CN111935746A - Method, device, terminal and storage medium for acquiring communication parameters - Google Patents
Method, device, terminal and storage medium for acquiring communication parameters Download PDFInfo
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Abstract
The embodiment of the application discloses a method, a device, a terminal and a storage medium for obtaining communication parameters, and belongs to the technical field of communication. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a form of a product of a first matrix, a second matrix and a transpose matrix of the first matrix, then use the solved inverse matrix of the first matrix and the solved inverse matrix of the second matrix to obtain a third matrix, then combine the transpose matrix of the first matrix to obtain an inverse matrix of the autocorrelation matrix, and based on the inverse matrix and a corresponding cross-correlation matrix, obtain communication parameters for improving the quality of the communication signals. The evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, so that the complexity of calculating the communication parameters is reduced, and the efficiency of obtaining the communication parameters is improved.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a method, an apparatus, a terminal, and a storage medium for acquiring a communication parameter.
Background
With the rapid development of wireless communication technology, accurate noise condition estimation is of great significance for improving signal quality. Wherein, the accurate calculation of the channel estimation coefficient has a large influence on the signal quality.
In the related art, the situation of signals changes in real time. Therefore, the terminal can calculate the current real-time channel estimation coefficient in real time, which is of great help to improve the signal quality. In a possible calculation mode, the terminal needs to perform matrix inversion operation in real time when calculating the real-time channel estimation coefficient. Generally, a terminal performs matrix inversion by using a Cholesky (chinese: Cholesky) decomposition method, and then calculates to obtain a channel estimation coefficient.
Disclosure of Invention
The embodiment of the application provides a method, a device, a terminal and a storage medium for acquiring communication parameters. The technical scheme is as follows:
according to an aspect of the present application, there is provided a method of acquiring communication parameters, the method including:
acquiring an autocorrelation matrix of a communication signal;
performing matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix, wherein the autocorrelation matrix is the product of the first matrix, the second matrix and a transposed matrix of the first matrix;
obtaining a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second matrix;
calculating an inverse matrix of the autocorrelation matrix according to the third matrix and the transposed matrix of the first matrix;
and acquiring communication parameters according to the inverse matrix of the autocorrelation matrix and the corresponding cross-correlation matrix, wherein the communication parameters are used for improving the quality of the communication signals.
According to another aspect of the present application, there is provided an apparatus for acquiring communication parameters, the apparatus comprising:
the matrix acquisition module is used for acquiring an autocorrelation matrix of the communication signal;
a matrix decomposition module, configured to perform matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix, where the autocorrelation matrix is a product of the first matrix, the second matrix, and a transposed matrix of the first matrix;
the matrix calculation module is used for obtaining a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second matrix;
a matrix inversion module, configured to calculate an inverse matrix of the autocorrelation matrix according to the third matrix and a transposed matrix of the first matrix;
and the parameter acquisition module is used for acquiring communication parameters according to the inverse matrix of the autocorrelation matrix and the corresponding cross-correlation matrix, wherein the communication parameters are used for improving the quality of the communication signals.
According to another aspect of the present application, there is provided a terminal comprising a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method for obtaining communication parameters as provided in the various aspects of the present application.
According to another aspect of the present application, there is provided a computer-readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the method for obtaining communication parameters as provided in the various aspects of the present application.
According to one aspect of the present application, a computer program product is provided that includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the methods provided in the various alternative implementations of the above-described aspect of obtaining communication parameters.
In the method for acquiring the communication parameters, the terminal can acquire the inverse matrix of the autocorrelation matrix by adopting a mode of firstly decomposing and then solving the inverse matrix after acquiring the autocorrelation matrix of the communication signals. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a form of a product of a first matrix, a second matrix and a transpose matrix of the first matrix, then use an inverse matrix of the first matrix and an inverse matrix of the second matrix that have been solved to obtain a third matrix, then combine the transpose matrix of the first matrix to calculate an inverse matrix of the autocorrelation matrix, and based on the inverse matrix and a corresponding cross-correlation matrix, the terminal can obtain corresponding communication parameters for improving the quality of the communication signal. The evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, so that the complexity of calculating the communication parameters is reduced, and the efficiency of obtaining the communication parameters is improved.
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In order to more clearly describe the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a process for obtaining wiener filter coefficients according to an embodiment of the present disclosure;
fig. 2 is a block diagram of a terminal according to an exemplary embodiment of the present application;
fig. 3 is a flowchart of a method for obtaining communication parameters according to an exemplary embodiment of the present application;
fig. 4 is a flowchart of a method for obtaining communication parameters according to another exemplary embodiment of the present application;
fig. 5 is a schematic diagram illustrating a method for acquiring communication parameters according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a comparison of multiplication complexity between two possible implementations of the scheme shown in FIG. 5;
FIG. 7 is a diagram illustrating a comparison of the complexity of addition between two possible implementations of the scheme of FIG. 5;
fig. 8 is a block diagram illustrating a structure of an apparatus for acquiring communication parameters according to an exemplary embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In the description of the present application, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connected" are to be interpreted broadly, e.g., as being fixed or detachable or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art. Further, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The present application describes a method for obtaining communication parameters, which is directly helpful to improve the quality of a received signal or to improve the quality of a transmitted signal in a wireless communication system. In one possible scenario, the communication parameter is a channel estimation coefficient. It should be noted that the communication coefficient may also be another parameter related to the matrix inversion process, and this is not limited in this embodiment of the application.
