A wireless signal channel estimation method, device, apparatus and storage medium

Abstract

The embodiment of the application provides a wireless signal channel estimation method, device, equipment and storage medium, the method comprises the following steps: obtaining the corresponding pilot signal column vector Y from the receiving signal of the receiving antenna M according to the pilot subcarrier distribution mode of the sending antenna N NM ; obtaining the channel conversion matrix of the pilot subcarrier of the sending antenna N from the time domain channel to the frequency domain; obtaining the pilot diagonal matrix X of the sending antenna N N ; estimating the time domain channel from the sending antenna N to the receiving antenna M according to the pilot signal column vector Y NM , the channel conversion matrix and the pilot diagonal matrix X N ; obtaining the frequency domain estimation of the effective subcarrier from the sending antenna N to the receiving antenna M according to the time domain channel estimation column vector. The embodiment of the application can be used for OFDM or SC-FDMA waveform channel estimation, and the LS estimation is used in the time domain, so that the problem that the edge subcarrier estimation is inaccurate is solved.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a wireless signal channel estimation method, apparatus, device, and storage medium. Background Technology

[0002] There are various existing methods for wireless signal channel estimation, such as LS estimation and transform domain noise suppression estimation, MMSE estimation, etc.

[0003] MMSE estimation is highly complex and relies on the correlation coefficients between different subcarriers. There are various methods for obtaining these correlation coefficients, some based on channel models. However, channel models may not accurately reflect real-world scenarios, leading to performance degradation.

[0004] Although LS estimation does not require a channel module, its performance is very poor at low signal-to-noise ratios. Transform domain suppression improves the performance, but it has a significant impact on edge subcarriers, resulting in inaccurate estimations and requiring additional processing. Summary of the Invention

[0005] In view of this, embodiments of this application provide a wireless signal channel estimation method, apparatus, device, and storage medium that can be used for OFDM or SC-FDMA waveform channel estimation. By employing LS estimation in the time domain, it not only solves the problem of inaccurate estimation of edge subcarriers, but also achieves channel estimation performance for other subcarriers that is comparable to existing frequency domain noise-suppressed LS estimation.

[0006] In a first aspect, embodiments of this application provide a wireless signal channel estimation method, wherein the pilot subcarriers of the wireless signal are arranged in a comb pattern among the effective subcarriers. The method includes: obtaining a corresponding pilot signal column vector Y from the received signal of the receiving antenna M according to the pilot subcarrier distribution pattern of the transmitting antenna N. NM Each row of this vector corresponds to a pilot subcarrier; the channel transformation matrix of the pilot subcarriers of the transmitting antenna N from the time domain to the frequency domain is obtained. Each row of this matrix corresponds to a pilot subcarrier, and its width is the length of the cyclic prefix; thus, the pilot diagonal matrix X of the transmitting antenna N is obtained. N According to the pilot signal column vector Y NM Channel conversion matrix and pilot diagonal array X N The time-domain channel from the transmitting antenna N to the receiving antenna M is estimated to obtain the corresponding time-domain channel estimation column vector. Based on the time-domain channel estimation column vector Obtain the frequency domain estimate of the effective subcarriers from the transmitting antenna N to the receiving antenna M.

[0007] As described above, by using LS estimation in the time domain to first obtain the time domain channel estimate, and then using this estimate to obtain the frequency domain channel estimate, not only is the problem of inaccurate estimation of edge subcarriers solved, but the channel estimation performance for other subcarriers is also comparable to that of existing frequency domain noise-suppressed LS estimation.

[0008] In one possible implementation of the first aspect, the method based on the pilot signal column vector Y NM Channel conversion matrix and pilot diagonal array X N The time-domain channel from the transmitting antenna N to the receiving antenna M is estimated to obtain the corresponding time-domain channel estimation column vector. Includes: channel transformation matrix and pilot diagonal array X N The product matrix is ​​decomposed using Singular Value Decomposition (SVD) to obtain singular values ​​greater than a set value, and an estimated matrix G of the pseudo-inverse matrix of the product matrix is ​​constructed based on these values. N Based on the estimated matrix G of the pseudo-inverse matrix N and pilot signal column vector Y NM The product matrix yields the corresponding time-domain channel estimation column vector.

[0009] Based on the above, the time-domain channel estimation column vector is obtained using the above method. This is used to obtain better frequency domain channel estimation.

[0010] In one possible implementation of the first aspect, the result of the SVD decomposition is U∑V H The estimated matrix G of the pseudo-inverse matrix N for The first K diagonal elements are The rest are 0, σ1, σ2, ..., σ K These are the singular values ​​of the first K diagonal elements, respectively, and are greater than the set value.

[0011] Based on the above method, non-zero singular values ​​greater than a set value are obtained, thereby obtaining an accurate time-domain channel estimation column vector.

