Channel estimation method and device and electronic equipment

Abstract

The invention provides a channel estimation method and device and electronic equipment, and is applied to the technical field of communication, and the method comprises the steps: carrying out the channel estimation of first reference signals of a plurality of pilot frequency positions corresponding to a current channel, and obtaining a channel estimation value corresponding to each pilot frequency position; wherein the plurality of pilot frequency positions have the same beam and node position; determining a channel feature corresponding to a channel where the first reference signal is located according to the channel estimation value, and determining a plurality of initial filtering orders according to the second reference signal; wherein the number of the initial filtering orders is not greater than the number of the pilot frequency positions; determining a target filtering order from the plurality of initial filtering orders by using the channel characteristics; and determining a target filtering matrix according to the target filtering order, and performing filtering and non-pilot position interpolation on the channel estimation value by using the target filtering matrix to obtain a target channel estimation result. And the deviation between the channel statistical characteristics and the actual statistical characteristics is reduced, so that the channel estimation result is more accurate.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular, to a channel estimation method and apparatus, and an electronic device. Background Art

[0002] In modern commercial communication systems, such as Long-Term Evolution (LTE) systems, New Radio (NR) systems, and Wireless Fidelity (Wi-Fi) systems, etc., the transmission and reception of data are mostly based on coherent demodulation methods. Taking NR as an example, channel estimation is usually performed based on pilots, and then channel equalization, demodulation, and decoding processes are carried out.

[0003] Taking the transmission of the NR Physical Downlink Shared Channel (PDSCH) as an example, a User Equipment (UE) measures the channel based on the Demodulation Reference Signals (DMRS) of the PDSCH to obtain the channel information of the PDSCH. During the channel estimation process, first, the least squares (LS) estimation is performed on the channel response at the pilot positions according to the transmitted pilot data, and then, based on the statistical information of the channel (for example: second-order statistical matrix, etc.), filtering and interpolation are performed based on the LS estimation result of the obtained channel response, while further eliminating the additive noise interference, the channel response information at non-pilot positions is obtained.

[0004] In the prior art, in the process of determining the statistical information of the channel, there may be a large deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics, resulting in serious performance loss. Summary of the Invention

[0005] The purpose of the embodiments of this application is to provide a channel estimation method and apparatus, and an electronic device, so as to solve the technical problem of a large deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a channel estimation method, including: performing channel estimation on a first reference signal at a plurality of pilot positions corresponding to a current channel to obtain a channel estimation value corresponding to each pilot position; wherein the plurality of pilot positions have the same beam and node positions; determining a channel feature corresponding to the channel where the first reference signal is located according to the channel estimation value, and determining a plurality of initial filtering orders according to a second reference signal; wherein the number of the initial filtering orders is not greater than the number of the pilot positions; determining a target filtering order from the plurality of initial filtering orders by using the channel feature; determining a target filtering matrix according to the target filtering order, and filtering the channel estimation value and interpolating non-pilot positions by using the target filtering matrix to obtain a target channel estimation result.

[0007] In the above solution, a suitable target filtering order can be determined from a plurality of initial filtering orders according to the channel feature corresponding to the channel where the first reference signal is located; the above target filtering order is used to determine a corresponding target filtering matrix, that is, the statistical feature of the channel, and the channel estimation can be realized by using the target filtering matrix. Among them, since the filtering order can be adaptively selected according to the channel feature corresponding to the channel where the first reference signal is located, the deviation between the obtained statistical feature of the channel and the actual statistical feature can be reduced, so that the result of channel estimation is more accurate.

[0008] In an optional implementation manner, the channel feature includes a covariance matrix, and the determining a target filtering order from the plurality of initial filtering orders by using the channel feature includes: calculating a correlation coefficient error corresponding to the plurality of initial filtering orders according to the covariance matrix corresponding to the channel where the first reference signal is located and the covariance matrix corresponding to the channel where the second reference signal is located; determining the maximum value of the initial filtering orders with the correlation coefficient error not greater than an error threshold as the target filtering order. In the above solution, the channel feature may include a covariance matrix. At this time, the correlation coefficient error corresponding to the plurality of initial filtering orders can be calculated according to the covariance matrix corresponding to the channel where the first reference signal is located and the covariance matrix corresponding to the channel where the second reference signal is located, so as to determine the target filtering order according to the above correlation coefficient error. Among them, since the filtering order can be adaptively selected according to the covariance matrix corresponding to the channel where the first reference signal is located, the deviation between the obtained statistical feature of the channel and the actual statistical feature can be reduced, so that the result of channel estimation is more accurate.

[0009] In an alternative embodiment, the channel feature includes the root mean square delay spread. Determining the target filtering order from the multiple initial filtering orders by using the channel feature includes: determining the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located; determining the initial filtering order corresponding to the difference as the target filtering order; where each initial filtering order corresponds to a difference range. In the above solution, the channel feature may include the root mean square delay spread. At this time, the target filtering order can be determined according to the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located. Among them, since the filtering order can be adaptively selected according to the root mean square delay spread corresponding to the channel where the first reference signal is located, the deviation between the statistical feature of the obtained channel and the actual statistical feature can be reduced, so that the result of channel estimation is more accurate.

