CN115695097B

CN115695097B - Channel equalization method, device, apparatus and storage medium

Info

Publication number: CN115695097B
Application number: CN202110875086.2A
Authority: CN
Inventors: 蔡月宏
Original assignee: Datang Mobile Communications Equipment Co Ltd
Current assignee: Datang Mobile Communications Equipment Co Ltd
Priority date: 2021-07-30
Filing date: 2021-07-30
Publication date: 2024-10-11
Anticipated expiration: 2041-07-30
Also published as: CN115695097A

Abstract

The embodiment of the invention provides a channel equalization method, device and apparatus and a storage medium, wherein the method comprises the following steps: after receiving the signal sent by the base station, channel estimation is performed on the signal to obtain channel estimation information. Noise measurement is performed based on the channel estimation information to obtain noise information. Based on the noise information, a cross-correlation value representing the cross-correlation of noise between different receiving antennas of the terminal and an autocorrelation value representing the self-correlation of noise of each receiving antenna are calculated. If the ratio of the cross correlation value to the autocorrelation value is smaller than or equal to the preset ratio, selecting the MRC algorithm as a target algorithm, otherwise, selecting the IRC algorithm as the target algorithm. And carrying out channel equalization processing on the signals by adopting the selected target algorithm based on the channel estimation information and the noise information. The scheme provided by the embodiment of the invention can be applied to select the channel equalization algorithm, thereby realizing a better channel equalization effect.

Description

Channel equalization method, device, apparatus and storage medium

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a channel equalization method, device, apparatus, and storage medium.

Background

The signal sent by the base station is affected by intersymbol interference caused by multipath transmission delay in the transmission process, so that the signal received by the terminal is different from the signal sent by the base station. In order to eliminate the influence of intersymbol interference on signals, a terminal can perform channel equalization processing on the signals after receiving the signals so as to restore the signals sent by the base station.

In the prior art, various different channel equalization algorithms can be adopted to realize signal equalization processing, but the equalization effects of the various different channel equalization algorithms in different application scenes are different, so that a channel equalization scheme is needed to be provided to select the channel equalization algorithm based on the actual application scene, and further a better channel equalization effect is achieved.

Disclosure of Invention

The embodiment of the invention aims to provide a channel equalization method, device and apparatus and a storage medium, so that a channel equalization algorithm is selected based on an actual application scene in the process of performing channel equalization processing on signals, and a good channel equalization effect is realized. The specific technical scheme is as follows:

In a first aspect, an embodiment of the present invention provides a channel equalization method, where the method includes:

after receiving a signal sent by a base station, carrying out channel estimation on the signal to obtain channel estimation information;

noise measurement is carried out based on the channel estimation information, and noise information is obtained;

calculating a cross-correlation value representing the cross-correlation of noise between different receiving antennas of the terminal and an autocorrelation value representing the self-correlation of noise of each receiving antenna based on the noise information;

If the ratio of the cross correlation value to the autocorrelation value is smaller than or equal to a preset ratio, selecting an MRC algorithm as a target algorithm, otherwise, selecting the IRC algorithm as the target algorithm;

And carrying out channel equalization processing on the signal by adopting the selected target algorithm based on the channel estimation information and the noise information.

In one embodiment of the present invention, the information estimation information is recorded in a channel estimation matrix, the noise information is recorded in a noise covariance matrix, and the channel equalization processing is performed on the signal by using the selected target algorithm based on the channel estimation information and the noise information, including:

Obtaining the product of the conjugate transpose of the channel estimation matrix and the inverse of the noise matrix as a first result, wherein if the target algorithm is an MRC algorithm, the noise matrix is: the diagonal matrix with the same value of the element at the diagonal line is the same as the value of the element at the diagonal line of the noise covariance matrix, and if the target algorithm is an IRC algorithm, the noise matrix is: the noise covariance matrix;

obtaining a product of the first result and the channel estimation matrix as a second result;

obtaining an inverse matrix of the sum of the second result and the identity matrix as a third result;

And carrying out channel equalization processing on the signals based on the first result and the third result to obtain a channel equalization processing result.

In one embodiment of the present invention, after the channel equalization processing is performed on the signal by using the selected target algorithm based on the channel estimation information and the noise information, the method further includes:

Based on the channel estimation information and the noise information, obtaining a correction factor for performing error adjustment on a channel equalization processing result;

and adjusting the channel equalization processing result based on the correction factor to obtain a correction result.

In one embodiment of the present invention, the information estimation information is recorded in a channel estimation matrix, the noise information is recorded in a noise covariance matrix, and the correction factor for performing error adjustment on a channel equalization processing result is obtained based on the channel estimation information and the noise information, including:

obtaining a diagonal matrix with the value of the element at the diagonal line as the signal-to-noise ratio of the signal based on the channel estimation matrix and the noise covariance matrix, and taking the diagonal matrix as the signal-to-noise ratio matrix;

and obtaining a correction factor for carrying out error adjustment on the channel equalization processing result based on the signal-to-noise ratio matrix.

In one embodiment of the present invention, the performing channel equalization processing on the signal by using the selected target algorithm based on the channel estimation information and the noise information includes:

converting the data in the channel estimation information, the noise information and the signals into fixed point numbers;

And carrying out channel equalization processing on the converted signal by adopting the selected target algorithm based on the converted channel estimation information and the noise information.

In one embodiment of the present invention, the information estimation information is recorded in a channel estimation matrix, the noise information is recorded in a noise covariance matrix, the signals are represented in the form of a signal matrix, and the channel equalization processing is performed on the converted signals by using a selected target algorithm based on the converted channel estimation information and the noise information, including:

For each row of the conjugate transpose of the converted channel estimation matrix, shifting the element in the row to the left by a first bit, wherein the value of the first bit is as follows: the minimum of the real and imaginary parts of the elements in the row;

Obtaining the product of the adjusted conjugate transpose matrix and the inverse matrix of the converted noise matrix as a fourth result, wherein if the target algorithm is an MRC algorithm, the noise matrix is: the diagonal matrix with the same value of the element at the diagonal line is the same as the value of the element at the diagonal line of the noise covariance matrix, and if the target algorithm is an IRC algorithm, the noise matrix is: the noise covariance matrix;

For each row in the fourth result, each element in the row is shifted to the left by a second bit, wherein the value of the second bit is as follows: the minimum of the real and imaginary parts of the elements in the row;

For each column in the converted channel estimation matrix, each element in the column is shifted left by a third bit, wherein the value of the third bit is as follows: a difference between a first bit corresponding to a target row and a reserved bit number, the target row being: a row in a conjugate transpose of the channel estimation matrix having the same row number as the column number of the column;

Right shifting each element in the converted signal matrix by a fourth bit;

And carrying out channel equalization processing on the adjusted signal by adopting the selected target algorithm based on the adjusted fourth result, the adjusted channel estimation matrix, the first bit, the second bit and the third bit.

