CN111935746B - 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 acquiring communication parameters, which belong to the technical field of communication, wherein the terminal can acquire an inverse matrix of an autocorrelation matrix by adopting a mode of decomposing and then solving the inverse matrix after acquiring the autocorrelation matrix of a communication signal. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a product of the first matrix, the second matrix and the transposed matrix of the first matrix, then obtain a third matrix by using the solved inverse matrix of the first matrix and the inverse matrix of the second matrix, then combine the transposed matrix of the first matrix to obtain the inverse matrix of the autocorrelation matrix, and acquire the communication parameters for improving the quality of the communication signal based on the inverse matrix and the corresponding cross-correlation matrix. Because the evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, the complexity of calculating the communication parameters is reduced, and the efficiency of acquiring the communication parameters is improved.
Description
Technical Field
The embodiment of the application relates to the technical field of communication, in particular to a method, a device, a terminal and a storage medium for acquiring communication parameters.
Background
With the rapid development of wireless communication technology, accurate estimation of noise conditions is of great importance for improving signal quality. Wherein the accurate computation of the channel estimation coefficients has a large impact on the signal quality.
In the related art, since the signal condition varies in real time. Therefore, the terminal can calculate the current real-time channel estimation coefficient in real time, which is important for improving the signal quality. In one possible calculation mode, the terminal needs to perform matrix inversion operation in real time when calculating the real-time channel estimation coefficient. In general, the terminal performs matrix inversion by using Cholesky (chinese: cholesky) decomposition method, and further calculates 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 transpose 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 including:
the matrix acquisition module is used for acquiring an autocorrelation matrix of the communication signal;
the matrix decomposition module is used for carrying out 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 the 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;
the matrix inversion module is used for calculating an inverse matrix of the autocorrelation matrix according to the third matrix and the transpose 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 having stored therein at least one instruction loaded and executed by the processor to implement a method of acquiring communication parameters as provided in various aspects of the present application.
According to another aspect of the present application, there is provided a computer readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement a method of acquiring communication parameters as provided by 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 computer instructions are read from the computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the methods provided in various alternative implementations of the above-described aspects of acquiring communication parameters.
In the method for acquiring the communication parameters provided by the application, after the terminal acquires the autocorrelation matrix of the communication signal, the terminal can acquire the inverse matrix of the autocorrelation matrix in a mode of decomposing and then solving the inverse matrix. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a product of the first matrix, the second matrix and the transposed matrix of the first matrix, then obtain a third matrix by using the solved inverse matrix of the first matrix and the inverse matrix of the second matrix, then combine the transposed matrix of the first matrix to calculate and obtain the inverse matrix of the autocorrelation matrix, and based on the inverse matrix and the corresponding cross-correlation matrix, the terminal can obtain corresponding communication parameters for improving the quality of communication signals. Because the evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, the complexity of calculating the communication parameters is reduced, and the efficiency of acquiring the communication parameters is improved.
Drawings
In order to more clearly describe the technical solutions of the embodiments of the present application, the drawings that are needed 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 other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic flow chart of obtaining wiener filter coefficients according to an embodiment of the present application;
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 acquiring communication parameters provided in an exemplary embodiment of the present application;
FIG. 4 is a flowchart of a method for acquiring communication parameters according to another exemplary embodiment of the present application;
fig. 5 is a schematic diagram of a method for obtaining 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 schematic diagram of a comparison of the complexity of the addition between two possible implementations of the scheme shown in FIG. 5;
fig. 8 is a block diagram of an apparatus for acquiring communication parameters according to an exemplary embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the 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, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
In the description of the present application, it should 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 should be noted that, unless explicitly specified and limited otherwise, the terms "connected," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context. Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The present application describes a method for obtaining communication parameters, which are described as being helpful for improving the quality of a received signal or for improving 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 other parameters related to the matrix inversion process, which is not limited in the embodiment of the present application.
As one possible channel estimation scheme, wiener filtering scheme is generally used for channel estimation in 3GPP LTE system and 5G NR (New Radio) communication system. The wiener filtering scheme involves a coefficient calculation process. The coefficient calculation process generally adopts a preset system or a method for calculating coefficients in real time. Illustratively, the wiener filter coefficient calculation process is shown in fig. 1. Fig. 1 is a schematic flow chart of obtaining wiener filter coefficients according to an embodiment of the present application. 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 wiener filter coefficient calculation process shown in fig. 1, the signal-to-noise ratio estimation module 110 obtains a signal-to-noise ratio. The correlation generation module 120 inputs the correlation signal to the autocorrelation matrix generation module 140 based on a delay spread (des) or a doppler spread (dos) and the estimated correlation. Meanwhile, the pilot distance pattern generation module 130 will also input signals to the autocorrelation matrix generation module 140 according to the type of pilot signals. The autocorrelation matrix generation module 140 will generate an autocorrelation matrix (ACM).
At the same time, 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).
Subsequently, the autocorrelation matrix generated by the autocorrelation matrix generation module 140 is input to the matrix inversion module 160 to determine the inverse of the autocorrelation matrix, which is input to the wiener coefficient calculation module 170. The wiener coefficient calculation module 170 calculates the wiener coefficients 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 the wiener coefficient in real time may be adopted. The processing procedure of the matrix inversion module 160 is described below. First, the terminal performs Cholesky decomposition (also called trigonometric decomposition) on an autocorrelation matrix (which may also be referred to as a target matrix). Cholesky decomposition enables a symmetrically positive matrix Φ to be represented as a lower triangular matrix L and its transpose L H I.e. the decomposition of the product of (a), i.e. the (b) is performed.