[0010] In an alternative embodiment, determining the target filtering order from the multiple initial filtering orders by using the channel feature includes: inputting the channel estimation value into a pre-trained neural network to obtain the target filtering order output by the neural network; where the neural network extracts the channel feature corresponding to the channel where the first reference signal is located based on the channel estimation value, and determines the target filtering order based on the channel feature. In the above solution, the channel feature corresponding to the channel where the first reference signal is located can be extracted by using a pre-trained neural network, so as to determine the target filtering order from the multiple initial filtering orders according to the above channel feature. Among them, since the filtering order can be adaptively selected after using the neural network to extract the channel feature, the deviation between the statistical feature of the obtained channel and the actual statistical feature can be reduced, so that the result of channel estimation is more accurate.

[0011] In an alternative embodiment, the channel feature includes the covariance matrix and the root mean square delay. Determining the target filtering order from the multiple initial filtering orders by using the channel feature includes: determining the intermediate filtering order corresponding to each channel feature according to each channel feature; determining the minimum value among the intermediate filtering orders as the target filtering order. In the above solution, when the channel feature includes the covariance matrix and the root mean square delay, the minimum value among the multiple intermediate filtering orders can be determined as the target filtering order, so as to ensure the accuracy of the channel estimation result.

[0012] In an alternative embodiment, the channel estimation values include multiple sets of channel estimation sub-values, where each set of channel estimation sub-values is obtained by performing channel estimation on a plurality of PRGs; the determining, according to the channel estimation values, the channel characteristics corresponding to the channel where the first reference signal is located includes: for each set of channel estimation sub-values, determining the channel sub-characteristics corresponding to this set of channel estimation sub-values; the using the channel characteristics to determine the target filtering order from the multiple initial filtering orders includes: for each set of channel sub-characteristics, determining the target sub-filtering order corresponding to this set of channel sub-characteristics from the multiple initial filtering orders according to this set of channel sub-characteristics; and determining the target filtering order corresponding to each channel estimation sub-value according to the multiple target sub-filtering orders. In the above solution, the channel can be divided into multiple groups, so the channel estimation values can also be divided into multiple sets of channel estimation sub-values; for each set of channel estimation sub-values, the target sub-filtering order can be determined according to the corresponding channel self-characteristics; finally, the target filtering order corresponding to each channel estimation sub-value can be determined according to the multiple target sub-filtering orders. Among them, for each set of channel estimation sub-values, the filtering order can be adaptively selected according to the channel sub-characteristics. Therefore, the deviation between the statistical characteristics of the obtained channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0013] In an alternative embodiment, the determining the target filtering order corresponding to each channel estimation sub-value according to the multiple target sub-filtering orders includes: determining the minimum value among the multiple target sub-filtering orders as the target filtering order corresponding to each channel estimation sub-value; or, determining the target sub-filtering order corresponding to the channel estimation sub-value with the largest number of pilot points as the target filtering order corresponding to each channel estimation sub-value; or, determining the target sub-filtering order of each set of channel estimation sub-values as the target filtering order corresponding to it.

[0014] In a second aspect, an embodiment of the present application provides a channel estimation apparatus, including: an estimation module, configured to perform channel estimation on the first reference signals at multiple pilot positions corresponding to a current channel to obtain channel estimation values corresponding to each pilot position respectively; where the multiple pilot positions have the same beam and node positions; a first determination module, configured to determine the channel characteristics corresponding to the channel where the first reference signal is located according to the channel estimation values, and determine multiple initial filtering orders according to a second reference signal; where the number of the initial filtering orders is not greater than the number of the pilot positions; a second determination module, configured to determine a target filtering order from the multiple initial filtering orders by using the channel characteristics; and a third determination module, configured to determine a target filtering matrix according to the target filtering order, and filter the channel estimation values and interpolate non-pilot positions by using the target filtering matrix to obtain a target channel estimation result.

[0015] In the above solution, an appropriate target filtering order can be determined from multiple initial filtering orders according to the channel characteristics corresponding to the channel where the first reference signal is located; the above target filtering order is used to determine the corresponding target filtering matrix, that is, the statistical characteristics of the channel, and the channel can be estimated by using this target filtering matrix. Among them, since the filtering order can be adaptively selected according to the channel characteristics corresponding to the channel where the first reference signal is located, the deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0016] In an alternative embodiment, the channel characteristics include a covariance matrix, and the second determination module is specifically configured to: calculate the correlation coefficient errors corresponding to the multiple initial filtering orders according to the covariance matrix corresponding to the channel where the first reference signal is located and the covariance matrix corresponding to the channel where the second reference signal is located; determine the maximum value of the initial filtering orders whose correlation coefficient errors are not greater than the error threshold as the target filtering order. In the above solution, the channel characteristics may include a covariance matrix. At this time, the correlation coefficient errors corresponding to the multiple initial filtering orders can be calculated according to the covariance matrix corresponding to the channel where the first reference signal is located and the covariance matrix corresponding to the channel where the second reference signal is located, so as to determine the target filtering order according to the above correlation coefficient errors. Among them, since the filtering order can be adaptively selected according to the covariance matrix corresponding to the channel where the first reference signal is located, the deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0017] In an alternative embodiment, the channel characteristics include the root mean square delay spread, and the second determination module is specifically configured to: determine the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located; determine the initial filtering order corresponding to the difference as the target filtering order; where each initial filtering order corresponds to a difference range. In the above solution, the channel characteristics may include the root mean square delay spread. At this time, the target filtering order can be determined according to the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located. Among them, since the filtering order can be adaptively selected according to the root mean square delay spread corresponding to the channel where the first reference signal is located, the deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0018] In an alternative embodiment, the second determination module is specifically configured to: input the channel estimation value into a pre-trained neural network to obtain the target filtering order output by the neural network; wherein, the neural network extracts the channel characteristics corresponding to the channel where the first reference signal is located based on the channel estimation value, and determines the target filtering order based on the channel characteristics. In the above solution, the channel characteristics corresponding to the channel where the first reference signal is located can be extracted by using a pre-trained neural network, so as to determine the target filtering order from multiple initial filtering orders according to the above channel characteristics. Among them, since the channel characteristics can be extracted by using a neural network and the filtering order can be adaptively selected, the deviation between the statistical characteristics of the obtained channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0019] In an alternative embodiment, the channel characteristics include a covariance matrix and a root mean square delay, and the second determination module is specifically configured to: determine an intermediate filtering order corresponding to each channel characteristic according to each channel characteristic; determine the minimum value of the intermediate filtering orders as the target filtering order. In the above solution, when the channel characteristics include a covariance matrix and a root mean square delay, the minimum value of multiple intermediate filtering orders can be determined as the target filtering order, so as to ensure the accuracy of the channel estimation result.