In a second aspect, an embodiment of the present invention provides a channel equalization apparatus, including a memory, a transceiver, and a processor:

A memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:

In one embodiment of the present invention, the information estimation information is recorded in a channel estimation matrix, the noise information is recorded in a noise covariance matrix, and the channel equalization processing is performed on the signal by using the selected target algorithm based on the channel estimation information and the noise information, which specifically includes:

In one embodiment of the present invention, the information estimation information is recorded in a channel estimation matrix, the noise information is recorded in a noise covariance matrix, and a correction factor for performing error adjustment on a channel equalization processing result is obtained based on the channel estimation information and the noise information, and specifically includes:

In one embodiment of the present invention, the performing channel equalization processing on the signal by using the selected target algorithm based on the channel estimation information and the noise information specifically includes:

In one embodiment of the present invention, the information estimation information is recorded in a channel estimation matrix, the noise information is recorded in a noise covariance matrix, the signals are represented in a signal matrix form, and the channel equalization processing is performed on the converted signals by using a selected target algorithm based on the converted channel estimation information and the noise information, which specifically includes:

Right shifting each element in the converted signal matrix by a fourth bit;

In a third aspect, an embodiment of the present invention provides a channel equalization apparatus, where the apparatus includes:

the channel information obtaining module is used for carrying out channel estimation on the signals after receiving the signals sent by the base station to obtain channel estimation information;

the noise information obtaining module is used for carrying out noise measurement based on the channel estimation information to obtain noise information;

A correlation value calculation module for calculating a cross correlation value representing the cross correlation of noise between different receiving antennas of the terminal and an autocorrelation value representing the self correlation of noise of each receiving antenna based on the noise information;

the algorithm selection module is used for selecting an MRC algorithm as a target algorithm if the ratio of the cross correlation value to the autocorrelation value is smaller than or equal to a preset ratio, or selecting the IRC algorithm as the target algorithm;

And the equalization processing module is used for carrying out channel equalization processing on the signals by adopting the selected target algorithm based on the channel estimation information and the noise information.

In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having a computer program stored therein, which when executed by a processor, implements the method steps of any of the first aspects.

In a fifth aspect, embodiments of the present invention also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method steps of any of the first aspects described above.

The embodiment of the invention has the beneficial effects that:

the embodiment of the invention provides a channel equalization method, which is characterized in that after a terminal receives a signal sent by a base station, the terminal carries out channel estimation processing on the signal to obtain channel estimation information. Noise measurement is performed based on the channel estimation information to obtain noise information. And calculates a cross-correlation value representing the cross-correlation of noise between different receiving antennas of the terminal and an autocorrelation value representing the self-correlation of noise of each receiving antenna based on the noise information. If the ratio of the cross correlation value to the autocorrelation value is smaller than or equal to the preset ratio, the MRC algorithm is used for carrying out channel equalization processing on the signals. Otherwise, the IRC algorithm is used for carrying out channel equalization processing on the signals.

From the above, if the ratio between the cross correlation value and the autocorrelation value is smaller than or equal to the preset ratio, it is indicated that compared with the autocorrelation value, the cross correlation value is smaller, that is, the cross correlation of the noise between different receiving channels is smaller, and the ratio of the noise in the signal to the inter-symbol interference is lower, so that the MRC algorithm suitable for performing the channel equalization processing under the condition of low inter-symbol interference can be adopted to perform the channel equalization processing on the signal. Otherwise, IRC algorithm suitable for carrying out channel equalization processing under condition of high intersymbol interference can be adopted to carry out channel equalization processing on signals. Therefore, the embodiment of the invention can select the MRC algorithm or the IRC algorithm to perform channel equalization processing on the signals based on the actual application scene, thereby realizing better channel equalization effect.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.

Fig. 1 is a flow chart of a first channel equalization method according to an embodiment of the present invention;

fig. 2 is a schematic diagram of an application scenario provided in an embodiment of the present invention;

Fig. 3 is a flow chart of a second channel equalization method according to an embodiment of the present invention;

fig. 4 is a flow chart of a third channel equalization method according to an embodiment of the present invention;

fig. 5 is a flow chart of a fourth channel equalization method according to an embodiment of the present invention;

fig. 6 is a flowchart of a fifth channel equalization method according to an embodiment of the present invention;

Fig. 7 is a flowchart of a sixth channel equalization method according to an embodiment of the present invention;

fig. 8 is a schematic structural diagram of a channel equalization device according to an embodiment of the present invention;

fig. 9 is a schematic structural diagram of a channel equalization apparatus according to an embodiment of the present invention.

Detailed Description

In the embodiment of the invention, the term "and/or" describes the association relation of the association objects, which means that three relations can exist, for example, a and/or B can be expressed as follows: 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 term "plurality" in embodiments of the present invention means two or more, and other adjectives are similar.

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention are included in the scope of protection of the present invention.

In order to select a channel equalization algorithm based on an actual application scene and further achieve a good channel equalization effect, the embodiment of the invention provides a channel equalization method, device and storage medium.

The embodiment of the invention provides a channel equalization method, which comprises the following steps:

after receiving signals sent by a base station, carrying out channel estimation on the signals to obtain channel estimation information;

Calculating a cross correlation value representing the cross correlation of noise between different receiving antennas of the terminal and an autocorrelation value representing the self correlation of noise of each receiving antenna based on the noise information;

If the ratio of the cross-correlation value to the autocorrelation value is smaller than or equal to a preset ratio, selecting an MRC algorithm as a target algorithm, otherwise, selecting the IRC algorithm as the target algorithm;

and performing channel equalization processing on the signal by adopting the selected target algorithm based on the channel estimation information and the noise information.

Referring to fig. 1, a flow chart of a first channel equalization method according to an embodiment of the present invention is shown, where the method includes the following steps S101 to S105.

The main execution body of the channel equalization method can be a terminal, such as a mobile phone, a computer, a tablet computer and the like.

S101: after receiving the signal sent by the base station, channel estimation is carried out on the signal to obtain channel estimation information.

After the base station transmits a signal, the transmitted signal may be interfered by a channel and noise, so that the signal received by the terminal is different from the signal transmitted by the base station.

Specifically, the signal received by the terminal may be represented by the following formula:

y＝Hx+u

wherein y is a signal received by the terminal, x is a signal transmitted by the base station, H is channel interference superimposed on x, and u is noise interference superimposed on x.

In addition, the signal may be a signal belonging to one RB (Resource Block) group, and specifically, the frequency domain bandwidth may be divided to obtain different RB groups, and the division may be random.