As one possible channel estimation scheme, the wiener filtering scheme is commonly used for channel estimation in a 3GPP LTE system and a 5G NR (New Radio over the air) communication system. The wiener filtering scheme involves a coefficient calculation process. The coefficient calculation process usually adopts a preset system or a method for calculating the coefficient in real time. Schematically, the wiener filter coefficient calculation process is shown in fig. 1. Fig. 1 is a schematic flowchart of a process for obtaining wiener filter coefficients according to an embodiment of the present disclosure. In fig. 1, a signal-to-noise ratio (SNR) estimation module 110, a correlation generation module 120, a pilot distance pattern generation module 130, an autocorrelation matrix generation module 140, a cross-correlation matrix generation module 150, a matrix inversion module 160, and a wiener coefficient calculation module 170 are included.
In the process of obtaining the wiener filter coefficient shown in fig. 1, the snr estimation module 110 obtains the snr. The correlation generation module 120 inputs a correlation signal to the autocorrelation matrix generation module 140 according to the delay spread (des) or doppler spread (dos), and the estimated correlation. Meanwhile, the pilot distance pattern generation module 130 also inputs a signal to the autocorrelation matrix generation module 140 according to the type of the pilot signal. The autocorrelation matrix generation module 140 will generate an autocorrelation matrix (ACM).
Meanwhile, the correlation generation module 120 and the pilot distance pattern generation module 130 also input signals to the cross-correlation matrix generation module 150, so that the cross-correlation matrix generation module 150 generates a cross-correlation matrix (CCM).
The autocorrelation matrix generated by the autocorrelation matrix generation module 140 is then input to a matrix inversion module 160 to find the inverse of the autocorrelation matrix, which is input to a wiener coefficient calculation module 170. The wiener coefficient calculation module 170 calculates a wiener coefficient according to the inverse matrix and the cross-correlation matrix of the input autocorrelation matrix.
In the wiener coefficient calculation process shown in fig. 1, a method of calculating wiener coefficients in real time may be employed. The processing procedure for the matrix inversion module 160 is described as follows. First, the terminal performs Cholesky decomposition (also called triangle decomposition) on an autocorrelation matrix (also called a target matrix). The Cholesky decomposition can represent a symmetric positive definite matrix Φ as a lower triangular matrix L and its transpose LHThe decomposition of the product of (c), i.e. the decomposition of the product of (d).
Φ＝LLH
Wherein, for
j ═ 0, 1., (n-1), the j-th column main diagonal element of the L decomposition is:
for i ═ k., (n-1), the jth column, row i element of L is:
from the above calculation process, in the Cholesky decomposition process, there is an operation of solving an evolution when solving the main diagonal element.
In the subsequent calculation process, an L matrix is obtained by using Cholesky decomposition, and the L matrix is inverted according to the theoretical relationship between the inverse matrix and the L matrix to obtain the L-1. And then carrying out matrix multiplication to obtain an inverse matrix of the target matrix. The concrete formula is as follows:
Φ-1＝(LLH)-1＝(L-1)HL-w
let Z be L-1I.e. by
Then phi-1＝ZHZ。
As can be seen from the above description, Cholesky decomposition is a positive definite matrix for the input matrix required to be decomposed, and when the input matrix does not satisfy the positive definite condition, the computing system will have the phenomenon that the self-matrix is not reversible.
Since Cholesky decomposition requires that the autocorrelation matrix to be decomposed satisfies a positive definite condition, that is, all eigenvalues of the matrix must be greater than zero, the diagonal elements of the lower triangular matrix obtained by decomposition are also greater than zero. And when the matrix to be decomposed does not meet the positive definite condition, the matrix to be decomposed is irreversible. That is, in the current coefficient real-time calculation process, the inverse matrix and the real-time coefficient calculated according to the real-time channel correlation and the channel parameters cannot be obtained, but the preset robust filter coefficient is used to ensure that the filter coefficient matrix available in the data stream of the current coefficient calculation is available.
On the other hand, the calculation method provided in fig. 1 requires an extraction operation. In the evolution operation, if the evolution operation is realized by hardware, the terminal realizes the evolution operation by a CORDIC algorithm or a table look-up method. (1) If the evolution is executed by the CORDIC algorithm, the computational complexity and the time delay are both large. (2) If the table look-up method is adopted, the table meeting the dynamic range of the current data needs to be prestored, the occupied space of the memory of the terminal can be increased, and the requirement on the memory space is higher.
In order to solve the drawback of the computational complexity of the above solution of calculating the inverse of the autocorrelation matrix. The embodiment of the application improves the scheme for acquiring the communication parameters, and is introduced as follows.
In the calculation of the current communication parameters, the terminal usually adopts the channel estimation coefficients required for calculating the wiener filter of the current time slot (slot) in real time. In this case, the terminal can calculate a corresponding channel estimation coefficient based on different pilot distance patterns, different channel parameters, and different numbers of bundled resource blocks (bundle RBs).
Illustratively, in the calculation process of each channel estimation coefficient, an N × N matrix inversion operation process is included. It should be noted that N is the number of pilot sample points (tap) of the currently calculated filter coefficient, that is, the dimension of the inverted autocorrelation matrix is N × N, so that each slot needs to calculate the inverse matrix of multiple sets of matrices in real time. When the bit width and the operation amount of the single matrix inversion are large, the calculation complexity of the whole terminal or the system for calculating the channel estimation coefficient is high. Based on the current situation, the calculation time delay of matrix operation is reduced by simplifying the complexity of single matrix inversion, so that the coefficient calculation time delay is reduced, the system time sequence requirement is met, and the complexity of time sequence control is simplified.
