial for the formulation of communication standards, the manufacturing of equipment and instruments, and the simulation and deployment planning of wireless communication systems. Therefore, how to establish channel models for high-speed scenarios has always been a key research focus in academia. Conducting field tests in high-speed scenarios presents several limitations. First, high speed is a constraint on testing; how to stably deploy measurement equipment in a high-speed moving environment is a challenge. Second, due to the nature of high speed, recording the same channel accuracy as in low-speed scenarios requires a shorter signal acquisition cycle, necessitating both faster sampling rates and more storage space, thus placing higher demands on channel measurement and acquisition equipment. Finally, the environment itself can also be a limiting factor. For example, in an unfinished railway tunnel, high-speed trains cannot operate, and only slow-moving test vehicles can provide testing conditions.

[0004] Currently, there are some existing methods for high-speed channel modeling, and high-speed channel modeling can also be summarized into channel modeling. Channel modeling methods mainly include statistical channel modeling methods, deterministic channel modeling methods, and semi-deterministic channel modeling methods. Among them, (1) the statistical modeling method, also known as the parametric modeling method, mainly relies on channel measurement. By conducting actual measurements in a certain area, various important statistical characteristics of the channel are summarized from a large amount of measured data to obtain empirical formulas for wireless propagation. It is a channel model established based on various statistical characteristics of the wireless channel. This method summarizes various important statistical characteristics of the channel from a large amount of measured data by conducting actual measurements in a certain area to obtain empirical formulas for wireless propagation. This method requires actual testing, which consumes certain costs, and is difficult to carry out in high-speed scenarios due to the influence of the testing environment. (2) The deterministic modeling method uses the specific geographical and morphological information of the propagation environment to analyze and predict the wireless propagation model based on electromagnetic wave propagation theory or optical ray theory. This method requires simulating the channel through environmental and physical principles. In the case of pure simulation, the channel model is idealized and is mainly limited by the accuracy of the simulation environment. It also ignores the influence of random noise in the channel. These factors will cause the modeling results to deviate from the real scene. In addition, deterministic channel modeling also requires certain computing power from the equipment. Furthermore, in order to improve their accuracy and maintain consistency with the experimental results, the formulas derived when applying the deterministic channel modeling method to general urban or indoor environments need to be appropriately modified according to the experimental results. (3) Semi-deterministic channel modeling is between statistical modeling and deterministic modeling. It combines deterministic and statistical factors, integrates the advantages of both, is more in line with the actual scene, has low complexity, and can better conform to the actual environment. It can accurately calculate most wireless channel models. However, this method may be difficult to adapt to channels in dynamically changing scenarios. In mobile environments, the model may need to be dynamically adjusted to better describe the actual situation. Moreover, the model accuracy is limited by both the accuracy of statistical modeling and the accuracy of the actual environment, and is not suitable for all scenarios. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a method, device, medium, and product for converting low-speed to high-speed channel models.

[0006] To achieve the above objectives, the present invention provides the following solution.

[0007] A method for converting a low-speed to a high-speed channel model, the method comprising: conducting low-speed channel measurement activities or static fixed-point channel measurement activities in a target scenario, and acquiring the channel impulse response; low speed refers to an operating speed lower than a set value; high speed refers to an operating speed higher than or equal to the set value.

[0008] A wireless channel parameter extraction algorithm is used to extract energy domain channel parameters and time delay domain channel parameters from the channel impulse response.

[0009] The three-dimensional channel information of the multi-tap is determined based on the energy domain channel parameters and the time delay domain channel parameters.

[0010] The three-dimensional channel information from the multi-tap sources is combined to form a statistical channel model; the statistical channel model is a channel model for low-speed and stationary scenarios.

[0011] Angle domain information is extracted based on the channel impulse response, and a channel model for the target high-speed scenario is obtained by combining velocity information and a statistical channel model.