In one embodiment of the present invention, after receiving a signal, preliminary channel estimation may be performed based on the received signal and a preset local signal to obtain preliminary channel estimation information, and frequency domain filtering difference processing may be performed on the preliminary channel estimation information to obtain channel estimation information.

Specifically, after receiving a signal, pilot symbol data is extracted from frequency domain data of the received signal. The predetermined local signal may be pilot symbol data, and then a complex conjugate product of pilot symbol data extracted from frequency domain data of the signal and the local signal may be calculated to obtain the preliminary channel estimation information.

In addition, the frequency domain filtering difference processing can be performed on the initial channel estimation information by adopting an MMSE (Minimum Mean Square Error ) frequency domain filtering interpolation mode to obtain the channel estimation information.

Specifically, the channel estimation information obtained after the signal is subjected to channel estimation may be represented in the form of a channel estimation matrix, where each row of the channel estimation matrix corresponds to one receiving antenna of the terminal, and each column of the channel estimation matrix corresponds to one transmitting antenna of the base station, so that the channel estimation matrix is a matrix of N _rx×N_tx, where N _rx is the number of receiving antennas of the terminal, and N _tx is the number of transmitting antennas of the base station.

The above processes of obtaining the preliminary channel estimation information and processing the frequency domain filtering difference value can be completed by the prior art, and the embodiments of the present invention will not be repeated.

The base station may transmit signals based on MIMO (Multiple-in Multipleout, multiple input Multiple output) technology, and the base station may transmit signals via a plurality of transmitting antennas, and the terminal may receive signals via a plurality of receiving antennas.

Referring to fig. 2, a schematic diagram of an application scenario is provided in an embodiment of the present invention.

As can be seen, the base station passes through the transmitting antennas 1-n. The terminal can receive signals through the receiving antennas 1-m and m receiving antennas. The arrow between each transmit antenna and each receive antenna represents one signal transmission path.

S102: and carrying out noise measurement based on the channel estimation information to obtain noise information.

In one embodiment of the present invention, the noise information may be calculated based on the channel estimation information and the preliminary channel estimation information.

Specifically, in the case where the channel estimation information is represented in the form of a channel estimation matrix and the preliminary channel estimation information is represented in the form of a preliminary channel estimation matrix, a difference between the preliminary channel estimation matrix and the channel estimation matrix may be calculated, and a calculation result may be obtained as the preliminary noise information. The initial noise information is also a matrix, each row in the initial noise information corresponds to a receiving antenna, each column corresponds to a transmitting antenna, and the initial noise information is a matrix of N _rx×N_tx. Each element in the initial noise information represents the magnitude of noise interference that a signal transmitted from a transmitting antenna corresponding to the present column receives during transmission to a receiving antenna corresponding to the present row. And carrying out noise covariance calculation based on the initial noise information to obtain a noise covariance matrix serving as the noise information. Specifically, the product between the initial noise information and the conjugate transpose of the initial noise information may be calculated to obtain the noise covariance matrix.

Wherein each row in the noise covariance matrix corresponds to one receiving antenna of the terminal, and each column also corresponds to one receiving antenna of the terminal. The noise covariance matrix is thus a matrix of N _rx×N_rx. Each element in the noise covariance matrix may represent a correlation between a receiving antenna corresponding to a row where the element is located and a receiving antenna corresponding to a column where the element is located, where the larger the value of the element is, the larger the represented correlation is.

The above process of obtaining noise information may be completed by the prior art, and this will not be repeated in the embodiments of the present invention.

S103: based on the noise information, a cross-correlation value representing the cross-correlation of noise between different receiving antennas of the terminal and an autocorrelation value representing the self-correlation of noise of each receiving antenna are calculated.

Specifically, for each two receiving antennas, a sub-cross-correlation value indicating the cross-correlation between the two receiving antennas may be obtained based on the noise information. And then calculating the cross-correlation value based on the sub-cross-correlation value. For each receiving antenna, a sub-autocorrelation value indicating the autocorrelation of the receiving antenna may be obtained based on the noise information, and the autocorrelation value may be calculated based on the sub-autocorrelation value.

The sum of squares of the individual sub-cross-correlation values may be calculated as the cross-correlation value. The sum of the products between the sub-correlation values of the different receive antennas may be calculated as the above-mentioned autocorrelation value.

Wherein, in the case where the noise information is represented in the form of a noise covariance matrix, all elements other than the elements located at the diagonal are the sub-cross correlation values, and the elements located at the diagonal are the sub-autocorrelation values. The cross-correlation value can be calculated according to the following formula:

Wherein, Y is the cross correlation value, ruu is the noise covariance matrix, r is the row number of the element in the noise covariance matrix, and q is the column number of the element in the noise covariance matrix.

In addition, the above autocorrelation value can be calculated according to the following formula:

Wherein, the above-mentioned Z is the above-mentioned autocorrelation value, ruu is the above-mentioned noise covariance matrix, r is the line number and column number of the element in the above-mentioned noise covariance matrix, q is the line number and column number of the element in the above-mentioned noise covariance matrix.

S104: and if the ratio of the cross correlation value to the autocorrelation value is smaller than or equal to a preset ratio, selecting the MRC algorithm as a target algorithm, otherwise, selecting the IRC algorithm as the target algorithm.

If the ratio between the cross correlation value and the autocorrelation value is smaller than or equal to the preset ratio, the cross correlation of the noise between different receiving antennas is smaller, otherwise, the cross correlation of the noise between different receiving antennas is larger. The preset proportion takes on any value between 0 and 1.

In addition, the MRC (Maximum Ratio Combining, maximum carrier-to-interference ratio combining) algorithm may be an algorithm in the prior art, or may be other algorithms derived based on the MRC algorithm in the prior art, where the derived algorithm also belongs to the MRC algorithm mentioned in the embodiment of the present invention. The IRC (INTERFERENCE REJECTION COMBINING ) algorithm can be an algorithm in the prior art, and can also be other algorithms derived on the basis of the IRC algorithm in the prior art, and the derived algorithm also belongs to the IRC algorithm mentioned in the embodiment of the invention.

S105: and performing channel equalization processing on the signal by adopting the selected target algorithm based on the channel estimation information and the noise information.

In the embodiment of the invention, the parameters for performing the channel equalization processing can be calculated by adopting the selected target algorithm based on the channel estimation information and the noise information, and the parameters are multiplied by the received signals, so that the channel equalization processing on the signals is completed.

In addition, when the signals belonging to different subcarriers and different symbols in the signals are subjected to channel equalization processing, it is necessary to extract information corresponding to the subcarrier and the symbol from the channel estimation information and the noise information for each subcarrier and each symbol when the signals belonging to the subcarrier and the symbol are subjected to channel equalization processing, so that the signals belonging to the subcarrier and the symbol are subjected to channel equalization processing.