Φ＝LL H
Wherein for the following
j=0, 1, (n-1), the j-th column main diagonal element of the L decomposition is:
for i=k., (n-1), column j, row i elements of L are:
as is clear from the above calculation process, in the Cholesky decomposition process, an evolution operation exists when the main diagonal element is obtained.
In the subsequent calculation process, calculating an L matrix by using Cholesky decomposition, and inverting the L matrix according to the theoretical relationship between the inverse matrix and the L matrix to obtain L -1 . And multiplying the matrix to obtain the inverse matrix of the target matrix. The specific formula is as follows:
Φ -1 ＝(LL H ) -1 ＝(L -1 ) H L -w
let z=l -1 I.e.
Phi is -1 ＝Z H Z。
From the above description, cholesky decomposition is a positive definite matrix for an input matrix that is required to be decomposed, and when the input matrix does not meet a positive definite condition, a phenomenon that the self matrix is irreversible occurs in the computing system.
Since Cholesky decomposition requires that the autocorrelation matrix to be decomposed satisfies a positive condition, i.e., all eigenvalues of the matrix must be greater than zero, the diagonal elements of the decomposed lower triangular matrix are also greater than zero. 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 usable inverse matrix and real-time coefficient calculated according to the real-time channel correlation and the channel parameter cannot be obtained, but the preset robust filter coefficient is used, so that the usable filter coefficient matrix is ensured in the data stream calculated by the current coefficient.
On the other hand, since the calculation method provided in fig. 1 requires the execution of the evolution operation. In the evolution operation, if the evolution operation is realized by adopting hardware, the terminal realizes the evolution operation by a CORDIC algorithm or a table look-up method. (1) If the evolution is performed by adopting the CORDIC algorithm, the computation complexity and the time delay are both relatively large. (2) If a table look-up method is adopted, a table meeting the current data dynamic range needs to be pre-stored, the memory occupied space of the terminal can be increased, and the requirement on the memory space is high.
In order to solve the disadvantage of the complexity of the scheme for calculating the inverse of the autocorrelation matrix in the above scheme. The embodiment of the application improves the scheme for acquiring the communication parameters, and is described as follows.
In the calculation of the current communication parameters, the terminal typically calculates in real time the channel estimation coefficients required for the wiener filtering of the current slot in each slot (slot). 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, each channel estimation coefficient calculation process includes an n×n matrix inversion operation process. It should be noted that N is the number of pilot samples (taps) of the currently calculated filter coefficient, that is, the dimension of the inverse autocorrelation matrix is n×n, so that each slot needs to calculate the inverse matrix of the 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 method and the device enable the calculation time delay of matrix operation to be reduced through simplification of the complexity of inverting a single matrix, 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 by the embodiment of the application, a channel estimation coefficient real-time calculation scheme based on an enhanced matrix inversion process is provided. Firstly, the embodiment can select the correlation coefficient according to the reversibility judgment indication, and protect diagonal line elements of the input target matrix, so that the target matrix to be subjected to inversion can better meet the requirement of matrix positive determination, and the irreversibility of the target matrix is avoided. Next, the present embodiment employs LDL H Decomposing, avoiding the evolution operation in matrix decomposing, 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 decomposing, and further simplifying the inversion calculation complexity. Finally, in this embodiment, wiener filter coefficients are calculated according to the inverse of the cross correlation matrix and the autocorrelation matrix, and the validity of the coefficients is determined and then output to the channel estimation filterA wave module.
For ease of understanding of the schemes shown in the embodiments of the present application, several terms appearing in the embodiments of the present application are described below.
3GPP (third Generation partnership project, 3rd Generation Partnership Project).
LTE (long term evolution ).
5G NR (fifth generation mobile communication New air interface, 5G New Radio).
RS (Reference Signal).
SNR (signal to noise ratio ).
Cholesky decomposition (Cholesky decomposition ).
CORDIC (coordinate rotation digital computing method, coordinate Rotation Digital Computer).
Slot (Slot).
The method for acquiring the communication parameters according to the embodiment of the application can be applied to a terminal with computing capability, and the terminal has the capability of receiving and transmitting radio frequency signals. The terminals may include cell phones, tablet computers, laptops, desktop computers, computer-integrated machines, servers, workstations, televisions, set-top boxes, smart glasses, smart watches, digital cameras, MP4 play terminals, MP5 play terminals, learning machines, point-to-read machines, electronic books, electronic dictionaries, vehicle-mounted terminals, virtual Reality (VR) play terminals, or augmented Reality (Augmented Reality, AR) play terminals, etc.
Referring to fig. 2, fig. 2 is a block diagram of a terminal according to an exemplary embodiment of the present application, where, as shown in fig. 2, the terminal includes a processor 220 and a memory 240, where at least one instruction is stored in the memory 240, and the instruction is loaded and executed by the processor 220 to implement a method for acquiring a communication parameter according to each method embodiment 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 can acquire 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 transpose 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.