In the scheme designed in the embodiment of the application, a scheme for calculating the channel estimation coefficient in real time based on the enhanced matrix inversion process is provided. First, the embodiment can judge the indication selection according to the reversibilityAnd selecting a correlation coefficient, and performing protection processing on diagonal elements of the input target matrix, so that the target matrix to be inverted can better meet the requirement of positive determination of the matrix, and the irreversibility of the target matrix is avoided. Next, this example uses LDLHAnd decomposing, avoiding the evolution operation in matrix decomposition, and obtaining the inverse matrix of the target matrix by adopting a backward recursion loop iteration method based on the L matrix and the D matrix after decomposition, thereby further simplifying the complexity of inversion calculation. Finally, in this embodiment, a wiener filter coefficient is calculated according to the cross-correlation matrix and the inverse matrix of the autocorrelation matrix, and the validity of the coefficient is judged and then output to the channel estimation filtering module.
In order to make the solution shown in the embodiments of the present application easy to understand, several terms appearing in the embodiments of the present application will be described below.
3GPP (third Generation Partnership Project, 3rd Generation Partnership Project).
LTE (Long Term Evolution ).
5G NR (New air interface for fifth generation mobile communication, 5G New Radio).
RS (Reference Signal).
SNR (Signal to noise ratio).
Cholesky decomposition (Cholesky decomposition).
CORDIC (Coordinate Rotation Digital Computer).
Slot (time Slot).
For example, the method for acquiring communication parameters shown in the embodiment of the present application may be applied to a terminal with computing capability, where the terminal has the capability of transceiving radio frequency signals. The terminal may include a mobile phone, a tablet computer, a laptop computer, a desktop computer, a computer all-in-one machine, a server, a workstation, a television, a set-top box, smart glasses, a smart watch, a digital camera, an MP4 player terminal, an MP5 player terminal, a learning machine, a point-and-read machine, an electronic book, an electronic dictionary, a vehicle-mounted terminal, a Virtual Reality (VR) player terminal, an Augmented Reality (AR) player terminal, or the like.
Referring to fig. 2, fig. 2 is a block diagram of a terminal according to an exemplary embodiment of the present application, and as shown in fig. 2, the terminal includes a processor 220 and a memory 240, where the memory 240 stores at least one instruction, and the instruction is loaded and executed by the processor 220 to implement a method for acquiring communication parameters according to various method embodiments of the present application.
In the present application, the terminal 200 is an electronic device having a function of acquiring communication parameters. The terminal 200 is able to obtain an autocorrelation matrix of the communication signal; performing matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix, wherein the autocorrelation matrix is the product of the first matrix, the second matrix and a transposed matrix of the first matrix; obtaining a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second matrix; calculating an inverse matrix of the autocorrelation matrix according to the third matrix and the transposed matrix of the first matrix; and acquiring communication parameters according to the inverse matrix of the autocorrelation matrix, wherein the communication parameters are used for improving the quality of the communication signals.
The Memory 240 may include a Random Access Memory (RAM) or a Read-Only Memory (ROM). Optionally, the memory 240 includes a non-transitory computer-readable medium. The memory 240 may be used to store instructions, programs, code sets, or instruction sets. The memory 240 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing various method embodiments described below, and the like; the storage data area may store data and the like referred to in the following respective method embodiments.
Illustratively, the embodiments shown in the present application can be applied to NR modem chips. The NR modem chip includes a filtering module.
Referring to fig. 3, fig. 3 is a flowchart of a method for obtaining communication parameters according to an exemplary embodiment of the present application. The method for acquiring the communication parameters can be applied to the terminal shown above. In fig. 3, the method for acquiring the communication parameters includes:
at step 310, an autocorrelation matrix of the communication signal is obtained.
In the embodiment of the application, the terminal can execute the calculation scheme for acquiring the communication parameters through the specified integrated circuit component. Wherein the integrated circuit assembly may be a chip or other circuit assembly having equivalent functionality. In one possible scheme for obtaining the autocorrelation matrix, the terminal can calculate the autocorrelation matrix through parameters such as signal-to-noise ratio, pilot distance pattern, and the like.
It should be noted that, the embodiments of the present application do not limit other schemes capable of calculating the autocorrelation matrix.
In this embodiment, the terminal can perform matrix decomposition on the autocorrelation matrix. It should be noted that, when the decomposition is performed on the first matrix and the second matrix, the original autocorrelation matrix may be expressed as a product of the first matrix, the second matrix, and a transpose of the first matrix. Since the decomposition does not involve an squaring operation, the matrix inversion scheme provided by the present embodiment can reduce the complexity of the operation.
And 330, obtaining a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second matrix.
In this embodiment, after the autocorrelation matrix is decomposed, the terminal may obtain a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second proof obtained through decomposition. It should be noted that, the elements in the first matrix and the second matrix are already calculated in the decomposition process. Therefore, to facilitate subsequent calculations, a third matrix is introduced. The third matrix may be a product of an inverse of the first matrix and an inverse of the second matrix.
In the embodiment of the present application, the terminal will calculate the inverse of the autocorrelation matrix according to the third matrix and the transposed matrix of the first matrix, which are intermediate quantities. It should be noted that, since the first matrix and the second matrix are both calculated in the foregoing steps. Therefore, in this step, each element in the autocorrelation matrix needs to be calculated by combining the third matrix and the transpose of the first matrix.
And 350, acquiring communication parameters according to the inverse matrix of the autocorrelation matrix and the corresponding cross-correlation matrix, wherein the communication parameters are used for improving the quality of the communication signals.