Specifically, the manner of calculating the above parameters based on the MRC algorithm or the IRC algorithm belongs to the prior art, and the embodiments of the present invention will not be described in detail.

Referring to fig. 3, a flow chart of a second channel equalization method according to an embodiment of the present invention is shown.

As can be seen from the figure, after receiving the signal, the signal is input to the pilot signal extraction module to perform pilot received signal extraction on the signal. And inputting the extracted pilot frequency receiving signals and the local signals into an initial channel estimation module for initial channel estimation to obtain initial channel estimation information. And inputting the initial channel estimation information into an MMSE filtering difference module, and performing MMSE filtering difference processing on the initial channel estimation information to obtain the channel estimation information. The initial channel estimation information and the channel estimation information are input into a noise measurement module to obtain initial noise information. And inputting the initial noise information into a noise covariance calculation module to obtain noise information. And inputting the channel estimation information and the noise information into a channel equalization module to perform noise estimation on the signal.

The pilot signal extraction module, the initial channel estimation module and the MMSE filtering difference module which are outlined by the dotted line frame belong to a channel estimation system, and the noise measurement module, the noise covariance calculation module and the input channel equalization module which are outlined by the dotted line frame belong to a channel equalization system.

Referring to fig. 4, a flow chart of a third channel equalization method according to an embodiment of the present invention is shown, and in comparison with the embodiment shown in fig. 1, in the case that the information estimation information is recorded in the channel estimation matrix and the noise information is recorded in the noise covariance matrix, the step S105 may be implemented by the following steps S105A-S105D.

S105A: and obtaining the product of the conjugate transpose matrix of the channel estimation matrix and the inverse matrix of the noise matrix as a first result.

If the target algorithm is an MRC algorithm, the noise matrix is: the diagonal matrix having the same value of the element at the diagonal line as the diagonal line of the noise covariance matrix, that is, if the target algorithm is an MRC algorithm, the values of the elements except the element at the diagonal line in the noise covariance matrix may be set to 0, so as to obtain the noise matrix.

If the target algorithm is an IRC algorithm, the noise matrix is: the noise covariance matrix.

Specifically, if the dimension of the channel estimation matrix is N _rx×N_tx, the dimension of the conjugate transpose of the channel estimation matrix is N _tx×N_rx, the noise matrix is the same as the noise covariance matrix, and if the dimension is N _rx×N_rx, the inverse of the noise matrix is the same as the matrix having the dimension of N _rx×N_rx. The product of the conjugate transpose of the channel estimation matrix and the inverse of the noise matrix has a dimension N _tx×N_rx, i.e., the first result has a dimension N _tx×N_rx.

In addition, if the channel equalization processing is performed on signals belonging to different subcarriers and different symbols in the signals, when the channel equalization processing is performed on signals belonging to the subcarriers and the symbols for each subcarrier and each symbol, a matrix having a dimension v×n _rx corresponding to the subcarrier and the symbol may be extracted from the obtained channel estimation matrix as the channel estimation matrix, and then the subsequent calculation may be performed.

S105B: and obtaining the product of the first result and the channel estimation matrix as a second result.

Specifically, since the dimension of the first result is N _tx×N_rx and the dimension of the channel estimation matrix is N _rx×N_tx, the dimension of the product of the first result and the channel estimation matrix is N _tx×N_tx.

S105C: and obtaining an inverse matrix of the sum of the second result and the identity matrix as a third result.

Specifically, the identity matrix is a matrix with a value of 1 of an element at a diagonal line, the number of rows being the same as that of the second result, and the number of columns being the same as that of the second result. The identity matrix is likewise a matrix of N _tx×N_tx.

In one embodiment of the present invention, the sum of the second result and the identity matrix may be calculated first, where the dimension of the calculated sum is N _tx×N_tx, and then the sum is inverted, and as a third result, the dimension of the calculated third result is N _tx×N_tx.

S105D: and carrying out channel equalization processing on the signals based on the first result and the third result to obtain a channel equalization processing result.

In the first embodiment of the present invention, the product between the third result and the first result may be obtained first to obtain the parameter matrix for performing the channel equalization processing, where the dimension of the third result is N _tx×N_tx, and the dimension of the first result is N _tx×N_rx, and the dimension of the product between the third result and the first result is N _tx×N_rx, that is, the dimension of the parameter matrix is N _tx×N_rx. And multiplying the parameter matrix with the signals to obtain a channel equalization processing result.

In the second embodiment of the present invention, the product of the first result and the signal may be calculated first, where the dimension of the first result is N _tx×N_rx, the dimension of the signal is N _rx×N_tx, the dimension of the product obtained by calculation is N _tx×N_tx, and the channel equalization result is calculated by multiplying the third result by the product.

Specifically, in the channel equalization processing according to the first embodiment, the process of multiplying the matrix of the primary dimension N _tx×N_tx by the matrix of the dimension N _tx×N_rx and the process of multiplying the matrix of the primary dimension N _tx×N_rx by the matrix of the dimension N _rx×N_tx are involved.

In the channel equalization processing according to the second embodiment, a process of multiplying the matrix of the primary dimension N _tx×N_rx by the matrix of the dimension N _rx×N_tx and a process of multiplying the matrix of the primary dimension N _tx×N_tx by the matrix of the dimension N _tx×N_tx are involved.

Since the smaller the dimension of the matrix is, the less calculation resources are required in the calculation process, and the number N _rx of terminal receiving antennas in a realistic application scenario is often greater than the number N _tx of transmitting antennas of the base station. The use of the second embodiment for the channel equalization process can reduce the amount of computation required in the process of performing the channel equalization process.

In another embodiment of the present invention, the above parameter matrix may be calculated based on the following formula z.

W＝(H^HR^-1H+I)^-1H^HR^-1

Wherein W is a parameter matrix, H is a channel estimation matrix, H ^H is a conjugate transpose of the channel estimation matrix, R is the noise matrix, and I is the identity matrix.

Specifically, H ^HR^-1 in the above formula is the product of the conjugate transpose of the channel estimation matrix and the inverse of the noise matrix, which is the first result. H ^HR^-1 H is the product of the first result and the channel estimation matrix, i.e. the second result. (H ^HR-¹H+I)^-1 is the inverse of the sum of the second result and the identity matrix, i.e., the third result.

