Double codebook estimation method and system

Abstract

The invention provides a double-codebook estimation method and system, and the method comprises the steps: obtaining a double-codebook matrix W and an autocorrelation matrix R of a channel state matrix # imgabs0 #, the number of rows Nt of the double-codebook matrix W being the number of array antennas, the number of columns N1 being the number of spatial streams, the number of Nr being the number of receiving antennas, and N1 being far less than Nt; performing eigenvalue decomposition processing on the autocorrelation matrix R in the initial calculation formula WHRW to obtain a decomposition calculation formula # imgabs1 # lambdai as an eigenvalue of the autocorrelation matrix R after eigenvalue decomposition processing, and si as an eigenvector of the autocorrelation matrix R after eigenvalue decomposition processing; and converting the decomposition calculation formula into an alternative calculation formula # imgabs2 #, substituting the alternative calculation formula # imgabs2 # into a preset codebook measurement index function, and screening out an optimal codebook. According to the invention, the efficiency of estimating the double codebooks by the UE side can be improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a dual codebook estimation method and system thereof. Background Art

[0002] Most of the current advanced communication systems use large-scale array antenna shaping solutions on the base station side to enhance the coverage of the base station's transmitted signals and improve base station performance. In this scenario, the UE (user equipment) needs to estimate the codebook to assist the base station side in completing the shaping factor selection.

[0003] In the process of estimating the codebook on the UE side, in order to reduce the number of bits fed back by the UE, protocols such as NR (New Radio) / LTE (Long Term Evolution) provide a two-level codebook cascade, that is, a set of dual codebooks, for the UE to select the optimal codebook for reporting. Among them, the matrix composed of the dual codebook is matrix W, and the number of rows of matrix W is N t is the number of array antennas, the number of columns N l is the number of spatial streams, and W=W1W2, where the dimension of matrix W1 is N t ×2L, the dimension of matrix W2 is 2L×N l , W1 is a block diagonal matrix based on a fixed beam cluster and matched to the long-term channel information, also known as the inner codebook matrix; matrix W2 is the outer codebook matrix, which is used to implement frequency-selective beam selection and co-phasing functions and matches the short-term channel information; L is the number of selected beam vectors.

[0004] In the existing process of selecting the codebook on the UE side, W is usually directly calculated. H The value of RW is substituted into the codebook metric function f(W H RW) is calculated, and then according to f(W H The optimal codebook is selected based on the operation result of RW), where R is the channel state matrix The autocorrelation matrix of N r is the number of receiving antennas.

[0005] However, since the channel state matrix The dimension is large, which leads to high complexity of the dual codebook estimation on the UE side, thereby reducing the efficiency of the dual codebook estimation on the UE side. Summary of the Invention

[0006] In order to solve the above problems, the present invention provides a dual codebook estimation method and system thereof, by changing the initial calculation formula W H The autocorrelation matrix R in RW is processed by eigenvalue decomposition to reduce W HThe complexity of RW calculation is reduced, thereby improving the efficiency of dual codebook estimation on the UE side.

[0007] In a first aspect, the present invention provides a dual codebook estimation method, applied to a user equipment side, the method comprising:

[0008] Get the dual codebook matrix W and channel state matrix The autocorrelation matrix R of the double codebook matrix W has the number of rows N t is the number of array antennas, the number of columns N l is the number of spatial streams, N r is the number of receiving antennas, N l Less than N t ;

[0009] For the initial calculation formula W H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula λ i is the eigenvalue after eigenvalue decomposition of the autocorrelation matrix R, s i is the eigenvector after eigenvalue decomposition of the autocorrelation matrix R;

[0010] Convert decomposed calculation formula into alternative calculation formula

[0011] Substitute the alternative calculation formula into the preset codebook metric function to screen out the optimal codebook.

[0012] Optionally, the initial calculation formula W H The steps of performing eigenvalue decomposition processing on the autocorrelation matrix R in RW include:

[0013] The initial calculation formula W is calculated by Householder algorithm or Laplace algorithm H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula

[0014] Optionally, obtain the dual codebook matrix W and the channel state matrix The steps of calculating the autocorrelation matrix R include:

[0015] Get the inner codebook matrix and the outer codebook matrix. The dimension of the inner codebook matrix is N. t ×2L, the dimension of the outer codebook matrix is 2L×N l ;

[0016] The dual codebook matrix W is calculated by multiplying the inner codebook matrix and the outer codebook matrix.