In the application scenario provided by the embodiment of the application, the terminal can obtain the communication parameters according to the inverse matrix of the autocorrelation matrix and the corresponding cross-correlation matrix, and the communication parameters are used for improving the quality of the communication signals.
In this application, the communication signal may be a received signal or a transmitted signal, and this application is not limited to this application scenario. The embodiment of the application can be applied to the processing process of the communication signals, the communication parameters are finally obtained by processing the autocorrelation matrix of the communication signals, and then the communication signals are optimized through the communication parameters, so that the signal quality of the communication signals is finally improved.
In a possible implementation manner, the terminal can further encapsulate the module aiming at the matrix inversion process in the above steps to form an independent operation component, and the independent operation component is applied to other processes needing matrix inversion in the communication field. For example, the matrix inversion process can be encapsulated into IP for matrix inversion in the whitening matrix calculation process of the signal demodulation or channel state feedback module.
In summary, in the method for acquiring communication parameters provided by the present application, after acquiring the autocorrelation matrix of the communication signal, the terminal may acquire the inverse matrix of the autocorrelation matrix by decomposing first and then performing inverse matrix. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a form of a product of a first matrix, a second matrix and a transpose matrix of the first matrix, then use an inverse matrix of the first matrix and an inverse matrix of the second matrix that have been solved to obtain a third matrix, then combine the transpose matrix of the first matrix to calculate an inverse matrix of the autocorrelation matrix, and based on the inverse matrix and a corresponding cross-correlation matrix, the terminal can obtain corresponding communication parameters for improving the quality of the communication signal. The evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, so that the complexity of calculating the communication parameters is reduced, and the efficiency of obtaining the communication parameters is improved.
Based on the scheme disclosed in the previous embodiment, the terminal can further optimize the scheme for obtaining the communication parameters based on matrix inversion in detail from the following four aspects. (1) Using LDLHDecomposition replaces the traditional Cholesky decomposition; (2) preprocessing the input autocorrelation matrix, namely protecting input data; (3) obtaining an inverse matrix phi of the target matrix phi by backward recursion loop iteration-1(ii) a (4) In the whole wiener filter systemAnd in the number calculation process, a reversibility and effectiveness judgment module is added. For details, reference is made to the following examples.
Referring to fig. 4, fig. 4 is a flowchart of a method for obtaining communication parameters according to another exemplary embodiment of the present application. The method for acquiring the communication parameters can be applied to the terminal shown above. In fig. 4, the method for acquiring communication parameters includes:
In the embodiment of the application, the terminal can acquire the signal-to-noise ratio of the channel through a specified hardware component or a data channel before calculating the autocorrelation matrix.
In step 412, a pilot distance pattern is obtained.
Illustratively, the terminal can obtain a corresponding pilot distance pattern.
In step 413, a correlation coefficient is obtained.
In the embodiment of the present application, the correlation coefficient may include a statistical correlation coefficient and an estimated correlation coefficient.
In the embodiment of the present application, the communication parameter includes a channel estimation coefficient.
In step 421, in response to the element of the maximum value in the autocorrelation matrix being located on the main diagonal, matrix decomposition is performed on the autocorrelation matrix to obtain a first matrix and a second matrix.
Step 422, in response to the absolute value of the main diagonal element of the second matrix being less than or equal to the second threshold value, the statistical correlation coefficient is taken as the correlation coefficient.
Illustratively, after the terminal uses the statistical correlation coefficient as the communication coefficient, the terminal will calculate the autocorrelation matrix again according to the statistical correlation coefficient, that is, re-execute step 413 and the subsequent steps.
And 431, obtaining a main diagonal element of a third matrix according to the main diagonal element of the second matrix, wherein the main diagonal element of the second matrix and the main diagonal element of the third matrix are reciprocal.
It should be noted that, if an element is an element in the ith row and the jth column, the row number of the element is i, and the column number of the element is j.
In another possible embodiment, the terminal may implement the values of the calculation elements shown in step 433 by performing step (1) and step (2).
And (1) determining elements to be calculated, which are not calculated to obtain numerical values, aiming at elements in an upper triangular area of an inverse matrix of the autocorrelation matrix.
And (2) calculating the numerical value of the element to be calculated in response to the maximum row number and the maximum column number of the element to be calculated.
In summary, in the method for obtaining communication parameters provided by the present application, the terminal can determine the autocorrelation matrix through the signal-to-noise ratio, the pilot distance pattern, and the correlation coefficient, and after obtaining the autocorrelation matrix of the communication signal, obtain the inverse matrix of the autocorrelation matrix by means of first decomposing and then solving the inverse matrix. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a form of a product of a first matrix, a second matrix and a transpose matrix of the first matrix, then use an inverse matrix of the first matrix and an inverse matrix of the second matrix that have been solved to obtain a third matrix, then combine the transpose matrix of the first matrix to calculate an inverse matrix of the autocorrelation matrix, and based on the inverse matrix and a corresponding cross-correlation matrix, the terminal can obtain corresponding communication parameters for improving the quality of the communication signal. The evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, so that the complexity of calculating the communication parameters is reduced, and the efficiency of obtaining the communication parameters is improved.
The method for acquiring the communication parameters provided by the embodiment can also perform an autocorrelation matrix adjustment process on the input target matrix, that is, input data is protected, and the probability of matrix irreversible occurrence is reduced from a data source. The robustness of the matrix inversion process is improved, and the control complexity and the calculation complexity which are brought by the occurrence of irreversible time are avoided.
