A channel estimation method, apparatus, storage medium, and electronic device

Abstract

This application provides a channel estimation method, apparatus, storage medium, and electronic device. The method includes: dividing multiple paths of an acquired channel into clusters to obtain N multipath sets, where N is a natural number greater than or equal to 1; determining the correlation coefficient of each multipath set; and determining target correlation coefficients for the N multipath sets based on the correlation coefficients of each multipath set, wherein the correlation coefficients and target correlation coefficients represent the channel's temporal variation. This application solves the problem in related technologies of difficulty in estimating the temporal correlation coefficient of channels in high-speed scenarios, thereby improving the accuracy of estimating the temporal correlation coefficient of channels.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to a channel estimation method, apparatus, storage medium, and electronic device. Background Technology

[0002] In some algorithms in the field of communication, channel estimation before filtering is a very important process. As a key parameter for channel estimation, the method for determining the correlation coefficient of the channel in the time direction also needs to be continuously developed with the advancement of related technologies.

[0003] In related technologies, mathematical analysis is usually used to estimate the correlation coefficient of the channel in the time direction, but this calculation method is not effective when dealing with devices that are moving at high speeds.

[0004] No effective solutions have yet been proposed to address the aforementioned problems in the relevant technologies. Summary of the Invention

[0005] This application provides a channel estimation method, apparatus, storage medium, and electronic device to at least solve the problem in the related art of difficulty in estimating the correlation coefficient of the channel in the time direction under high-speed scenarios.

[0006] According to one embodiment of this application, a channel estimation method is provided, comprising: dividing multiple paths of an acquired channel into clusters to obtain N multipath sets, wherein N is a natural number greater than or equal to 1; determining the correlation coefficient of each of the multipath sets; and determining target correlation coefficients of the N multipath sets based on the correlation coefficients of each of the multipath sets, wherein the correlation coefficients and the target correlation coefficients are used to represent the changes of the channel in the time direction.

[0007] In an exemplary embodiment, the multiple paths of the acquired channel are divided into clusters to obtain N multipath sets, including: dividing the multiple paths into clusters according to the Doppler frequency offset and time delay of each of the above paths to obtain N multipath sets.

[0008] In an exemplary embodiment, the multiple paths are clustered according to the Doppler frequency offset and time delay of each of the aforementioned paths to obtain N sets of the aforementioned multipaths, including: determining the strongest path among the multiple paths, wherein the strongest path is used to represent the path whose fading factor is greater than a first preset threshold; determining the target Doppler frequency offset and target time delay of the strongest path; determining the path among the multiple paths for which the absolute value of the frequency offset between the Doppler frequency offset and the target Doppler frequency offset is less than or equal to a second preset threshold, and the absolute value of the time delay difference between the time delay and the target time delay is less than or equal to a third preset threshold, to obtain a multipath set; performing the same operation as determining the multipath set on the remaining paths to obtain the remaining N-1 sets of the aforementioned multipaths, wherein the remaining paths include paths among the multiple paths other than the aforementioned multipath sets.

[0009] In one exemplary embodiment, for each of the aforementioned multipath sets, determining the correlation coefficient of each of the aforementioned multipath sets includes: determining the correlation coefficient of the aforementioned multipath set using the time delay and energy of all paths in the aforementioned multipath set.

[0010] In an exemplary embodiment, determining the target correlation coefficients of N multipath sets based on the correlation coefficient of each multipath set includes: merging the Doppler frequency offsets of all paths in the multipath set using a preset merging algorithm to obtain the Doppler frequency offset of the multipath set; determining a first average power based on the fading factor of each path in the multipath set, wherein the first average power represents the average power of the multipath set within a preset time period; accumulating the first average power to obtain a second average power, wherein the second average power represents the average power of the N multipath sets within the preset time period; and calculating the target correlation coefficient using the second average power, the first average power of each multipath set, the Doppler frequency offset of each multipath set, the correlation coefficient of each multipath set, and the preset time period.

[0011] In an exemplary embodiment, determining the average power of the multipath set within the preset time period to obtain the second average power of the multipath set includes: determining the fading factor of each path in the multipath set; and calculating the second average power of the multipath set using the fading factor of each path.