[0020] In an alternative embodiment, the channel estimation value includes multiple groups of channel estimation sub-values, where each group of channel estimation sub-values is obtained by performing channel estimation on a plurality of PRGs; the first determination module is specifically configured to: for each group of channel estimation sub-values, determine a channel sub-characteristic corresponding to the group of channel estimation sub-values according to the group of channel estimation sub-values; the second determination module is specifically configured to: for each group of channel sub-characteristics, determine a target sub-filtering order corresponding to the group of channel sub-characteristics from the multiple initial filtering orders; determine the target filtering order corresponding to each channel estimation sub-value according to the multiple target sub-filtering orders. In the above solution, the channel can be divided into multiple groups, so the channel estimation value can also be divided into multiple groups of channel estimation sub-values; for each group of channel estimation sub-values, the target sub-filtering order can be determined according to the corresponding channel self-characteristics; finally, the target filtering order corresponding to each channel estimation sub-value can be determined according to the multiple target sub-filtering orders. Among them, for each group of channel estimation sub-values, the filtering order can be adaptively selected according to the channel sub-characteristics, so the deviation between the statistical characteristics of the obtained channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0021] In an alternative embodiment, the second determination module is further configured to: determine the minimum value among the multiple target sub-filter orders as the target filter order corresponding to each channel estimation sub-value; or, determine the target sub-filter order corresponding to the channel estimation sub-value with the largest number of pilot points as the target filter order corresponding to each channel estimation sub-value; or, determine the target sub-filter order of each group of channel estimation sub-values as the target filter order corresponding thereto.

[0022] In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, a memory, and a bus; the processor and the memory complete communication with each other through the bus; the memory stores computer program instructions executable by the processor, and the processor can execute the channel estimation method as described in the first aspect by invoking the computer program instructions.

[0023] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores computer program instructions. When the computer program instructions are run by a computer, the computer is enabled to execute the channel estimation method as described in the first aspect.

[0024] To make the above objects, features, and advantages of the present application more obvious and understandable, specific embodiments of the present application are hereinafter given, and detailed descriptions are made in conjunction with the accompanying drawings as follows. Description of the Drawings

[0025] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a flowchart of a channel estimation method provided by an embodiment of the present application;

[0027] Figure 2 It is a structural block diagram of a channel estimation device provided by an embodiment of the present application;

[0028] Figure 3 It is a structural block diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0029] Before introducing the channel estimation method provided in the embodiments of the present application, it should be noted that for the convenience of understanding, the solutions provided in the embodiments of the present application are all embodiments in PDSCH / PDCCH in the NR communication system; for LTE and other communication systems, the channel estimation method provided in the embodiments of the present application is still applicable, and those skilled in the art can perform the channel estimation method in the corresponding communication system in combination with the following introduction.

[0030] Taking the second-order statistic channel multipath delay spread as an example, when the channel multipath delay spread is small, the coherent bandwidth of the channel will be relatively large. At this time, the statistical correlation of the channel responses at adjacent frequency points is higher, and the noise reduction ability at a high filtering order (i.e., the number of input LS channel response results used to filter and generate the channel response of one frequency point) is stronger. As the channel multipath delay spread increases, the statistical correlation of the channel responses at adjacent frequency points decreases, and the contribution of the frequency points where the input LS channel responses farther away from the frequency point to be filtered and obtained to the noise reduction benefit becomes lower.

[0031] If the channel statistical characteristics obtained by the QCL-specified reference signal are mismatched with the current channel, there may be an error between the channel delay spread obtained by the QCL-specified reference signal and the actual channel delay spread. If the channel delay spread obtained by the QCL-specified reference signal is less than the actual channel delay spread, the LS results of the far-end frequency points that are no longer significantly correlated will interfere with the channel response results of the current frequency point to be calculated, resulting in a decline in estimation performance; if the deviation is extremely large, the channels with low far-end correlation will be introduced as interference, resulting in the interference in the channel estimation result rising above the noise floor reduction, making the filtering process show a negative gain.

[0032] Based on the above analysis, when the deviation between the obtained channel statistical characteristics and the actual statistical characteristics is large, the channel interference outside the coherent bandwidth can be avoided by reducing the filtering order. Next, the technical solutions in the embodiments of the present application will be described in conjunction with the accompanying drawings in the embodiments of the present application. In the channel estimation method provided in the embodiments of the present application, an appropriate filtering order can be determined, thereby improving the performance of channel estimation.