The method for acquiring the communication parameters provided by this embodiment can also use the decomposition process of the first matrix and the second matrix to replace the evolution operation in the traditional matrix decomposition process in the process of matrix inversion decomposition, thereby simplifying the complexity of system implementation.
The method for acquiring the communication parameters provided by the embodiment can ensure the reliability of the wiener coefficient calculation result through multiple reversibility judgment and validity judgment.
Referring to fig. 5, fig. 5 is a schematic diagram illustrating a method for acquiring communication parameters according to an embodiment of the present disclosure. In the implementation shown in fig. 5, the following 7 stages are involved.
And (1) selecting and judging a correlation coefficient.
According to the judgment instruction 1 output by the reversibility judgment module 1, the judgment instruction 2 output by the reversibility judgment module 2 or the judgment instruction 3 output by the coefficient validity judgment module 3, if any judgment instruction value in the three judgment instructions is No, selecting a statistical correlation coefficient as the output of the correlation coefficient selection judgment module; otherwise, selecting 'estimation correlation coefficient' as the output of the correlation coefficient selection judgment module.
In the initial state, (1) when the estimated correlation coefficient is not obtained yet, the statistical correlation coefficient is selected as the output of the correlation coefficient selection judgment module. (2) When the "estimated correlation coefficient" is available (i.e., when the block for estimating the correlation coefficient starts to have a valid output), the "estimated correlation coefficient" is used as the output of the correlation coefficient selection judgment block by default.
Stage (2) autocorrelation matrix phi and cross-correlation matrix theta calculation
And calculating a channel estimation autocorrelation matrix phi by using the correlation coefficient output by the correlation coefficient selection judgment module, the SNR value output by the SNR estimation module and the pilot distance pattern output by the pilot distance pattern calculation module. And calculating a channel estimation cross-correlation matrix theta by using the correlation coefficient output by the correlation coefficient selection judgment module and the pilot distance pattern output by the pilot distance pattern calculation module.
The calculation method of the autocorrelation matrix comprises the following steps:
wherein, R (delta k) is the output of the relation number selection judgment module in the stage (1). K isj-kiIs the distance between RS REj and RS REi.
Is the noise power. I is an N × N unit array.
The calculation method of the cross-correlation matrix comprises the following steps:
Φhh′＝[R(k0-ki)R(k1-ki)…R(kN-1-ki)]
wherein, R (delta k) is the output of the relation number selection judgment module in the stage (1). K isj-kiIs the distance between RS REj and the filter output position REi.
And (3) adjusting an autocorrelation matrix.
The autocorrelation matrix calculated in stage (2) is adjusted as follows:
if the SNR value used in the stage (2) to calculate the autocorrelation matrix is greater than TH1 (threshold value 1) dB, then add 10^ (TH-1/10) on the main diagonal elements of the autocorrelation matrix; otherwise, the module passes through.
And a stage (4) reversibility judgment 1 module.
Counting the maximum values of all elements in the autocorrelation matrix phi and the positions of the maximum values in the matrix, if the maximum values appear on the main diagonal line of the matrix phi, outputting a judgment indication value of 'Yes' by the reversibility judgment 1 module, and continuing to perform next LDL (low-density lipoprotein) stepHCarrying out decomposition operation; otherwise, outputting the judgment indication value to be No, stopping the coefficient calculation process at this time, jumping back to 1 to perform correlation coefficient selection judgment, selecting the 'statistical correlation coefficient' as the output of the correlation coefficient selection judgment module, and repeating the stages (1) to (4) to perform calculation.
Stage (5) LDLHAnd (5) decomposing operation.
If the target matrix phi is a symmetric matrix and all sequence masters of the target matrix phi are not zero, phi can be uniquely decomposed into:
Φ＝LDLH
where L is a lower triangular matrix with a main diagonal element of 1, D is a diagonal matrix (with non-zero values on the main diagonal elements only), D ═ diag (D)ii). That is to say that the first and second electrodes,
ljj＝1(j＝0，…，(N-1))
in the above formula, the problem of high computational complexity of the evolution operation involved in the Cholesky decomposition method operation is that LDL is a complex ofHThis problem is avoided in the decomposition process.
Schematically, using LDLHDecomposing the solved L matrix and D matrix according to the solved inverse matrix phi-1And the theoretical relation of the L matrix and the D matrix, respectively inverting the L matrix and the D matrix, and then performing matrix multiplication to obtain an inverse matrix of the target matrix.
And a stage (6) reversibility judgment 2 module.
Schematically, LDL in stage (5)HIn the decomposition process, numerical analysis needs to be performed on the D matrix elements obtained by decomposition, and if the D matrix elements meet the following requirements: if the absolute value of the main diagonal element of the D matrix is less than TH2 (a second threshold value), or if the main diagonal element of the D matrix is a negative value, the reversibility judgment 1 module outputs a judgment indication value of "No", at this time, the coefficient calculation process is terminated, the stage (1) is skipped to for correlation coefficient selection judgment, the "statistical correlation coefficient" is selected as the output of the correlation coefficient selection judgment module, and the stages (1) to (6) are repeated for calculation; otherwise, the judgment indicated value is output as Yes, and the backward recursion inversion process in the stage (7) is started.
And stage (7) of backward recursive inversion.