[0012] In an exemplary embodiment, after determining the target correlation coefficients of N multipath sets based on the correlation coefficients of each multipath set, the method further includes performing channel estimation on the channel using the target correlation coefficients, including: determining filter parameters of a filter based on the target correlation coefficients; filtering the signal transmitted through the channel using the filter and according to the filter parameters; and / or, the correlation coefficients include the Pearson correlation coefficient of the channel in the time direction.

[0013] According to another embodiment of this application, a channel estimation apparatus is provided, comprising: a partitioning module, configured to partition multiple multipaths of an acquired channel into clusters to obtain N multipath sets, wherein N is a natural number greater than or equal to 1; a first determining module, configured to determine the correlation coefficient of each of the multipath sets; and a second determining module, configured to determine target correlation coefficients of the N multipath sets based on the correlation coefficient of each of the multipath sets, wherein the correlation coefficients and the target correlation coefficients are used to represent the changes of the channel in the time direction.

[0014] In an exemplary embodiment, the partitioning module includes a partitioning submodule, configured to partition multiple paths into clusters based on the Doppler frequency offset and time delay of each path, thereby obtaining N sets of the multipath.

[0015] In an exemplary embodiment, the partitioning module includes: a first determining submodule, configured to determine the strongest path among a plurality of paths, wherein the strongest path represents a path whose fading factor is greater than a first preset threshold; a second determining submodule, configured to determine the target Doppler frequency offset and target delay of the strongest path; a third determining submodule, configured to determine paths among the plurality of paths for which the absolute value of the frequency offset between the Doppler frequency offset and the target Doppler frequency offset is less than or equal to a second preset threshold, and the absolute value of the delay difference between the delay and the target delay among the plurality of paths is less than or equal to a third preset threshold, thereby obtaining a multipath set; and an execution submodule, configured to perform the same operation as determining the multipath set on the remaining paths, thereby obtaining a remaining N-1 multipath sets, wherein the remaining paths include paths among the plurality of paths other than the multipath sets.

[0016] In an exemplary embodiment, the first determining module includes a fourth determining submodule, configured to determine the correlation coefficient of the multipath set using the time delay and energy of all paths in the multipath set.

[0017] In an exemplary embodiment, the second determining module includes: a merging submodule, configured to merge the Doppler frequency offsets of all paths in the multipath set using a preset merging algorithm to obtain the Doppler frequency offset of the multipath set; a fifth determining submodule, configured to determine a first average power based on the fading factor of each path in each multipath set, wherein the first average power represents the average power of each multipath set within a preset time period; a sixth determining submodule, configured to accumulate the first average power to obtain a second average power, wherein the second average power represents the average power of N multipath sets within the preset time period; and a calculation submodule, configured to calculate the target correlation coefficient using the second average power, the first average power of each multipath set, the Doppler frequency offset of each multipath set, the correlation coefficient of each multipath set, and the preset time period.

[0018] In an exemplary embodiment, the sixth determining submodule includes: a determining unit for determining the fading factor of each path in the multipath set; and a calculating unit for calculating the second average power of the multipath set using the fading factor of each path.

[0019] In an exemplary embodiment, the estimation module includes: a seventh determining submodule, configured to determine filter parameters based on the target correlation coefficients of N multipath sets after determining the target correlation coefficients of each multipath set; a filtering submodule, configured to filter the signal transmitted through the channel using the filter and according to the filtering parameters; and / or, the correlation coefficients include the Pearson correlation coefficient of the channel in the time direction.

[0020] According to yet another embodiment of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to perform the steps in any of the above method embodiments when it is run.