[0017] Optionally, the autocorrelation matrix R includes at most N l feature vectors.

[0018] In a second aspect, the present invention provides a dual codebook estimation system, which is applied to a user equipment side, and the system includes:

[0019] An acquisition module is configured to obtain the dual codebook matrix W and the channel state matrix The autocorrelation matrix R of the double codebook matrix W has the number of rows N t is the number of array antennas, the number of columns N l is the number of spatial streams, N r is the number of receiving antennas, N l Less than N t ;

[0020] The decomposition module is configured to calculate the initial formula W H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula λ i is the eigenvalue after eigenvalue decomposition of the autocorrelation matrix R, s i is the eigenvector after eigenvalue decomposition of the autocorrelation matrix R;

[0021] A conversion module configured to convert the decomposed calculation formula into an alternative calculation formula The alternative calculation formula is substituted into the preset codebook metric function to screen out the optimal codebook.

[0022] Optionally, the decomposition module is further configured to calculate the initial calculation formula W by Householder algorithm or Laplace algorithm. H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula

[0023] Optionally, the acquisition module includes:

[0024] The acquisition submodule is configured to obtain the inner codebook matrix and the outer codebook matrix. The dimension of the inner codebook matrix is N t ×2L, the dimension of the outer codebook matrix is 2L×N l ;

[0025] The calculation submodule is configured to calculate the dual codebook matrix W by multiplying the inner codebook matrix by the outer codebook matrix.

[0026] Optionally, the autocorrelation matrix R includes at most N l feature vectors.

[0027] In a third aspect, the present invention provides a chip, comprising:

[0028] at least one processor; and

[0029] a memory communicatively connected to at least one processor; wherein,

[0030] The memory stores instructions that can be executed by at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform any of the above methods.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, any of the above methods is implemented.

[0032] The dual codebook estimation method and system provided by the embodiment of the present invention utilizes the sparsity of the channel state matrix to calculate the initial calculation formula W H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition, and the decomposition calculation formula obtained is converted into an alternative calculation formula so that W H The complexity of RW calculation is reduced from the original O(N t 2 ) is reduced to O(N t ), which reduces W H The complexity of RW calculation is reduced, thereby improving the efficiency of dual codebook estimation on the UE side. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the conventional technology, the following briefly introduces the drawings required for use in the embodiments or the conventional technology descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 This is a schematic flowchart of a dual codebook estimation method according to an embodiment of the present application;

[0035] Figure 2 FIG. 1 is a schematic structural diagram of a dual codebook estimation system according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] To facilitate understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The accompanying drawings provide embodiments of the present application. However, the present application may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application.

[0038] When used herein, the singular forms "a", "an", and "the" may also include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include / comprise" or "have" and the like specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof.

[0039] In a first aspect, an embodiment of the present invention provides a dual codebook estimation method, which is applied to a user equipment side. Figure 1 , the method includes steps S101 to S103:

[0040] Step S101: Obtain the dual codebook matrix W and the channel state matrix The autocorrelation matrix R of .

[0041] Among them, the number of rows of the dual codebook matrix W is N t is the number of array antennas, the number of columns N l is the number of spatial streams, N r is the number of receiving antennas, N l Less than N t ; The autocorrelation matrix R includes at most N l feature vectors.

[0042] The number of array antennas is configured on the base station side and sent to the UE through signaling. t According to the NR protocol, the value may be 4, 8, 12, 16, 24 or 32. l The value may be 1, 2, 3 or 4 depending on the capability of the UE.

[0043] Specifically, since the number of antennas of the mobile receiver antennas on the user equipment side is much smaller than the number of antennas on the base station side, the autocorrelation matrix R is a sparse matrix with a maximum of N l non-zero eigenvectors. In this embodiment, N l Much smaller than N t , that is, N l <<N t , I will not elaborate on this.

[0044] Step S102: Initial calculation formula W HThe autocorrelation matrix R in RW is processed by eigenvalue decomposition (EVD) to obtain the decomposition calculation formula

[0045] Among them, λ i is the eigenvalue after eigenvalue decomposition of the autocorrelation matrix R, s i is the eigenvector after eigenvalue decomposition of the autocorrelation matrix R.

[0046] Step S103: Convert the decomposed calculation formula into an alternative calculation formula The alternative calculation formula is substituted into the preset codebook metric function to screen out the optimal codebook.

