Method, device, storage medium and electronic device for estimating pilot position channel

Abstract

The present disclosure relates to a method, device, storage medium and electronic device for estimating a pilot position channel, the method comprising: obtaining a frequency domain channel estimation result of the pilot position channel; determining the noise power corresponding to the frequency domain channel estimation result; obtaining a time-frequency domain autocorrelation matrix corresponding to the pilot position channel, and calculating a filter coefficient according to the time-frequency domain autocorrelation matrix and the noise power; and calculating the time-frequency domain channel estimation result of the pilot position channel according to the filter coefficient and the frequency domain channel estimation result. The time-frequency domain channel estimation result of the pilot position channel is calculated based on the frequency domain channel estimation result and the noise power of the pilot position channel, so that the noise power only needs to be estimated once. Compared with performing two noise estimations in the related art, the number of calculations is reduced, the calculation error is further reduced, and the performance can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of communications, and in particular to a method, device, storage medium and electronic device for estimating a pilot position channel. Background Art

[0002] OFDM (Orthogonal Frequency Division Multiplexing) technology is a representative technology in multi-carrier technology. In OFDM technology, a given channel is divided into many orthogonal sub-channels in the frequency domain, and the sub-carrier spectrum is allowed to partially overlap. As long as the sub-carriers are mutually orthogonal, the data signal can be separated from the aliased sub-carriers. OFDM technology has the ability to combat inter-symbol interference and can provide high spectrum efficiency. Therefore, it is regarded as one of the most likely transmission technologies to be adopted by the next generation of wireless mobile communication systems. OFDM technology has been widely used in many fields such as digital subscriber loop, digital audio / video broadcasting, wireless local area network and wireless metropolitan area network.

[0003] In order to ensure that the communication system has good performance in the wireless mobile channel environment, it is necessary to estimate the time-varying multipath wireless fading channel as accurately as possible, which is particularly difficult in the case of high-speed movement. In OFDM systems, in order to improve the transmission rate and quality, coherent demodulation is generally used, which requires effective channel estimation. The performance of the channel estimation module directly affects the bit error rate performance of the entire OFDM system. It can be considered that the quality of channel estimation plays a key role in the performance of the OFDM system.

[0004] Currently, a carrier-assisted modulation channel estimation method is usually used, that is, a pilot signal is inserted into the transmitted data stream, the pilot is extracted at the receiving end, the channel response at the pilot position is obtained by calculation, and then the channel response at other positions without a pilot is estimated by interpolation. Summary of the invention

[0005] During the research on related technologies, the inventors found that in the related technologies, when calculating the channel response at the pilot position, the time domain data is processed first, and then the frequency domain data is processed, and finally the channel response result at the pilot position is obtained, wherein the processing of the time domain data includes filtering and interpolation, and the processing of the frequency domain data also includes filtering and interpolation. However, since the processing of the time domain data and the processing of the frequency domain data are implemented separately, it is necessary to estimate the noise once in the frequency domain direction and the time domain direction, resulting in high computational complexity of the channel estimation and insufficient channel estimation performance. In order to solve the above problems, the present disclosure provides a method, device, storage medium and electronic device for estimating a pilot position channel:

[0006] In a first aspect, a method for estimating a pilot position channel is provided, comprising:

[0007] Obtaining a frequency domain channel estimation result of a pilot position channel;

[0008] Determining a noise power corresponding to the frequency domain channel estimation result;

[0009] Acquire a time-frequency domain autocorrelation matrix corresponding to the pilot position channel, and calculate a filter coefficient according to the time-frequency domain autocorrelation matrix and the noise power;

[0010] The time-frequency domain channel estimation result of the pilot position channel is calculated according to the filter coefficient and the frequency-domain channel estimation result.

[0011] Optionally, obtaining a frequency domain channel estimation result of a pilot position channel includes:

[0012] A least squares channel estimation result of the pilot position channel is obtained.

[0013] Optionally, obtaining a time-frequency domain autocorrelation matrix corresponding to the pilot position channel includes:

[0014] Obtaining a frequency domain autocorrelation matrix and a time domain autocorrelation matrix of the pilot position channel;

[0015] The time-frequency domain autocorrelation matrix is ​​obtained by using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix.