In the case of the scheme (7-1), LDL is generally performed on the object matrixHAfter decomposition, solving the object matrix phi-1The inverse matrix method of (3) is:
Φ-1＝(LDLH)-1＝(L-1)HD-1L-1
let Z be L-1I.e. by
Then phi-1＝ZHD-1Z。
In the case of the scheme (7-2), LDL is applied to the objective matrixHAfter the decomposition, the mixture is subjected to a treatment,is not based on the inverse matrix Φ in the above description-1And LDLHAnd (5) performing inversion on the theoretical relationship of the L matrix and the D matrix obtained by decomposition. According to the characteristics of the upper triangular matrix, the inverse matrix of the target matrix is obtained by adopting backward recursion loop iteration according to columns, so that the complexity of inversion calculation is further simplified. The specific backward recursion inversion method comprises the following steps:
let phi-1＝P，
Let B be D-1L-1Then, then
LHP＝B
Let LHWhen M is equal to
MP＝B
The above equation is written in the form of matrix elements (taking the input Φ matrix as a 3 × 3 matrix for example), i.e.
It can be seen that since the autocorrelation matrix and its inverse are Hermitian matrices, the P matrix can be directly expressed in the form of a conjugate symmetric matrix.
For each element of the B matrix on the right side of the equation, the order of the backward recursion in columns is:
at this time, it can be seen that in iteratively solving the inverse matrix P in a column-wise backward recursion loop, only the main diagonal elements { B ] of the B matrix are presentii0, …, (N-1) participates in the operation, and N is the dimension of the input Φ matrix. Thus, the computation of the B matrix can be simplified to the dominant diagonal element BiiI.e. no other lower triangular elements of the B matrix than the main diagonal elements need to be calculated. Principal diagonal element { biiThe calculation method comprises the following steps:
wherein d isiiBeing the main diagonal elements of the D matrix, i.e.
D＝diag(dii)
diiCalculated already in phase (5).
The M matrix can be obtained by performing a simple conjugate transpose operation on the L matrix obtained by the calculation in the stage (5), that is, the M matrix is obtained by
The calculation formula of the P matrix is as follows:
when i > j, the number of the first and second groups,
and (8) calculating the wiener filter coefficient.
From the cross-correlation matrix phi calculated in stage (2) and the inverse matrix phi of the autocorrelation matrix calculated in stage (7)-1Calculating wiener filter coefficient W ═ theta phi-1。
And (9) a coefficient validity judgment 3 module.
And (3) judging the effectiveness of the wiener filter coefficient calculated in the stage (8), and if the two are satisfied: if the absolute value of the real part or the imaginary part of any wiener coefficient is greater than TH3 or the square value of the sum of the wiener filter coefficient vectors is greater than TH4, the effectiveness judgment 3 module outputs a judgment indication value of No, at this time, the coefficient calculation process is terminated, the stage (1) is returned to for correlation coefficient selection judgment, the statistical correlation coefficient is selected as the output of the correlation coefficient selection judgment module, and the stages (1) to (8) are repeated to calculate the wiener filter coefficient and output the wiener filter coefficient to the filter module; otherwise, outputting the judgment indicating value as Yes, and outputting the currently calculated wiener filter coefficient to the filter module.
The TH3 (third threshold) and the TH4 (fourth threshold) are preset threshold values that can be configured by software.
The wiener filter coefficients output in the stage (9), which are finally output by the coefficient calculation module, can be provided to a subsequent channel estimation filter module for channel estimation wiener filtering.
Referring to fig. 6 and 7, fig. 6 is a schematic diagram showing a comparison of multiplication complexity between two possible implementations of the scheme shown in fig. 5. Fig. 7 is a diagram illustrating a comparison of the complexity of addition between two possible implementations of the scheme of fig. 5. As can be seen from the contents shown in fig. 6 and 7, the solutions shown in fig. 5 and 4 can reduce the complexity of the terminal for obtaining the communication parameters.
In fig. 6, a curve 610 represents the multiplication complexity of the scheme (7-1) in the stage (7) in the scheme shown in fig. 5, and a curve 620 represents the multiplication complexity of the scheme (7-2) in the backward recursive inversion of the stage (7) in the scheme shown in fig. 5.
In fig. 7, a curve 710 represents the addition complexity of the scheme (7-1) in the stage (7) in the scheme shown in fig. 5, and a curve 720 represents the addition complexity of the scheme (7-2) in the backward recursive inversion of the stage (7) in the scheme shown in fig. 5.
The dimensions of the autocorrelation matrices analyzed in fig. 6 and 7 include 4 × 4,8 × 8,12 × 12,16 × 16,20 × 20. Referring to fig. 6 and 7, it can be seen that the inversion Method (labeled Target Method in the figure) of the scheme (7-2) is much less complex than the direct inversion Method (labeled Legacy Method in the figure). Taking the matrix dimension of 12x12 as an example, using scheme (7-2), 572 complex multiplications (complex multiplications) +495 complex additions (complex additions) are required, while the inversion scheme of scheme (7-1) requires 936 complex multiplications +726 complex additions. When the matrix dimension is enlarged to 20x20, using scheme (7-2), 2660 times complex by +2451 times complex addition are required, while scheme (7-1) requires 4200 times complex by +3610 times complex addition. The computational complexity described here is a comparison of the computational complexity for a single matrix inversion. In actual products, usually in one slot (slot), the inverse matrix of dozens or even hundreds of matrices is calculated, and at this time, the absolute amount of reduction of the system complexity is more obvious by using the backward recursion inversion scheme recommended in the scheme.
The method for acquiring the communication parameters provided by the embodiment can perform an autocorrelation matrix adjustment process on the input target matrix, that is, input data is protected, and the probability of matrix irreversible occurrence is reduced from a data source. The robustness of the matrix inversion process is improved, and the control complexity and the calculation complexity which are brought by the occurrence of irreversible time are avoided.