[0047] It should be noted that the preset codebook metric quality function used to calculate the performance of the dual codebook includes but is not limited to the existing codebook metric function, which will not be described in detail in this embodiment.

[0048] At the same time, the present invention does not protect the specific content of the preset codebook metric quality function mentioned above. In addition, due to the sparsity of the channel state matrix, the present invention can obtain W by replacing the calculation formula H RW calculated The complex multiplication is reduced to Since N l Much smaller than N t , so calculate W H The complexity of RW is reduced from the original O(N t 2 ) is reduced to O(N t ). Although the method provided by the present invention brings additional workload of EVD decomposition of the N-dimensional matrix R, due to the sparsity of R, EVD can also adopt a method with lower complexity, such as the Householder algorithm or the Laplace algorithm, and the EVD decomposition is calculated only once, the workload of EVD decomposition of the N-dimensional matrix R in the present invention does not significantly increase the complexity of estimating the double codebook, and is relatively simple compared to the existing direct calculation of W H The complexity of RW is reduced, and the present invention can still reduce the complexity of estimating the dual codebook.

[0049] At the same time, there may be dozens or hundreds of selection sets for W, and EVD decomposition can provide information such as channel correlation, which can be used to estimate RI (rank indication) and provide important information for related algorithm decisions. This embodiment will not go into details about this.

[0050] Furthermore, in the existing calculation W H In the process of RW, the first step is to multiply the autocorrelation matrix R by the W matrix, which is N t ×Nt The matrix and N t ×N l Matrix multiplication, complex multiplication operation is N t N l ×N t Complex multiplication; then, with W H The complex multiplication operation of matrix multiplication is N l 2 ×N t Complex multiplication, from which we can know the existing calculation W H The RW process includes Complex multiplication.

[0051] In the present invention, W H The autocorrelation matrix R in RW is subjected to EVD decomposition, and the decomposition calculation formula after EVD decomposition is converted into an alternative calculation formula. The specific process can be performed by the following formula 1.

[0052]

[0053] It should be noted that the approximation in the last step of the above formula is based on the fact that the number of antennas on the mobile receiver side is much smaller than the number of antennas on the base station side, so the autocorrelation matrix R is a sparse matrix, with a maximum of N l non-zero eigenvectors, N l <<N. Among them, 1×N t Vector and N t ×N l Matrix multiplication, that is, Nt×N l Complex multiplication, W H s i and Multiplying them together is N l ×1 vector and 1xN l Vector multiplication, the result is N l ×N l Matrix, because it is a symmetric matrix, we only need to calculate the results of the main diagonal and upper half-angle matrix, that is, 1+2+…N l times complex multiplication, equal to Therefore, the present invention calculates W H The RW process includes times complex multiplication. Since N l Much smaller than N t , so the present invention calculates W H The complexity of RW is reduced from the original O(N t 2 ) is reduced to O(N t ).

[0054] In an optional embodiment, the dual codebook matrix W and the channel state matrix are obtained The steps of calculating the autocorrelation matrix R include:

[0055] Get the inner codebook matrix and the outer codebook matrix. The dimension of the inner codebook matrix is N. t ×2L, the dimension of the outer codebook matrix is 2L×N l ; By multiplying the inner codebook matrix and the outer codebook matrix, the double codebook matrix W is calculated.

[0056] The dual codebook estimation method provided in this embodiment can reduce the matrix dimension of the estimated dual codebook through EVD decomposition without loss of generality, thereby reducing the complexity of estimating the dual codebook. At the same time, this method is applicable to communication systems using dual codebooks, including but not limited to LTE / NR.

[0057] In a second aspect, the present invention provides a dual codebook estimation system 200, which is applied to a user equipment side. Figure 2 , the dual codebook estimation system 200 includes:

[0058] Acquisition module 201 is configured to acquire the dual codebook matrix W and the channel state matrix The autocorrelation matrix R of the double codebook matrix W has the number of rows N t is the number of array antennas, the number of columns N l is the number of spatial streams, N r is the number of receiving antennas, N l Much smaller than N t ;

[0059] Decomposition module 202 is configured to calculate the initial formula W H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula λ i is the eigenvalue after eigenvalue decomposition of the autocorrelation matrix R, s i is the eigenvector after eigenvalue decomposition of the autocorrelation matrix R;

[0060] The conversion module 203 is configured to convert the decomposed calculation formula into an alternative calculation formula The alternative calculation formula is substituted into the preset codebook metric function to screen out the optimal codebook.