Processor 220 may include one or more processing cores. The processor 220 connects various parts within the overall terminal 200 using various interfaces and lines, performs various functions of the terminal 200 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 240, and invoking data stored in the memory 240. Alternatively, the processor 220 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 220 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 220 and may be implemented by a single chip.
The Memory 240 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (ROM). Optionally, the memory 240 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 240 may be used to store instructions, programs, code, a set of codes, or a set of instructions. 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 the various method embodiments described below, etc.; the storage data area may store data and the like referred to in the following respective method embodiments.
Illustratively, the embodiments shown herein can be applied in an NR modem chip. 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 of acquiring communication parameters may be applied to the terminal shown above. In fig. 3, the method for acquiring communication parameters includes:
In step 310, an autocorrelation matrix of the communication signal is obtained.
In the embodiment of the application, the terminal can execute a calculation scheme for acquiring the communication parameters through the designated integrated circuit component. The integrated circuit component may be a chip or other equivalent functional circuit component. In one possible scheme for obtaining the autocorrelation matrix, the terminal can calculate the autocorrelation matrix by parameters such as signal-to-noise ratio, pilot distance pattern, etc.
It should be noted that, the embodiments of the present application are not limited to other schemes capable of calculating the autocorrelation matrix.
In step 320, the autocorrelation matrix is subjected to matrix decomposition 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.
In this embodiment, the terminal can perform matrix decomposition on the autocorrelation matrix. The first matrix and the second matrix are decomposed, and the original autocorrelation matrix may be expressed as a product of the first matrix, the second matrix, and the transpose matrix of the first matrix. Since the decomposition does not involve the evolution operation, the matrix inversion scheme provided in this embodiment can reduce the complexity of the operation.
Step 330, obtaining a third matrix according to the inverse of the first matrix and the inverse of the second matrix.
In the embodiment of the application, after the self-correlation matrix is decomposed, the terminal can obtain the third matrix according to the inverse matrix of the first matrix obtained by decomposition and the second verified inverse matrix. It should be noted that, since both the first matrix and the second matrix have completed the element calculation in the decomposition process. Therefore, to facilitate subsequent computation, 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.
Step 340, calculating the inverse of the autocorrelation matrix based on the third matrix and the transpose of the first matrix.
In the embodiment of the present application, the terminal calculates the inverse of the autocorrelation matrix based on the third matrix and the transpose of the first matrix as 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 in combination with the third matrix and the transpose matrix 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 either a received signal or a transmitted signal, which is not limited in this application scenario. The method and the device 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, the communication signals are optimized through the communication parameters, and the signal quality of the communication signals is finally improved.
In one possible implementation manner, the terminal can also encapsulate the module for the matrix inversion process in the above steps to form an independent operation component, which is applied to other processes needing to perform matrix inversion in the communication field. For example, the matrix inversion process may be encapsulated as IP, and applied to matrix inversion in a whitening matrix calculation process of a signal demodulation or channel state feedback module.
In summary, in the method for acquiring communication parameters provided in the present application, after acquiring the autocorrelation matrix of the communication signal, the terminal can acquire the inverse matrix of the autocorrelation matrix by decomposing and then inverting the autocorrelation matrix. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a product of the first matrix, the second matrix and the transposed matrix of the first matrix, then obtain a third matrix by using the solved inverse matrix of the first matrix and the inverse matrix of the second matrix, then combine the transposed matrix of the first matrix to calculate and obtain the inverse matrix of the autocorrelation matrix, and based on the inverse matrix and the corresponding cross-correlation matrix, the terminal can obtain corresponding communication parameters for improving the quality of communication signals. Because the evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, the complexity of calculating the communication parameters is reduced, and the efficiency of acquiring the communication parameters is improved.
Based on the scheme disclosed in the previous embodiment, the terminal can also optimize the scheme of acquiring the communication parameters based on matrix inversion in detail from the following four aspects. (1) Use of LDL H The decomposition replaces the traditional Cholesky decomposition; (2) Preprocessing the input autocorrelation matrix, namely protecting the input data; (3) Inverse matrix phi of target matrix phi is obtained by adopting backward recursion cyclic iteration -1 The method comprises the steps of carrying out a first treatment on the surface of the (4) In the whole wiener filter coefficient calculation process, a reversibility and effectiveness judgment module is added. For details, please refer 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 of acquiring communication parameters may be applied to the terminal shown above. In fig. 4, the method for acquiring communication parameters includes:
step 411, the signal to noise ratio of the channel is obtained.
In the embodiment of the application, the terminal can acquire the signal-to-noise ratio of the channel through a designated hardware component or a data channel before calculating the autocorrelation matrix.
At step 412, a pilot distance pattern is obtained.
Illustratively, the terminal can acquire a corresponding pilot range 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 step 414, an autocorrelation matrix is calculated based on the signal-to-noise ratio of the channel, the pilot distance pattern, and the correlation coefficient.
In step 415, in response to the signal-to-noise ratio of the channel being greater than the first threshold, the main diagonal element of the autocorrelation matrix is incremented by a target increment, the target increment being a positive number.
In the embodiment of the application, the communication parameters include channel estimation coefficients.
In step 421, in response to the element with the largest value in the autocorrelation matrix being located on the main diagonal, the autocorrelation matrix is subjected to matrix decomposition, so as to obtain a first matrix and a second matrix.
In 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, the statistical correlation coefficient is used as the correlation coefficient.
