Channel estimation method and device, electronic equipment, storage medium and chip
By calculating the time-frequency domain eigenvalue and eigenvector of channel estimation, the minimum mean square error MMSE filter matrix is determined, which solves the problem of high complexity of existing channel estimation methods and realizes efficient channel estimation.
Patent Information
- Application Number
- CN202410224366.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-02-28
- Publication Date
- 2025-07-25
AI Technical Summary
The existing channel estimation methods are complex and take a lot of time, and require a more efficient channel estimation method.
By receiving the reference signal and pilot sequence, the least squares LS estimation result of each pilot point is determined, the eigenvalue matrix and eigenvector of the time domain autocorrelation matrix and frequency domain autocorrelation matrix are calculated, and the minimum mean square error MMSE filter matrix is determined based on these matrices and vectors, and the channel estimation result of each frequency point on each OFDM symbol is finally determined.
The difficulty and complexity of channel estimation are reduced, the efficiency of channel estimation is improved, and the accuracy and reliability of channel estimation are ensured.
Smart Images
Figure CN120378255A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of communication technologies, and in particular, to a channel estimation method, apparatus, electronic device, storage medium, and chip. Background Art
[0002] Channel estimation is used to estimate the channel characteristics through which a signal passes during transmission, so that the receiving end can correctly decode and recover the original signal, thereby improving the resistance of the communication system to interference and noise. It is the key to the development of communication systems.
[0003] However, existing channel estimation methods have high complexity and require a large amount of time. Therefore, a more efficient channel estimation method is needed. Summary of the Invention
[0004] The present disclosure aims to solve at least one of the technical problems in the related art to some extent.
[0005] To this end, the first object of the present application is to propose a channel estimation method to reduce the complexity of channel estimation and improve the efficiency of channel estimation.
[0006] The second object of the present application is to propose a channel estimation apparatus.
[0007] The third object of the present application is to propose an electronic device.
[0008] The fourth object of the present application is to propose a computer-readable storage medium.
[0009] The fifth object of the present application is to propose a chip.
[0010] To achieve the above object, an embodiment of the first aspect of the present disclosure provides a channel estimation method, including:
[0011] Determine the least squares (LS) estimation result corresponding to each pilot point according to the received reference signal and pilot sequence;
[0012] Determine the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols and the frequency-domain autocorrelation matrix of N pilot points in the two adjacent OFDM symbols according to the LS estimation result, where N is a positive integer greater than 1;
[0013] Determine the first eigenvalue matrix of the time-domain autocorrelation matrix, the second eigenvalue matrix of the frequency-domain autocorrelation matrix, and the eigenvectors;
[0014] Determine the minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in the two adjacent OFDM symbols based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvectors;
[0015] Determine the channel estimation result of each frequency point on each of the OFDM symbols according to the MMSE filtering matrix and the LS estimation result corresponding to every two adjacent OFDM symbols.
[0016] An embodiment of the second aspect of the present disclosure provides a channel estimation apparatus, including:
[0017] A first determination module, configured to determine the least squares (LS) estimation result corresponding to each pilot point according to the received reference signal and pilot sequence;
[0018] A second determination module, configured to determine the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols and the frequency-domain autocorrelation matrix of N pilot points in the two adjacent OFDM symbols according to the LS estimation result, where N is a positive integer greater than 1;
[0019] A third determination module, configured to determine a first eigenvalue matrix of the time-domain autocorrelation matrix, a second eigenvalue matrix of the frequency-domain autocorrelation matrix, and eigenvectors;
[0020] A fourth determination module, configured to determine the minimum mean square error (MMSE) filtering matrix corresponding to each of the OFDM symbols in the two adjacent OFDM symbols based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvectors;
[0021] A fifth determination module, configured to determine the channel estimation result of each frequency point on each of the OFDM symbols according to the MMSE filtering matrix and the LS estimation result corresponding to every two adjacent OFDM symbols.
[0022] An embodiment of the third aspect of the present disclosure provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the channel estimation method provided in the embodiment of the first aspect of the present disclosure is implemented.
[0023] An embodiment of the fourth aspect of the present disclosure provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the channel estimation method provided in the embodiment of the first aspect of the present disclosure is implemented.
[0024] An embodiment of the fifth aspect of the present disclosure provides a chip, including a processor and an interface; the processor is configured to read instructions to execute a computer program, and when the computer program is executed by the processor, the channel estimation method provided in the embodiment of the first aspect is implemented.