When the channel equalization is performed, the above-mentioned parameter matrix W may be multiplied by the signal y to obtain a channel equalization result, that is, (H ^HR^-1H+I)^-1H^HR^-1 multiplied by y.) so that if the channel equalization is performed in the above first embodiment, the third result (H ^HR^-1H+I)^-1 multiplied by the first result H ^HR^-1, calculated to obtain W, and then multiplied by y to obtain a channel equalization result, and if the channel equalization is performed in the above second embodiment, the first result H ^HR^-1 may be multiplied by y and then multiplied by the third result (H ^HR^-1H+I)^-1 multiplied by H ^HR^-1 y) to obtain a channel equalization result.

In addition, the formula z for calculating the parameter matrix can be calculated based on an MRC algorithm and an IRC algorithm.

Specifically, the MRC algorithm in the prior art is:

Wherein, W is a parameter matrix, λ is a parameter, H is a channel estimation matrix, H ^H is a conjugate transpose matrix of the channel estimation matrix, R is a noise matrix, and R is a diagonal matrix having the same value of an element at a diagonal line as that of the noise covariance matrix.

Since the signal y=hx+u, in theory, the signal is subjected to channel equalization, that is, the signal y is multiplied by the W, so that H in the signal y can be eliminated, and the signal x sent by the base station is obtained. It can be considered that W multiplied by H gives a constant C, and it can be assumed that wh=c

λ＝2C(H^HR^-1H)^-1

If c=1 is assumed, it is possible to obtain

W＝(H^HR^-1H)^-1H^HR^-1

Adding identity matrix I into the matrix to obtain

W＝(H^HR^-1H+I)^-1H^HR^-1

In addition, the IRC algorithm in the prior art is as follows:

W＝(H^HH+Ruu)^-1H

wherein Ruu is the noise covariance matrix.

The derivation is performed on the basis of w= (H ^HH+Ruu)^-1 H) described above, and can be obtained:

W＝Ruu^-1H-Ruu^-1H(I+H^HRuu^-1H)^-1H^HRuu^-1H

further:

W＝Ruu^-1H(I+H^HRuu^-1H)^-1

w is then ^H＝(H^HRuu^-1H+I)^-1H^HRuu^-1

Since the difference between W ^H and W is mainly that the positions of the elements in the matrix are different, the influence on the value of the elements is small, so that the above formula can be simplified as follows:

W＝(H^HRuu^-1H+I)^-1H^HRuu^-1

Therefore, the expression of the MRC algorithm and the IRC algorithm may be unified as the formula z.

In addition, the complexity of data processing of IRC algorithm in the prior art is OThus, the data processing complexity of the above formula z is OWhere N _sym is the number of processed symbols in a single slot and N _sc is the number of processed subcarriers. Because the N _rx is often larger than N _tx in a realistic application scenario, improving the IRC algorithm to the formula z and then performing channel equalization processing by using the IRC algorithm can reduce the computational resources consumed in the process of performing channel equalization processing.

Furthermore, the dimension of the matrix H ^H H+Ruu needing inversion in the formula of the IRC algorithm in the prior art is N _rx×N_rx, the dimension of the matrix H ^HRuu^-1 H+I needing inversion in the improved formula is N _tx×N_tx, and the dimension of the matrix needing inversion is reduced, so that the problem of pathological inversion can be avoided in the process of carrying out channel equalization processing by adopting the IRC algorithm after improvement.

From the above, in the scheme provided by the embodiment of the invention, no matter whether the target algorithm is the IRC algorithm or the MRC algorithm, the process of channel equalization processing can be completed through the same flow, but the noise matrix used in the process of channel equalization processing is the noise covariance matrix when the target algorithm is the IRC algorithm, and the noise matrix used in the process of channel equalization processing is the diagonal matrix with the same value as the element at the diagonal of the noise covariance matrix when the target algorithm is the MRC algorithm. Therefore, the scheme provided by the embodiment of the invention can unify the flow of the channel equalization processing of the MRC algorithm and the IRC algorithm. In addition, when the target algorithm is the IRC algorithm, the flow provided by the embodiment of the invention is used for carrying out channel equalization processing, so that the calculated amount required in the process of carrying out the channel equalization processing can be reduced, and the efficiency of carrying out the channel equalization processing is improved.

Referring to fig. 5, a flow chart of a fourth channel equalization method according to an embodiment of the present invention, compared with the embodiment shown in fig. 1, further includes the following steps S106 to S107 after the step S105.

S106: and obtaining a correction factor for performing error adjustment on a channel equalization processing result based on the channel estimation information and the noise information.

In one embodiment of the present invention, when the channel estimation information is recorded in the channel estimation matrix and the noise information is recorded in the noise covariance matrix, the process of performing the channel equalization process based on the embodiment shown in fig. 3 belongs to the process of performing the eccentricity estimation, and the step S106 may be implemented by the following steps a to B to eliminate the error in the channel equalization result caused by the eccentricity estimation.

Step A: and obtaining a diagonal line matrix with the value of the element at the diagonal line as the signal-to-noise ratio of the signal based on the channel estimation matrix and the noise covariance matrix, and taking the diagonal line matrix as the signal-to-noise ratio matrix.

Specifically, the second result may be obtained by calculating based on the channel estimation matrix and the noise covariance matrix, and a diagonal matrix having the same value of the element at the diagonal line as the value of the element at the diagonal line in the second result may be obtained as the signal-to-noise ratio matrix.

And (B) step (B): and obtaining a correction factor for carrying out error adjustment on the channel equalization processing result based on the signal-to-noise ratio matrix.

In one embodiment of the present invention, the sum of the snr matrix and the identity matrix may be calculated, and the sum value obtained by calculation may be divided by the snr matrix to obtain the correction factor.

Specifically, the correction factor may be calculated according to the following formula:

wherein Corrector is the correction factor and SNR is the SNR matrix.

S107: and adjusting the channel equalization processing result based on the correction factor to obtain a correction result.

In one embodiment of the present invention, the correction factor may be multiplied by a channel equalization result to adjust the channel equalization result to obtain a correction result.

From the above, since there may be an error in the channel equalization result obtained by performing the channel equalization on the signal, a correction factor may be calculated, and the channel equalization result is adjusted based on the correction factor, so as to remove the error and improve the accuracy of the channel equalization result.

Referring to fig. 6, a flowchart of a fifth channel equalization method according to an embodiment of the present invention may be implemented by the following steps S105E-S105F, compared with the embodiment shown in fig. 1.

S105E: and converting the data in the channel estimation information, the noise information and the signals into fixed point numbers.

In one embodiment of the present invention, the channel estimation information, the noise information, and the data included in the signal may be floating point numbers, and the data included in the channel estimation information may be fixed point numbers based on a first scale, converted to fixed point numbers, fixed point numbers based on a second scale, and fixed point numbers based on a third scale, thereby converting the channel estimation information and the noise information to fixed point numbers.