[0016] Optionally, using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix to obtain the time-frequency domain autocorrelation matrix includes:

[0017] Calculating a first Kronecker product using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix;

[0018] The first Kronecker product is used as the time-frequency domain autocorrelation matrix.

[0019] Optionally, calculating a first Kronecker product using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix comprises:

[0020] Performing dimensionality reduction processing on the frequency domain autocorrelation matrix to obtain a dimensionality reduction matrix;

[0021] Calculating a second Kronecker product using the dimension reduction matrix and the time domain autocorrelation matrix;

[0022] The second Kronecker product is taken as the first Kronecker product.

[0023] Optionally, the frequency domain autocorrelation matrix is ​​subjected to dimensionality reduction processing to obtain a dimensionality reduction matrix, including:

[0024] Performing singular value decomposition on the frequency domain autocorrelation matrix to obtain a left singular value matrix, singular values ​​and a right singular value matrix;

[0025] The left singular value matrix, the singular values ​​and the right singular value matrix are used as the dimension reduction matrix.

[0026] Optionally, calculating a filter coefficient according to the time-frequency domain autocorrelation matrix and the noise power includes:

[0027] Based on the minimum mean square error criterion, the filter coefficient is calculated according to the time-frequency domain autocorrelation matrix and the noise power.

[0028] In a second aspect, a device for estimating a pilot position channel is provided, comprising:

[0029] An acquisition unit, used to acquire a frequency domain channel estimation result of a pilot position channel;

[0030] A determination unit, configured to determine a noise power corresponding to the frequency domain channel estimation result;

[0031] A processing unit, configured to obtain a time-frequency domain autocorrelation matrix corresponding to the pilot position channel, and calculate a filter coefficient according to the time-frequency domain autocorrelation matrix and the noise power;

[0032] A calculation unit is used to calculate the time-frequency domain channel estimation result of the pilot position channel according to the filter coefficient and the frequency-domain channel estimation result.

[0033] In a third aspect, an electronic device is provided, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0034] The memory is used to store computer programs;

[0035] The processor is used to execute the program stored in the memory to implement the pilot position channel estimation method described in the first aspect.

[0036] According to a fourth aspect, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the method for estimating the pilot position channel according to the first aspect is implemented.

[0037] The above technical solution provided by the embodiment of the present disclosure has the following advantages compared with the related art:

[0038] The technical solution provided by the embodiment of the present disclosure calculates the time-frequency domain channel estimation result of the pilot position channel based on the frequency domain channel estimation result and the noise power of the pilot position channel. Therefore, it is only necessary to estimate the noise power once. Compared with performing two noise estimations in the related art, the number of calculations is reduced, the calculation error is further reduced, and the performance can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 A schematic diagram of a flow chart of a method for estimating a pilot position channel in an embodiment of the present disclosure;

[0042] Figure 2 It is a flowchart of another method for estimating a pilot position channel in an embodiment of the present disclosure;

[0043] Figure 3 It is a flowchart of another method for estimating a pilot position channel in an embodiment of the present disclosure;

[0044] Figure 4 A schematic diagram of a flow chart of a method for estimating a pilot position channel in an embodiment of the present disclosure;

[0045] Figure 5 A schematic diagram of a flow chart of a method for estimating a pilot position channel in an embodiment of the present disclosure;

[0046] Figure 6 It is a structural schematic diagram of a device for estimating a pilot position channel in an embodiment of the present disclosure;

[0047] Figure 7 Schematic diagram of the structure of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0049] In OFDM systems, effective channel estimation is required to improve transmission rate and quality. Currently, considering the independence of channels in the time domain and frequency domain in OFDM systems, channel estimation can be decomposed into two parts: channel estimation in the frequency domain and channel estimation in the time domain.

[0050] Regardless of whether it is channel estimation in the frequency domain or channel estimation in the time domain, it includes two parts: one is the estimation of the reference signal channel, and the other is the interpolation of the data channel.