[0025] The channel estimation method, apparatus, electronic device, storage medium, and chip provided by the present disclosure have the following beneficial effects:
[0026] In the embodiments of the present disclosure, first, according to the received reference signals and pilot sequences, the least squares (LS) estimation results corresponding to each pilot point are determined. Then, based on the LS estimation results, the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols and the frequency-domain autocorrelation matrix of N pilot points in two adjacent OFDM symbols are determined. Next, eigenvalue decomposition is performed to determine the first eigenvalue matrix of the time-domain autocorrelation matrix, the second eigenvalue matrix of the frequency-domain autocorrelation matrix, and the eigenvectors. After that, based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvectors, the minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in two adjacent OFDM symbols is determined. And according to the MMSE filtering matrices corresponding to every two adjacent OFDM symbols and the LS estimation results, the channel estimation results of each frequency point on each OFDM symbol are determined. Thus, by jointly estimating two adjacent OFDM symbols in two dimensions of time and frequency domains, not only the accuracy and reliability of channel estimation are ensured, but also the difficulty and complexity of channel estimation are reduced, and the efficiency of channel estimation is improved.
[0027] Additional aspects and advantages of the present disclosure will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] The above and / or additional aspects and advantages of the present disclosure will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:
[0029] Figure 1 is a schematic flowchart of a channel estimation method provided by an embodiment of the present disclosure;
[0030] Figure 2 is a schematic flowchart of a channel estimation method provided by another embodiment of the present disclosure;
[0031] Figure 3 is a schematic structural diagram of a channel estimation device provided by an embodiment of the present disclosure;
[0032] Figure 4 shows a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0033] Embodiments of the present disclosure will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as limiting the present disclosure.
[0034] The channel estimation method, apparatus, electronic device, storage medium, and chip according to the embodiments of the present disclosure will be described below with reference to the accompanying drawings.
[0035] Figure 1 It is a schematic flowchart of a channel estimation method provided by an embodiment of the present disclosure.
[0036] In the embodiments of the present disclosure, this channel estimation method is exemplified by being configured in a channel estimation apparatus, and this channel estimation apparatus can be applied to any electronic device, so that the electronic device can quickly obtain the status information of the channel by executing the channel estimation method provided by the present disclosure, thereby improving the reliability of the communication system.
[0037] It should be noted that the channel estimation method of the present disclosure is applicable to communication systems using Orthogonal Frequency Division Multiplexing (OFDM) signals, such as New Radio (NR) communication systems, Long Term Evolution (LTE) communication systems, etc. The present disclosure does not limit this.
[0038] As Figure 1 shown, the channel estimation method may include the following steps:
[0039] Step 101: Determine the least squares (LS) estimation result corresponding to each pilot point according to the received reference signal and pilot sequence.
[0040] Among them, the reference signal (RS) is a known signal transmitted by the transmitter of the communication system.
[0041] Optionally, the pilot sequence corresponding to each pilot point may be determined according to the configuration parameters of the channel corresponding to the received reference signal.
[0042] Among them, the configuration parameters may include at least one of Orthogonal Frequency Division Multiplexing (OFDM) symbol index, subframe index number, cell physical ID, etc. In the present disclosure, the configuration parameters for calculating the pilot sequence can be determined according to the actual communication system, and the present disclosure does not limit this.
[0043] In the embodiments of the present disclosure, the expression of the received reference signal may be as shown in the following formula (1):
[0044] y(l,k) = h(l,k) * x(l,k) + n (1)
[0045] Among them, l represents the time domain position, and k represents the frequency domain position. (l, k) represents the position of the pilot point in the time domain and frequency domain, y(l, k) represents the received signal at the time-frequency position (l, k), h(l, k) represents the channel at the time-frequency position (l, k), x(l, k) represents the pilot sequence transmitted at the time-frequency position (l, k), and n represents the noise.
[0046] It should be noted that the formula of the pilot sequence x(l, k) transmitted at the time-frequency position (l, k) can be determined in the form shown in the following formula (2):
[0047]
[0048] Among them, c(2m) and c(2m + 1) are pseudo-random sequences, and the expression of this sequence is shown in the following formula (3):
[0049] c(n) = (x1(n + Nc) + x2(n + Nc)) mod 2 (3)
[0050] Among them, Nc is a parameter used to describe the channel resource allocation in the communication system. The Nc configurations of different channels are different. For example, in the Physical Downlink Control Channel (PDCCH), the value of Nc is 1600. And x1(n + Nc) in formula (3) satisfies the polynomial shown in the following formula (4), and x2(n + Nc) satisfies the polynomial shown in the following formula (5):
[0051] x1(n + 31) = (x1(n + 3) + x1(n)) mod 2 (4)
[0052] x2(n + 31) = (x2(n + 3) + x2(n + 2) + x2(n + 1) + x2(n)) mod 2 (5)
[0053] In addition, x1(0) = 1, x1(n) = 0, n = 1, 2, 3,...., 30. x2(n) is the value of the nth bit of the scrambling sequence initial value C init and C init is determined by the configuration parameters of the channel.
[0054] For example, the expression of C init in the PDCCH channel is shown in the following formula (6):
[0055]
[0056] Among them, is the number of symbols symb in the current slot, is the serial number of the current slot in the current frame, NID This is the scrambling ID for the Demodulation Reference Signal (DMRS).