In step 423, in response to the main diagonal element of the second matrix being negative, the statistical correlation coefficient is taken as the correlation coefficient.
In step 424, the statistical correlation coefficient is taken as the correlation coefficient in response to the element of the maximum value in the autocorrelation matrix being outside the main diagonal.
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, the step 413 and the subsequent steps are performed again.
Step 431, obtaining the main diagonal elements of the third matrix according to the main diagonal elements of the second matrix, where the main diagonal elements of the second matrix and the main diagonal elements of the third matrix are reciprocal.
Step 432, obtaining the values of the elements of the transposed matrix of the first matrix according to the values of the elements of the first matrix.
In step 433, for the upper triangular region of the inverse matrix of the autocorrelation matrix, the values of the elements are calculated in response to the maximum number of rows in which the elements are located and the maximum number of columns in which the elements are located.
If an element is an element in the ith row and the jth column, the number of rows of the element is i, and the number of columns of the element is j.
In another possible implementation, the terminal may implement the values of the computing elements shown in step 433 by performing step (1) and step (2).
And (1) determining the elements to be calculated for which the numerical value is not calculated aiming at the elements in the upper triangular area of the inverse matrix of the autocorrelation matrix.
And (2) calculating the numerical value of the element to be calculated in response to the fact that the number of rows of the element to be calculated is maximum and the number of columns of the element to be calculated is maximum.
In step 441, wiener filter coefficients are calculated based on the autocorrelation matrices and the corresponding cross correlation matrices.
In step 442, in response to the absolute value of the real or imaginary part of the wiener filter coefficient being greater than the third threshold, the statistical correlation coefficient is taken as the correlation coefficient.
In step 443, the statistical correlation coefficient is taken as the correlation coefficient in response to the square value of the sum of the wiener filter coefficient vectors being greater than the fourth threshold value.
In summary, in the method for acquiring communication parameters provided in the present application, the terminal can determine the autocorrelation matrix through the signal-to-noise ratio, the pilot frequency distance pattern and the correlation coefficient, and acquire the inverse matrix of the autocorrelation matrix by decomposing and then inverting the autocorrelation matrix of the communication signal. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a product of the first matrix, the second matrix and the transposed matrix of the first matrix, then obtain a third matrix by using the solved inverse matrix of the first matrix and the inverse matrix of the second matrix, then combine the transposed matrix of the first matrix to calculate and obtain the inverse matrix of the autocorrelation matrix, and based on the inverse matrix and the corresponding cross-correlation matrix, the terminal can obtain corresponding communication parameters for improving the quality of communication signals. Because the evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, the complexity of calculating the communication parameters is reduced, and the efficiency of acquiring 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, namely, perform protection processing on the input data, and reduce the probability of matrix irreversibility from the data source. The robustness of the matrix inversion process is improved, and the control complexity and the calculation complexity caused by irreversible time are prevented from being improved.
The method for acquiring the communication parameters 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 matrix inversion decomposition process, so that the system implementation complexity is simplified.
The method for acquiring the communication parameters provided by the embodiment can also 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 of a method for acquiring communication parameters according to an embodiment of the present application. In the implementation shown in fig. 5, the following 7 stages are included.
And (3) selecting and judging the correlation coefficient in the stage (1).
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 'statistical correlation coefficient' as the output of the correlation coefficient selection judgment module; otherwise, the 'estimated correlation coefficient' is selected as the output of the correlation coefficient selection judging module.
In the initial state, (1) when the estimated correlation coefficient is not obtained yet, "statistical correlation coefficient" is selected as the output of the correlation coefficient selection judgment module. (2) When "estimated correlation coefficient" is available (i.e., when the module for estimating correlation coefficient starts to have a valid output), the "estimated correlation coefficient" is used by default as the output of the correlation coefficient selection judgment module.
Stage (2) autocorrelation matrix Φ and cross-correlation matrix Θ computation
The channel estimation autocorrelation matrix phi is calculated by using the correlation coefficient output by the correlation coefficient selection judgment module, the SNR value output by the signal-to-noise ratio 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 judging module and the pilot frequency distance pattern output by the pilot frequency distance pattern calculating module.
The method for calculating the autocorrelation matrix comprises the following steps:
wherein R (Δk) is the output of the correlation coefficient selection determination module in stage (1). Δk=k j -k i Is the distance between RS REj and RS REi.Is the noise power. I is an N unit array.
The method for calculating the cross-correlation matrix comprises the following steps:
Φ hh′ ＝[R(k 0 -k i )R(k 1 -k i )…R(k N-1 -k i )]
wherein R (Δk) is the output of the correlation coefficient selection determination module in stage (1). Δk=k j -k i Is the distance of RS REj from the filtered output position REi.
Stage (3) autocorrelation matrix adjustment.
The autocorrelation matrix calculated in the step (2) is adjusted as follows:
if the SNR value used in the calculation of the autocorrelation matrix in the stage (2) is larger than TH1 (threshold value 1) dB, adding 10 (TH 1/10); otherwise, the module is transparent.
And (3) a reversibility judgment 1 module.
Counting the maximum value of all elements in the autocorrelation matrix phi and the position of the maximum value in the matrix, if the maximum value appears on the main diagonal of the matrix phi, outputting a judgment indication value of Yes by the reversibility judgment 1 module, and continuing to carry out the LDL of the next step H A decomposition operation; otherwise, outputting a judgment instruction value of 'No', stopping the coefficient calculation process, jumping back to 1 to perform correlation coefficient selection judgment, selecting 'statistical correlation coefficient' as output of a correlation coefficient selection judgment module, and repeating the steps (1) to (4) to perform calculation.