[0051] Among them, when estimating the reference signal (that is, pilot) part of the channel, the following channel model can be used:

[0052] Y p =X p H+W (1)

[0053] Where H is the channel response, X p is the signal sent by the transmitter at the pilot position, Y p is the pilot signal extracted by the receiving end from the received data stream, and W is the observation noise.

[0054] After the channel response at the pilot position is obtained, the channel response of the data portion channel can be obtained by combining the channel response at the pilot position with an interpolation method.

[0055] Based on the above analysis, it can be seen that when performing channel estimation, the noise needs to be estimated once in the time domain and once in the frequency domain, which leads to high computational complexity of channel estimation. In addition, since noise estimation needs to be performed twice, deviations are easily introduced, resulting in poor channel estimation performance.

[0056] In order to solve the above problem, an embodiment of the present disclosure provides a method for estimating a pilot position channel, which can be applied to an OFDM system.

[0057] like Figure 1 As shown, the method may specifically include the following steps:

[0058] Step 101: Obtain a frequency domain channel estimation result of a pilot position channel.

[0059] Optionally, the obtained frequency domain channel estimation result of the pilot position channel may be a least squares (LS, Least-Square) channel estimation result.

[0060] In this embodiment, the LS channel estimation result is to estimate the parameter H in formula (1) using the LS algorithm. According to the LS algorithm, it is assumed that the closer the output signal obtained after channel estimation is to the actual received signal, the smaller the error of the corresponding channel estimation value. Therefore, it is necessary to calculate the power difference between the signal received by the actual channel and the output signal after channel estimation. When the power difference is the smallest, the corresponding channel estimation value is the smallest, which is the channel estimation value of the LS algorithm. Therefore, the function in formula (2) is minimized:

[0061]

[0062] Among them, Y p is a vector composed of the received signals at the pilot subcarriers at the receiving end;

[0063] is the pilot output signal obtained after channel estimation;

[0064] is the estimated value of the channel response H, and J is the power difference.

[0065] Based on equation (2), we can obtain equation (3):

[0066]

[0067] Based on formula (3), the LS channel estimation result can be obtained as follows:

[0068]

[0069] Among them, H LS is the LS channel estimation result.

[0070] Step 102: Determine the noise power corresponding to the frequency domain channel estimation result.

[0071] In this embodiment, the noise power can be estimated by a power delay profile (PDP).

[0072] Optionally, when obtaining the noise power, the data in the frequency domain may be converted into the time domain. Therefore, after obtaining H, an inverse Fourier transform may be performed on H to obtain a time domain impulse response, and the time domain impulse response may be calculated to obtain the PDP.

[0073] The specific implementation process is as follows:

[0074] Using formula (5) to calculate H LSAdd 0 at the end to make N IFFT length;

[0075]

[0076] Among them, N RS -1 is the number of reference signals.

[0077] Where h is the channel estimation value in the time domain, IDFT is the inverse Fourier transform operation, and N IFFT is the number of sampling points for inverse Fourier transform.

[0078] According to formula (6), h is used to calculate the time domain impulse response P:

[0079] P(i)=|h(i)| 2 ,i=0..N IFFT -1 (6)

[0080] Wherein, h(i) is the channel estimation value of the i-th sampling point in the time domain after Fourier transform according to equation (5), and P(i) is the power value of the channel estimation corresponding to the i-th sampling point in the time domain.

[0081] Set the length to N IFFT The PDPs are divided into 16 groups, and the average power of the kth group is:

[0082]

[0083] Set the noise window width to ensure that W noise There is only noise in the group, and the effective signal can only be concentrated on the remaining 16-W noise Within the group, as long as W noise The internal average power can be regarded as the noise power.

[0084]

[0085] i noise =argminPow(i) (9)

[0086] P noise =Pow(i noise ) (10)

[0087] Among them, W noise To make the length N IFFT After the PDPs are divided into 16 groups, the number of groups with only noise;

[0088] Pow(i) is the power of the i-th group;

[0089] i noiseis the index of the group corresponding to the smallest Pow(i) among the 16 Pow(i) obtained in formula (8);

[0090] P noise is the noise power, that is, the noise power is the average power of the group with the smallest power value.

[0091] Step 103: Obtain a time-frequency domain autocorrelation matrix corresponding to the pilot position channel, and calculate a filter coefficient according to the time-frequency domain autocorrelation matrix and noise power.