[0012] Optionally, low-speed channel measurement activities or static fixed-point channel measurement activities can be carried out in the target scenario, and the channel impulse response can be obtained, specifically including: obtaining baseband I / Q data and transmitted signal frequency domain data.

[0013] After windowing the ratio of the baseband I / Q data to the frequency domain data of the transmitted signal, an inverse Fourier transform is performed to obtain the channel impulse response.

[0014] Optionally, a wireless channel parameter extraction algorithm is used to extract energy domain channel parameters and time delay domain channel parameters from the channel impulse response, specifically including: determining the steady-state time window.

[0015] Information is extracted from the channel impulse response within the steady-state time window to obtain multipath information; the multipath information includes the time delay and amplitude of each multipath.

[0016] The multipath information is clustered and merged to obtain amplitude information and time delay information.

[0017] The amplitude information is fitted with a distribution, and the optimal distribution of the amplitude is obtained by using an optimal distribution selection algorithm.

[0018] The amplitude information and the optimal amplitude distribution are used as energy domain channel parameters, and the time delay information is used as time delay domain channel parameters.

[0019] Optionally, angle domain information is extracted based on the channel impulse response, and a channel model under the target high-speed scenario is obtained by combining velocity information and a statistical channel model. Specifically, this includes: estimating the angle of arrival of the channel impulse response to obtain angle spectrum information.

[0020] The direction of arrival of the multipath wave is obtained by performing a spectral peak search on the angular spectrum information.

[0021] The arrival angle of the multipath is determined based on the direction of arrival information.

[0022] The Doppler shift of the multipath is determined based on the angle of arrival, the multipath's moving speed, the carrier center frequency, and the speed of light in a vacuum.

[0023] The Doppler spread is obtained by performing energy-weighted calculations on the Doppler frequency shift.

[0024] Based on the Doppler extension, the statistical channel model is converted into a channel model for the target high-speed scenario.

[0025] Optionally, the formula for determining the Doppler frequency shift is:

[0026] In the formula, υ i For the Doppler frequency shift of the i-th multipath, v i Let f be the receiver's moving speed at the sampling time corresponding to the i-th multipath in the target high-speed scenario, f be the carrier center frequency, and θ be the receiver's moving speed. i Let be the angle of arrival of the i-th multipath, and c be the speed of light in a vacuum.

[0027] Optionally, the formula for calculating the Doppler extension is:

[0028] In the formula, S υ,j For the Doppler extension of the j-th tap, υ i For the Doppler frequency shift of the i-th multipath, α i Let be the amplitude of the i-th multipath.

[0029] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the steps of the low-speed to high-speed channel model conversion method described in any of the preceding claims.

[0030] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the low-speed to high-speed channel model conversion method described in any of the preceding claims.

[0031] A computer program product includes a computer program that, when executed by a processor, implements the steps of the low-speed to high-speed channel model conversion method described in any of the preceding claims.

[0032] According to specific embodiments provided by the present invention, the following technical effects are disclosed: The present invention obtains a channel model by conducting statistical channel modeling through channel measurements in low-speed, controllable scenarios, and then theoretically derives a channel model for high-speed scenarios based on this model, overcoming the limitation that testing in high-speed scenarios is difficult. Furthermore, in the process of obtaining the channel model, only traditional statistical model processing is performed, eliminating the need for high-performance hardware simulations like those used in deterministic channel modeling, the need for obtaining map information of the actual scenario, and the absence of theoretical simplification of the model, making it a reasonable extension of the actual channel. Finally, it possesses the high-precision statistical characteristics of the channel model established through low-speed testing and is applicable to extensions to any low-speed scenario, exhibiting the advantages of semi-deterministic channel modeling. Attached Figure Description

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 This is a flowchart of the low-speed to high-speed channel model conversion method provided in Embodiment 1 of the present invention.

[0035] Figure 2 This is a schematic diagram of the overall architecture of the channel measurement system provided in Embodiment 1 of the present invention.