Specifically, the scaling may record a first number of bits included in each fixed point number after conversion and a second number of bits belonging to an integer part in the fixed point number. In the process of converting the floating point number into the fixed point number, the floating point number needs to shift the difference between the first bit number and the second bit number to the left. Each time the floating point number shifts one bit to the left, the obtained fixed point number can be amplified by two times on the basis of the original floating point number.

The first calibration, the second calibration and the third calibration may be the same or different, and the first calibration and the second calibration may be preset fixed values or may be determined based on a preset accumulated combined bit width between receiving antennas.

For example, the first scale may be a preset fixed value (16, 5), which indicates that the fixed point number obtained by conversion contains 16 bits in total, and the first 5 bits in the fixed point number are integer parts contained in the floating point number before conversion, that is, the last 11 bits in the fixed point number are fractional parts contained in the floating point number before conversion.

The second scaling may be determined based on accumulating the combined bit widths between the antennas for a predetermined period, e.g., (16, 1+δ _rs11),δ_rs11 is the accumulated combined bit width between the antennas).

The third scaling may be a fixed value (16, 1).

S105F: and carrying out channel equalization processing on the converted signal by adopting the selected target algorithm based on the converted channel estimation information and the noise information.

Specifically, the scheme of performing the channel equalization processing on the signal based on the channel estimation information and the noise information is similar to the above-described step S105, and the difference is that only the channel estimation information and the noise information used in the above-described step S105F are subjected to the fix-point processing, and the data in the channel estimation information and the noise information are amplified after the fix-point processing, so that the data in the obtained channel processing result needs to be reduced after the channel equalization processing is performed based on the amplified data.

For example, if the data included in the channel estimation information is amplified by 2 ³ times and the data included in the noise information is amplified by 2 ² times, the data in the obtained channel processing result may be multiplied by 2 ^-5 after the channel processing result is calculated.

In one embodiment of the present invention, the above step S105F may be implemented by the following steps S105F1 to S105F6, which are not described in detail herein.

From the above, before the channel equalization processing is performed on the signal based on the channel estimation information and the noise information, the data in the channel estimation information, the noise information and the signal are subjected to the localization processing, that is, the data is converted from the floating point number to the fixed point number. Since the terminal consumes more computing resources in the process of processing the floating point number, and part of the data processor is not suitable for performing the floating point number calculation, if the data are all the floating point numbers, the requirement on the data processor in the process of performing the channel equalization processing is higher. The embodiment of the invention adjusts each floating point number to the fixed point number and then carries out the channel equalization processing, so that the computing resources consumed in the channel equalization processing process can be reduced, and the embodiment of the invention can be suitable for various data processors. And the original numerical value of part of data is smaller, the influence on the channel equalization processing result is smaller in the process of carrying out the channel equalization processing, and the influence is easily ignored, so that the accuracy of the channel equalization processing result is lower. After the floating point number is converted into the fixed point number, the numerical value of the data is increased, and for partial data with smaller original numerical value, the influence degree of the data on the channel equalization processing result can be improved by amplifying and then performing data processing, so that the accuracy of the channel equalization processing result can be improved.

Referring to fig. 7, a flowchart of a sixth channel equalization processing method according to an embodiment of the present invention is shown, and compared with the embodiment shown in fig. 6, the above step S105F may be implemented by the following steps S105F1-S105F 6.

S105F1: for each row of the conjugate transpose of the converted channel estimation matrix, the elements in that row are left shifted by the first bit.

Wherein, the value of the first bit is: the minimum of the real and imaginary parts of the elements in the row. For each row, the values of the real part and the imaginary part of each element in the row can be determined respectively, so that the minimum value in the determined real part and imaginary part is determined.

In one embodiment of the present invention, the first bit shifted to the left of the element is equal to the first bit to the power of 2 of the value of the element, and the value of the data in the channel equalization result calculated based on the conjugate transpose of the channel estimation matrix after the amplification is also amplified.

The first bit corresponding to the p-th row may be represented by δ _s1,p.

In addition, since the elements in the channel estimation matrix are converted into fixed point numbers, the process of calculating the conjugate transpose matrix based on the converted channel estimation matrix belongs to the process of calculating fixed point numbers. The conjugate transpose is scaled identically to the channel estimation matrix, and may be (16, 5), for example.

S105F2: and obtaining the product of the adjusted conjugate transpose matrix and the inverse matrix of the converted noise matrix as a fourth result.

The elements contained in the adjusted conjugate transpose matrix and the converted noise matrix are fixed point numbers, so that the process of calculating the fourth result belongs to the calculation of the fixed point numbers.

In addition, since the p-th row element in the adjusted conjugate transpose matrix is shifted to the left by δ _s1,p bits, the product ratio of the adjusted conjugate transpose matrix and the inverse matrix of the converted noise matrix is shifted to the left by δ _s1,p bits compared with the product ratio of the conjugate transpose matrix before adjustment and the inverse matrix of the converted noise matrix, which is equivalent to the fourth result shifted to δ _s1,p-δ_rs11 bits except for the accumulated combined bits δ _rs11 between the reserved receiving antennas in the elements in the noise matrix.

In order to counteract the influence of the element left shift in the fourth result on the channel equalization result after the channel equalization process is performed, δ _s1,p-δ_rs11 opposite number- δ _s1,p+δ_rs11 may be used as a first left shift factor, so as to restore the channel equalization process result in the case that the element is not left shifted based on the first left shift factor in the subsequent process.

The fourth result may be scaled similarly to the conjugate transpose, and in the case where the conjugate transpose is scaled by (16, 5), the fourth result may be scaled by (16, 5).

Otherwise, the manner in which the fourth result is obtained in the step S105F2 is similar to that in the step S105A, and the embodiment of the present invention will not be repeated.

S105F3: for each row in the fourth result, each element in the row is shifted to the left by a second bit.

Wherein, the second bit has a value of: the minimum of the real and imaginary parts of the elements in the row. For each row, the values of the real part and the imaginary part of each element in the row can be determined respectively, so that the minimum value in the determined real part and imaginary part is determined.

For the elements in the p-th row, it can be seen from the foregoing that the p-th row element in the fourth result has been shifted to the left by δ _s1,p-δ_rs11 bits, and on the basis of this, each element in the row is shifted to the left by a second bit δ _s2,p, so that the p-th row element is shifted to the left by δ _s1,p-δ_rs11+δ_s2,p bits, and the first left shifting factor is updated to δ _rs11-δ_s1,p-δ_s2,p.

In addition, the scaling of the fourth result may be adjusted, for example, the scaling of the fourth result is adjusted from (16, 5) to (16, 1), which corresponds to shifting the decimal point of each element in the fourth result by 4 bits, that is, shifting each element by 4 bits, so that the first left shifting factor is updated to δ _rs11-δ_s1,p-δ_s2,p +4.