[0033] Please refer to Figure 1 , Figure 1 , which is a flowchart of a channel estimation method provided in an embodiment of the present application. The channel estimation method may include the following steps:

[0034] Step S101: Perform channel estimation on the first reference signals at multiple pilot positions corresponding to the current channel to obtain channel estimation values respectively corresponding to each pilot position.

[0035] Step S102: Determine the channel characteristics corresponding to the channel where the first reference signal is located according to the channel estimation values, and determine multiple initial filtering orders according to the second reference signal.

[0036] Step S103: Determine a target filtering order from multiple initial filtering orders by using channel characteristics.

[0037] Step S104: Determine a target filtering matrix according to the target filtering order, and filter the channel estimation value and interpolate non-pilot positions by using the target filtering matrix to obtain a target channel estimation result.

[0038] Specifically, in the above step S101, channel estimation can be performed on the first reference signal of the current channel. When performing channel estimation, multiple pilot positions can be selected, so as to obtain the channel estimation value corresponding to each pilot position.

[0039] It should be noted that the embodiments of the present application do not make specific limitations on the specific implementation manners of the multiple pilot positions, and those skilled in the art can make appropriate adjustments according to actual situations. Among them, the above multiple pilot positions can have the same beam and node positions. As an implementation manner, the number of the above pilot positions can be greater than the filtering order; as another implementation manner, the above pilot positions can be continuous or discontinuous.

[0040] In the above step S102, according to the channel estimation value obtained in the above step S101, the channel characteristics corresponding to the channel where the first reference signal is located can be determined.

[0041] It should be noted that the embodiments of the present application do not make specific limitations on the specific implementation manners of the channel characteristics, and those skilled in the art can make appropriate adjustments according to actual situations. For example, the channel characteristics can include covariance matrix, root mean square delay spread, etc. Correspondingly, the embodiments of the present application do not make specific limitations on the specific implementation manners of determining the above channel characteristics, and those skilled in the art can make appropriate adjustments according to actual situations.

[0042] In addition, multiple initial filtering orders can also be determined according to a second reference signal, where the number of the initial filtering orders is not greater than the number of the pilot positions. Among them, according to the differences of current communication systems, the specific implementation manners of the above second reference signal can be different. For example, for NR communication systems and LTE communication systems, the second reference signal can be a reference signal corresponding to the QCL relationship; for other communication systems, the second reference signal can be a user-defined reference signal.

[0043] In the above step S103, by using the channel characteristics determined in the above step S102, a target filtering order can be determined from the multiple initial filtering orders obtained in the above step S102.

[0044] In the above step S104, according to the above target filtering order, a suitable target filtering matrix can be determined from a pre-generated filtering matrix obtained based on the second reference signal; alternatively, the target filtering matrix can also be re-generated according to the above filtering order.

[0045] Using the above target filtering matrix, the channel estimation value can be filtered and interpolated at non-pilot positions, so as to obtain the target channel estimation result.

[0046] In the above solution, a suitable target filtering order can be determined from multiple initial filtering orders according to the channel characteristics corresponding to the channel where the first reference signal is located; the above target filtering order is used to determine the corresponding target filtering matrix, that is, the statistical characteristics of the channel, and the channel can be estimated by using this target filtering matrix. Among them, since the filtering order can be adaptively selected according to the channel characteristics corresponding to the channel where the first reference signal is located, the deviation between the statistical characteristics of the obtained channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0047] Further, on the basis of the above embodiment, the channel characteristics may include a covariance matrix. In this implementation manner, the above step S103 may specifically include the following steps:

[0048] Step 1), calculate the correlation coefficient errors corresponding to multiple initial filtering orders according to the covariance matrix corresponding to the channel where the first reference signal is located and the covariance matrix corresponding to the channel where the second reference signal is located.

[0049] Step 2), determine the maximum value of the initial filtering orders whose correlation coefficient errors are not greater than the error threshold as the target filtering order.

[0050] Specifically, according to the channel estimation value corresponding to the channel where the first reference signal is located, a K-order normalized statistical covariance matrix R composed of Neyman-Pearson correlation coefficients can be calculated; similarly, the channel estimation covariance matrix can be statistically obtained according to the second reference signal

[0051] In the above steps 1)-step 2), for k = K, K-1,...1, the correlation coefficient R in the covariance matrix R corresponding to the channel where the first reference signal is located can be calculated in turn k,1 and the covariance matrix corresponding to the channel where the first reference signal is located in the correlation coefficient between the correlation coefficient errors until M k is not greater than the error threshold M th and then stop; among them, the notified k can be used as the target filtering order K opt .

[0052] As an implementation, the calculation of the covariance matrix R corresponding to the channel where the first reference signal is located can rely on all or part of the values in the channel estimation values.

[0053] As another implementation, for the calculation of the covariance matrix R corresponding to the channel where the first reference signal is located, only the first column of the matrix can be calculated, or only some of the elements in the first column can be calculated to reduce the complexity.