[0012] In one possible implementation of the first aspect, the column vector is estimated based on the time-domain channel. Obtaining the frequency domain estimate of the effective subcarriers from the transmitting antenna N to the receiving antenna M includes: in the time domain channel estimation column vector Pad with zeros to make its length N FFT N FFT The number of points in the FFT / IFFT operation of the wireless signal is greater than the number of effective subcarriers of the wireless signal; the zero-padded column vector Perform N FFT The point FFT transform is performed, and the frequency domain estimate is obtained from the data of the effective subcarriers in the transform result.

[0013] As shown above, by padding the time-domain channel estimation column vector with zeros and converting it to the frequency domain, an accurate frequency domain estimate can be obtained.

[0014] In one possible implementation of the first aspect, the method of obtaining the channel transformation matrix of the pilot subcarrier from the time domain channel to the frequency domain is... Includes: obtaining N FFT DFT matrix F, N FFT The number of points in the FFT / IFFT operation of the wireless signal is greater than the number of effective subcarriers of the wireless signal. The first row of the matrix corresponds to the DC subcarrier, and each row from the second row onwards corresponds to one subcarrier; from N... FFT The channel transformation matrix is ​​formed by taking the number of preceding cyclic prefixes of each row of the pilot subcarriers corresponding to the transmit antenna N from the DFT matrix F.

[0015] From the above, through N FFT The DFT matrix F yields the channel transformation matrix from the time domain to the frequency domain for pilot subcarriers of different transmit antennas, which can be used to achieve LS estimation in the time domain.

[0016] In one possible implementation of the first aspect, the corresponding pilot signal column vector Y is obtained from the received signal from the receiving antenna M according to the pilot subcarrier distribution of the transmitting antenna N. NM This includes: after synchronizing, frequency offset compensating, and removing CP from the signal received by the receiving antenna M, obtaining the effective signal of each pilot symbol according to the pilot subcarrier distribution on antenna N; performing an FFT transform on the effective signal, obtaining the frequency domain signal on the pilot subcarrier from the transform result, and forming a pilot signal column vector Y. NM .

[0017] As described above, after synchronization, frequency offset compensation, and CP removal, the pilot signal column vector is obtained for accurate channel estimation.

[0018] In one possible implementation of the first aspect, it further includes: acquiring system parameters of the wireless signal, which include one of the following: FFT / IFFT point count N. FFT Number of cyclic prefix points N CP Number of effective subcarriers N use Pilot length N P N FFT It is greater than the number of effective subcarriers of the wireless signal.

[0019] The above describes how system parameters of wireless signals are obtained to accurately acquire various matrices and vectors.

[0020] Secondly, embodiments of this application provide a wireless signal channel estimation apparatus, comprising: a pilot receiving module, configured to obtain a corresponding pilot signal column vector Y from the received signal of the receiving antenna M according to the pilot subcarrier distribution of the transmitting antenna N. NM Each row of this vector corresponds to a pilot subcarrier; the matrix acquisition module is used to obtain the pilot diagonal matrix X of the transmitting antenna N. N Channel transformation matrix from time domain to frequency domain for pilot subcarriers Channel conversion matrix Each row corresponds to a pilot subcarrier, with a width equal to the length of the cyclic prefix; the time-domain estimation module is used to estimate the pilot signal column vector Y. NM Channel conversion matrix and pilot diagonal array X N The time-domain channel from the transmitting antenna N to the receiving antenna M is estimated to obtain the corresponding time-domain channel estimation column vector. The frequency domain estimation module is used to estimate column vectors based on the time-domain channel. Obtain the frequency domain estimate of the effective subcarriers from the transmitting antenna N to the receiving antenna M.

[0021] As described above, by using LS estimation in the time domain, the time domain channel estimate is obtained first, and then the frequency domain channel estimate is obtained accordingly. This not only solves the problem of inaccurate estimation of edge subcarriers, but also achieves channel estimation performance for other subcarriers that is comparable to existing frequency domain noise-suppressing LS estimation.

[0022] In one possible implementation of the second aspect, the time-domain estimation module is specifically used for: adjusting the channel transformation matrix. and pilot diagonal array X N The product matrix is ​​decomposed using Singular Value Decomposition (SVD) to obtain singular values ​​greater than a set value, and an estimated matrix G of the pseudo-inverse matrix of the product matrix is ​​constructed based on these values. N Based on the estimated matrix G of the pseudo-inverse matrix N and pilot signal column vector Y NM The product matrix yields the corresponding time-domain channel estimation column vector.

[0023] Based on the above, the time-domain channel estimation column vector is obtained using the above method. This is used to obtain better frequency domain channel estimation.

[0024] In one possible implementation of the second aspect, the result of the SVD decomposition is U∑V H The estimated matrix G of the pseudo-inverse matrix N for The first K diagonal elements are The rest are 0, σ1, σ2, ..., σ K These are the singular values ​​of the first K diagonal elements, respectively, and are greater than the set value.

[0025] Based on the above method, non-zero singular values ​​greater than a set value are obtained, and the time-domain channel estimation column vector is obtained accordingly.