[0036] Figure 3 This is a flowchart of CIR acquisition using frequency domain channel measurement as an example, provided in Embodiment 1 of the present invention.

[0037] Figure 4 The flowchart illustrates the implementation of the low-speed to high-speed channel model conversion method provided in Embodiment 1 of the present invention.

[0038] Figure 5 The flowchart for channel modeling provided in Embodiment 1 of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] The purpose of this invention is to provide a method, device, medium, and product for converting low-speed to high-speed channel models, which aims to overcome the limitation that it is difficult to conduct tests in high-speed scenarios, and does not require consuming computing power to simulate the channel. It has high-precision statistical characteristics of the channel during low-speed testing, and is applicable to any low-speed scenario extension, with the characteristics of semi-deterministic channel modeling.

[0041] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] Example 1

[0043] In some scenarios, conducting channel measurements under high-speed conditions is difficult, such as in the case of unfinished railway tunnels. Obtaining channel information under high-speed conditions is relatively challenging. Taking a tunnel as an example, during tunnel construction, to ensure the safety of personnel and handle potential emergencies, test vehicles can only operate at a low speed, such as 10 km / h, while the target operating speed can reach up to 350 km / h. Using the method provided in this embodiment, existing low-speed test vehicles can be used to conduct channel measurement activities to obtain a channel model (e.g., a TDL channel model) under the target's high-speed operating conditions. Based on this, this embodiment provides a method for converting low-speed to high-speed channel models, such as... Figure 1 and Figure 4 As shown, the method includes steps 100 to 104.

[0044] Step 100: Conduct low-speed channel measurement activities or static fixed-point channel measurement activities in the target scenario, and obtain the channel impulse response. Low speed refers to the operating speed below the set value. High speed refers to the operating speed above or equal to the set value.

[0045] Channel measurement involves transmitting a known signal at the transmitter and recording the received baseband in-phase / quadrature (I / Q) data at the receiver, and then analyzing the channel impulse response (CIR). Channel measurement systems are now relatively mature, primarily consisting of a transmitter and a receiver. The transmitter architecture includes a channel measurement signal generation module, digital up-conversion, a digital-to-analog converter (DAC), a power amplifier, and a transmit antenna. The receiver architecture includes a receive antenna, a low-noise amplifier, a down-conversion module, an analog-to-digital converter (ADC), an I / Q data storage module, and a real-time data logger. The transmitter's channel measurement signal generation module generates the measurement signal, which can utilize existing mature baseband signals such as pseudo-noise sequences (PN). The digital up-conversion shifts the baseband signal to the target transmit band, and the DAC converts the digital signal into an analog signal, which is then amplified and transmitted through the antenna. After receiving the signal, the receiving antenna amplifies the signal-to-noise ratio using a low-noise amplifier, then down-converts it to baseband. Finally, the ADC module converts the analog signal into a digital I / Q signal and stores it in the hardware. A real-time information recorder records real-time information such as the receiver's speed and GPS location. Additionally, the transceiver requires a reference clock for time-frequency synchronization. The overall architecture of the channel measurement system is as follows: Figure 2 As shown.

[0046] After measurement, the acquired I / Q data needs to be processed to obtain the Channel Impulse Response (CIR). This involves first acquiring the baseband I / Q data and the transmitted signal frequency domain data. Then, the ratio of the baseband I / Q data to the transmitted signal frequency domain data is windowed and followed by an inverse Fourier transform to obtain the channel impulse response. Finally, the above steps are performed on the I / Q data of each channel at the receiving end to obtain the CIR of all channels. This embodiment uses frequency domain channel measurement as an example, and the process is as follows: Figure 3 As shown.

[0047] in, Figure 3 The process shown in the figure is formalized as shown in formula (1).

[0048]

[0049] Where Y(f) is the frequency-domain received signal (i.e., baseband I / O data), X(f) is the frequency-domain transmitted signal (i.e., transmitted signal frequency-domain data), and f window This indicates that the signal is windowed, i.e., the signal within the effective bandwidth frequency domain is obtained. IFFT represents the inverse Fourier transform, and h(τ) is the channel impulse response.