[0021] According to yet another embodiment of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0022] This application effectively avoids estimation errors that may be caused by large differences in Doppler frequency offsets of multipaths in different directions in high-speed scenarios by dividing multipaths into clusters, that is, calculating the Doppler frequency offset and correlation coefficient of multiple multipath sets separately, and then estimating the overall correlation coefficient of all multipaths. It solves the problem of difficulty in estimating the correlation coefficient of the channel in the time direction in related technologies, and achieves the effect of improving the accuracy of estimating the correlation coefficient of the channel in the time direction. Attached Figure Description

[0023] Figure 1 This is a hardware structure block diagram of a mobile terminal according to an embodiment of the channel estimation method of this application;

[0024] Figure 2 This is a flowchart of a channel estimation method according to an embodiment of this application;

[0025] Figure 3 This is a structural block diagram of a channel estimation device according to an embodiment of this application. Detailed Implementation

[0026] The embodiments of this application will be described in detail below with reference to the accompanying drawings and examples.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0028] The methods and embodiments provided in this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal using a channel estimation method according to an embodiment of this application. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0029] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the channel estimation method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0030] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0031] This embodiment provides a channel estimation method. Figure 2 This is a flowchart based on an embodiment of this application, such as... Figure 2 As shown, the process includes the following steps:

[0032] Step S202: Divide the multiple paths of the acquired channel into clusters to obtain N multipath sets, where N is a natural number greater than or equal to 1.

[0033] Step S204: Determine the correlation coefficient for each of the above multipath sets;

[0034] Step S206: Determine the target correlation coefficients of N multipath sets based on the correlation coefficients of each of the multipath sets, wherein the correlation coefficients and the target correlation coefficients are used to represent the changes of the channel in the time direction.

[0035] The entity performing the above steps can be a computer, a processor capable of loading and running programs, or other processing devices or units with similar processing capabilities, but is not limited to these.

[0036] In the above embodiments, the multipath refers to the multiple paths of a signal from the transmitting end to the receiving end. Methods for clustering multiple multipaths include, but are not limited to, clustering based on multipath Doppler frequency offset, multipath time delay, and multipath fading factor. Methods for obtaining multiple multipaths include, but are not limited to, obtaining multiple multipaths through simulation, obtaining multiple multipaths through direct detection, and obtaining multiple multipaths through mathematical analysis of the received signal.

[0037] This application effectively avoids estimation errors that may be caused by large differences in Doppler frequency offsets of multipaths in different directions in high-speed scenarios by dividing multipaths into clusters, that is, calculating the Doppler frequency offset and correlation coefficient of multiple multipath sets separately, and then estimating the overall correlation coefficient of all multipaths. It also solves the problem in related technologies that it is difficult to estimate the correlation coefficient of the channel in the time direction in high-speed scenarios.

[0038] In one exemplary embodiment, the acquired channel paths are clustered to obtain N multipath sets. This includes: clustering the multiple paths according to the Doppler frequency offset and delay of each path to obtain N multipath sets. In the above embodiment, clustering the multiple multipaths according to the Doppler frequency offset and delay of each multipath includes, but is not limited to, selecting the multipath with the largest fading factor from all existing multipaths, calculating the difference between the Doppler frequency offset of the selected multipath and the Doppler frequency offset of the multipath with the largest fading factor, determining the multipath with a difference less than a preset threshold and the multipath with the largest fading factor as a cluster (one cluster corresponds to one multipath set), and repeating the above process in the remaining multipaths. In the above embodiment, the preset threshold may include, but is not limited to, 5%, 10%, or 15% of the maximum fading factor of the multiple multipaths. Of course, the above is merely an illustrative example of the preset threshold, and the preset threshold may be all possible fading factor values ​​that meet the clustering requirements. By taking into account the potential impact of Doppler frequency offset on correlation coefficients from multipath propagation in different directions under high-speed conditions, the method described above enables rapid multipath clustering and effectively improves the estimation efficiency of correlation coefficients.

[0039] In an exemplary embodiment, the multiple paths are clustered according to the Doppler frequency offset and time delay of each path to obtain N multipath sets, including: determining the strongest path among the multiple paths, wherein the strongest path represents a path whose fading factor is greater than a first preset threshold; determining the target Doppler frequency offset and target time delay of the strongest path; determining paths among the multiple paths where the absolute value of the frequency offset between the Doppler frequency offset and the target Doppler frequency offset is less than or equal to a second preset threshold, and the absolute value of the time delay difference between the time delay and the target time delay is less than or equal to a third preset threshold, to obtain a multipath set; performing the same operation as determining the multipath set on the remaining paths to obtain the remaining N-1 multipath sets, wherein the remaining paths include paths among the multiple paths other than the multipath sets. In the above embodiment, the fading factor of the strongest path satisfying the first preset threshold includes the fading factor of the strongest path exceeding the first threshold. When there are multiple fading factors satisfying the first preset threshold, any multipath corresponding to the fading factor satisfying the first preset threshold can be arbitrarily selected as the strongest path. By taking into account the potential impact of Doppler frequency offset on correlation coefficients from multipath propagation in different directions under high-speed scenarios, the method described above enables rapid multipath clustering and effectively improves the estimation efficiency of correlation coefficients.