[0054] In the above solution, the channel characteristics may include the covariance matrix. In this case, the correlation coefficient errors corresponding to multiple initial filtering orders can be calculated based on the covariance matrix corresponding to the channel where the first reference signal is located and the covariance matrix corresponding to the channel where the second reference signal is located, and then the target filtering order can be determined based on the above correlation coefficient errors. Among them, since the filtering order can be adaptively selected according to the covariance matrix corresponding to the channel where the first reference signal is located, the deviation between the statistical characteristics of the obtained channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0055] Further, based on the above embodiments, the channel characteristics may include the root mean square delay spread. In this implementation, step S103 may specifically include the following steps:

[0056] Step 1), determine the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located;

[0057] Step 2), determine the initial filtering order corresponding to the difference as the target filtering order.

[0058] Specifically, first, the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located can be determined, and then the initial filtering order corresponding to the difference can be determined as the target filtering order, where each initial filtering order corresponds to a difference range.

[0059] As an implementation, the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located can be compared: if the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located is small, a larger filtering order can be selected as the target filtering order; if the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located is large, a smaller filtering order can be selected as the target filtering order.

[0060] In the above solution, the channel characteristics may include the root mean square delay spread. At this time, the target filtering order can be determined according to the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located. Among them, since the filtering order can be adaptively selected according to the root mean square delay spread corresponding to the channel where the first reference signal is located, the deviation between the statistical characteristics of the obtained channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0061] Further, on the basis of the above embodiment, step S103 may specifically include the following steps:

[0062] Input the channel estimation value into a pre-trained neural network to obtain the target filtering order output by the neural network; wherein, the neural network extracts the channel characteristics corresponding to the channel where the first reference signal is located based on the channel estimation value, and determines the target filtering order based on the channel characteristics.

[0063] Specifically, a large amount of training data can be used to train the neural network in advance, so as to obtain a pre-trained neural network for determining the target filtering order corresponding to the channel where the first reference signal is located.

[0064] Among them, as an implementation manner, the specific implementation manner of the channel characteristics may not be limited, and the neural network extracts based on the channel estimation value; as another implementation manner, the specific implementation manner of the channel characteristics may also be limited. For example, the channel characteristics may include a covariance matrix. At this time, the channel estimation value, the covariance matrix corresponding to the channel where the second reference signal is located, and the correlation coefficient error can be input into the pre-trained neural network at the same time, so as to obtain the target filtering order output by the neural network.

[0065] In the above solution, the channel characteristics corresponding to the channel where the first reference signal is located can be extracted by using a pre-trained neural network, so as to determine the target filtering order from multiple initial filtering orders according to the above channel characteristics. Among them, since the filtering order can be adaptively selected after using the neural network to extract the channel characteristics, the deviation between the statistical characteristics of the obtained channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0066] Further, on the basis of the above embodiment, the channel characteristics may include both the covariance matrix and the root mean square delay. In this implementation manner, step S103 may specifically include the following steps:

[0067] Step 1), determine the intermediate filtering order corresponding to each channel characteristic according to each channel characteristic.

[0068] Step 2), determine the minimum value in the intermediate filtering order as the target filtering order.

[0069] Specifically, as an implementation manner, the number of channel characteristics can be one. At this time, a target filtering order can be calculated according to this channel characteristic.

[0070] As another implementation manner, the number of channel characteristics can be multiple. At this time, the channel characteristics can simultaneously include the covariance matrix and the root mean square delay spread. According to each channel characteristic, its corresponding intermediate filtering order can be determined. According to the above multiple intermediate filtering orders, the minimum value in the intermediate filtering orders can be determined as the target filtering order.

[0071] It should be noted that the embodiments of the present application do not make specific limitations on the specific implementation manners of determining the intermediate filtering order according to each channel characteristic. The specific implementation manners of determining the intermediate filtering order for each channel characteristic can be the same or different; the specific implementation manners of determining the intermediate filtering order for each channel characteristic can adopt any one of the above embodiments.

[0072] In the above solution, when the channel characteristics include the covariance matrix and the root mean square delay spread, the minimum value in the multiple intermediate filtering orders can be determined as the target filtering order, so as to ensure the accuracy of the channel estimation result.

[0073] Further, on the basis of the above embodiments, the channel estimation value can include multiple groups of channel estimation sub-values. Among them, each group of channel estimation sub-values is obtained by performing channel estimation on several PRGs. Taking transmission channels such as PDSCH / PDCCH in the NR communication system as an example, according to the beamforming scheme and precoding scheme used at the transmitting end, the channel is divided into different precoding resource block groups (PRGs). There are different possibilities for precoding between PRGs of different channels, resulting in different forms of the equivalent channel response. Therefore, the channel change characteristics can be extracted through the PRGs, and then the subsequent filtering matrix selection or generation can be performed by synthesizing the feature extraction results of different PRGs.

[0074] In this implementation manner, step S102 above can specifically include the following steps:

[0075] For each group of channel estimation sub-values, determine the channel sub-characteristics corresponding to this group of channel estimation sub-values.

[0076] Specifically, for the received signals of each PRG, channel estimation can be performed on each of them, so as to obtain the channel estimation sub-values corresponding to each PRG; the channel estimation sub-values corresponding to the above multiple PRGs are the above multiple groups of channel estimation sub-values.

[0077] Therefore, for each set of channel estimation sub-values, channel sub-features can be extracted, so as to obtain the channel sub-features corresponding to each set of channel estimation sub-values.

[0078] It should be noted that the channel sub-features corresponding to each set of channel estimation sub-values can be of the same type. For example, they are all covariance matrices; or, they can also be of different types. For example, some are covariance matrices and some are root mean square delay spreads.