[0026] In one possible implementation of the second aspect, the frequency domain estimation module is specifically used to include: estimating the time domain channel column vector. Pad with zeros to make its length N FFT N FFT The number of points in the FFT / IFFT operation of the wireless signal is greater than the number of effective subcarriers of the wireless signal; the zero-padded column vector Perform an NFFT-point FFT transform and obtain the frequency domain estimate based on the data of the effective subcarriers from the transform result.

[0027] As shown above, by padding the time-domain channel estimation column vector with zeros and converting it to the frequency domain, an accurate frequency domain estimate can be obtained.

[0028] In one possible implementation of the second aspect, the matrix acquisition module obtains the channel transformation matrix of the pilot subcarrier from the time-domain channel to the frequency domain. Includes: obtaining N FFT DFT matrix F, N FFT The number of points in the FFT / IFFT operation of the wireless signal is greater than the number of effective subcarriers of the wireless signal. The first row of the matrix corresponds to the DC subcarrier, and each row from the second row onwards corresponds to one subcarrier; from N... FFT The channel transformation matrix is ​​formed by taking the number of preceding cyclic prefixes of each row of the pilot subcarriers corresponding to the transmit antenna N from the DFT matrix F.

[0029] From the above, through N FFT The DFT matrix F yields the channel transformation matrix from the time domain to the frequency domain for pilot subcarriers of different transmit antennas, which can be used to achieve LS estimation in the time domain.

[0030] In one possible implementation of the second aspect, the pilot receiving module is specifically used to: synchronize, compensate for frequency offset, and remove CP from the signal received by the receiving antenna M; obtain the effective signal of each pilot symbol according to the pilot subcarrier distribution on the antenna N; perform FFT transformation on the effective signal; obtain the frequency domain signal on the pilot subcarrier from the transformation result; and form a pilot signal column vector Y. NM .

[0031] As described above, after synchronization, frequency offset compensation, and CP removal, the pilot signal column vector is obtained for accurate channel estimation.

[0032] In one possible implementation of the second aspect, it further includes: a parameter acquisition module, configured to acquire system parameters of the wireless signal, which include one of the following: FFT / IFFT point count N. FFT Number of cyclic prefix points N CP Number of effective subcarriers N use Pilot length N P N FFT It is greater than the number of effective subcarriers of the wireless signal.

[0033] The above describes how system parameters of wireless signals are obtained to accurately acquire various matrices and vectors.

[0034] Thirdly, embodiments of this application provide a computing device, including,

[0035] bus;

[0036] A communication interface, which is connected to the bus;

[0037] At least one processor connected to the bus; and

[0038] At least one memory is connected to the bus and stores program instructions that, when executed by the at least one processor, cause the at least one processor to perform any of the embodiments described in the first aspect of this application.

[0039] Fourthly, embodiments of this application provide a computer-readable storage medium having program instructions stored thereon, which, when executed by a computer, cause the computer to perform any of the embodiments described in the first aspect. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the comb-like arrangement of wireless signal pilot subcarriers in the frequency domain, according to an embodiment of a wireless signal channel estimation method of this application.

[0041] Figure 2 This is a flowchart illustrating an embodiment of a wireless signal channel estimation method according to this application;

[0042] Figure 3 This is a flowchart illustrating a second embodiment of a wireless signal channel estimation method according to this application.

[0043] Figure 4 This is a schematic diagram of the comb-like arrangement of wireless signal pilot subcarriers in the frequency domain, according to Embodiment 2 of a wireless signal channel estimation method of this application.

[0044] Figure 5 This is a schematic diagram of the structure of a wireless signal channel estimation device according to a first embodiment of the present application;

[0045] Figure 6 This is a schematic diagram of a second embodiment of a wireless signal channel estimation device according to this application;

[0046] Figure 7 This is a schematic diagram of the structure of a computing device according to various embodiments of this application. Detailed Implementation

[0047] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0048] In the following description, the terms “first, second, third, etc.” or module A, module B, module C, etc. are used only to distinguish similar objects or different embodiments, and do not represent a specific ordering of objects. It is understood that a specific order or sequence may be interchanged where permitted so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0049] In the following description, the labels of the steps, such as S110, S120, etc., do not necessarily mean that the steps will be executed in this way. The order of the steps can be interchanged or executed simultaneously if permitted.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0051] This application provides a wireless signal channel estimation method, apparatus, device, and storage medium. The method includes: obtaining a corresponding pilot signal column vector Y from the received signal of the receiving antenna M according to the pilot subcarrier distribution of the transmitting antenna N. NM Each row of this vector corresponds to a pilot subcarrier; the channel transformation matrix of the pilot subcarriers of the transmitting antenna N from the time domain to the frequency domain is obtained. Each row of this matrix corresponds to a pilot subcarrier, and its width is the length of the cyclic prefix; thus, the pilot diagonal matrix X of the transmitting antenna N is obtained. N According to the pilot signal column vector Y NM Channel conversion matrix and pilot diagonal array X NThe time-domain channel from the transmitting antenna N to the receiving antenna M is estimated to obtain the corresponding time-domain channel estimation column vector. Based on the time-domain channel estimation column vector Obtain the frequency domain estimate of the effective subcarriers from the transmitting antenna N to the receiving antenna M.