Stage (5) LDL H And (5) decomposing operation.
If the target matrix Φ is a symmetric matrix and all its sequential principals are not zero, Φ can be uniquely decomposed into:
Φ＝LDL H
where L is a lower triangular matrix and the main diagonal element is 1, D is a diagonal matrix (with non-zero values only on the main diagonal element), d=diag (D ii ). That is to say,
l jj ＝1(j＝0，…，(N-1))
in the above equation, the problem of high computational complexity of the evolution operation is involved in the Cholesky decomposition process, and LDL H This problem is avoided in the decomposition method.
Illustratively, LDL is used H The L matrix and D matrix obtained by decomposition can be based on the inverse matrix phi -1 And the theoretical relation between the L matrix and the D matrix, respectively inverting the L matrix and the D matrix, and multiplying the L matrix and the D matrix by the matrix to obtain an inverse matrix of the target matrix.
And (3) a reversibility judgment module 2.
Schematically, LDL in stage (5) H In the decomposition process, numerical analysis is required to be carried out on D matrix elements obtained by decomposition, and if the numerical analysis is satisfied: the absolute value of the main diagonal element of the D matrix < = TH2 (second threshold value), or the main diagonal element of the D matrix is negative, the reversibility judgment 1 module outputs a judgment indication value of No, the coefficient calculation process is ended at the moment, the process jumps back to the stage (1) to carry out the 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 to carry out calculation; otherwise, outputting a judgment instruction value of Yes, and starting a backward recursion inversion process in the stage (7).
Stage (7), backward recursion inversion.
In scheme (7-1), LDL is generally performed on the target matrix H After decomposition, solve the target matrix phi -1 The method of the inverse matrix of (a) is as follows:
Φ -1 ＝(LDL H ) -1 ＝(L -1 ) H D -1 L -1
let z=l -1 I.e.
Phi is -1 ＝Z H D -1 Z。
In the scheme (7-2), LDL is performed on the target matrix H After decomposition, it is no longer based on the inverse matrix Φ in the above description -1 And LDL H The theoretical relationship between the L matrix and the D matrix obtained by decomposition is inverted. According to the characteristics of the upper triangular matrix, the inverse matrix of the target matrix is obtained by adopting the column-wise backward recursion loop iteration, so that the inversion calculation complexity is further simplified. The specific backward recursion inversion method comprises the following steps:
let phi -1 ＝P，
Let b=d -1 L -1 Then
L H P＝B
Let L H =m, then
MP＝B
Writing the above in the form of matrix elements (taking the input Φ matrix as 3x3 matrix as an example), i.e
It can be seen that since the autocorrelation matrix and its inverse are both Hermitian matrices, the P matrix can be represented directly 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 column backward recursion is:
at this time, it can be seen that in the process of solving the inverse matrix P by column backward recursion loop iteration, only the main diagonal elements { B } of the B matrix ii I=0, …, (N-1) participates in the operation, N being the dimension of the input Φ matrix. Thus, the computation of the B matrix can be reduced to the principal diagonal element { B } ii Calculation of the B matrix, i.e. without calculating other lower triangle elements than the main diagonal elements. Major diagonal element { b ii The calculation method of the } is as follows:
wherein d ii For the main diagonal elements of the D matrix, i.e.
D＝diag(d ii )
d ii Has been calculated in stage (5).
M matrix can be obtained by simple conjugate transposition operation of L matrix calculated in stage (5), namely
The calculation formula of the P matrix is as follows:
when i is greater than j, the method comprises,
stage (8), calculating wiener filter coefficients.
From the cross correlation matrix Φ calculated in stage (2) and the inverse matrix Φ of the autocorrelation matrix calculated in stage (7) -1 Calculating wiener filter coefficient w=ΘΦ -1 。
And (9) judging the coefficient effectiveness by a 3-module.
And (3) judging the effectiveness of the coefficients of the wiener filter calculated in the step (8), if the coefficients satisfy the following conditions: if the absolute value of the real part or the imaginary part of any wiener coefficient is larger than TH3, or if the square value of the sum of wiener filter coefficient vectors is larger than TH4, outputting a judgment instruction value of 'No' by the effectiveness judgment 3 module, stopping the coefficient calculation process, jumping back to the stage (1) to perform correlation coefficient selection judgment, selecting a 'statistical correlation coefficient' as the output of the correlation coefficient selection judgment module, repeating the steps (1) to (8) to calculate the wiener filter coefficient, and outputting the wiener filter coefficient to the filter module; otherwise, outputting the judgment instruction value of Yes, and outputting the currently calculated wiener filter coefficient to a filter module.
The TH3 (third threshold) and TH4 (fourth threshold) are preset threshold values which can be configured through software.
The wiener filter coefficients output in the stage (9), namely the final output of the coefficient calculation module, can be provided for 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 schematic diagram of a comparison of the complexity of the addition between two possible implementations of the scheme shown in fig. 5. As can be seen from the contents shown in fig. 6 and 7, the schemes shown in fig. 5 and 4 can reduce the complexity of the terminal to obtain the communication parameters.