[0050] Step 101: Use a wireless channel parameter extraction algorithm to extract energy domain channel parameters and time delay domain channel parameters from the channel impulse response.

[0051] Based on the generalized stationary uncorrelated assumption, channel characteristics remain relatively stable over a period of time. Therefore, after obtaining the CIR, the quasi-steady-state time window needs to be calculated first. After obtaining the window information, multipath information is extracted from the CIR within the window. Then, resolvable multipaths are clustered and merged to extract channel parameters in both the energy domain and the time delay domain. The steady-state time window can be obtained using many existing methods, such as matrix correlation distance discrimination and spectral divergence discrimination. Multipath information includes the time delay and amplitude of each multipath. Multipath information can be extracted using methods including, but not limited to, interference-eliminating peak search methods or artificial intelligence-based methods. A single multipath information is represented as [α]. i ,τ i ], α i τ represents the amplitude of the i-th multipath. i Let represent the resolvable multipath delay of the i-th path, where i is the resolvable multipath index. Cluster merging methods include, but are not limited to, traditional machine learning methods or methods based on kurtosis and region theory.

[0052] The multipath resulting from the merging of resolvable multipaths is called an indiscriminable multipath, also known as a tap. A single tap is represented as [A]. j ,T j A j Let T be the amplitude of the j-th tap. j Let be the delay of the j-th tap, and j be the tap index. The amplitude values ​​of the merged taps are fitted with a distribution, and the optimal distribution is obtained using an algorithm based on the Akaike information standard. The obtained distribution is denoted by F, and generally, distributions including but not limited to Rice, Rayleigh, Weber, Nakagami, and Suzuki are used for distribution fitting.

[0053] Furthermore, in step 101, the principle of wireless channel parameter extraction is as follows: wireless channel characteristics are related to the environmental characteristics of its location. Wireless channel fading characteristics are divided into large-scale fading and small-scale fading. Changes in received signal strength over a long distance belong to large-scale fading, which are mainly caused by obstacles blocking the transceiver. This type of fading is also called shadow fading because it is caused by the shadow area formed by the obstruction, and generally follows a log-normal distribution. Since different interferences on the propagation path may be blocked by the same obstacle, log-normal shadow fading often has statistical correlation. Small-scale fading refers to the rapid change in signal amplitude caused by the superposition of multipath signals at the receiving end after multipath propagation. Small-scale fading is mainly divided into three types: frequency-selective fading, time-selective fading, and spatial-selective fading. Frequency-selective fading is caused by the spread in the time direction due to fading at different frequencies. Time-selective fading is caused by the spread in the frequency direction due to the Doppler shift caused by the rapid movement of the mobile station. Spatial selective fading is caused by the inconsistency of characteristics at different locations and along different transmission paths. Relevant large- and small-scale propagation characteristic parameters for channel modeling include... Figure 5 As shown.

[0054] Step 102: Determine the three-dimensional channel information of the multi-tap based on the energy domain channel parameters and the time delay domain channel parameters. Based on the specific implementation process of step 101 above, in this embodiment, the three-dimensional channel information of the multi-tap can be represented as [A j ,T j ,F j ], where F j This represents the optimal distribution and characteristic parameters of the j-th tap.

[0055] Step 103: Combine the three-dimensional channel information from the multi-tap sources to form a statistical channel model. This statistical channel model is for low-speed and stationary scenarios. Based on this, the three-dimensional channel information from the multi-tap sources forms the channel model for low-speed / stationary scenarios.

[0056] Based on the above description, steps 100-103 are essentially the modeling process of the statistical channel.

[0057] Step 104: Extract angle domain information based on channel impulse response, and combine it with velocity information and statistical channel model to obtain channel model for target high-speed scenario.