[0092] Optionally, the filter coefficient may be determined according to a minimum mean-square error (MMSE) criterion, and the determined filter coefficient may be:

[0093] W=R(R+σ 2 I) -1 (11)

[0094] Among them, W is the filter coefficient, R is the time-frequency domain autocorrelation matrix, σ 2 is the noise power, and I is the unit matrix. In which, combined with formula (10), σ 2 P noise .

[0095] In order to reduce the amount of calculation, in this embodiment, the time-frequency domain autocorrelation matrix can be represented by a frequency domain autocorrelation matrix and a time domain autocorrelation matrix. In specific implementation, an optional implementation method is as follows: Figure 2 As shown, step 103 may include:

[0096] Step 201: Obtain a frequency domain autocorrelation matrix and a time domain autocorrelation matrix of a pilot position channel.

[0097] Among them, the autocorrelation matrix is ​​a matrix in which the original matrix and the correlation matrix are the same matrix.

[0098] Specifically, for the autocorrelation matrix, the element in the i-th row and j-th column of the correlation matrix is ​​the correlation coefficient between the i-th column and the j-th column of the original matrix.

[0099] Moreover, the autocorrelation matrix is ​​a conjugate symmetric positive definite Toeplitz matrix.

[0100] Step 202: Obtain a time-frequency domain autocorrelation matrix using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix.

[0101] Considering the orthogonality of the time domain correlation matrix and the frequency domain correlation matrix, when the time-frequency domain autocorrelation matrix is ​​obtained by using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix, as Figure 3 As shown, step 202 may include:

[0102] Step 301: Calculate the first Kronecker product using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix.

[0103] Optionally, step 301 may be expressed as:

[0104]

[0105] Among them, R is the time-frequency domain autocorrelation matrix, R F is the frequency domain autocorrelation matrix of the pilot position channel, R T is the time domain autocorrelation matrix of the pilot position channel, stands for Kronecker product.

[0106] Step 302: Use the first Kronecker product as the time-frequency domain autocorrelation matrix.

[0107] Since the Kronecker product makes R T With R F The dimensions of R are extended together, which will result in a huge amount of calculation. In order to reduce the amount of calculation, this embodiment considers T and / or R F Simplify.

[0108] Optionally, taking into account R T is a real matrix, and since the number of symbols at the pilot position in the time domain is not large (no more than 4), R T is a real matrix with a dimension not exceeding 4×4, so we can mainly consider R F Simplification of the matrix. Figure 4 As shown, step 301 may include:

[0109] Step 401: Perform dimensionality reduction processing on the frequency domain autocorrelation matrix to obtain a dimensionality reduction matrix.

[0110] Step 402: Calculate the second Kronecker product using the dimension reduction matrix and the time domain autocorrelation matrix.

[0111] Step 403: Use the second Kronecker product as the first Kronecker product.

[0112] Optionally, this embodiment provides a method for implementing dimensionality reduction processing on the frequency domain autocorrelation matrix, such as Figure 5 As shown, step 401 may include:

[0113] Step 501: Perform singular value decomposition on the frequency domain autocorrelation matrix to obtain a left singular value matrix, singular values, and a right singular value matrix.

[0114] Optionally, for R F If we perform SVD decomposition (Singular Value Decomposition) on the matrix, we will get:

[0115]

[0116] Among them, U F is a unitary matrix, satisfying U F and Orthogonal, that is

[0117] Optionally, U F For a triangular array, is the orthogonal matrix of the triangular matrix, V F It is a diagonal matrix.

[0118] Step 502: Use the left singular value matrix, the singular values ​​and the right singular value matrix as dimension reduction matrices.

[0119] Step 104: Calculate the time-frequency domain channel estimation result of the pilot position channel according to the filter coefficient and the frequency-domain channel estimation result.

[0120] In this embodiment, the calculated time-frequency domain channel estimation result may be a time-frequency domain channel estimation result based on a minimum mean square error (MMSE) criterion.