[0057] Then, by using the least squares (LS) estimation for the expressions of the above-obtained reference signal and pilot sequence, the LS estimation result corresponding to each pilot point (l,k) can be obtained, as shown in the following formula (7):
[0058] h LS (l,k) = y(l,k) * x H (l,k) (7)
[0059] Where, h LS (l,k) is the least squares (LS) estimation result corresponding to the pilot point (l,k), and x H (l,k) is the conjugate of the pilot sequence.
[0060] Step 102: Determine the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols, and the frequency-domain autocorrelation matrix of N pilot points in two adjacent OFDM symbols according to the LS estimation result.
[0061] Where, N is a positive integer greater than 1.
[0062] It can be understood that in an OFDM communication system, multiple subcarriers with different frequencies can be transmitted simultaneously at the same moment. Then, the multiple subcarriers transmitted at the same moment form an OFDM symbol, and there are multiple pilot points on one OFDM symbol. Then, after a certain time interval, the next OFDM symbol can be transmitted. That is to say, according to the characteristics of different channels, the time-domain distance between two adjacent OFDM symbols may be inconsistent. Therefore, in the present disclosure, for the channel to be estimated, the time-domain autocorrelation matrix of every two adjacent OFDM symbols in this channel can be obtained.
[0063] For example, for the PDCCH channel, the time-domain distance between any two adjacent OFDM symbols is 1. For example, the indexes of three OFDM symbols within the same time slot can be symb0, symb1, and symb2. Correspondingly, the time-domain autocorrelation matrix between symb0 and symb1 can be obtained, and the time-domain autocorrelation matrix between symb1 and symb2 can be obtained. For the Physical Downlink Shared Channel (PDSCH), the time-domain distance between two adjacent OFDM symbols may not be fixed. The positions of the OFDM symbols in the time domain may be symb1, symb3, and symb7. The distance between symb1 and symb3 is 2, and the distance between symb3 and symb7 is 4. At this time, for this PDSCH channel, the time-domain autocorrelation matrix between symb1 and symb2, and the time-domain autocorrelation matrix between symb3 and symb7 can be obtained.
[0064] In the embodiments of the present disclosure, the time-domain correlation coefficient can be obtained through the channel measurement module to determine the time-domain autocorrelation matrix of every two adjacent OFDM symbols. For example, the measured Doppler values can be binned, and then the LS estimation results can be fitted with different Dopplers to find the Doppler value with the minimum Mean Squared Error (MSE). Then, the time-domain autocorrelation matrix can be obtained through Bessel (besselj) function modeling. The present disclosure does not limit this.
[0065] It can be understood that since the time-domain autocorrelation matrix represents the correlation between two OFDM symbols, the time-domain autocorrelation matrix is a 2*2 matrix.
[0066] In the embodiments of the present disclosure, the frequency-domain correlation coefficient can be obtained through the channel measurement module to determine the frequency-domain autocorrelation matrix of N pilot points in two adjacent OFDM symbols. For example, different CIRLen channel impulse responses can be fitted to find the CIRLen with the minimum MSE, and the frequency-domain autocorrelation matrix can be obtained through sinc function modeling. The present disclosure does not limit this.
[0067] It should be noted that the number of pilot points on each OFDM symbol can be determined according to the bandwidth of the channel. The more the bandwidth, the more the number of different frequency points. When generating the frequency-domain autocorrelation matrix, the N pilot points corresponding to the matrix in the OFDM symbol should be continuous in the frequency domain. The value of N can be determined according to the processing capabilities of the system in actual applications, etc. For example, it can be 10, 20, 50, etc. The present disclosure does not limit this.
[0068] Step 103: Determine the first eigenvalue matrix of the time-domain autocorrelation matrix, the second eigenvalue matrix of the frequency-domain autocorrelation matrix, and the eigenvector.
[0069] Among them, the first eigenvalue matrix includes the time-domain eigenvalues corresponding to each OFDM symbol in two adjacent OFDM symbols. The second eigenvalue matrix includes the frequency-domain eigenvalues corresponding to each of the N pilot points.
[0070] Optionally, the time-domain autocorrelation matrix can be subjected to eigenvalue decomposition based on a preset unitary matrix to obtain the first eigenvalue matrix.
[0071] It can be understood that the time-domain autocorrelation matrix is a real matrix of size 2*2. By performing eigenvalue decomposition on the 2*2 real matrix, it can be known that each element in the eigenvector of the time-domain autocorrelation matrix is always + / -sqrt(2), that is, the eigenvector is a fixed value. Therefore, a unitary matrix can be determined in advance to be used for eigenvalue decomposition of the time-domain autocorrelation matrix.