The method for acquiring the communication parameters provided by this embodiment can also use the decomposition process of the first matrix and the second matrix to replace the evolution operation in the traditional matrix decomposition process in the process of matrix inversion decomposition, thereby simplifying the complexity of system implementation.
The method for acquiring the communication parameters provided by the embodiment can ensure the reliability of the wiener coefficient calculation result through multiple reversibility judgment and validity judgment.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Referring to fig. 8, fig. 8 is a block diagram illustrating a structure of an apparatus for acquiring communication parameters according to an exemplary embodiment of the present application. The means for acquiring communication parameters may be implemented as all or part of the terminal by software, hardware or a combination of both. The device includes:
a matrix obtaining module 810, configured to obtain an autocorrelation matrix of a communication signal;
a matrix decomposition module 820, configured to perform matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix, where the autocorrelation matrix is a product of the first matrix, the second matrix, and a transpose of the first matrix;
a matrix calculation module 830, configured to obtain a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second matrix;
a matrix inversion module 840, configured to calculate an inverse matrix of the autocorrelation matrix according to the third matrix and a transposed matrix of the first matrix;
a parameter obtaining module 850, configured to obtain a communication parameter according to an inverse matrix of the autocorrelation matrix and a corresponding cross-correlation matrix, where the communication parameter is used to improve the quality of the communication signal.
In an optional embodiment, the matrix inversion module 840 is configured to obtain a main diagonal element of the third matrix according to a main diagonal element of the second matrix, where the main diagonal element of the second matrix and the main diagonal element of the third matrix are reciprocal; obtaining the numerical value of the element of the transpose matrix of the first matrix according to the numerical value of the element of the first matrix; for an upper triangular region of an inverse of the autocorrelation matrix, a numerical value of an element is calculated in response to the element having a maximum number of rows and a maximum number of columns.
In an optional embodiment, the matrix inversion module 840 is configured to determine, for an element in an upper triangular region of an inverse matrix of the autocorrelation matrix, an element to be calculated for which no numerical value is calculated;
and responding to the maximum row number and the maximum column number of the element to be calculated, and calculating the numerical value of the element to be calculated.
In an optional embodiment, the matrix obtaining module 810 is configured to obtain a signal-to-noise ratio of a channel; acquiring a pilot frequency distance pattern; obtaining a correlation coefficient; calculating the autocorrelation matrix according to the signal-to-noise ratio of the channel, the pilot frequency distance pattern and the correlation coefficient; and adding a target increment to a main diagonal element of the autocorrelation matrix in response to the signal-to-noise ratio of the channel being greater than a first threshold value, wherein the target increment is a positive number. Wherein the communication parameters comprise channel estimation coefficients.
In an optional embodiment, the apparatus further includes an execution module, where the execution module is configured to perform the matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix in response to that an element of a maximum numerical value in the autocorrelation matrix is located on a main diagonal; and in response to the element of the maximum value in the autocorrelation matrix being outside the main diagonal, taking a statistical correlation coefficient as the correlation coefficient.
In an optional embodiment, the execution module is further configured to take a statistical correlation coefficient as the correlation coefficient in response to an absolute value of a main diagonal element of the second matrix being less than or equal to a second threshold value; or, in response to the main diagonal element of the second matrix being negative, taking a statistical correlation coefficient as the correlation coefficient.
In an alternative embodiment, the parameter obtaining module 850 is configured to calculate a wiener filter coefficient according to the autocorrelation matrix and the corresponding cross-correlation matrix; taking a statistical correlation coefficient as the correlation coefficient in response to the absolute value of the real part or the imaginary part of the wiener filter coefficient being greater than a third threshold value; or, in response to the square value of the sum of the wiener filter coefficient vectors being greater than the fourth threshold value, taking the statistical correlation coefficient as the correlation coefficient.
In an alternative embodiment, the execution module processes the communication signal according to the wiener filter coefficients in response to the absolute value of the real part or the imaginary part of the wiener filter coefficients being less than or equal to the third threshold value and the square value of the sum of the vectors of the wiener filter coefficients being less than or equal to the fourth threshold value.
In summary, in the apparatus for acquiring communication parameters provided in the present application, after acquiring the autocorrelation matrix of the communication signal, the terminal may acquire the inverse matrix of the autocorrelation matrix by performing decomposition and then performing inversion. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a form of a product of a first matrix, a second matrix and a transpose matrix of the first matrix, then use an inverse matrix of the first matrix and an inverse matrix of the second matrix that have been solved to obtain a third matrix, then combine the transpose matrix of the first matrix to calculate an inverse matrix of the autocorrelation matrix, and based on the inverse matrix and a corresponding cross-correlation matrix, the terminal can obtain corresponding communication parameters for improving the quality of the communication signal. The evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, so that the complexity of calculating the communication parameters is reduced, and the efficiency of obtaining the communication parameters is improved.
The device for acquiring communication parameters provided by this embodiment can also perform an autocorrelation matrix adjustment process on the input target matrix, that is, input data is protected, and the probability that the matrix is irreversible is reduced from the data source. The robustness of the matrix inversion process is improved, and the control complexity and the calculation complexity which are brought by the occurrence of irreversible time are avoided.
The device for acquiring communication parameters provided by this embodiment can also use the decomposition process of the first matrix and the second matrix to replace the evolution operation in the traditional matrix decomposition process in the process of matrix inversion decomposition, thereby simplifying the complexity of system implementation.