In fig. 6, curve 610 represents the multiplication complexity of scheme (7-1) in stage (7) of the scheme shown in fig. 5, and curve 620 represents the multiplication complexity of scheme (7-2) in the backward recursive inversion of stage (7) of the scheme shown in fig. 5.
In fig. 7, curve 710 represents the added complexity of scheme (7-1) in stage (7) of the scheme shown in fig. 5, and curve 720 represents the added complexity of scheme (7-2) in the backward recursive inversion of stage (7) of 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. In connection with fig. 6 and 7, it can be seen that the inversion scheme (7-2) (labeled Target Method in the figure) is much less complex than the direct inversion scheme (labeled Legacy Method in the figure). Taking the example of a matrix dimension of 12x12, 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, 2660 complex +2451 complex additions are required using scheme (7-2), while 4200 complex +3610 complex additions are required for scheme (7-1). The computational complexity described herein is a comparison of the computational complexity for a single matrix inversion. In practical products, the inverse matrix of tens or even hundreds of matrices is usually calculated in one slot (slot), and the absolute amount of system complexity reduction is more obvious when the backward recursion inversion scheme recommended in the scheme is used.
The method for acquiring the communication parameters can perform an autocorrelation matrix adjustment process on the input target matrix, namely, input data is protected, and the probability of matrix irreversibility is reduced from a data source. The robustness of the matrix inversion process is improved, and the control complexity and the calculation complexity caused by irreversible time are prevented from being improved.
The method for acquiring the communication parameters 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 matrix inversion decomposition process, so that the system implementation complexity is simplified.
The method for acquiring the communication parameters provided by the embodiment can also ensure the reliability of the wiener coefficient calculation result through multiple reversibility judgment and validity judgment.
The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
Referring to fig. 8, fig. 8 is a block diagram illustrating an apparatus for acquiring communication parameters according to an exemplary embodiment of the present application. The means for obtaining the communication parameters may be implemented as all or part of the terminal by software, hardware or a combination of both. The device comprises:
A matrix acquisition module 810, configured to acquire 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 matrix 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 the transpose matrix of the first matrix;
and the parameter acquisition module 850 is configured to acquire communication parameters according to the inverse matrix of the autocorrelation matrix and the corresponding cross correlation matrix, where the communication parameters are used to improve the quality of the communication signal.
In an alternative embodiment, the matrix inversion module 840 is configured to obtain a main diagonal element of the third matrix according to the 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 values of the elements of the transposed matrix of the first matrix according to the values of the elements of the first matrix; and for the upper triangular area of the inverse matrix of the autocorrelation matrix, responding to the maximum number of rows in which the element is positioned and the maximum number of columns in which the element is positioned, and calculating the numerical value of the element.
In an alternative embodiment, the matrix inversion module 840 is configured to determine, for an element in an upper triangle area of an inverse matrix of the autocorrelation matrix, an element to be calculated for which a value is not calculated;
and responding to the fact that the number of rows of the element to be calculated is maximum and the number of columns of the element to be calculated is maximum, and calculating the numerical value of the element to be calculated.
In an alternative embodiment, the matrix acquiring module 810 is configured to acquire a signal-to-noise ratio of a channel; acquiring a pilot frequency distance pattern; acquiring 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 the main diagonal element of the autocorrelation matrix in response to the signal-to-noise ratio of the channel being greater than a first threshold, wherein the target increment is a positive number. Wherein the communication parameters include channel estimation coefficients.
In an optional embodiment, the apparatus further includes an execution module, where the execution module is configured to execute the step of performing matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix in response to an element with a maximum value in the autocorrelation matrix being located on a main diagonal; and taking the statistical correlation coefficient as the correlation coefficient in response to the element with the largest numerical value in the autocorrelation matrix being positioned outside the main diagonal.
In an alternative embodiment, the execution module is further configured to, in response to the absolute value of the main diagonal element of the second matrix being less than or equal to a second threshold, use a statistical correlation coefficient as the correlation coefficient; 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 wiener filter coefficients according to the autocorrelation matrix and the corresponding cross correlation matrix; 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, taking the statistical correlation coefficient as the correlation coefficient; or, in response to the square value of the sum of the wiener filter coefficient vectors being greater than a fourth threshold value, taking the statistical correlation coefficient as the correlation coefficient.
In an alternative embodiment, the executing module processes the communication signal according to the wiener filter coefficient in response to the absolute value of the real part or the imaginary part of the wiener filter coefficient being smaller than or equal to the third threshold value, and the square value of the sum of the wiener filter coefficient vectors being smaller 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 decomposing and then inverting the autocorrelation matrix. In a specific implementation process, the terminal can decompose the autocorrelation matrix into a product of the first matrix, the second matrix and the transposed matrix of the first matrix, then obtain a third matrix by using the solved inverse matrix of the first matrix and the inverse matrix of the second matrix, then combine the transposed matrix of the first matrix to calculate and obtain the inverse matrix of the autocorrelation matrix, and based on the inverse matrix and the corresponding cross-correlation matrix, the terminal can obtain corresponding communication parameters for improving the quality of communication signals. Because the evolution operation involved in Cholesky decomposition is avoided in the decomposition process of the autocorrelation matrix, the complexity of calculating the communication parameters is reduced, and the efficiency of acquiring the communication parameters is improved.