[0040] In an exemplary embodiment, for each of the aforementioned multipath sets, determining the correlation coefficient of each multipath set includes: determining the correlation coefficient of the multipath set using the time delay and energy of all paths in the multipath set. In the above embodiment, the preset merging algorithm includes, but is not limited to: calculating the average Doppler frequency offset of all multipaths in the multipath set; determining the weight of the multipaths based on their energy, and then calculating the average Doppler frequency offset of all multipaths; and using the maximum value of the Doppler frequency offset of the multipaths as the Doppler frequency offset of the multipath set. By taking into account the potential impact of Doppler frequency offsets of multipaths in different directions on the correlation coefficient in high-speed scenarios, the calculation of the Doppler frequency offset of the multipath set is completed quickly, effectively improving the estimation efficiency of the correlation coefficient.

[0041] In an exemplary embodiment, determining the target correlation coefficients of N multipath sets based on the correlation coefficient of each multipath set includes: merging the Doppler frequency offsets of all paths in the multipath sets using a preset merging algorithm to obtain the Doppler frequency offsets of the multipath sets; determining a first average power based on the fading factor of each path in the multipath sets, wherein the first average power represents the average power of the multipath sets within a preset time period; summing the first average power to obtain a second average power, wherein the second average power represents the average power of the N multipath sets within the preset time period; and calculating the target correlation coefficients using the second average power, the first average power of each multipath set, the Doppler frequency offset of each multipath set, the correlation coefficient of each multipath set, and the preset time period. In the above embodiment, calculating the target correlation coefficients based on power, Doppler frequency offset, and preset time period considers the possible influence of Doppler frequency offsets of multipaths in different directions on the correlation coefficients in high-speed scenarios while achieving rapid multipath clustering, effectively improving the estimation efficiency of correlation coefficients.

[0042] In an exemplary embodiment, determining the average power of the multipath set within the preset time period to obtain a second average power of the multipath set includes: determining the fading factor of each path in the multipath set; and calculating the second average power of the multipath set using the fading factor of each path. In the above embodiment, calculating the average power based on the fading factor and using mathematical analysis effectively improves the estimation efficiency of the correlation coefficient.

[0043] In an exemplary embodiment, after determining the target correlation coefficients for N multipath sets based on the correlation coefficients of each multipath set, the method further includes performing channel estimation on the channel using the target correlation coefficients, including: determining filter parameters of a filter based on the target correlation coefficients; filtering the signal transmitted through the channel using the filter and according to the filter parameters; and / or, the correlation coefficients include the Pearson correlation coefficient of the channel in the time direction. In the above embodiments, the filter can be a Wiener filter or other filters that require channel estimation before design. Adjusting the filter parameters using the target correlation coefficients effectively improves filter performance and enhances the filtering effect of the filter.

[0044] The present invention will now be described with reference to specific embodiments. The calculation process of the correlation coefficient includes the following steps:

[0045] Step S301: Obtain the multipath set L (corresponding to the multiple multipaths mentioned above), and for each multipath l∈L, obtain its fading factor a. l Delay τ l ;

[0046] Step S302: For each multipath l∈L, estimate its Doppler frequency offset f l ;

[0047] Step S303: Cluster according to the following strategy (corresponding to the multipath set mentioned above). Assume the clustered multipath set is C0, C1, ..., C N-1 Where N is a preset parameter, clustering is completed recursively as follows:

[0048] a) In the first execution, i = 0. Let S i Let S be the set of multipaths after the i≥0th successful clustering, where S -1 =L.

[0049] b) Select S i-1 The strongest multipath in, that is, let To obtain its delay and Doppler frequency deviation

[0050] c) Let C i ={l} is the set of all multipaths that simultaneously satisfy the following conditions:

[0051] l∈S i-1 ;

[0052]

[0053]

[0054] d) Let S i =S i-1 -C i .