[0058] In practical applications, step 104 is essentially a model transformation process, which can be: First, perform Direction of Angle (DOA) estimation on the input multi-channel CIR to obtain angular spectrum information. DOA estimation can employ methods including, but not limited to, multiple signal classification.

[0059] Then, spectral peak search is performed on the angular spectrum to obtain the resolvable multipath wave direction information θ. The spectral peak search can be carried out using methods including, but not limited to, maximum estimation.

[0060] The Doppler frequency shift of the multipath can be derived from the incoming wave direction information obtained by the above DOA solution and the real-time recorded running speed. The principle is shown in the following equation (2).

[0061]

[0062] Among them, υ i For the Doppler frequency shift of the i-th multipath, v i Let f be the receiver's moving speed at the sampling time corresponding to the i-th multipath in the target high-speed scenario, f be the carrier center frequency, and θ be the receiver's moving speed. i Let be the angle of arrival of the i-th multipath, and c be the speed of light in a vacuum.

[0063] Furthermore, after obtaining the Doppler frequency shift for each multipath, the same method as provided in steps 102 and 103 for merging resolvable paths into indistinguishable paths needs to be used*(*marked in the text). Figure 4 In the middle, the Doppler frequency shift of the resolvable multipath within each cluster is calculated by energy weighting, i.e., the Doppler spread S of each Tap. υ The principle is shown in equation (3).

[0064]

[0065] Among them, S υ,j For the Doppler extension of the j-th Tap.

[0066] Finally, Tap's modeling result can be represented as [A] j ,T j ,F j ,S υ,j Thus, the TDL model considering high-speed mobility was obtained.

[0067] Based on the above description, this invention establishes a low-speed / static TDL channel model by utilizing the distinguishable multipath energy and time delay information extracted under low-speed / static constraints, and then derives the Doppler frequency shift of the distinguishable multipath by combining angle domain information, and calculates the Doppler spread of the Tap, thus successfully converting the low-speed / static channel model into a channel model for high-speed scenarios.

[0068] Compared with the prior art, the technical solution provided by the present invention also has the following advantages.

[0069] 1. This invention uses data from statistical modeling of low-speed controllable scenarios as a basis, and derives a channel model for high-speed scenarios through theoretical derivation, thus overcoming the limitation that it is difficult to conduct tests in high-speed scenarios.

[0070] 2. This invention does not require high-performance hardware for simulation, does not require obtaining map information of the actual scene, does not adopt theoretical setup, and is a reasonable extension of the actual channel.

[0071] 3. This invention possesses high-precision statistical characteristics of the channel model established by low-speed testing, and is applicable to any low-speed scenario extension, thus having the advantage of semi-deterministic channel modeling.

[0072] Furthermore, existing channel modeling schemes are mainly divided into statistical channel modeling, deterministic channel modeling, and semi-deterministic channel modeling. More resources can be invested in developing high-precision, high-capacity measurement systems to conduct high-speed scenario measurements for statistical channel modeling. Alternatively, high-performance ray tracing simulation experiments can be conducted using high-performance computing platforms to obtain channel impulse responses by constructing high-speed scenario environments. After conducting statistical channel modeling, this invention can also consider incorporating environmental factors to optimize the modeling results and make them more closely match real-world scenarios, thus combining the two existing schemes into semi-deterministic channel modeling.

[0073] Example 2

[0074] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the low-speed to high-speed channel model conversion method in Embodiment 1.

[0075] Example 3

[0076] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the low-speed to high-speed channel model conversion method in Embodiment 1.

[0077] Example 4

[0078] A computer program product includes a computer program that, when executed by a processor, implements the steps of the low-speed to high-speed channel model conversion method in Embodiment 1.

[0079] Example 5

[0080] A computer device, which may be a database, includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores pending transactions. The I / O interfaces facilitate information exchange between the processor and external devices. The communication interface enables communication with external terminals via a network connection. When executed by the processor, the computer program implements the low-speed to high-speed channel model conversion method described in Embodiment 1.