[0052] The technical solution of this application embodiment is used for OFDM or SC-FDMA waveform channel estimation, which is suitable for both SISO and MIMO scenarios. It uses LS estimation in the time domain, which not only solves the problem of inaccurate estimation of edge subcarriers, but also has channel estimation performance for other subcarriers that is comparable to existing frequency domain noise-suppressed LS estimation.

[0053] The embodiments of this application are described below with reference to the accompanying drawings. First, according to... Figure 1 and Figure 2 This paper introduces an embodiment of a wireless signal channel estimation method.

[0054] In one embodiment of a wireless signal channel estimation method, the subcarriers of the wireless signal include effective subcarriers, DC subcarriers, and guard subcarriers. The DC subcarrier is in the middle and does not transmit signals; the effective subcarriers are on both sides of the DC subcarrier and are used to transmit data and pilot signals; the guard subcarriers are located to the left of the low-frequency subcarriers and to the right of the high-frequency subcarriers; the pilot signals are arranged in a comb pattern in the frequency domain.

[0055] The system parameters and symbols for wireless signals are explained below.

[0056] parameter symbol FFT / IFFT points <![CDATA[N FFT ]]> Cyclic prefix length / number of points <![CDATA[N CP ]]> Number of effective subcarriers <![CDATA[N use ]]> pilot length <![CDATA[N p ]]>

[0057] Where, N FFT Greater than N use N use Greater than N p The number of left-side (low-frequency) guard subcarriers is... The number of guard subcarriers on the right (high frequency) is

[0058] Figure 1 This diagram illustrates a comb-like arrangement of wireless signal pilot subcarriers in the frequency domain (guard subcarriers are not shown) according to an embodiment of a wireless signal channel estimation method. DC subcarriers are DC subcarriers, without signal or pilot signals; the left side represents negative frequencies, and the right side represents positive frequencies. Each side has... One effective subcarrier.

[0059] p(n) (1≤n≤Np) is a pilot sequence, mapped onto subcarriers sequentially from low to high frequency, arranged in a comb pattern. For example, That is, the number of pilot subcarriers is half the number of effective subcarriers, and they are distributed at intervals among the effective subcarriers.

[0060] Figure 2The flowchart of a wireless signal channel estimation method according to the present application is shown, including steps S120 to S140.

[0061] S110: Obtain the corresponding pilot signal column vector Y from the received signal from the receiving antenna M according to the pilot subcarrier distribution pattern of the transmitting antenna N. NM .

[0062] Wherein, the pilot signal column vector Y NM Each row corresponds to a pilot subcarrier signal in the frequency domain. The row numbers of each pilot subcarrier are arranged in ascending order according to the size of the pilot subcarrier, or in descending order. In all embodiments of this application, the elements in the matrix or vector that involve the order of subcarriers are arranged in the same way, either in ascending order according to the size of the subcarriers or in descending order according to the size of the subcarriers; for ease of explanation, they are all arranged in ascending order.

[0063] The arrangement is from low frequency to high frequency, i.e., y nm (i) is the frequency domain received signal corresponding to the subcarrier where the pilot p(i) of the transmitting antenna N is located, 1≤i≤N p .

[0064] In this embodiment, the receiving antenna M is a receiving antenna of the receiving node, and the transmitting antenna N is a transmitting antenna of the transmitting node. In the various embodiments of this application, M in the subscript represents the receiving antenna M, and N in the subscript represents the transmitting antenna N.

[0065] In some embodiments, after synchronizing, frequency offset compensating, and removing CP (cyclic prefix) from the signal received by the receiving antenna M, the effective signal of each pilot symbol is obtained according to the pilot subcarrier distribution on the antenna N; the effective signal is then subjected to an FFT transform, and the frequency domain signal on the pilot subcarrier is obtained from the transform result, forming a pilot signal column vector Y. NM .

[0066] In some embodiments, system parameters of the wireless signal are first obtained, including one of the following: FFT / IFFT points N. FFT Number of cyclic prefix points N CP Number of effective subcarriers N use Pilot length N P N FFT It is greater than the number of effective subcarriers of the wireless signal.

[0067] S120: Obtain the channel transformation matrix from the time domain to the frequency domain for the pilot subcarriers of the transmitting antenna N. And the pilot diagonal array X of the transmitting antenna N N .

[0068] Among them, YNM The relationship between the pilot signal and the transmitting antenna N satisfies equation (1).

[0069] Y NM =X N h NMf +w (1)

[0070] Among them, X N This is a pilot diagonal array, where each element on the diagonal corresponds to the frequency domain signal of a pilot subcarrier on the transmitting antenna N. The row numbers corresponding to each pilot subcarrier increase in order of the pilot subcarrier size, X N (i, i) represents the intensity of pilot p(i) of transmitting antenna N, 1 ≤ i ≤ N p h NMf For N p A ×1 column vector, representing the frequency domain channel column vector from transmitting antenna N to receiving antenna M. w is N p A ×1 column vector, representing noise.