[0072] In the embodiments of the present disclosure, by multiplying the time-domain autocorrelation matrix on the left and right by the preset unitary matrix respectively, the first eigenvalue matrix can be obtained, and its formula expression is as shown in formula (8) below:
[0073]
[0074] Among them, is the preset unitary matrix, is the time-domain autocorrelation matrix, is the time-domain eigenvalue corresponding to the first OFDM symbol in two adjacent OFDM symbols, is the time-domain eigenvalue corresponding to the second OFDM symbol in two adjacent OFDM symbols.
[0075] And, by performing eigenvalue decomposition on the frequency-domain autocorrelation matrix the second eigenvalue matrix of the frequency-domain autocorrelation matrix and the eigenvector U f .
[0076] Step 104: Based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvector, determine the minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in two adjacent OFDM symbols.
[0077] In the embodiments of the present disclosure, first, based on the first time-domain eigenvalue, the second eigenvalue matrix, and the eigenvector corresponding to the first OFDM symbol among two adjacent OFDM symbols, the minimum mean squared error (MMSE) filtering matrix corresponding to the first symbol is determined, and its corresponding calculation formula is shown in the following formula (9):
[0078]
[0079] Wherein, is the frequency-domain cross-correlation matrix, which is generated in the same way as the frequency-domain autocorrelation matrix, but has a different frequency-domain interval from that configured for the frequency-domain autocorrelation matrix. SNR is the signal-to-noise ratio.
[0080] Then, based on the second time-domain eigenvalue, the second eigenvalue matrix, and the eigenvector corresponding to the second OFDM symbol among two adjacent OFDM symbols, the MMSE filtering matrix corresponding to the second symbol is determined, and its corresponding calculation formula is shown in the following formula (10):
[0081]
[0082] Step 105: Determine the channel estimation result of each frequency point on each OFDM symbol according to the MMSE filtering matrix and the LS estimation result corresponding to each two adjacent OFDM symbols.
[0083] In the embodiments of the present disclosure, first, based on the MMSE filtering matrix corresponding to each OFDM symbol, the LS estimation results corresponding to the pilot points on each OFDM symbol are filtered to obtain the time-frequency fusion channel estimation result of each OFDM symbol.
[0084] For example, two adjacent OFDM symbols are symb0 and symb1, the MMSE filtering matrix corresponding to symb0 is C0, and the MMSE filtering matrix corresponding to symb1 is C1. Then, the time-frequency fusion channel estimation result of symb0 can be obtained by multiplying C0 with the LS estimation results of different pilot points on symb0, and the time-frequency fusion channel estimation result of symb1 can be obtained by multiplying C1 with the LS estimation results of different pilot points on symb1.
[0085] It should be noted that the MMSE filtering matrix is an N*N matrix. Therefore, when filtering using the MMSE filtering matrix, only the LS estimation results of N pilot points on the OFDM symbol can be filtered each time. However, the number of pilot points on the OFDM symbol may be more than N. Therefore, the pilot points on the OFDM symbol can be grouped and filtered using the MMSE filtering matrix until all pilot points are filtered to obtain the time-frequency fusion channel estimation result of the OFDM symbol.
[0086] Then, based on the time-frequency fused channel estimation results of every two adjacent OFDM symbols, the channel estimation results of each frequency point on each OFDM symbol can be determined.
[0087] Optionally, half of the sum of the time-frequency fused channel estimation results of the m-th frequency point on the i-th symbol and the time-frequency fused channel estimation results of the m-th frequency point on the (i + 1)-th symbol can be determined as the channel estimation results of the m-th frequency point on the i-th symbol.
[0088] Then, half of the difference between the time-frequency fused channel estimation results of the m-th frequency point on the i-th symbol and the time-frequency fused channel estimation results of the m-th frequency point on the (i + 1)-th symbol can be determined as the channel estimation results of the m-th frequency point on the (i + 1)-th symbol. Here, i and m are natural numbers respectively.
[0089] In the embodiments of the present disclosure, first, based on the received reference signals and pilot sequences, the least squares (LS) estimation results corresponding to each pilot point are determined. Then, based on the LS estimation results, the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols and the frequency-domain autocorrelation matrix of N pilot points in two adjacent OFDM symbols are determined. Then, eigenvalue decomposition is performed to determine the first eigenvalue matrix of the time-domain autocorrelation matrix, the second eigenvalue matrix of the frequency-domain autocorrelation matrix, and the eigenvectors. After that, based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvectors, the minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in two adjacent OFDM symbols is determined. And based on the MMSE filtering matrices corresponding to every two adjacent OFDM symbols and the LS estimation results, the channel estimation results of each frequency point on each OFDM symbol are determined. Thus, by jointly estimating two adjacent OFDM symbols in two dimensions of time domain and frequency domain, not only the accuracy and reliability of channel estimation are ensured, but also the difficulty and complexity of channel estimation are reduced, and the efficiency of channel estimation is improved.