[0055] e) From S i Begin, continue with step a) until C. N-1 It has been confirmed.

[0056] In the above clustering strategy, Th a ≥0 and Th f ≥0 represents the clustering delay and Doppler frequency offset thresholds, respectively. The set AB consists of elements that belong to A but not to B.

[0057] Step S304: For each cluster C i The Doppler frequency offset f of each multipath is then merged using a preset merging algorithm. l (l∈C i ) are merged to obtain

[0058] Step S305: For each cluster C i Using only C i Multipath propagation, estimating the correlation coefficient Ri (Δt);

[0059] Step S306: Combine the correlation coefficients R according to the following formula. i (Δt):

[0060]

[0061] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0062] This embodiment also provides a channel estimation apparatus for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0063] Figure 3 This is a structural block diagram of a channel estimation apparatus according to an embodiment of this application, such as... Figure 3 As shown, the device includes a partitioning module for dividing multiple multipaths of the acquired channel into clusters to obtain N multipath sets, where N is a natural number greater than or equal to 1; a first determining module for determining the Doppler frequency offset and correlation coefficient of each of the multipath sets; and a second determining module for determining the target correlation coefficients of the N multipath sets based on the Doppler frequency offset and correlation coefficient of each of the multipath sets, where the correlation coefficients and the target correlation coefficients are used to represent the changes of the channel in the time direction.

[0064] In an exemplary embodiment, the partitioning module includes a partitioning submodule, configured to partition multiple paths into clusters based on the Doppler frequency offset and time delay of each path, thereby obtaining N sets of the multipath.

[0065] In an exemplary embodiment, the partitioning module includes: a first determining submodule, configured to determine the strongest path among a plurality of paths, wherein the strongest path represents a path whose fading factor is greater than a first preset threshold; a second determining submodule, configured to determine the target Doppler frequency offset and target delay of the strongest path; a third determining submodule, configured to determine paths among the plurality of paths for which the absolute value of the frequency offset between the Doppler frequency offset and the target Doppler frequency offset is less than or equal to a second preset threshold, and the absolute value of the delay difference between the delay and the target delay among the plurality of paths is less than or equal to a third preset threshold, thereby obtaining a multipath set; and an execution submodule, configured to perform the same operation as determining the multipath set on the remaining paths, thereby obtaining a remaining N-1 multipath sets, wherein the remaining paths include paths among the plurality of paths other than the multipath sets.

[0066] In an exemplary embodiment, the first determining module includes a fourth determining submodule, configured to determine the correlation coefficient of the multipath set using the time delay and energy of all paths in the multipath set.

[0067] In an exemplary embodiment, the second determining module includes: a merging submodule, configured to merge the Doppler frequency offsets of all paths in the multipath set using a preset merging algorithm to obtain the Doppler frequency offset of the multipath set; a fifth determining submodule, configured to determine a first average power based on the fading factor of each path in each multipath set, wherein the first average power represents the average power of each multipath set within a preset time period; a sixth determining submodule, configured to accumulate the first average power to obtain a second average power, wherein the second average power represents the average power of N multipath sets within the preset time period; and a calculation submodule, configured to calculate the target correlation coefficient using the second average power, the first average power of each multipath set, the Doppler frequency offset of each multipath set, the correlation coefficient of each multipath set, and the preset time period.

[0068] In an exemplary embodiment, the sixth determining submodule includes: a determining unit for determining the fading factor of each path in the multipath set; and a calculating unit for calculating the second average power of the multipath set using the fading factor of each path.

[0069] In an exemplary embodiment, the estimation module includes: a seventh determining submodule, configured to determine filter parameters based on the target correlation coefficients of N multipath sets after determining the target correlation coefficients of each multipath set; a filtering submodule, configured to filter the signal transmitted through the channel using the filter and according to the filtering parameters; and / or, the correlation coefficients include the Pearson correlation coefficient of the channel in the time direction.

[0070] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when run.

[0071] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0072] Embodiments of this application also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0073] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0074] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0075] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0076] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the principles of this application should be included within the scope of protection of this application.