[0071] Among them, the channel transformation matrix Each row corresponds to a pilot subcarrier, with a width equal to the length of the cyclic prefix. The row numbers for each pilot subcarrier increase in order of subcarrier size. Channel transition matrix For N FFT N-dimensional DFT matrix F p ×N CP The dimensionless submatrix, where the i-th row corresponds to the subcarrier containing pilot p(i), 1≤i≤N p All rows are N FFT The first N rows of the corresponding row vectors in the 3D DFT matrix F CP List.

[0072] In some embodiments, N is obtained first. FFT DFT matrix F, N FFT The number of points in the FFT / IFFT operation of the wireless signal is greater than the number of effective subcarriers of the wireless signal. The first row of this matrix corresponds to the DC subcarrier, and each row from the second row onwards corresponds to one subcarrier; from N... FFT The dimensional DFT matrix F yields the number of preceding cyclic prefixes N for each row of the pilot subcarriers corresponding to the transmit antenna N. CP The elements form the channel conversion matrix. Typically, it is assumed that time synchronization is accurate and no inter-symbol interference is introduced. That is, the FFT window is taken towards the CP direction, retaining only the pilot subcarriers in matrix F and removing redundant columns of matrix F to obtain the channel transformation matrix.

[0073] For example, N FFT The 3D DFT matrix is ​​represented by equation (2).

[0074]

[0075] Where, N FFT Multiplying the dimensional DFT matrix F by the column vector a is equivalent to performing a DFT transformation on a. Multiplying the i-th row by a yields the i-th frequency domain value, where 1 ≤ i ≤ N. FFT .

[0076] S130: Based on the pilot signal column vector Y NM Channel conversion matrix and pilot diagonal array X N The time-domain channel from the transmitting antenna N to the receiving antenna M is estimated to obtain the corresponding time-domain channel estimation column vector.

[0077] Among them, h NMf Write it as a channel transformation matrix With the time-domain channel column vector h NMt The form of the multiplication is shown in equation (3).

[0078]

[0079] Among them, h NMt Let N be the time-domain channel column vector from transmitting antenna N to receiving antenna M, with N rows. CP The time-domain channel column vector h is obtained by using the LS estimation method in the time domain. NMt The estimated vector, i.e., the column vector of the time-domain channel estimation.

[0080] In some embodiments, the LS estimation algorithm is used in the time domain to obtain the time-domain channel estimation column vector using the least squares method. Specifically, the channel transformation matrix is ​​as follows: and pilot diagonal array X N The product matrix is ​​decomposed using Singular Value Decomposition (SVD) to obtain singular values ​​greater than a set value, and an estimated matrix G of the pseudo-inverse matrix of the product matrix is ​​constructed based on these values. N According to the estimated matrix G N and pilot signal column vector Y NM The product matrix yields the corresponding time-domain channel estimation column vector.

[0081] In some embodiments, the result of the SVD decomposition is shown in equation (4). The estimated matrix G of the pseudo-inverse matrix. N As shown in equation (5).

[0082]

[0083] Wherein, the estimated matrix G of the pseudo-inverse matrix N As shown in equation (5).

[0084]

[0085] in, The first K diagonal elements are The rest are 0; σ1, σ2, ..., σ K These are the singular values ​​of the first K diagonal elements, respectively, and are greater than the set value, which, for example, is a very small number (e.g., 0.001 or 0.0001).

[0086] Among them, due to the pilot of the transmitting antenna N and N FFT The dimensional DFT matrix F is known, and X is known. N and It can also be considered as known, therefore G N You can obtain and save it in advance.

[0087] S140: Column vector estimated based on time-domain channel Obtain the frequency domain estimate of the effective subcarriers from the transmitting antenna N to the receiving antenna M.

[0088] In some embodiments, in the time-domain channel estimation column vector Pad with zeros to make its length N FFT ; for the column vector padded with zeros Perform N FFT The point FFT transform is performed, and the frequency domain estimate is obtained from the data of the effective subcarriers in the transform result.

[0089] This algorithm can guarantee the accuracy of the effective subcarrier channel estimation within the signal bandwidth, but it does not guarantee the accuracy of the guard subcarrier. Therefore, the time-domain channel estimation will differ from the actual channel.

[0090] In the SISO scenario, both the transmitting antenna N and the receiving antenna M are simply referred to as antennas in this embodiment, and the annotations for N and M in various symbols can be omitted, while the steps remain unchanged. In the MIMO scenario, the transmitting antenna N and the receiving antenna M, as well as the annotations for N and M in various symbols, remain unchanged in this embodiment. The pilot signals of different transmitting antennas are staggered on the subcarriers. When performing channel estimation for each receiving antenna, a corresponding channel conversion matrix is ​​constructed based on the pilot data of different transmitting antennas and the subcarriers. and pilot diagonal array X N Thus, G is obtained. N matrix.