[0079] At this time, step S103 above can specifically include the following steps:

[0080] Step 1), for each set of channel sub-features, determine the target sub-filter order corresponding to the set of channel sub-features from multiple initial filter orders according to the set of channel sub-features.

[0081] Step 2), determine the target filter order corresponding to each channel estimation sub-value according to multiple target sub-filter orders.

[0082] Specifically, in step 1) above, for each set of channel sub-features, the target sub-filter order can be determined. That is, the target sub-filter order corresponding to the set of channel sub-features is determined from multiple initial filter orders according to the set of channel sub-features.

[0083] It should be noted that the embodiments of the present application do not specifically limit the specific implementation manner of determining the target sub-filter order according to each channel sub-feature. The specific implementation manners of determining the target sub-filter order for each channel sub-feature can be the same or different; the specific implementation manner of determining the target sub-filter order for each channel sub-feature can adopt any one of the above embodiments.

[0084] In step 2) above, the target filter order corresponding to each channel estimation sub-value can be determined according to multiple target sub-filter orders. It can be understood that the target filter orders corresponding to each channel estimation sub-value can be the same or different.

[0085] In the above solution, the channel can be divided into multiple groups, so the channel estimation values can also be divided into multiple groups of channel estimation sub-values; for each group of channel estimation sub-values, the target sub-filter order can be determined according to the corresponding channel self-features; finally, the target filter order corresponding to each channel estimation sub-value can be determined according to multiple target sub-filter orders. Among them, for each group of channel estimation sub-values, the filter order can be adaptively selected according to the channel sub-features. Therefore, the deviation between the statistical features of the obtained channel and the actual statistical features can be reduced, so that the result of channel estimation is more accurate.

[0086] Further, based on the above embodiments, the step of determining the target filtering order corresponding to each channel estimation sub-value according to multiple target sub-filtering orders may specifically include the following steps:

[0087] Determine the minimum value among the multiple target sub-filtering orders as the target filtering order corresponding to each channel estimation sub-value; alternatively, determine the target sub-filtering order corresponding to the channel estimation sub-value with the largest number of pilot points as the target filtering order corresponding to each channel estimation sub-value; or, determine the target sub-filtering order of each group of channel estimation sub-values as its corresponding target filtering order.

[0088] Specifically, as an implementation manner, each channel estimation sub-value obtains its respective optimal target sub-filtering order, and the smallest target filtering order among all target sub-filtering orders can be selected as the target filtering order of all channel estimation sub-values.

[0089] As another implementation manner, since the covariance statistics are different due to the different magnitudes of the respective channel estimation sub-values, the target filtering order corresponding to the channel estimation sub-value with the largest number of pilot points among all target sub-filtering orders can be selected as the target filtering order of all channel estimation sub-values.

[0090] As yet another implementation manner, the target sub-filtering order of each group of channel estimation sub-values can be determined as its corresponding target filtering order.

[0091] Please refer to Figure 2 , Figure 2 , which is a structural block diagram of a channel estimation device provided by an embodiment of the present application. The channel estimation device 200 includes: an estimation module 201, configured to perform channel estimation on the first reference signals at multiple pilot positions corresponding to the current channel to obtain channel estimation values corresponding to each pilot position; wherein, the multiple pilot positions have the same beam and node positions; a first determination module 202, configured to determine the channel characteristics corresponding to the channel where the first reference signal is located according to the channel estimation values, and determine multiple initial filtering orders according to the second reference signal; wherein, the number of the initial filtering orders is not greater than the number of the pilot positions; a second determination module 203, configured to determine the target filtering order from the multiple initial filtering orders by using the channel characteristics; a third determination module 204, configured to determine a target filtering matrix according to the target filtering order, and filter the channel estimation values and interpolate non-pilot positions by using the target filtering matrix to obtain a target channel estimation result.

[0092] In the above solution, the appropriate target filtering order can be determined from multiple initial filtering orders according to the channel characteristics corresponding to the channel where the first reference signal is located; the above target filtering order is used to determine the corresponding target filtering matrix, that is, the statistical characteristics of the channel, and the channel can be estimated by using this target filtering matrix. Among them, since the filtering order can be adaptively selected according to the channel characteristics corresponding to the channel where the first reference signal is located, the deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0093] Further, on the basis of the above embodiment, the channel characteristics include a covariance matrix, and the second determination module 203 is specifically configured to: calculate the correlation coefficient error corresponding to the multiple initial filtering orders according to the covariance matrix corresponding to the channel where the first reference signal is located and the covariance matrix corresponding to the channel where the second reference signal is located; determine the maximum value of the initial filtering orders whose correlation coefficient error is not greater than the error threshold as the target filtering order.

[0094] In the above solution, the channel characteristics may include a covariance matrix. At this time, the correlation coefficient error corresponding to the multiple initial filtering orders can be calculated according to the covariance matrix corresponding to the channel where the first reference signal is located and the covariance matrix corresponding to the channel where the second reference signal is located, so as to determine the target filtering order according to the above correlation coefficient error. Among them, since the filtering order can be adaptively selected according to the covariance matrix corresponding to the channel where the first reference signal is located, the deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0095] Further, on the basis of the above embodiment, the channel characteristics include the root mean square delay spread, and the second determination module 203 is specifically configured to: determine the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located; determine the initial filtering order corresponding to the difference as the target filtering order; where each initial filtering order corresponds to a difference range.

