Dual codebook estimation method and system thereof
By performing eigenvalue decomposition on the autocorrelation matrix R of the channel state matrix, the problem of high complexity in dual codebook estimation on the UE side is solved, and a more efficient estimation process is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 归芯科技(深圳)有限公司
- Filing Date
- 2024-02-07
- Publication Date
- 2026-07-21
AI Technical Summary
In the existing technology, the user equipment (UE) side has high complexity when estimating the dual codebook, resulting in low estimation efficiency.
By performing eigenvalue decomposition on the autocorrelation matrix R in the initial calculation formula WHRW, the computational complexity is reduced, and it is transformed into an alternative calculation formula to select the optimal codebook.
The complexity of dual-codebook estimation has been reduced from O(Nt2) to O(Nt), thus improving estimation efficiency.
Smart Images
Figure CN120454768B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wireless communication technology, and in particular to a dual codebook estimation method and system. Background Technology
[0002] For most advanced communication systems, large-scale array antenna shaping schemes are used on the base station side to enhance the coverage of the base station's transmitted signals and improve the base station's performance. In this scenario, the UE (User Equipment) needs to estimate the codebook to assist the base station side in selecting the shaping factor.
[0003] During the codebook estimation process on the UE side, to reduce the number of bits reported by the UE, protocols such as NR (New Radio) / LTE (Long Term Evolution) provide a two-level codebook concatenation, i.e., a set of dual codebooks, for the UE to select the optimal codebook for reporting. The matrix formed by the dual codebooks is matrix W, and matrix W has N rows. t N represents the number of array antennas and the number of columns. l Let W be the number of spatial flows, and let W = W1W2, where the dimension of matrix W1 is N. t The dimension of matrix W2 is 2L×N. l W1 is a block diagonal matrix based on a fixed beam cluster and matched to long-time channel information, also known as the inner codebook matrix; W2 is the outer codebook matrix, used to implement frequency-selective beam selection and co-phase function and matched to short-time channel information; L is the number of selected beam vectors.
[0004] In the current process of selecting a codebook on the UE side, W is usually calculated directly. H The value of RW is then substituted into the codebook metric function f(W). H The calculation is performed in RW), and then based on f(W) H The optimal codebook is selected from the results of the RW operation, where R is the channel state matrix. The autocorrelation matrix, i.e. N r This represents the number of receiving antennas.
[0005] However, due to the channel state matrix The large dimensionality of the codebook results in high complexity for the UE to estimate the dual codebook, thus reducing the efficiency of the UE in estimating the dual codebook. Summary of the Invention
[0006] To address the aforementioned problems, the present invention provides a dual-codebook estimation method and system, which, through the initial calculation formula W... H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to reduce W. HThis reduces the complexity of RW calculations, thereby improving the efficiency of the UE side in estimating the dual codebook.
[0007] In a first aspect, the present invention provides a dual codebook estimation method, applied to the user equipment side, the method comprising:
[0008] Obtain the dual codebook matrix W and the channel state matrix. The autocorrelation matrix R, and the number of rows N of the double codebook matrix W. t N represents the number of array antennas and the number of columns. l N represents the number of space flows. r N represents the number of receiving antennas. 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 s represents the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition. i The eigenvectors are the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition.
[0010] Convert the decomposition calculation formula into a substitute calculation formula.
[0011] The substitution calculation formula is substituted into the preset codebook metric function to select the optimal codebook.
[0012] Optionally, for the initial calculation formula W H The steps for eigenvalue decomposition of the autocorrelation matrix R in RW include:
[0013] The initial calculation formula W is obtained by using either the Haushold algorithm or the 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 for obtaining the autocorrelation matrix R include:
[0015] Obtain the inner codebook matrix and the outer codebook matrix. The dimension of the inner codebook matrix is N. t ×2L, the outer codebook matrix has a dimension of 2L×N. l ;
[0016] The double 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 1 eigenvector.