[0091] In summary, the first embodiment of a wireless signal channel estimation method is used for OFDM or SC-FDMA waveform channel estimation, which is suitable for both SISO and MIMO scenarios. It uses LS estimation in the time domain to first obtain the time domain channel estimate, and then obtains the frequency domain channel estimate based on it. This not only solves the problem of inaccurate estimation of edge subcarriers, but also achieves channel estimation performance for other subcarriers that is comparable to existing frequency domain noise-suppressing LS estimation.

[0092] The following is based on Figure 3 and Figure 4 This paper introduces a second embodiment of a wireless signal channel estimation method.

[0093] Embodiment 2 of a wireless signal channel estimation method is a specific implementation of Embodiment 1 of the same method, and possesses all its advantages. For simplicity, Embodiment 2 uses the SISO scenario as an example, where the transmitting antenna N and receiving antenna M are simply referred to as antennas, and the annotations for N and M in various symbols can be omitted.

[0094] Figure 3 The flowchart of a second embodiment of a wireless signal channel estimation method is shown, including steps S210 to S250.

[0095] S210: System parameters for obtaining wireless signals.

[0096] The system parameters obtained are as follows.

[0097] parameter symbol FFT / IFFT points 2048 CP points 144 Number of effective subcarriers 1200 pilot length 600

[0098] Figure 4 The diagram shows the comb-like arrangement of pilots in the frequency domain (guard subcarriers are not shown) in Embodiment 2 of a wireless signal channel estimation method. In this embodiment, a pilot subcarrier is set every other effective subcarrier, resulting in 600 pilot subcarriers out of 1200 effective subcarriers.

[0099] S220: Obtain N FFT Given a 3D DFT matrix F and a pilot diagonal matrix X, the channel transformation matrix for the pilot subcarriers from the time domain to the frequency domain is obtained from matrix F. Build and save The estimated matrix G of the pseudo-inverse matrix.

[0100] Among them, from N FFT The channel transformation matrix is ​​formed by extracting the first 144 columns from the rows corresponding to the 600 pilot subcarriers in the DFT matrix F. matrix The size is 600*114, and the diagonal matrix X is 600*600.

[0101] Among them, for the matrix Perform SVD decomposition to obtain Let ∑ be a 600×144 matrix. Set all elements in the ∑ matrix that are less than a set value (e.g., less than 0.001 or 0.0001) to 0. It is also a 600×144 matrix, with the first K diagonal elements being large singular values, σ1, σ2, ..., σ K K represents the number of non-zero numbers, and the rest are 0.

[0102] Among them, matrix For a 144×600 matrix, the first K diagonal elements are: The rest are 0.

[0103] Where, N FFT Since the DFT matrix F and the pilot diagonal matrix X are both known, Each matrix can be obtained in advance, thus allowing G to be obtained and stored in advance.

[0104] S230: The receiver performs synchronization, frequency offset compensation, and CP removal on the signal received by the receiving antenna to obtain a valid received signal.

[0105] S240: Obtain the valid signal of the pilot symbol in the valid received signal, perform FFT on it, and extract the frequency domain signal at the pilot subcarrier in the FFT result to obtain the pilot signal column vector Y.

[0106] Among them, the pilot signal column vector Y is obtained as a 600×1 pilot signal column vector Y. Each row corresponds to a pilot subcarrier. The row numbers of each pilot subcarrier are arranged in ascending order of pilot subcarrier size. y(i) is the frequency domain received signal corresponding to the subcarrier where pilot p(i) is located, and 1≤i≤600.

[0107] S250: Obtain the time-domain channel estimation column vector based on matrix G and pilot signal column vector Y. right After zero-padding, perform an FFT transform, extract the data of the effective subcarriers from the transformed result, and estimate the channel of the effective subcarriers.

[0108] in, right After padding with zeros to 2048 points, perform a 2048-point FFT on the padded result, extract the data of 1200 effective subcarriers, and you can obtain the channel estimate for all effective subcarriers.

[0109] The following is combined with Figure 5 This paper introduces an embodiment of a wireless signal channel estimation device.

[0110] An embodiment of a wireless signal channel estimation device is provided to implement the method described in embodiment one of a wireless signal channel estimation method, and has all its advantages.

[0111] Figure 5 The structure of a wireless signal channel estimation device according to an embodiment 1 is shown, including: a pilot receiving module 510, a matrix obtaining module 520, a time domain estimation module 530, and a frequency domain estimation module 540.

[0112] Pilot receiving module 510 is used to obtain the corresponding pilot signal column vector Y from the received signal of receiving antenna M according to the pilot subcarrier distribution of transmitting antenna N. NM For its working principle and advantages, please refer to step S110 of Embodiment 1 of a wireless signal channel estimation method.

[0113] Matrix acquisition module 520 is used to obtain the channel transformation matrix of the pilot subcarriers of the transmitting antenna N from the time domain channel to the frequency domain. And the pilot diagonal array X of the transmitting antenna N N For its working principle and advantages, please refer to step S120 of Embodiment 1 of a wireless signal channel estimation method.