[0096] In the above solution, the channel characteristics may include the root mean square delay spread. At this time, the target filtering order can be determined according to the difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located. Among them, since the filtering order can be adaptively selected according to the root mean square delay spread corresponding to the channel where the first reference signal is located, the deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0097] Further, based on the above embodiments, the second determination module 203 is specifically configured to: input the channel estimation value into a pre-trained neural network to obtain the target filtering order output by the neural network; wherein, the neural network extracts the channel characteristics corresponding to the channel where the first reference signal is located based on the channel estimation value, and determines the target filtering order based on the channel characteristics.

[0098] In the above solution, a pre-trained neural network can be used to extract the channel characteristics corresponding to the channel where the first reference signal is located, so as to determine the target filtering order from multiple initial filtering orders according to the above channel characteristics. Among them, since the channel characteristics can be adaptively selected for filtering order after using the neural network to extract the channel characteristics, the deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0099] Further, based on the above embodiments, the channel characteristics include a covariance matrix and a root mean square delay, and the second determination module 203 is specifically configured to: determine an intermediate filtering order corresponding to each channel characteristic according to each channel characteristic; determine the minimum value of the intermediate filtering orders as the target filtering order.

[0100] In the above solution, when the channel characteristics include a covariance matrix and a root mean square delay, the minimum value of multiple intermediate filtering orders can be determined as the target filtering order, so as to ensure the accuracy of the channel estimation result.

[0101] Further, based on the above embodiments, the channel estimation value includes multiple groups of channel estimation sub-values, where each group of channel estimation sub-values is obtained by performing channel estimation on a plurality of PRGs; the first determination module 202 is specifically configured to: for each group of channel estimation sub-values, determine the channel sub-characteristics corresponding to the group of channel estimation sub-values according to the group of channel estimation sub-values; the second determination module is specifically configured to: for each group of channel sub-characteristics, determine the target sub-filtering order corresponding to the group of channel sub-characteristics from the multiple initial filtering orders; determine the target filtering order corresponding to each channel estimation sub-value according to the multiple target sub-filtering orders.

[0102] In the above solution, the channel can be divided into multiple groups, so the channel estimation value can also be divided into multiple groups of channel estimation sub-values; for each group of channel estimation sub-values, the target sub-filtering order can be determined according to the corresponding channel self-characteristics; finally, the target filtering order corresponding to each channel estimation sub-value can be determined according to the multiple target sub-filtering orders. Among them, for each group of channel estimation sub-values, the filtering order can be adaptively selected according to the channel sub-characteristics, so the deviation between the obtained statistical characteristics of the channel and the actual statistical characteristics can be reduced, so that the result of channel estimation is more accurate.

[0103] Further, based on the above embodiments, the second determination module 203 is further configured to: determine the minimum value among the multiple target sub-filter orders as the target filter order corresponding to each channel estimation sub-value; or, determine the target sub-filter order corresponding to the channel estimation sub-value with the largest number of pilot points as the target filter order corresponding to each channel estimation sub-value; or, determine the target sub-filter order of each group of channel estimation sub-values as the target filter order corresponding thereto.

[0104] Please refer to Figure 3 , Figure 3 FIG. is a structural block diagram of an electronic device provided by an embodiment of the present application. The electronic device 300 includes: at least one processor 301, at least one communication interface 302, at least one memory 303, and at least one communication bus 304. Among them, the communication bus 304 is used to implement direct connection communication between these components. The communication interface 302 is used to communicate signaling or data with other node devices. The memory 303 stores machine-readable instructions executable by the processor 301. When the electronic device 300 runs, the processor 301 communicates with the memory 303 through the communication bus 304. When the machine-readable instructions are called by the processor 301, the above channel estimation method is executed.

[0105] For example, the processor 301 of the embodiment of the present application can read a computer program from the memory 303 through the communication bus 304 and execute the computer program to implement the following method: Step S101: Perform channel estimation on the first reference signals at multiple pilot positions corresponding to the current channel to obtain channel estimation values corresponding to each pilot position respectively. Step S102: Determine the channel characteristics corresponding to the channel where the first reference signal is located according to the channel estimation values, and determine multiple initial filter orders according to the second reference signal. Step S103: Determine the target filter order from the multiple initial filter orders by using the channel characteristics. Step S104: Determine the target filter matrix according to the target filter order, and use the target filter matrix to filter the channel estimation values and interpolate non-pilot positions to obtain the target channel estimation result.

[0106] Among them, the processor 301 includes one or more, which may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 301 may be a general-purpose processor, including a central processing unit (CPU), a microcontroller unit (MCU), a network processor (NP), or other conventional processors; it may also be a dedicated processor, including a neural-network processing unit (NPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Moreover, when there are multiple processor 301s, a part of them may be general-purpose processors, and another part may be dedicated processors.

[0107] The memory 303 includes one or more, which may be, but are not limited to, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0108] It can be understood that Figure 3 the structure shown is only schematic, and the electronic device 300 may also include more or fewer components than Figure 3 those shown in Figure 3 or have a different configuration from Figure 3Each component shown in the figure may be implemented by hardware, software, or a combination thereof. In the embodiments of the present application, the electronic device 300 may be, but is not limited to, physical devices such as desktop computers, laptop computers, smart phones, smart wearable devices, in-vehicle devices, etc., and may also be virtual devices such as virtual machines. Additionally, the electronic device 300 does not necessarily have to be a single device, and may also be a combination of multiple devices, such as a server cluster, etc.