[0081] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0082] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided by this invention may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided by this invention may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0083] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Similar or identical parts between the various embodiments can be referred to mutually. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for converting low-speed to high-speed channel models, characterized in that, The method includes: Conduct low-speed channel measurement activities or static fixed-point channel measurement activities in the target scenario and obtain the channel impulse response; low speed refers to the operating speed below a set value; high speed refers to the operating speed above or equal to the set value; A wireless channel parameter extraction algorithm is used to extract energy domain channel parameters and time delay domain channel parameters from the channel impulse response. The three-dimensional channel information of the multi-tap is determined based on the energy domain channel parameters and the time delay domain channel parameters; The three-dimensional channel information from the multi-tap sources is combined to form a statistical channel model; the statistical channel model is a channel model for low-speed and stationary scenarios. Angle spectrum information is obtained by estimating the angle of arrival of the channel impulse response; The direction of arrival of the multipath wave is obtained by performing a spectral peak search on the angular spectrum information; Determine the arrival angle of the multipath based on the direction of arrival information; The Doppler shift of the multipath is determined based on the angle of arrival, the multipath velocity, the carrier center frequency, and the speed of light in a vacuum. The Doppler spread is obtained by energy-weighted calculation of the Doppler frequency shift; wherein, after obtaining the Doppler frequency shift of each multipath, the Doppler spread within each cluster is calculated by energy-weighted calculation of the Doppler frequency shift of the resolvable multipath within each cluster, thus obtaining the Doppler spread of each tap; the calculation formula for the Doppler spread is: In the formula, S υ,j For the Doppler extension of the j-th tap, υ i For the Doppler frequency shift of the i-th multipath, α i For the i-th multipath amplitude, Based on the Doppler extension, the statistical channel model is converted into a channel model for the target high-speed scenario; A low-speed / static TDL channel model is established by using the distinguishable multipath energy and time delay information extracted from measurements under low-speed / static constraints. Then, the Doppler frequency shift of the distinguishable multipath is derived by combining angle domain information, and the Doppler spread of the taps is calculated. The low-speed / static channel model is then converted into a channel model for high-speed scenarios.

2. The method for converting low-speed to high-speed channel models according to claim 1, characterized in that, Conduct low-speed channel measurement activities or static fixed-point channel measurement activities in the target scenario, and obtain the channel impulse response, specifically including: Acquire baseband I / Q data and transmit signal frequency domain data; After windowing the ratio of the baseband I / Q data to the frequency domain data of the transmitted signal, an inverse Fourier transform is performed to obtain the channel impulse response.

3. The method for converting low-speed to high-speed channel models according to claim 1, characterized in that, A wireless channel parameter extraction algorithm is used to extract energy domain channel parameters and time delay domain channel parameters from the channel impulse response, specifically including: Determine the steady-state time window; Information is extracted from the channel impulse response within the steady-state time window to obtain multipath information; the multipath information includes the time delay and amplitude of each multipath. The multipath information is clustered and merged to obtain amplitude information and time delay information; The amplitude information is fitted with a distribution, and the optimal distribution of the amplitude is obtained by using an optimal distribution selection algorithm; The amplitude information and the optimal amplitude distribution are used as energy domain channel parameters, and the time delay information is used as time delay domain channel parameters.

4. The method for converting low-speed to high-speed channel models according to claim 1, characterized in that, The formula for determining the Doppler frequency shift is: In the formula, υ i For the Doppler frequency shift of the i-th multipath, v i Let f be the receiver's moving speed at the sampling time corresponding to the i-th multipath in the target high-speed scenario, f be the carrier center frequency, and θ be the receiver's moving speed. i Let be the angle of arrival of the i-th multipath, and c be the speed of light in a vacuum.

5. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the low-speed to high-speed channel model conversion method according to any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the low-speed to high-speed channel model conversion method according to any one of claims 1-4.

7. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the low-speed to high-speed channel model conversion method according to any one of claims 1-4.

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