[0090] It should be noted that when performing channel estimation, channel estimation needs to be performed on all frequency points on all OFDM symbols included in the channel. Therefore, according to the steps of the above embodiments, after obtaining the channel estimation results of each frequency point on two adjacent OFDM symbols, it is also necessary to continue to repeat the operation to perform channel estimation on other two adjacent OFDM symbols until the channel estimation results of all frequency points on all OFDM symbols are obtained.
[0091] For example, the OFDM symbols included in the channel are symb0, symb1, and symb2. First, channel estimation is performed on symb0 and symb1 to obtain the estimation results corresponding to symb0 and symb1 respectively. Then, channel estimation is performed on symb1 and symb2 to obtain the estimation result corresponding to symb2. Since both calculations include the OFDM symbol symb1, but since symb0 and symb2 are different, the results of the two channel estimations for symb1 may be different. At this time, only the channel estimation result obtained by symb1 in any one calculation can be retained, or, when estimating symb1 and symb2, only symb2 can be estimated. The present disclosure does not limit this.
[0092] Alternatively, before determining the time-domain autocorrelation matrix and frequency-domain autocorrelation matrix of two OFDM symbols, all OFDM symbols included in the channel can be grouped in pairs first, and then channel estimation can be performed on all groups simultaneously.
[0093] Figure 2 It is a schematic flowchart of a channel estimation method provided by an embodiment of the present disclosure. As Figure 2 shown, the channel estimation method may include the following steps:
[0094] Step 201, determine the least squares (LS) estimation result corresponding to each pilot point according to the received reference signal and pilot sequence.
[0095] For the description of the above step 201, reference can be specifically made to the above embodiment, which will not be elaborated here.
[0096] Step 202, determine the number K of OFDM symbols included in the channel of the received reference signal.
[0097] In the embodiment of the present disclosure, the number K of OFDM symbols included in the channel can be determined according to the design of the communication system and the requirements of actual applications, or can also be determined according to factors such as the change speed of the channel, signal-to-noise ratio, and bandwidth of the system. The present disclosure does not limit this. For example, in the case of a relatively fast channel change or a relatively low signal-to-noise ratio, in order to ensure accurate channel estimation results, the number of OFDM symbols can be increased; the larger the bandwidth, usually more OFDM symbols are required.
[0098] Step 203, when K is an even number, divide the K OFDM symbols into K / 2 groups.
[0099] Among them, the OFDM symbols included in each group are different, and the indexes of the two OFDM symbols included in each group are adjacent.
[0100] For example, when the number of OFDM symbols included in a channel is 4, four adjacent OFDM symbols are symb0, symb1, symb2, and symb3 in sequence. Since 4 is an even number, the OFDM symbols can be divided into 4 / 2 = 2 groups, namely the first group of symb0 and symb1, and the second group of symb2 and symb3.
[0101] Optionally, when K is an odd number, the K OFDM symbols can be divided into (K + 1) / 2 groups.
[0102] Among them, the OFDM symbols included in the first (K + 1) / 2 - 1 groups are different, and the (K + 1) / 2 - 1th group and the (K + 1) / 2th group both include the (K - 1)th OFDM symbol, and the indices of the two OFDM symbols included in each group are adjacent.
[0103] For example, when the number of OFDM symbols included in a channel is 5, five adjacent OFDM symbols are symb0, symb1, symb2, symb3, and symb4 in sequence. Since 5 is an odd number, the OFDM symbols can be divided into (5 + 1) / 2 = 3 groups, namely the first group of symb0 and symb1, the second group of symb2 and symb3, and the third group of symb3 and symb4. Then the OFDM symbols included in the first group and the second group are different, and the second group and the third group both include the 4th OFDM symbol symb3.
[0104] Step 204: According to the LS estimation result, determine the time-domain autocorrelation matrix of two OFDM symbols in each group, and the frequency-domain autocorrelation matrix of N pilot points in two OFDM symbols in each group.
[0105] Step 205: Determine the first eigenvalue matrix of the time-domain autocorrelation matrix, the second eigenvalue matrix of the frequency-domain autocorrelation matrix, and the eigenvectors.
[0106] Step 206: Based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvectors, determine the minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in two OFDM symbols in each group.
[0107] Step 207: According to the MMSE filtering matrix corresponding to each OFDM symbol and the LS estimation result, determine the channel estimation result of each frequency point on each OFDM symbol.
[0108] For the descriptions of the above steps 204 to 207, specific reference can be made to the above embodiments, which will not be elaborated here.
[0109] In the embodiments of the present disclosure, first, the number K of OFDM symbols included in the channel for receiving the reference signal is determined. When K is an even number, the K OFDM symbols are divided into K / 2 groups. Then, the time-domain autocorrelation matrix of two OFDM symbols in each group and the frequency-domain autocorrelation matrix of N pilot points in two OFDM symbols in each group are determined. Furthermore, the channel estimation result of each frequency point on each OFDM symbol in each group is determined. Thus, by grouping the OFDM symbols included in the channel in pairs and performing channel estimation within multiple groups simultaneously, the efficiency of channel estimation is further improved.