[0121] Optionally, the formula that can be used when calculating the time-frequency domain channel estimation result according to the filter coefficient based on the minimum mean square error criterion and the frequency domain channel estimation result is:

[0122]

[0123] in, is the estimation result based on the minimum mean square error criterion.

[0124] In the technical solution provided by the embodiments of the present disclosure, since the time-frequency domain channel estimation result of the pilot position channel is calculated based on the frequency domain channel estimation result and the noise power of the pilot position channel, it is only necessary to estimate the noise power once. Compared with performing noise estimation twice in the related technology, the number of calculations is reduced, the calculation error is further reduced, and the performance is improved.

[0125] Exemplarily, the following calculation process for calculating the minimum mean square error estimation result is provided:

[0126]

[0127]

[0128] Based on the same concept, an estimation device for a pilot position channel is provided in an embodiment of the present disclosure. The specific implementation of the device can be found in the description of the method embodiment part, and the repeated parts will not be repeated. Figure 6 As shown, the device mainly includes:

[0129] An acquisition unit 601 is used to acquire a frequency domain channel estimation result of a pilot position channel;

[0130] A determination unit 602, configured to determine a noise power corresponding to a frequency domain channel estimation result;

[0131] The processing unit 603 is used to obtain a time-frequency domain autocorrelation matrix corresponding to the pilot position channel, and calculate a filter coefficient according to the time-frequency domain autocorrelation matrix and noise power;

[0132] The calculation unit 604 is used to calculate the time-frequency domain channel estimation result of the pilot position channel according to the filter coefficient and the frequency-domain channel estimation result.

[0133] Optionally, the acquiring unit 601 is used to:

[0134] Get the least squares channel estimation result of the pilot position channel.

[0135] Optionally, the processing unit 603 is configured to:

[0136] Obtaining a frequency domain autocorrelation matrix and a time domain autocorrelation matrix of the pilot position channel;

[0137] The time-frequency domain autocorrelation matrix is ​​obtained by using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix.

[0138] Optionally, the processing unit 603 is configured to:

[0139] Using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix, calculate the first Kronecker product;

[0140] The first Kronecker product is taken as the time-frequency domain autocorrelation matrix.

[0141] Optionally, the processing unit 603 is configured to:

[0142] Perform dimensionality reduction processing on the frequency domain autocorrelation matrix to obtain a dimensionality reduction matrix;

[0143] Using the dimension reduction matrix and the time domain autocorrelation matrix, calculate the second Kronecker product;

[0144] The second Kronecker product is taken as the first Kronecker product.

[0145] Optionally, the processing unit 603 is configured to:

[0146] Perform singular value decomposition on the frequency domain autocorrelation matrix to obtain a left singular value matrix, singular values, and a right singular value matrix;

[0147] The left singular value matrix, singular values, and right singular value matrix are used as dimensionality reduction matrices.

[0148] Optionally, the calculation unit 604 is used to:

[0149] Based on the minimum mean square error criterion, the filter coefficients are calculated according to the time-frequency domain autocorrelation matrix and the noise power.

[0150] Based on the same concept, an electronic device is also provided in the embodiment of the present disclosure, such as Figure 7 As shown, the electronic device mainly includes: a processor 701, a communication interface 702, a memory 703 and a communication bus 704, wherein the processor 701, the communication interface 702 and the memory 703 communicate with each other through the communication bus 704. The memory 703 stores a program that can be executed by the processor 701, and the processor 701 executes the program stored in the memory 703 to implement the following steps:

[0151] Obtaining a frequency domain channel estimation result of a pilot position channel;

[0152] Determining a noise power corresponding to a frequency domain channel estimation result;

[0153] Obtaining a time-frequency domain autocorrelation matrix corresponding to the pilot position channel, and calculating a filter coefficient based on the time-frequency domain autocorrelation matrix and noise power;

[0154] The time-frequency domain channel estimation result of the pilot position channel is calculated based on the filter coefficient and the frequency-domain channel estimation result.

[0155] The communication bus 704 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0156] The communication interface 702 is used for communication between the electronic device and other devices.

[0157] The memory 703 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Alternatively, the memory may also be at least one storage device located away from the processor 701.