[0018] Secondly, this invention provides a dual codebook estimation system applied to the user equipment side, the system comprising:
[0019] The acquisition module is configured to acquire the dual codebook matrix W and the channel state matrix. The autocorrelation matrix R, and the number of rows N of the double codebook matrix W. t N represents the number of array antennas and the number of columns. l N represents the number of space flows. r N represents the number of receiving antennas. l Less than N t ;
[0020] The decomposition module is configured to perform calculations on the initial formula W. H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula. λ i s represents the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition. i The eigenvectors are the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition.
[0021] The conversion module is configured to convert the decomposed calculation formula into a substitute calculation formula. The optimal codebook is selected by substituting the substitution calculation formula into the preset codebook metric function.
[0022] Optionally, the decomposition module is also configured to perform a decomposition on the initial calculation formula W using either the Haushold algorithm or the 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 acquire the inner codebook matrix and the outer codebook matrix, where the dimension of the inner codebook matrix is N. t ×2L, the outer codebook matrix has a dimension of 2L×N. l ;
[0025] The computation submodule is configured to calculate the double codebook matrix W by multiplying the inner codebook matrix and the outer codebook matrix.
[0026] Optionally, the autocorrelation matrix R includes at most N l 1 eigenvector.
[0027] Thirdly, the present invention provides a chip, the chip comprising:
[0028] At least one processor; and
[0029] A memory that is communicatively connected to at least one processor; wherein,
[0030] The memory stores instructions that can be executed by at least one processor, such that at least one processor can perform any of the methods described above.
[0031] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement any of the methods described above.
[0032] The dual codebook estimation method and system provided in this invention utilize the sparsity of the channel state matrix to optimize the initial calculation formula W. H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition, and the resulting decomposition calculation formula is converted into a substitution calculation formula, so that W H The complexity of RW computation was initially O(N) t 2 ) reduced to O(N t ), reducing W H This reduces the complexity of RW calculations, thereby improving the efficiency of the UE side in estimating the dual codebook. Attached Figure Description
[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0034] Figure 1 This is a schematic flowchart illustrating a dual codebook estimation method according to an embodiment of this application;
[0035] Figure 2 This is a schematic structural diagram of a dual codebook estimation system according to an embodiment of this application. Detailed Implementation
[0036] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.
[0037] 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 particular embodiments only and is not intended to be limiting of the application.
[0038] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.
[0039] In a first aspect, one embodiment of the present invention provides a dual codebook estimation method, applied to the user equipment side, see [link to relevant documentation]. 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.
[0041] Wherein, the number of rows N of the double codebook matrix W t N represents the number of array antennas and the number of columns. l N represents the number of space flows. r N represents the number of receiving antennas. l Less than N t The autocorrelation matrix R contains at most N l 1 eigenvector.
[0042] The number of array antennas is configured on the base station side and sent to the UE via signaling. t According to the NR protocol, N may take the values 4, 8, 12, 16, 24, or 32. l Depending on the UE's capabilities, the value may be 1, 2, 3, or 4.
[0043] Specifically, since the number of antennas on the user equipment side of the mobile receiver is much smaller than the number of antennas on the base station side, the autocorrelation matrix R is a sparse matrix, with at most N antennas. l N non-zero feature vectors, in this embodiment, N l Much smaller than N t N l <<N t I won't go into too much detail about that.
[0044] Step S102: Apply the initial calculation formula W HThe autocorrelation matrix R in RW is subjected to eigenvalue decomposition (EVD) to obtain the decomposition calculation formula.
[0045] Where, λ i s represents the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition. i The eigenvectors are the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition.
[0046] Step S103: Convert the decomposition calculation formula into a substitution calculation formula The optimal codebook is selected by substituting the substitution calculation formula into the preset codebook metric function.
[0047] It should be noted that the preset codebook metric quality function is used to calculate the performance of dual codebooks, including but not limited to existing codebook metric functions. This embodiment will not elaborate on this further.