[0110] To implement the above embodiments, the present disclosure also proposes a channel estimation device.
[0111] Figure 3 It is a schematic structural diagram of the channel estimation device provided by the embodiments of the present disclosure.
[0112] As Figure 3 shown, the channel estimation device 300 may include:
[0113] A first determination module 301, configured to determine the least squares (LS) estimation result corresponding to each pilot point according to the received reference signal and pilot sequence;
[0114] A second determination module 302, configured to determine the time-domain autocorrelation matrix of every two adjacent orthogonal frequency-division multiplexing (OFDM) symbols and the frequency-domain autocorrelation matrix of N pilot points in two adjacent OFDM symbols according to the LS estimation result, where N is a positive integer greater than 1;
[0115] A third determination module 303, configured to determine a first eigenvalue matrix of the time-domain autocorrelation matrix, a second eigenvalue matrix of the frequency-domain autocorrelation matrix, and eigenvectors;
[0116] A fourth determination module 304, configured to determine the minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in two adjacent OFDM symbols based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvectors;
[0117] A fifth determination module 305, configured to determine the channel estimation result of each frequency point on each OFDM symbol according to the MMSE filtering matrix corresponding to every two adjacent OFDM symbols and the LS estimation result.
[0118] In some embodiments, the first determination module 301 is further configured to:
[0119] Determine the pilot sequence corresponding to each pilot point according to the configuration parameters corresponding to the channel for receiving the reference signal.
[0120] In some embodiments, the third determination module 303 is specifically configured to:
[0121] Perform eigenvalue decomposition on the time-domain autocorrelation matrix based on a preset unitary matrix to obtain a first eigenvalue matrix, where the first eigenvalue matrix includes time-domain eigenvalues corresponding to each OFDM symbol in two adjacent OFDM symbols.
[0122] In some embodiments, the fourth determination module 304 is specifically configured to:
[0123] Determine the MMSE filtering matrix corresponding to the first symbol based on the first time-domain eigenvalue, the second eigenvalue matrix, and the eigenvector corresponding to the first OFDM symbol in two adjacent OFDM symbols;
[0124] Determine the MMSE filtering matrix corresponding to the second symbol based on the second time-domain eigenvalue, the second eigenvalue matrix, and the eigenvector corresponding to the second OFDM symbol in two adjacent OFDM symbols.
[0125] In some embodiments, the fifth determination module 305 is specifically configured to:
[0126] Filter the LS estimation results corresponding to the frequency points on each OFDM symbol based on the MMSE filtering matrix corresponding to each OFDM symbol to obtain the time-frequency fusion channel estimation results of each OFDM symbol;
[0127] Determine the channel estimation results of each frequency point on each OFDM symbol according to the time-frequency fusion channel estimation results of every two adjacent OFDM symbols.
[0128] In some embodiments, the fifth determination module 305 is specifically configured to:
[0129] Determine half of the sum of the time-frequency fusion channel estimation result of the m-th frequency point on the i-th symbol and the time-frequency fusion channel estimation result of the m-th frequency point on the (i + 1)-th symbol as the channel estimation result of the m-th frequency point on the i-th symbol;
[0130] Determine half of the difference between the time-frequency fusion channel estimation result of the m-th frequency point on the i-th symbol and the time-frequency fusion channel estimation result of the m-th frequency point on the (i + 1)-th symbol as the channel estimation result of the m-th frequency point on the (i + 1)-th symbol, where i and m are natural numbers respectively.
[0131] In some embodiments, the second determination module 302 is specifically configured to:
[0132] Determine the number K of OFDM symbols included in the channel for receiving the reference signal;
[0133] When K is an even number, the K OFDM symbols are divided into K / 2 groups, where the OFDM symbols included in each group are different, and the indices of the two OFDM symbols included in each group are adjacent;
[0134] Determine the time-domain autocorrelation matrix of two OFDM symbols in each group.
[0135] In some embodiments, the second determination module 302 is specifically configured to:
[0136] Determine the number K of OFDM symbols included in the channel of the received reference signal;
[0137] When K is an odd number, the K OFDM symbols are divided into (K + 1) / 2 groups, where the OFDM symbols included in the first (K + 1) / 2 - 1 groups are different, and the (K + 1) / 2 - 1th group and the (K + 1) / 2th group both include the (K - 1)th OFDM symbol, and the indices of the two OFDM symbols included in each group are adjacent;
[0138] Determine the time-domain autocorrelation matrix of two OFDM symbols in each group.