In one embodiment of the present invention, the fourth result may be adjusted by multiplying the fourth result by a first left shift coefficient matrix in order to cancel the influence of the left shift of the element on the element value. The first left shift coefficient matrix is a diagonal matrix with the same line number as the fourth result, the value of an element at the diagonal line in each line in the left shift coefficient matrix is to the power of a first target factor with the value of 2, and the first target factor is a first left shift factor corresponding to the line with the same line number as the line in the fourth result.

That is, the elements on the diagonal of the first left shift coefficient matrix a ₁ are: where t is the number of rows of the fourth result.

S105F4: for each column in the transformed channel estimation matrix, each element in the column is shifted to the left by a third bit, respectively.

Wherein, the third bit has a value of: the difference between the first bit and the reserved bit number corresponding to the target row is: the row number is the same as the column number of the column in the conjugate transpose of the channel estimation matrix.

Specifically, for each column, the first bit of the target row corresponding to the column may be determined separately. Subtracting the reserved bit number from the first bit to obtain the third bit, and shifting each element in the column left by the third bit, which is equivalent to performing the top lattice processing of each element except the reserved bit in the column, that is, amplifying each element to the maximum extent.

The reserved bits may be the number of accumulated combined bits between the receive antennas reserved for the channel estimation matrix. The third bit of column p may be expressed as: delta _s1,p-δ_rs12, wherein delta _rs12 is the reserved bit number.

In order to counteract the influence of the left shift of the element in the channel estimation matrix on the channel equalization result after the channel equalization process is performed, the inverse number of the third bit, delta _s1,p+δ_rs12, may be used as a second left shift factor, so that the channel equalization result in the case that the element is not left-shifted is obtained by restoring based on the second left shift factor in the subsequent process.

In one embodiment of the present invention, to offset the influence of the left shift of the element on the element value, the channel estimation matrix may be adjusted by multiplying the channel estimation matrix by a second left shift coefficient matrix. The second left shift coefficient matrix is a diagonal matrix with the same column number as that of the channel estimation matrix, the value of an element at a diagonal line in each column in the second left shift coefficient matrix is to the power of a second target factor with 2, and the second target factor is a second left shift factor corresponding to the column with the same column number as that of the column in the channel estimation matrix.

That is, the elements on the diagonal of the second left shift coefficient matrix a ₂ are:

The number of columns of the channel estimation matrix is the same as the number of rows of the conjugate transpose matrix of the channel estimation matrix, and the fourth result is obtained by multiplying the conjugate transpose matrix of the channel estimation matrix by the inverse matrix of the noise matrix, so that the number of rows of the fourth result is the same as the number of rows of the conjugate transpose matrix, that is, the number of rows of the fourth result is the same as the number of columns of the channel estimation matrix. In addition, the number of rows of the first left-shift coefficient matrix is the same as the fourth result, and the number of columns of the second left-shift coefficient matrix is the same as the number of columns of the channel estimation matrix, so that the number of rows of the first left-shift coefficient matrix is the same as the number of columns of the second left-shift coefficient matrix, and the number of columns is denoted by t in the expression herein.

S105F5: and right shifting each element in the converted signal matrix by a fourth bit.

The scaling of the signal matrix may be (16, 1), and the fourth bit may be a preset reserved bit width value, so that each element in the signal matrix is shifted right by the fourth bit, which is equivalent to shifting the decimal point by the fourth bit, and the values of the elements in the signal matrix are unchanged before and after the element is shifted right. The scaling of the signal matrix may be (16, 1+ delta _rs4).

S105F6: and carrying out channel equalization processing on the adjusted signal by adopting the selected target algorithm based on the adjusted fourth result, the adjusted channel estimation matrix, the first bit, the second bit and the third bit.

Specifically, referring to the embodiment shown in fig. 4, further products of the fourth result and the channel transpose matrix can be obtained by the following formula:

Wherein H ^H′ is the conjugate transpose of the adjusted channel estimation matrix, H ^HR^-1 is the fourth result, H' is the adjusted channel estimation matrix, For the product of the fourth adjusted result and the adjusted channel estimation matrix, R _HH is the sum of the first left-shift coefficient matrix and the second left-shift coefficient matrixAnd (5) adjusting to obtain a result.

Specifically, the scale of R _HH is (16, 5).

Thereafter, the above R _HH may be subjected to an inversion process. Due toTherefore, R _HH is not a conjugate symmetric matrix, and the computation of inverting the non-conjugate symmetric matrix is complex, and more computing resources are required, so that R _HH can be further processed.

Specifically, a ₁ is decomposed into a first sub-left-shift coefficient matrix a ₀₁ and a second sub-left-shift coefficient matrix a ₁₁, where the elements on the diagonal of a ₀₁ areThe elements on the diagonal of A ₁₁ areA ₁＝A₀₁A₁₁.

Thus, the first and second substrates are bonded together,If it isThen

R '_HH is a conjugate symmetric matrix, and the method can be obtained by inverting R' _HH and compensating A ₀₁:

Multiplying the result by Obtaining a new product

Specifically, the scale of (R' _HH)^-1 is the same as R _HH, which is (16, 5).

In addition, the product between the fourth result after adjustment and the signal matrix after adjustment is calculated as a fifth resultThe third left shift factor corresponding to the fifth result is identical to the first left shift factor corresponding to the fourth result, δ _rs11-δ_s1,p-δ_s2,p +4, and the scaling of the fifth result is identical to the signal matrix, and (16, 1+δ _rs4).

Next, the above is calculatedAnd (3) withThe product of (R' _HH)^-1A₁ and (R)The product of the above A ₁ and the left phase shift of each element in the fifth result can be cancelled, and the above can be calculatedAnd multiplying the scaling coefficient to obtain a channel equalization processing result.

Specifically, the scaling factor may be based on (R' _HH)^-1 andThe difference between the scaling factors is determined by the scaling factor 2 to the power of the target factor (the number of bits in the fractional part of R' _HH)^-1 andThe difference between the number of bits in the medium fraction. The scale at (R' _HH)^-1) above is (16, 5),Is (16, 1+delta _rs4), the above scaling factor is

From the above, the original values of part of the elements in the channel estimation matrix and the noise matrix are smaller, so that the influence on the channel equalization result in the process of performing the channel equalization is smaller and is easy to ignore, and the accuracy of the channel equalization result is lower. The elements in the channel estimation matrix and the noise matrix are shifted to the left, the values of the elements can be amplified, and for partial data with smaller original values, the influence degree of each element on the channel equalization processing result can be improved by carrying out data processing after amplification, so that the accuracy of the channel equalization processing result can be improved.