The device for acquiring the communication parameters provided by the embodiment can also perform an autocorrelation matrix adjustment process on the input target matrix, namely, perform protection processing on the input data, and reduce the probability of matrix irreversibility from the data source. The robustness of the matrix inversion process is improved, and the control complexity and the calculation complexity caused by irreversible time are prevented from being improved.
The device for acquiring the communication parameters provided by the 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 matrix inversion decomposition process, so that the system implementation complexity is simplified.
The device for acquiring the communication parameters provided by the embodiment can also ensure the reliability of the wiener coefficient calculation result through multiple reversibility judgment and validity judgment.
Embodiments of the present application also provide a computer readable medium storing at least one instruction that is loaded and executed by the processor to implement the method of obtaining a communication parameter as described in the above embodiments.
It should be noted that: the apparatus for acquiring communication parameters provided in the foregoing embodiments is only exemplified by the division of the foregoing functional modules when executing the method for acquiring communication parameters, and in practical application, the foregoing functional allocation may be performed 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 functions described above. In addition, the device for acquiring the communication parameters provided in the foregoing embodiments and the method embodiment for acquiring the communication parameters belong to the same concept, and specific implementation processes of the device and the method embodiment are detailed in the detailed description of the method embodiment, which is not repeated here.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages 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 for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is merely illustrative of the possible embodiments of the present application and is not intended to limit the present application, but any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the present application are intended to be included within the scope of the present application.
Claims (4)
1. A method of acquiring communication parameters, the method comprising:
acquiring the signal-to-noise ratio of a channel;
acquiring a pilot frequency distance pattern;
acquiring a correlation coefficient of a communication signal, wherein the correlation coefficient is an estimated correlation coefficient;
calculating an autocorrelation matrix of the communication signal according to the signal-to-noise ratio of the channel, the pilot frequency distance pattern and the correlation coefficient;
adding a target increment to a main diagonal element of the autocorrelation matrix under the condition that the signal-to-noise ratio of the channel is greater than a first threshold value, wherein the target increment is a positive number;
Determining a statistical correlation coefficient as the correlation coefficient when the element with the maximum value in the autocorrelation matrix is located outside a main diagonal, and calculating the autocorrelation matrix of the communication signal from the signal-to-noise ratio of the channel, the pilot distance pattern and the correlation coefficient;
under the condition that the element with the maximum numerical value in the autocorrelation matrix is positioned on a main diagonal, carrying out 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;
determining a statistical correlation coefficient as the correlation coefficient in the case that the absolute value of the main diagonal element of the second matrix is less than or equal to a second threshold value, and calculating an autocorrelation matrix of the communication signal from the signal-to-noise ratio of the channel, the pilot distance pattern, and the correlation coefficient;
determining a statistical correlation coefficient as the correlation coefficient in the case that the main diagonal element of the second matrix is negative, and calculating an autocorrelation matrix of the communication signal from the signal-to-noise ratio of the channel, the pilot distance pattern, and the correlation coefficient;
In case the main diagonal elements of the second matrix do not satisfy the two above, the following steps are performed: obtaining a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second matrix;
obtaining a main diagonal element of the 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;
obtaining the values of the elements of the transposed matrix of the first matrix according to the values of the elements of the first matrix;
calculating an inverse matrix of the autocorrelation matrix according to the third matrix and the transpose matrix of the first matrix;
calculating wiener filter coefficients according to the inverse matrix of the autocorrelation matrix and the corresponding cross correlation matrix;
taking a statistical correlation coefficient as the correlation coefficient and calculating an autocorrelation matrix of the communication signal from the signal-to-noise ratio of the channel, the pilot distance pattern and the correlation coefficient when the absolute value of the real part or the imaginary part of the wiener filter coefficient is greater than a third threshold value or the square value of the sum of the wiener filter coefficient vectors is greater than a fourth threshold value;
And when the wiener filter coefficients do not meet the two conditions, outputting the currently calculated wiener filter coefficients to a filter module for subsequent channel estimation wiener filtering.