[0114] The time-domain estimation module 530 is used to estimate the time domain based on the pilot signal column vector Y. NM Channel conversion matrix and pilot diagonal array X N The time-domain channel from the transmitting antenna N to the receiving antenna M is estimated to obtain the corresponding time-domain channel estimation column vector. For its working principle and advantages, please refer to step S130 of Embodiment 1 of a wireless signal channel estimation method.

[0115] Frequency domain estimation module 540 is used to estimate column vectors based on the time domain channel. The frequency domain estimate of the effective subcarriers from the transmitting antenna N to the receiving antenna M is obtained. For its working principle and advantages, please refer to step S140 of Embodiment 1 of a wireless signal channel estimation method.

[0116] The following is combined with Figure 6 This paper introduces a second embodiment of a wireless signal channel estimation device.

[0117] A second embodiment of a wireless signal channel estimation device is used to implement the method described in the second embodiment of a wireless signal channel estimation method, and has all its advantages.

[0118] Figure 6 The structure of a second embodiment of a wireless signal channel estimation device is shown, including: a parameter acquisition module 610, a matrix acquisition module 620, a signal receiving module 630, a pilot receiving module 640, and a channel estimation module 650.

[0119] The parameter acquisition module 610 is used to acquire the system parameters of the wireless signal. For its working principle and advantages, please refer to step S210 of Embodiment 2 of a wireless signal channel estimation method.

[0120] Matrix acquisition module 620 is used to obtain N FFT Given a 3D DFT matrix F and a pilot diagonal matrix X, the channel transformation matrix for the pilot subcarriers from the time domain to the frequency domain is obtained from matrix F. Build and save The estimated matrix G is the pseudo-inverse matrix. For its working principle and advantages, please refer to step S220 of Embodiment 2 of a wireless signal channel estimation method.

[0121] The signal receiving module 630 is used by the receiver to synchronize, compensate for frequency offset, and remove CP from the signal received by the receiving antenna to obtain a valid received signal. For its working principle and advantages, please refer to step S230 of Embodiment 2 of a wireless signal channel estimation method.

[0122] The pilot receiving module 640 is used to acquire the effective signal of the pilot symbol in the effective received signal, perform an FFT on it, and extract the frequency domain signal at the pilot subcarrier in the FFT result to obtain the pilot signal column vector Y. For its working principle and advantages, please refer to step S240 of Embodiment 2 of a wireless signal channel estimation method.

[0123] The channel estimation module 650 is used to obtain the time-domain channel estimation column vector based on the matrix G and the pilot signal column vector Y. right After zero-padding, an FFT transform is performed, and the data of the effective subcarriers are extracted from the transformed result. Channel estimation of the effective subcarriers is then performed. For its working principle and advantages, please refer to step S250 of Embodiment 2 of a wireless signal channel estimation method.

[0124] This application also provides a computing device, which will be described below in conjunction with... Figure 7 Detailed introduction.

[0125] The computing device 700 includes a processor 710, a memory 720, a communication interface 730, and a bus 740.

[0126] It should be understood that the communication interface 730 in the computing device 700 shown in the figure can be used to communicate with other devices.

[0127] The processor 710 can be connected to the memory 720. The memory 720 can be used to store the program code and data. Therefore, the memory 720 can be a storage unit inside the processor 710, an external storage unit independent of the processor 710, or a component that includes both the storage unit inside the processor 710 and the external storage unit independent of the processor 710.

[0128] Optionally, the computing device 700 may also include a bus 740. The memory 720 and communication interface 730 can be connected to the processor 710 via the bus 740. The bus 740 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus 740 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one line is used in this figure, but this does not mean that there is only one bus or one type of bus.

[0129] It should be understood that in the embodiments of this application, the processor 710 may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. Alternatively, the processor 710 may employ one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0130] The memory 720 may include read-only memory and random access memory, and provides instructions and data to the processor 710. A portion of the processor 710 may also include non-volatile random access memory. For example, the processor 710 may also store device type information.

[0131] When the computing device 700 is running, the processor 710 executes computer execution instructions stored in the memory 720 to perform the operation steps of each method embodiment.

[0132] It should be understood that the computing device 700 according to the embodiments of this application can correspond to the corresponding subject in executing the methods according to the various embodiments of this application, and the above and other operations and / or functions of each module in the computing device 700 are respectively for implementing the corresponding processes of the methods of this embodiment. For the sake of brevity, they will not be described in detail here.

[0133] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0134] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0138] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is used to perform the operation steps of the various method embodiments.

[0140] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0141] Computer-readable signal media may include data signals transmitted in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which can send, transmit, or transmit programs for use by or in connection with an instruction execution system, apparatus, or device.

[0142] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0143] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0144] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, all of which fall within the scope of protection of this application.