[0061] In an optional embodiment, the decomposition module 202 is further configured to use the Householder algorithm or the Laplace algorithm to calculate the initial calculation formula W H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula

[0062] In an optional embodiment, the acquisition module 201 includes:

[0063] The acquisition submodule is configured to obtain the inner codebook matrix and the outer codebook matrix. The dimension of the inner codebook matrix is N t ×2L, the dimension of the outer codebook matrix is 2L×N l ;

[0064] The calculation submodule is configured to calculate the dual codebook matrix W by multiplying the inner codebook matrix by the outer codebook matrix.

[0065] In an optional embodiment, the autocorrelation matrix R includes at most N l feature vectors.

[0066] In a third aspect, an embodiment of the present invention provides a chip, comprising:

[0067] at least one processor; and

[0068] a memory communicatively connected to at least one processor; wherein,

[0069] The memory stores instructions that can be executed by at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform any of the above methods.

[0070] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, any of the above methods is implemented.

[0071] Throughout this specification, references to terms such as "some embodiments," "other embodiments," and "desired embodiments" indicate that a particular feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present application. The schematic descriptions of these terms throughout this specification do not necessarily refer to the same embodiment or example.

[0072] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A dual codebook estimation method, characterized in that: Applied to the user equipment side, the method includes: Get the dual codebook matrix W and channel state matrix The autocorrelation matrix R of the double codebook matrix W has the number of rows N t is the number of array antennas, the number of columns N l is the number of spatial streams, N r is the number of receiving antennas, N l Less than N t ; For the initial calculation formula W H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula λ i is the eigenvalue after eigenvalue decomposition of the autocorrelation matrix R, s i is the eigenvector after eigenvalue decomposition of the autocorrelation matrix R; Convert the decomposition calculation formula into an alternative calculation formula The alternative calculation formula is substituted into a preset codebook metric function to screen out the optimal codebook.

2. The method according to claim 1, characterized in that The initial calculation formula W H The steps of performing eigenvalue decomposition processing on the autocorrelation matrix R in RW include: The initial calculation formula W is calculated by Householder algorithm or Laplace algorithm H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula 3. The method according to claim 1, characterized in that The dual codebook matrix W and the channel state matrix are obtained. The steps of calculating the autocorrelation matrix R include: Get the inner codebook matrix and the outer codebook matrix, the dimension of the inner codebook matrix is N t ×2L, the dimension of the outer codebook matrix is 2L×N l ; The dual codebook matrix W is calculated by multiplying the inner codebook matrix and the outer codebook matrix.

4. The method according to any one of claims 1 to 3, characterized in that The autocorrelation matrix R includes at most N l feature vectors.

5. A dual codebook estimation system, characterized in that: Applied to the user equipment side, the system includes: An acquisition module is configured to obtain the dual codebook matrix W and the channel state matrix The autocorrelation matrix R of the double codebook matrix W has the number of rows N t is the number of array antennas, the number of columns N l is the number of spatial streams, N r is the number of receiving antennas, N l Less than N t ; The decomposition module is configured to calculate the initial formula W H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula λ i is the eigenvalue after eigenvalue decomposition of the autocorrelation matrix R, s i is the eigenvector after eigenvalue decomposition of the autocorrelation matrix R; A conversion module is configured to convert the decomposed calculation formula into an alternative calculation formula The alternative calculation formula is substituted into a preset codebook metric function to screen out the optimal codebook.

6. The system according to claim 5, characterized in that The decomposition module is further configured to calculate the initial calculation formula W by Householder algorithm or Laplace algorithm. H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula 7. The system according to claim 5, characterized in that The acquisition module includes: The acquisition submodule is configured to obtain an inner codebook matrix and an outer codebook matrix, wherein the dimension of the inner codebook matrix is N t ×2L, the dimension of the outer codebook matrix is 2L×N l ; The calculation submodule is configured to calculate the dual codebook matrix W by multiplying the inner codebook matrix and the outer codebook matrix.

8. The system according to any one of claims 8 to 7, characterized in that The autocorrelation matrix R includes at most N l feature vectors.

9. A chip, characterized in that: The chip includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which implement the method according to any one of claims 1 to 4 when executed by a processor.

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