[0139] For the functions and specific implementation principles of the above-mentioned modules in the embodiments of the present disclosure, reference may be made to the above-mentioned method embodiments, and details are not described herein again.
[0140] The channel estimation device in the embodiments of the present disclosure first determines the least squares LS estimation result corresponding to each pilot point according to the received reference signal and pilot sequence, and then determines the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing OFDM symbols and the frequency-domain autocorrelation matrix of N pilot points in two adjacent OFDM symbols according to the LS estimation result, and then performs eigenvalue decomposition to determine the first eigenvalue matrix of the time-domain autocorrelation matrix, the second eigenvalue matrix of the frequency-domain autocorrelation matrix and the eigenvector. Then, based on the first eigenvalue matrix, the second eigenvalue matrix and the eigenvector, determine the minimum mean square error MMSE filtering matrix corresponding to each OFDM symbol in two adjacent OFDM symbols, and determine the channel estimation result of each frequency point on each OFDM symbol according to the MMSE filtering matrix corresponding to every two adjacent OFDM symbols and the LS estimation result. Thus, by jointly estimating two adjacent OFDM symbols in two dimensions of time and frequency domains, not only the accuracy and reliability of channel estimation are ensured, but also the difficulty and complexity of channel estimation are reduced, and the efficiency of channel estimation is improved.
[0141] To implement the above embodiments, the present disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the channel estimation method proposed in the foregoing embodiments of the present disclosure.
[0142] To implement the above embodiments, the present disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the channel estimation method provided in the foregoing embodiments of the present disclosure.
[0143] To implement the above embodiments, the present application also provides a chip, which includes a processor and an interface. Optionally, the chip may further include a memory. The number of processors may be one or more, and the number of interfaces may be multiple. The processor is configured to read instructions to execute the channel estimation method provided in the foregoing embodiments.
[0144] Figure 4 The block diagram of an exemplary electronic device suitable for implementing the embodiments of the present disclosure is shown. Figure 4 The shown electronic device 12 is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.
[0145] As Figure 4 shown, the electronic device 12 is presented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).
[0146] The bus 18 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnection (PCI) bus.
[0147] The electronic device 12 typically includes a variety of computer system-readable media. These media can be any available media accessible by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.
[0148] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used for reading and writing on non-removable, non-volatile magnetic media ( Figure 4 not shown, commonly referred to as a "hard disk drive"). Although Figure 4 not shown in the figure, a disk drive for reading and writing on removable non-volatile disks (such as "floppy disks") and an optical disk drive for reading and writing on removable non-volatile optical disks (such as compact disc read only memory (CD-ROM), digital video disc read only memory (DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 through one or more data media interfaces. The memory 28 may include at least one program product having a set (such as at least one) of program modules that are configured to perform the functions of the embodiments of the present disclosure.
[0149] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods in the embodiments described in the present disclosure.
[0150] The electronic device 12 can also communicate with one or more external devices 14 (such as a keyboard, a pointing device, a display 24, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 12, and / or communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 22. Moreover, the electronic device 12 can also communicate with one or more networks (such as a Local Area Network (LAN), a Wide Area Network (WAN), and / or a public network, such as the Internet) through the network adapter 20. As shown in the figure, the network adapter 20 communicates with other modules of the electronic device 12 through the bus 18. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.
[0151] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the methods mentioned in the foregoing embodiments.
[0152] The technical solution of the present disclosure determines the minimum mean square error (MMSE) filtering matrices corresponding to two adjacent OFDM symbols respectively based on the time-domain autocorrelation matrix and the frequency-domain autocorrelation matrix of two adjacent OFDM symbols, so as to filter the least squares (LS) estimation results corresponding to each pilot point, and obtain the channel estimation results in both the time domain and the frequency domain. The complexity of channel estimation is reduced, and the efficiency of channel estimation is improved.
[0153] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.
[0154] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.
[0155] Any process or method description represented in a flowchart or otherwise described herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present disclosure includes additional implementations, where functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present disclosure pertain.
[0156] The logic and / or steps represented in a flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing a logical function and may be specifically implemented in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.
[0157] It should be understood that various parts of the present disclosure can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.
[0158] Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.
[0159] In addition, in each embodiment of the present disclosure, each functional unit can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0160] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.
Claims
1. A channel estimation method, characterized in that, Including: Determine the least squares (LS) estimation results corresponding to each pilot point according to the received reference signal and pilot sequence; According to the LS estimation results, determine the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols and the frequency-domain autocorrelation matrix of N pilot points in the two adjacent OFDM symbols, where N is a positive integer greater than 1; Determine the first eigenvalue matrix of the time-domain autocorrelation matrix, the second eigenvalue matrix of the frequency-domain autocorrelation matrix, and the eigenvectors; Based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvectors, determine the minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in the two adjacent OFDM symbols; According to the MMSE filtering matrices corresponding to every two adjacent OFDM symbols and the LS estimation results, determine the channel estimation results of each frequency point on each of the OFDM symbols.