Claims

1. A method for estimating wireless signal channels, characterized in that, The pilot subcarriers of the wireless signal are arranged in a comb pattern within the number of effective subcarriers, and the method includes: The corresponding pilot signal column vector Y is obtained from the received signal from the receiving antenna M according to the pilot subcarrier distribution of the transmitting antenna N. NM Each row of this vector corresponds to a pilot subcarrier; Obtain the pilot diagonal array X of the transmitting antenna N. N Channel transformation matrix from time domain to frequency domain for pilot subcarriers Channel conversion matrix Each row corresponds to one pilot subcarrier, and the width is the length of the cyclic prefix; Based on the pilot signal column vector Y NM Channel conversion matrix and pilot diagonal array X N The time-domain channel from the transmitting antenna N to the receiving antenna M is estimated to obtain the corresponding time-domain channel estimation column vector. Based on the time-domain channel estimation column vector Obtain the frequency domain estimate of the effective subcarriers from the transmitting antenna N to the receiving antenna M.

2. The method according to claim 1, characterized in that, The aforementioned based on the pilot signal column vector Y NM Channel conversion matrix and pilot diagonal array X N The time-domain channel from the transmitting antenna N to the receiving antenna M is estimated to obtain the corresponding time-domain channel estimation column vector. include: Channel transformation matrix and pilot diagonal array X N The product matrix is ​​decomposed using Singular Value Decomposition (SVD) to obtain singular values ​​greater than a set value, and an estimated matrix G of the pseudo-inverse matrix of the product matrix is ​​constructed based on these values. N ; Based on the estimated matrix G of the pseudo-inverse matrix N and pilot signal column vector Y NM The product matrix yields the corresponding time-domain channel estimation column vector.

3. The method according to claim 2, characterized in that, The result of the SVD decomposition is U∑V H The estimated matrix G of the pseudo-inverse matrix N for The first K diagonal elements are The rest are 0, σ1, σ2, ..., σ K These are the singular values ​​of the first K diagonal elements, respectively, and are greater than the set value.

4. The method according to claim 1, characterized in that, The column vector estimated based on the time-domain channel Obtaining frequency domain estimates of the effective subcarriers from transmitting antenna N to receiving antenna M includes: Time-domain channel estimation column vector Pad with zeros to make its length N FFT N FFT The number of points in the FFT / IFFT operation of the wireless signal is greater than the number of effective subcarriers of the wireless signal; The column vector after padding with zeros Perform N FFT The point FFT transform is performed, and the frequency domain estimate is obtained from the data of the effective subcarriers in the transform result.

5. The method according to claim 1, characterized in that, The method described above obtains the channel transformation matrix of the pilot subcarrier from the time domain to the frequency domain. include: Obtain N FFT DFT matrix F, N FFT The number of points in the FFT / IFFT operation of the wireless signal is greater than the number of effective subcarriers of the wireless signal. The first row of the matrix corresponds to the DC subcarrier, and each row from the second row onwards corresponds to one subcarrier. From N FFT The channel transformation matrix is ​​formed by taking the number of preceding cyclic prefixes of each row of the pilot subcarriers corresponding to the transmit antenna N from the DFT matrix F.

6. The method according to claim 1, characterized in that, The pilot signal column vector Y is obtained from the received signal from the receiving antenna M according to the pilot subcarrier distribution of the transmitting antenna N. NM ,include: After synchronizing, frequency offset compensating and removing CP from the signal received by the receiving antenna M, the effective signal of each pilot symbol is obtained according to the pilot subcarrier distribution on antenna N. Perform an FFT transform on the effective signal to obtain the frequency domain signal on the pilot subcarrier from the transform result, and form the pilot signal column vector Y. NM .

7. The method according to claim 1, characterized in that, Also includes: The system parameters for obtaining the wireless signal include one of the following: FFT / IFFT point count N. FFT Number of cyclic prefix points N CP Number of effective subcarriers N use Pilot length N P N FFT It is greater than the number of effective subcarriers of the wireless signal.

8. A wireless signal channel estimation device, characterized in that, include: The pilot receiving module is used to obtain the corresponding pilot signal column vector Y from the received signal of the receiving antenna M according to the pilot subcarrier distribution of the transmitting antenna N. NM Each row of this vector corresponds to a pilot subcarrier; The matrix acquisition module is used to obtain the pilot diagonal matrix X of the transmitting antenna N. N Channel transformation matrix from time domain to frequency domain for pilot subcarriers Channel conversion matrix Each row corresponds to one pilot subcarrier, and the width is the length of the cyclic prefix; The time-domain estimation module is used to estimate the time based on the pilot signal column vector Y. NM Channel conversion matrix and pilot diagonal array X N The time-domain channel from the transmitting antenna N to the receiving antenna M is estimated to obtain the corresponding time-domain channel estimation column vector. The frequency domain estimation module is used to estimate column vectors based on the time-domain channel. Obtain the frequency domain estimate of the effective subcarriers from the transmitting antenna N to the receiving antenna M.

9. A computing device, characterized in that, include, bus; A communication interface, which is connected to the bus; At least one processor is connected to the bus; as well as At least one memory connected to the bus and storing program instructions that, when executed by the at least one processor, cause the at least one processor to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores program instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7.

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