[0109] The embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium stores computer program instructions. When the computer program instructions are run by a computer, the computer is caused to execute the channel estimation method described in the foregoing method embodiments.

[0110] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods may be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another 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 displayed or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces. The indirect coupling or communication connection of the devices or units may be in an electrical, mechanical, or other form.

[0111] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] Furthermore, in each embodiment of the present application, the various functional modules may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0113] It should be noted that if a function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0114] In this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0115] The above are only embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A channel estimation method, characterized in that, Including: Performing channel estimation on first reference signals at multiple pilot positions corresponding to a current channel to obtain channel estimation values respectively corresponding to each pilot position; wherein, the multiple pilot positions have the same beam and node positions; Determining channel characteristics corresponding to the channel where the first reference signal is located according to the channel estimation values, and determining a plurality of initial filtering orders according to second reference signals; wherein, the number of the initial filtering orders is not greater than the number of the pilot positions; Determining a target filtering order from the plurality of initial filtering orders by using the channel characteristics; Determining a target filtering matrix according to the target filtering order, and filtering the channel estimation values and interpolating non-pilot positions by using the target filtering matrix to obtain a target channel estimation result.

2. The channel estimation method according to claim 1, characterized in that The channel characteristics include a covariance matrix, and the determining a target filtering order from the plurality of initial filtering orders by using the channel characteristics includes: Calculating correlation coefficient errors corresponding to the plurality of initial filtering orders according to the covariance matrix corresponding to the channel where the first reference signal is located and the covariance matrix corresponding to the channel where the second reference signal is located; Determining the maximum value among the initial filtering orders with correlation coefficient errors not greater than an error threshold as the target filtering order.

3. The channel estimation method according to claim 1, wherein The channel characteristics include root mean square delay spread, and the determining a target filtering order from the plurality of initial filtering orders by using the channel characteristics includes: Determining a difference between the root mean square delay spread corresponding to the channel where the first reference signal is located and the root mean square delay spread corresponding to the channel where the second reference signal is located; Determining the initial filtering order corresponding to the difference as the target filtering order; wherein, each initial filtering order corresponds to a difference range.

4. The channel estimation method according to claim 1, characterized in that The determining a target filtering order from the plurality of initial filtering orders by using the channel characteristics includes: Inputting the channel estimation values into a pre-trained neural network to obtain the target filtering order output by the neural network; wherein, the neural network extracts channel characteristics corresponding to the channel where the first reference signal is located based on the channel estimation values, and determines the target filtering order based on the channel characteristics.

5. The channel estimation method according to claim 1, wherein The channel characteristics include a covariance matrix and root mean square delay, and the determining a target filtering order from the plurality of initial filtering orders by using the channel characteristics includes: Determining an intermediate filtering order corresponding to each channel characteristic according to each channel characteristic; Determining the minimum value among the intermediate filtering orders as the target filtering order.

6. The channel estimation method according to claim 1, wherein The channel estimation values include multiple groups of channel estimation sub-values, wherein each group of channel estimation sub-values is obtained by performing channel estimation on a plurality of PRGs; The determining channel characteristics corresponding to the channel where the first reference signal is located according to the channel estimation values includes: For each group of channel estimation sub-values, determining channel sub-characteristics corresponding to the group of channel estimation sub-values according to the group of channel estimation sub-values; The determining a target filtering order from the plurality of initial filtering orders by using the channel characteristics includes: For each group of channel sub-characteristics, determining a target sub-filtering order corresponding to the group of channel sub-characteristics from the plurality of initial filtering orders; Determine the target filtering order corresponding to each channel estimation sub-value according to multiple target sub-filtering orders.

7. The channel estimation method according to claim 6, wherein The determining the target filtering order corresponding to each channel estimation sub-value according to multiple target sub-filtering orders includes: Determine the minimum value among the multiple target sub-filtering orders as the target filtering order corresponding to each channel estimation sub-value; Or, Determine the target sub-filtering order corresponding to the channel estimation sub-value with the largest number of pilot points as the target filtering order corresponding to each channel estimation sub-value; Or, Determine the target sub-filtering order of each group of channel estimation sub-values as its corresponding target filtering order.

8. A channel estimation device, characterized in that, Including: An estimation module, configured to perform channel estimation on the first reference signals at multiple pilot positions corresponding to the current channel, and obtain channel estimation values corresponding to each pilot position respectively; wherein, the multiple pilot positions have the same beam and node positions; A first determination module, configured to determine the channel characteristics corresponding to the channel where the first reference signal is located according to the channel estimation value, and determine multiple initial filtering orders according to the second reference signal; wherein, the number of the initial filtering orders is not greater than the number of the pilot positions; A second determination module, configured to determine the target filtering order from the multiple initial filtering orders by using the channel characteristics; A third determination module, configured to determine a target filtering matrix according to the target filtering order, and filter the channel estimation value and interpolate non-pilot positions by using the target filtering matrix to obtain a target channel estimation result.

9. An electronic device, characterized in that, Including: A processor, a memory, and a bus; The processor and the memory communicate with each other through the bus; The memory stores computer program instructions executable by the processor, and the processor can execute the channel estimation method according to any one of claims 1-7 by invoking the computer program instructions.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, and when the computer program instructions are run by the computer, the computer is made to execute the channel estimation method according to any one of claims 1-7.

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