2. The method according to claim 1, characterized in that, Before determining the least squares (LS) estimation results corresponding to each pilot point according to the received reference signal and pilot sequence, it further includes: Determine the pilot sequence corresponding to each pilot point according to the configuration parameters of the channel for receiving the reference signal.
3. The method according to claim 1, wherein The determining of the first eigenvalue matrix of the time-domain autocorrelation matrix includes: Perform eigenvalue decomposition on the time-domain autocorrelation matrix based on a preset unitary matrix to obtain the first eigenvalue matrix, where the first eigenvalue matrix includes the time-domain eigenvalues corresponding to each OFDM symbol in the two adjacent OFDM symbols.
4. The method according to claim 3, characterized in that, The determining of the minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in the two adjacent OFDM symbols based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvectors includes: Based on the first time-domain eigenvalue corresponding to the first OFDM symbol in the two adjacent OFDM symbols, the second eigenvalue matrix, and the eigenvectors, determine the MMSE filtering matrix corresponding to the first symbol; Based on the second time-domain eigenvalue corresponding to the first OFDM symbol in the two adjacent OFDM symbols, the second eigenvalue matrix, and the eigenvectors, determine the MMSE filtering matrix corresponding to the second symbol.
5. The method according to claim 3 or 4, characterized in that The determining of the channel estimation results of each frequency point on each of the OFDM symbols according to the MMSE filtering matrices corresponding to every two adjacent OFDM symbols and the LS estimation results includes: Based on the MMSE filtering matrix corresponding to each OFDM symbol, filter the LS estimation results corresponding to the frequency points on each OFDM symbol to obtain the time-frequency fusion channel estimation results of each OFDM symbol; According to the time-frequency fusion channel estimation results of every two adjacent OFDM symbols, determine the channel estimation results of each frequency point on each of the OFDM symbols.
6. The method according to claim 5, characterized in that The determining of the channel estimation results of each frequency point on each of the OFDM symbols according to the time-frequency fusion channel estimation results of every two adjacent OFDM symbols includes: Determine half of the sum of the time-frequency fused channel estimation result at the m-th frequency point of the i-th symbol and the time-frequency fused channel estimation result at the m-th frequency point of the (i + 1)-th symbol as the channel estimation result at the m-th frequency point of the i-th symbol; Determine half of the difference between the time-frequency fused channel estimation result at the m-th frequency point of the i-th symbol and the time-frequency fused channel estimation result at the m-th frequency point of the (i + 1)-th symbol as the channel estimation result at the m-th frequency point of the (i + 1)-th symbol, where i and m are natural numbers respectively.
7. The method according to any one of claims 1 to 6, characterized in that The determination of the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols includes: Determine the number K of OFDM symbols included in the channel that receives the reference signal; When K is an even number, divide the K OFDM symbols into K / 2 groups, where the OFDM symbols included in each group are different, and the indices of the two OFDM symbols included in each group are adjacent; Determine the time-domain autocorrelation matrix of two OFDM symbols in each group.
8. The method according to any one of claims 1-6, characterized in that, The determination of the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols includes: Determine the number K of OFDM symbols included in the channel that receives the reference signal; When K is an odd number, divide the K OFDM symbols into (K + 1) / 2 groups, where the OFDM symbols included in the first (K + 1) / 2 - 1 groups are different, and both the (K + 1) / 2 - 1 group and the (K + 1) / 2 group include the (K - 1)-th OFDM symbol, and the indices of the two OFDM symbols included in each group are adjacent; Determine the time-domain autocorrelation matrix of two OFDM symbols in each group.
9. A channel estimation device, characterized in that, The device includes: A first determination module, configured to determine the least squares (LS) estimation result corresponding to each pilot point according to the received reference signal and pilot sequence; A second determination module, configured to determine the time-domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols and the frequency-domain autocorrelation matrix of N pilot points in the two adjacent OFDM symbols according to the LS estimation result, where N is a positive integer greater than 1; A third determination module, configured to determine the first eigenvalue matrix of the time-domain autocorrelation matrix, the second eigenvalue matrix of the frequency-domain autocorrelation matrix, and the eigenvectors; A fourth determination module, configured to determine the minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in the two adjacent OFDM symbols based on the first eigenvalue matrix, the second eigenvalue matrix, and the eigenvectors; A fifth determination module, configured to determine the channel estimation result at each frequency point of each of the OFDM symbols according to the MMSE filtering matrix corresponding to every two adjacent OFDM symbols and the LS estimation result.
10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the channel estimation method according to any one of claims 1 - 8.
11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the channel estimation method according to any one of claims 1 - 8.
12. A chip, characterized in that, It includes a processor and an interface; the processor is used to read instructions to execute the channel estimation method according to any one of claims 1-8.