[0048] Furthermore, this invention does not protect the specific content of the preset codebook metric quality function mentioned above. Additionally, due to the sparsity of the channel state matrix, the W value of this invention can be obtained through substitution calculation formulas. H RW calculation Complex multiplication is reduced to Due to N l Much smaller than N t Therefore, calculate W H The complexity of RW was initially O(N). t 2 ) reduced to O(N t Although the method provided in this invention introduces additional workload for EVD decomposition of the N-dimensional matrix R, the sparsity of R and the fact that EVD can also be performed using low-complexity methods such as the Householder algorithm or the Laplace algorithm, coupled with the fact that EVD decomposition is calculated only once, mean that the workload of EVD decomposition of the N-dimensional matrix R in this invention does not significantly increase the complexity of estimating the dual codebook. Compared to existing methods that directly calculate W... H Despite the complexity of RW, this invention can still reduce the complexity of estimating dual codebooks.
[0049] Meanwhile, the selection set of W may be dozens or hundreds of times, and EVD decomposition can provide information such as channel correlation, which can be used to estimate RI (rank indicator) and provide important information for related algorithm decisions. This embodiment will not elaborate on this further.
[0050] Furthermore, in the existing calculations of W H In the RW process, the first step is to multiply the autocorrelation matrix R by the W matrix, specifically N. t ×Nt The matrix and N t ×N l Matrix multiplication, complex multiplication is N t N l ×N t Multiply by a complex number several times; then, combine with W. H The complex multiplication operation of matrix multiplication is N. l 2 ×N t Multiplication by a complex number, from which we can know the existing calculation of W H The RW process includes Complex number multiplication.
[0051] In this invention, W H The autocorrelation matrix R in RW is decomposed using EVD, and the decomposition calculation formula after EVD decomposition is converted into a substitute calculation formula. The specific process can be carried out using the following formula.
[0052]
[0053] It should be noted that the approximation in the last step of the above equation 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 at most N... l N non-zero eigenvectors l << N. Among them, For 1×N t Vector and N t ×N l Matrix multiplication, i.e., Nt×N l Multiplication by a complex number, W H s i and Multiplication is N l ×1 vector and 1xN l Vector multiplication results in N. l ×N l Since it's a symmetric matrix, we only need to calculate the results of the main diagonal and the upper half of the matrix, i.e., 1 + 2 + ... + N. l Multiplying by a complex number multiple times equals... Therefore, this invention calculates W. H The RW process includes Complex multiplication. Because N... l Much smaller than N t Therefore, this invention calculates W H The complexity of RW was initially O(N). t 2 ) reduced to O(N t ).
[0054] In one optional embodiment, the dual codebook matrix W and the channel state matrix are obtained. The steps for obtaining the autocorrelation matrix R include:
[0055] Obtain the inner codebook matrix and the outer codebook matrix. The dimension of the inner codebook matrix is N. t ×2L, the outer codebook matrix has a dimension of 2L×N. l The double codebook matrix W is calculated by multiplying the inner codebook matrix and the outer codebook matrix.
[0056] The dual-codebook estimation method provided in this embodiment can reduce the matrix dimension of the dual-codebook estimation through EVD decomposition without loss of generality, thereby reducing the complexity of dual-codebook estimation. At the same time, this method can be applied to communication systems that use dual-codebooks, including but not limited to LTE / NR.
[0057] Secondly, this invention provides a dual codebook estimation system 200, applied to the user equipment side, see [link to relevant documentation]. Figure 2 The dual-codebook estimation system 200 includes:
[0058] The acquisition module 201 is configured to acquire the dual codebook matrix W and the channel state matrix. The autocorrelation matrix R, and the number of rows N of the double codebook matrix W. t N represents the number of array antennas and the number of columns. l N represents the number of space flows. r N represents the number of receiving antennas. l Much smaller than N t ;
[0059] Decomposition module 202 is configured to perform initial calculations on formula W. H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula. λ i s represents the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition. i The eigenvectors are the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition.
[0060] Conversion module 203 is configured to convert the decomposition calculation formula into a substitution calculation formula. The optimal codebook is selected by substituting the substitution calculation formula into the preset codebook metric function.