2. An apparatus for acquiring communication parameters, the apparatus comprising:
the matrix acquisition module is used for acquiring the signal-to-noise ratio of the channel; acquiring a pilot frequency distance pattern; acquiring a correlation coefficient of a communication signal, wherein the correlation coefficient is an estimated correlation coefficient; calculating an autocorrelation matrix of the communication signal according to the signal-to-noise ratio of the channel, the pilot frequency distance pattern and the correlation coefficient; adding a target increment to a main diagonal element of the autocorrelation matrix under the condition that the signal-to-noise ratio of the channel is greater than a first threshold value, wherein the target increment is a positive number;
an execution module, configured to determine a statistical correlation coefficient as the correlation coefficient when an element with a maximum value in the autocorrelation matrix is located outside a main diagonal, and execute from the step of calculating the autocorrelation matrix of the communication signal according to the signal-to-noise ratio of the channel, the pilot distance pattern, and the correlation coefficient;
the matrix decomposition module is used for carrying out matrix decomposition on the autocorrelation matrix to obtain a first matrix and a second matrix under the condition that the element with the maximum numerical value in the autocorrelation matrix is positioned on a main diagonal, and the autocorrelation matrix is the product of the first matrix, the second matrix and the transposed matrix of the first matrix;
The execution module is further configured to determine a statistical correlation coefficient as the correlation coefficient when an absolute value of a main diagonal element of the second matrix is less than or equal to a second threshold value, and execute from the step of calculating an autocorrelation matrix of the communication signal according to a signal-to-noise ratio of the channel, the pilot distance pattern, and the correlation coefficient; determining a statistical correlation coefficient as the correlation coefficient in the case that the main diagonal element of the second matrix is negative, and calculating an autocorrelation matrix of the communication signal from the signal-to-noise ratio of the channel, the pilot distance pattern, and the correlation coefficient;
a matrix calculation module, configured to perform the following steps when the main diagonal elements of the second matrix do not satisfy the two conditions: obtaining a third matrix according to the inverse matrix of the first matrix and the inverse matrix of the second matrix;
the matrix inversion module is used for obtaining the 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 values of the elements of the transposed matrix of the first matrix according to the values of the elements of the first matrix; calculating an inverse matrix of the autocorrelation matrix according to the third matrix and the transpose matrix of the first matrix;
The parameter acquisition module is used for calculating a wiener filter coefficient according to the inverse matrix of the autocorrelation matrix and the corresponding cross correlation matrix; taking a statistical correlation coefficient as the correlation coefficient and calculating an autocorrelation matrix of the communication signal from the signal-to-noise ratio of the channel, the pilot distance pattern and the correlation coefficient when the absolute value of the real part or the imaginary part of the wiener filter coefficient is greater than a third threshold value or the square value of the sum of the wiener filter coefficient vectors is greater than a fourth threshold value; and when the wiener filter coefficients do not meet the two conditions, outputting the currently calculated wiener filter coefficients to a filter module for subsequent channel estimation wiener filtering.
3. A terminal comprising a processor, and a memory coupled to the processor, and program instructions stored on the memory, wherein the processor, when executing the program instructions, implements the method of obtaining communication parameters according to claim 1.
4. A computer readable storage medium having stored therein program instructions, which when executed by a processor implement the method of obtaining communication parameters as claimed in claim 1.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102123127A (en) * | 2010-12-31 | 2011-07-13 | 意法·爱立信半导体（北京）有限公司 | Coefficient calculation method and coefficient calculation device of time-domain channel estimation filter |
| CN103532888A (en) * | 2012-07-02 | 2014-01-22 | 中兴通讯股份有限公司 | Data processing apparatus and method for eliminating interference |
| CN105426345A (en) * | 2015-12-25 | 2016-03-23 | 南京大学 | Matrix inverse operation method |
| WO2016172980A1 (en) * | 2015-04-30 | 2016-11-03 | 华为技术有限公司 | Channel estimation method and device, and terminal |
| CN107241070A (en) * | 2016-03-29 | 2017-10-10 | 大唐移动通信设备有限公司 | A kind of method, FPGA the and DPD processing systems of determination DPD coefficients |
| CN107483090A (en) * | 2017-09-07 | 2017-12-15 | 深圳清华大学研究院 | The extensive mimo system precoding implementation method decomposed based on LDLT |
| CN108595637A (en) * | 2018-04-25 | 2018-09-28 | 攀枝花学院 | Various visual angles correlation analysis method based on data distribution characteristics |
| CN108828655A (en) * | 2018-03-19 | 2018-11-16 | 中国石油天然气集团有限公司 | Processing method, device and the computer storage medium of trace gather record and stacked section |
| CN109740117A (en) * | 2019-01-31 | 2019-05-10 | 广东工业大学 | A kind of robust and quick magnetic orientation algorithm |
-
2020
- 2020-08-14 CN CN202010820423.3A patent/CN111935746B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102123127A (en) * | 2010-12-31 | 2011-07-13 | 意法·爱立信半导体（北京）有限公司 | Coefficient calculation method and coefficient calculation device of time-domain channel estimation filter |
| CN103532888A (en) * | 2012-07-02 | 2014-01-22 | 中兴通讯股份有限公司 | Data processing apparatus and method for eliminating interference |
| WO2016172980A1 (en) * | 2015-04-30 | 2016-11-03 | 华为技术有限公司 | Channel estimation method and device, and terminal |
| CN105426345A (en) * | 2015-12-25 | 2016-03-23 | 南京大学 | Matrix inverse operation method |
| CN107241070A (en) * | 2016-03-29 | 2017-10-10 | 大唐移动通信设备有限公司 | A kind of method, FPGA the and DPD processing systems of determination DPD coefficients |
| CN107483090A (en) * | 2017-09-07 | 2017-12-15 | 深圳清华大学研究院 | The extensive mimo system precoding implementation method decomposed based on LDLT |
| CN108828655A (en) * | 2018-03-19 | 2018-11-16 | 中国石油天然气集团有限公司 | Processing method, device and the computer storage medium of trace gather record and stacked section |
| CN108595637A (en) * | 2018-04-25 | 2018-09-28 | 攀枝花学院 | Various visual angles correlation analysis method based on data distribution characteristics |
| CN109740117A (en) * | 2019-01-31 | 2019-05-10 | 广东工业大学 | A kind of robust and quick magnetic orientation algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 一种适用于MIMO系统MMSE检测的LDLT分解硬件实现;梁子豪等;《微电子学与计算机》;20180805(第08期);全文 * |
| 基于HLS的Cholesky分解矩阵求逆算法的设计;韩文俊等;《电子技术与软件工程》;20180907(第17期);全文 * |
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