The device for acquiring the communication parameters provided by the embodiment can ensure the reliability of the wiener coefficient calculation result through multiple reversibility judgment and validity judgment.
The embodiment of the present application further provides a computer-readable medium, where at least one instruction is stored, and the at least one instruction is loaded and executed by the processor to implement the method for acquiring communication parameters according to the above various embodiments.
It should be noted that: in the above embodiment, when the apparatus for acquiring communication parameters executes the method for acquiring communication parameters, only the division of the above functional modules is taken as an example, in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the above described functions. In addition, the apparatus for acquiring a communication parameter and the method for acquiring a communication parameter provided in the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the implementation of the present application and is not intended to limit the present application, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (12)
1. A method for obtaining communication parameters, the method comprising:
acquiring an autocorrelation matrix of a communication signal;
performing matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix, wherein the autocorrelation matrix is the product of the first matrix, the second matrix and a transposed matrix of the first matrix;
obtaining a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second matrix;
calculating an inverse matrix of the autocorrelation matrix according to the third matrix and the transposed matrix of the first matrix;
and acquiring communication parameters according to the inverse matrix of the autocorrelation matrix and the corresponding cross-correlation matrix, wherein the communication parameters are used for improving the quality of the communication signals.
2. The method of claim 1, wherein the computing an inverse of the autocorrelation matrix based on the third matrix and a transpose of the first matrix comprises:
obtaining main diagonal elements of the third matrix according to the main diagonal elements of the second matrix, wherein the main diagonal elements of the second matrix and the main diagonal elements of the third matrix are reciprocal;
obtaining the numerical value of the element of the transpose matrix of the first matrix according to the numerical value of the element of the first matrix;
for an upper triangular region of an inverse of the autocorrelation matrix, a numerical value of an element is calculated in response to the element having a maximum number of rows and a maximum number of columns.
3. The method of claim 2, wherein calculating the value of an element in response to the element having the largest number of rows and the largest number of columns for an upper triangular region of an inverse of the autocorrelation matrix comprises:
determining elements to be calculated, which are not calculated to obtain numerical values, aiming at elements in an upper triangular area of an inverse matrix of the autocorrelation matrix;
and responding to the maximum row number and the maximum column number of the element to be calculated, and calculating the numerical value of the element to be calculated.
4. The method of claim 3, wherein the communication parameters comprise channel estimation coefficients, and wherein obtaining the autocorrelation matrix of the communication signal comprises:
acquiring the signal-to-noise ratio of a channel;
acquiring a pilot frequency distance pattern;
obtaining a correlation coefficient;
calculating the autocorrelation matrix according to the signal-to-noise ratio of the channel, the pilot frequency distance pattern and the correlation coefficient;
and adding a target increment to a main diagonal element of the autocorrelation matrix in response to the signal-to-noise ratio of the channel being greater than a first threshold value, wherein the target increment is a positive number.
5. The method of claim 4, further comprising:
and performing matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix in response to the element of the maximum numerical value in the autocorrelation matrix being located on the main diagonal.
6. The method of claim 4, wherein obtaining the correlation coefficient comprises:
and in response to the element of the maximum value in the autocorrelation matrix being outside the main diagonal, taking a statistical correlation coefficient as the correlation coefficient.
7. The method of claim 5, wherein obtaining the correlation coefficient comprises:
taking a statistical correlation coefficient as the correlation coefficient in response to the absolute value of a main diagonal element of the second matrix being less than or equal to a second threshold value;
or the like, or, alternatively,
and in response to the main diagonal element of the second matrix being negative, taking a statistical correlation coefficient as the correlation coefficient.
8. The method of claim 4, wherein obtaining communication parameters according to an inverse matrix of the autocorrelation matrix and a corresponding cross-correlation matrix comprises:
calculating a wiener filter coefficient according to the autocorrelation matrix and the corresponding cross-correlation matrix;
taking a statistical correlation coefficient as the correlation coefficient in response to the absolute value of the real part or the imaginary part of the wiener filter coefficient being greater than a third threshold value;
or the like, or, alternatively,
and in response to the square value of the sum of the wiener filter coefficient vectors being greater than the fourth threshold value, taking the statistical correlation coefficient as the correlation coefficient.
9. The method of claim 8, further comprising:
and in response to the absolute value of the real part or the imaginary part of the wiener filter coefficient being less than or equal to the third threshold value and the square value of the sum of the wiener filter coefficient vectors being less than or equal to the fourth threshold value, processing the communication signal according to the wiener filter coefficient.
10. An apparatus for obtaining communication parameters, the apparatus comprising:
the matrix acquisition module is used for acquiring an autocorrelation matrix of the communication signal;
a matrix decomposition module, configured to perform matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix, where the autocorrelation matrix is a product of the first matrix, the second matrix, and a transposed matrix of the first matrix;
the matrix calculation module is used for obtaining a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second matrix;
a matrix inversion module, configured to calculate an inverse matrix of the autocorrelation matrix according to the third matrix and a transposed matrix of the first matrix;
and the parameter acquisition module is used for acquiring communication parameters according to the inverse matrix of the autocorrelation matrix and the corresponding cross-correlation matrix, wherein the communication parameters are used for improving the quality of the communication signals.
11. A terminal, characterized in that the terminal comprises a processor, a memory connected to the processor, and program instructions stored on the memory, which when executed by the processor implement the method of obtaining communication parameters according to any of claims 1 to 9.
12. A computer readable storage medium having stored thereon program instructions which, when executed by a processor, implement the method of obtaining communication parameters according to any one of claims 1 to 9.
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