[0061] In an alternative embodiment, the decomposition module 202 is further configured to perform a decomposition on the initial calculation formula W using either the Haushold algorithm or the Laplace algorithm. H The autocorrelation matrix R in RW is subjected to eigenvalue decomposition to obtain the decomposition calculation formula.
[0062] In one optional embodiment, the acquisition module 201 includes:
[0063] The acquisition submodule is configured to acquire the inner codebook matrix and the outer codebook matrix, where the dimension of the inner codebook matrix is N. t ×2L, the outer codebook matrix has a dimension of 2L×N. l ;
[0064] The computation submodule is configured to calculate the double codebook matrix W by multiplying the inner codebook matrix and the outer codebook matrix.
[0065] In one alternative embodiment, the autocorrelation matrix R includes at most N l 1 eigenvector.
[0066] Thirdly, one embodiment of the present invention provides a chip, the chip comprising:
[0067] At least one processor; and
[0068] A memory that is communicatively connected to at least one processor; wherein,
[0069] The memory stores instructions that can be executed by at least one processor, such that at least one processor can perform any of the methods described above.
[0070] Fourthly, one embodiment of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method described above.
[0071] In the description of this specification, the references to terms such as "some embodiments," "other embodiments," "ideal embodiments," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative descriptions of the above terms do not necessarily refer to the same embodiments or examples.
[0072] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should 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: Obtain the dual codebook matrix W and the channel state matrix. The autocorrelation matrix R, and the number of rows N of the double codebook matrix W. t The number of array antennas, the number of columns N represents the number of space flows. r The number of receiving antennas. Less than N t ; For the initial calculation formula The autocorrelation matrix R in the equation is subjected to eigenvalue decomposition to obtain the decomposition calculation formula. , s represents the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition. i The eigenvectors are the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition. Convert the decomposition calculation formula into a substitution calculation formula. The substitution calculation formula is then substituted into a preset codebook metric function to select the optimal codebook.
2. The method according to claim 1, characterized in that, The initial calculation formula The steps for eigenvalue decomposition of the autocorrelation matrix R in the model include: The initial calculation formula is obtained by using the Haushold algorithm or the Laplace algorithm. The autocorrelation matrix R in the equation is subjected to eigenvalue decomposition to obtain the decomposition calculation formula. .
3. The method according to claim 1, characterized in that, The acquisition of the dual codebook matrix W and the channel state matrix The steps for obtaining the autocorrelation matrix R include: Obtain the inner codebook matrix and the outer codebook matrix, wherein the dimension of the inner codebook matrix is N. t The dimension of the external codebook matrix is ; 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 1 eigenvector.
5. A dual-codebook estimation system, characterized in that, The system, applied to the user equipment side, includes: The acquisition module is configured to acquire the dual codebook matrix W and the channel state matrix. The autocorrelation matrix R, and the number of rows N of the double codebook matrix W. t The number of array antennas, the number of columns N represents the number of space flows. r The number of receiving antennas. Less than N t ; The decomposition module is configured to perform initial calculations on the formulas. The autocorrelation matrix R in the equation is subjected to eigenvalue decomposition to obtain the decomposition calculation formula. , s represents the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition. i The eigenvectors are the eigenvalues of the autocorrelation matrix R after eigenvalue decomposition. The conversion module is configured to convert the decomposition calculation formula into a replacement calculation formula. The substitution calculation formula is then substituted into a preset codebook metric function to select the optimal codebook.
6. The system according to claim 5, characterized in that, The decomposition module is also configured to perform initial calculations on the formulas using either the Haushold algorithm or the Laplace algorithm. The autocorrelation matrix R in the equation 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 acquire an inner codebook matrix and an outer codebook matrix, wherein the dimension of the inner codebook matrix is N. t The dimension of the external codebook matrix is ; 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 5 to 7, characterized in that, The autocorrelation matrix R includes at most 1 eigenvector.
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 to enable the at least one processor to perform the method as described in any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 4.