Claims

1. A channel estimation method, characterized in that, include: The acquired channels are divided into clusters to obtain N multipath sets, where N is a natural number greater than or equal to 1. Determine the correlation coefficient for each of the multipath sets; Based on the correlation coefficient of each multipath set, N target correlation coefficients of the multipath sets are determined, wherein the correlation coefficients and the target correlation coefficients are used to represent the changes of the channel in the time direction; The method involves dividing the multiple paths of the acquired channel into clusters to obtain N multipath sets, including: dividing the multiple paths into clusters according to the Doppler frequency offset and time delay of each path to obtain N multipath sets. Based on the Doppler frequency offset and time delay of each path, the multiple paths are clustered to obtain N multipath sets, including: determining the strongest path among the multiple paths, wherein the strongest path is used to represent the path whose fading factor is greater than a first preset threshold; determining the target Doppler frequency offset and target time delay of the strongest path; determining the path among the multiple paths where the absolute value of the frequency offset between the Doppler frequency offset and the target Doppler frequency offset is less than or equal to a second preset threshold, and the absolute value of the time delay difference between the time delay and the target time delay is less than or equal to a third preset threshold, to obtain a multipath set; performing the same operation as determining the multipath set on the remaining paths to obtain the remaining N-1 multipath sets, wherein the remaining paths include the paths among the multiple paths other than the multipath sets.

2. The method according to claim 1, characterized in that, For each of the multipath sets, the correlation coefficient of each multipath set is determined, including: The correlation coefficient of the multipath set is determined by using the time delay and energy of all paths in the multipath set.

3. The method according to claim 1, characterized in that, Based on the correlation coefficient of each multipath set, target correlation coefficients for N multipath sets are determined, including: The Doppler frequency offset of the multipath set is obtained by merging the Doppler frequency offsets of all paths in the multipath set using a preset merging algorithm. A first average power is determined based on the fading factor of each path in the multipath set, wherein the first average power is used to represent the average power of the multipath set within a preset time period. The first average power is summed to obtain the second average power, wherein the second average power is used to represent the average power of the N multipath sets within the preset time period; The target correlation coefficient is calculated using the second average power, the first average power of each multipath set, the Doppler frequency offset of each multipath set, the correlation coefficient of each multipath set, and the preset duration.

4. The method according to claim 1, characterized in that, After determining the target correlation coefficients of N multipath sets based on the correlation coefficient of each multipath set, the method further includes: Channel estimation is performed using the target correlation coefficient, wherein performing channel estimation using the target correlation coefficient includes: determining filter parameters of a filter based on the target correlation coefficient; filtering the signal transmitted through the channel using the filter and according to the filter parameters; and / or, the correlation coefficient includes the Pearson correlation coefficient of the channel in the time direction.

5. A channel estimation device, characterized in that, include: The partitioning module is used to partition the multiple paths of the acquired channel into clusters to obtain N multipath sets, where N is a natural number greater than or equal to 1. The first determining module is used to determine the correlation coefficient of each of the multipath sets, wherein the correlation coefficient is used to represent the correlation coefficient of the channel in the time direction; The second determining module is used to determine the target correlation coefficients of N multipath sets based on the correlation coefficient of each multipath set, wherein the target correlation coefficients are used to represent the degree of correlation between each of the multiple paths; The partitioning module includes a partitioning submodule, used to partition multiple paths into clusters based on the Doppler frequency offset and time delay of each path, to obtain N sets of multipaths. The partitioning module further includes: a first determining submodule, used to determine the strongest path among the multiple paths, wherein the strongest path represents a path whose fading factor is greater than a first preset threshold; a second determining submodule, used to determine the target Doppler frequency offset and target time delay of the strongest path; a third determining submodule, used to determine paths among the multiple paths for which the absolute value of the frequency offset between the Doppler frequency offset and the target Doppler frequency offset is less than or equal to a second preset threshold, and the absolute value of the time delay difference between the time delay and the target time delay is less than or equal to a third preset threshold, thereby obtaining a multipath set; and an execution submodule, used to perform the same operation as determining the multipath set on the remaining paths, thereby obtaining the remaining N-1 multipath sets, wherein the remaining paths include paths among the multiple paths other than the multipath sets.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of claims 1 to 4.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 4.

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