[0158] The above-mentioned processor 701 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., and can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0159] In another embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed on a computer, the computer executes the pilot position channel estimation method described in the above embodiment.

[0160] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instruction is loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instruction is transmitted from a website site, a computer, a server or a data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a tape, etc.), an optical medium (such as a DVD) or a semiconductor medium (such as a solid-state hard disk), etc.

[0161] It should be noted that, in this article, 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 such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0162] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for estimating a pilot position channel, characterized in that: include: Obtaining a frequency domain channel estimation result of a pilot position channel; Determining a noise power corresponding to the frequency domain channel estimation result; Acquire a time-frequency domain autocorrelation matrix corresponding to the pilot position channel, and calculate a filter coefficient according to the time-frequency domain autocorrelation matrix and the noise power; Calculate the time-frequency domain channel estimation result of the pilot position channel according to the filter coefficient and the frequency-domain channel estimation result; The obtaining of a time-frequency domain autocorrelation matrix corresponding to the pilot position channel comprises: Obtaining a frequency domain autocorrelation matrix and a time domain autocorrelation matrix of the pilot position channel; Using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix, the time-frequency domain autocorrelation matrix is ​​obtained; The step of obtaining the time-frequency domain autocorrelation matrix by using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix includes: Calculating a first Kronecker product using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix; Using the first Kronecker product as the time-frequency domain autocorrelation matrix; The step of calculating the first Kronecker product by using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix comprises: Performing dimensionality reduction processing on the frequency domain autocorrelation matrix to obtain a dimensionality reduction matrix; Calculating a second Kronecker product using the dimension reduction matrix and the time domain autocorrelation matrix; The second Kronecker product is taken as the first Kronecker product.

2. The method according to claim 1, characterized in that The obtaining of the frequency domain channel estimation result of the pilot position channel comprises: A least squares channel estimation result of the pilot position channel is obtained.

3. The method according to claim 1, characterized in that The dimensionality reduction processing of the frequency domain autocorrelation matrix to obtain a dimensionality reduction matrix includes: Performing singular value decomposition on the frequency domain autocorrelation matrix to obtain a left singular value matrix, singular values ​​and a right singular value matrix; The left singular value matrix, the singular values ​​and the right singular value matrix are used as the dimension reduction matrix.

4. The method according to any one of claims 1 to 3, characterized in that: The step of calculating the filter coefficient according to the time-frequency domain autocorrelation matrix and the noise power comprises: Based on the minimum mean square error criterion, the filter coefficient is calculated according to the time-frequency domain autocorrelation matrix and the noise power.

5. A device for estimating a pilot position channel, characterized in that: include: An acquisition unit, used to acquire a frequency domain channel estimation result of a pilot position channel; A determination unit, configured to determine a noise power corresponding to the frequency domain channel estimation result; A processing unit, configured to obtain a time-frequency domain autocorrelation matrix corresponding to the pilot position channel, and calculate a filter coefficient according to the time-frequency domain autocorrelation matrix and the noise power; The obtaining of a time-frequency domain autocorrelation matrix corresponding to the pilot position channel comprises: Obtaining a frequency domain autocorrelation matrix and a time domain autocorrelation matrix of the pilot position channel; Using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix, the time-frequency domain autocorrelation matrix is ​​obtained; The step of obtaining the time-frequency domain autocorrelation matrix by using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix includes: Calculating a first Kronecker product using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix; Using the first Kronecker product as the time-frequency domain autocorrelation matrix; The step of calculating the first Kronecker product by using the frequency domain autocorrelation matrix and the time domain autocorrelation matrix comprises: Performing dimensionality reduction processing on the frequency domain autocorrelation matrix to obtain a dimensionality reduction matrix; Calculating a second Kronecker product using the dimension reduction matrix and the time domain autocorrelation matrix; Taking the second Kronecker product as the first Kronecker product; A calculation unit is used to calculate the time-frequency domain channel estimation result of the pilot position channel according to the filter coefficient and the frequency-domain channel estimation result.

6. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; The memory is used to store computer programs; The processor is used to execute the program stored in the memory to implement the pilot position channel estimation method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for estimating a pilot position channel according to any one of claims 1 to 4 is implemented.

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