Corresponding to the channel equalization method, the embodiment of the invention also provides a channel equalization device.

Referring to fig. 8, a schematic structural diagram of a channel equalization device according to an embodiment of the present invention includes a memory 801, a transceiver 802, and a processor 803:

A memory 801 for storing a computer program; a transceiver 802 for transceiving data under control of the processor; a processor 803 for reading the computer program in the memory and performing the following operations:

Right shifting each element in the converted signal matrix by a fourth bit;

Referring to fig. 9, a schematic structural diagram of a channel equalization apparatus according to an embodiment of the present invention is provided, where the apparatus includes:

A channel information obtaining module 901, configured to perform channel estimation on a signal sent by a base station after receiving the signal, to obtain channel estimation information;

a noise information obtaining module 902, configured to perform noise measurement based on the channel estimation information, to obtain noise information;

A correlation value calculation module 903, configured to calculate, based on the noise information, a cross-correlation value that represents a cross-correlation of noise between different receiving antennas of the terminal, and an autocorrelation value that represents a self-correlation of noise of each receiving antenna;

An algorithm selection module 904, configured to select a maximum carrier-to-interference ratio combining MRC algorithm as a target algorithm if the ratio of the cross correlation value to the autocorrelation value is less than or equal to a preset ratio, or select an interference suppression combining IRC algorithm as a target algorithm;

And an equalization processing module 905, configured to perform channel equalization processing on the signal using the selected target algorithm based on the channel estimation information and the noise information.

In yet another embodiment of the present invention, there is also provided a computer readable storage medium having stored therein a computer program which when executed by a processor implements the steps of any of the above-described channel equalization methods.

When the computer readable storage medium provided by the embodiment of the invention is used for carrying out channel equalization processing, if the proportion between the cross-correlation value and the autocorrelation value is smaller than or equal to the preset proportion, the cross-correlation value is smaller, that is, the cross-correlation of noise among different receiving channels is smaller, and the proportion of inter-symbol interference in the noise in the signal is lower, so that an MRC algorithm suitable for carrying out channel equalization processing under the condition of low inter-symbol interference can be adopted for carrying out channel equalization processing on the signal. Otherwise, IRC algorithm suitable for carrying out channel equalization processing under condition of high intersymbol interference can be adopted to carry out channel equalization processing on signals. Therefore, the embodiment of the invention can select the MRC algorithm or the IRC algorithm to perform channel equalization processing on the signals based on the actual application scene, thereby realizing better channel equalization effect.

In yet another embodiment of the present invention, there is also provided a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the channel equalization methods of the above embodiments.

When the computer program product provided by the embodiment of the invention is used for carrying out channel equalization processing, if the ratio between the cross correlation value and the autocorrelation value is smaller than or equal to the preset ratio, the cross correlation value is smaller, that is, the cross correlation of noise among different receiving channels is smaller, and the ratio of inter-symbol interference in the noise in the signal is lower, so that an MRC algorithm suitable for carrying out channel equalization processing under the condition of low inter-symbol interference can be adopted for carrying out channel equalization processing on the signal. Otherwise, IRC algorithm suitable for carrying out channel equalization processing under condition of high intersymbol interference can be adopted to carry out channel equalization processing on signals. Therefore, the embodiment of the invention can select the MRC algorithm or the IRC algorithm to perform channel equalization processing on the signals based on the actual application scene, thereby realizing better channel equalization effect.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk Solid STATE DISK (SSD)), etc.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the alarm relationship determination apparatus, device, storage medium and computer program embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the embodiments of the present application and the equivalent techniques thereof, the present application is also intended to include such modifications and variations.

Claims

1. A method of channel equalization, the method comprising:

If the ratio of the cross correlation value to the autocorrelation value is smaller than or equal to a preset ratio, selecting a maximum carrier-to-interference ratio combining MRC algorithm as a target algorithm, otherwise, selecting an interference suppression combining IRC algorithm as the target algorithm;

based on the channel estimation information and the noise information, carrying out channel equalization processing on the signals by adopting the selected target algorithm;

The channel equalization processing is performed on the signal by adopting the selected target algorithm based on the channel estimation information and the noise information, and the channel equalization processing comprises the following steps:

For each row of the conjugate transpose of the converted channel estimation matrix, shifting the element in the row to the left by a first bit, wherein the value of the first bit is as follows: the minimum value in the real part and the imaginary part of the element in the row, and the information estimation information is recorded in a channel estimation matrix;

Obtaining the product of the adjusted conjugate transpose matrix and the inverse matrix of the converted noise matrix as a fourth result, wherein if the target algorithm is an MRC algorithm, the noise matrix is: the diagonal matrix with the same value of the element at the diagonal line is the same as the value of the element at the diagonal line of the noise covariance matrix, and if the target algorithm is an IRC algorithm, the noise matrix is: the noise covariance matrix, wherein the noise information is recorded in the noise covariance matrix;

Right shifting each element in the converted signal matrix by a fourth bit;

2. The method of claim 1, wherein the information estimation information is recorded in a channel estimation matrix and the noise information is recorded in a noise covariance matrix, wherein the performing channel equalization processing on the signal using the selected target algorithm based on the channel estimation information and the noise information comprises:

3. The method of claim 1, further comprising, after said channel equalization processing of said signal using said selected target algorithm based on said channel estimation information and said noise information:

4. The method of claim 3, wherein the information estimation information is recorded in a channel estimation matrix, the noise information is recorded in a noise covariance matrix, and the obtaining a correction factor for performing error adjustment on a channel equalization processing result based on the channel estimation information and the noise information comprises:

5. A channel equalization device comprising a memory, a transceiver, and a processor:

Right shifting each element in the converted signal matrix by a fourth bit;

6. The apparatus of claim 5, wherein the information estimation information is recorded in a channel estimation matrix, the noise information is recorded in a noise covariance matrix, and the signal is subjected to channel equalization processing by using the selected target algorithm based on the channel estimation information and the noise information, and the method specifically comprises:

7. The apparatus of claim 5, further comprising, after said channel equalization processing of said signal using said selected target algorithm based on said channel estimation information and said noise information:

8. The apparatus of claim 7, wherein the information estimation information is recorded in a channel estimation matrix, the noise information is recorded in a noise covariance matrix, and the correction factor for performing error adjustment on the channel equalization processing result is obtained based on the channel estimation information and the noise information, specifically comprising:

9. A channel equalization apparatus, the apparatus comprising:

the equalization processing module is used for carrying out channel equalization processing on the signals by adopting the selected target algorithm based on the channel estimation information and the noise information;

the equalization processing module is specifically configured to:

Right shifting each element in the converted signal matrix by a fourth bit;

10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-4.