Signal processing method and device, electronic equipment, chip and storage medium

Abstract

The invention provides a signal processing method and device, electronic equipment, a chip and a storage medium, and relates to the field of communication, and the method comprises the steps: carrying out the noise estimation of a receiving signal received by at least one receiving antenna, so as to obtain an initial noise correlation matrix of the receiving signal; according to the initial noise correlation matrix, determining an interference scale factor used for representing the proportion of white noise and interference in the received signal; according to the interference scale factor, correcting the initial noise correlation matrix to obtain a target noise correlation matrix; and performing noise and interference suppression on the received signal based on the target noise correlation matrix to obtain a demodulation signal. Therefore, the difference between the estimated noise correlation matrix and the real noise correlation matrix can be reduced, the accuracy of noise correlation matrix estimation is improved, and on the basis, noise and interference suppression is performed on the received signal based on the accurate noise correlation matrix, so that the finally obtained demodulation signal is closer to the real sending signal.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a signal processing method, apparatus, electronic device, chip, and storage medium. Background Technology

[0002] In practical multiple-input multiple-output (MIMO) systems, the presence of interference can significantly affect the received signal at the receiver. Therefore, it is essential to adopt effective channel equalization techniques to suppress interference and improve the performance of MIMO systems. Summary of the Invention

[0003] This application proposes a signal processing method, apparatus, electronic device, chip, and storage medium to at least partially solve one of the technical problems in the related art.

[0004] One embodiment of this application proposes a signal processing method, comprising: performing noise estimation on a received signal received by at least one receiving antenna to obtain an initial noise correlation matrix of the received signal; determining an interference scaling factor based on the initial noise correlation matrix; wherein the interference scaling factor is used to characterize the ratio of white noise to interference in the received signal; correcting the initial noise correlation matrix based on the interference scaling factor to obtain a target noise correlation matrix; and performing noise and interference suppression on the received signal based on the target noise correlation matrix to obtain a demodulated signal.

[0005] Another embodiment of this application proposes a signal processing apparatus, comprising: an estimation module for performing noise estimation on a received signal received by at least one receiving antenna to obtain an initial noise correlation matrix of the received signal; a determination module for determining an interference scaling factor based on the initial noise correlation matrix; wherein the interference scaling factor characterizes the ratio of white noise to interference in the received signal; a correction module for correcting the initial noise correlation matrix based on the interference scaling factor to obtain a target noise correlation matrix; and a suppression module for performing noise and interference suppression on the received signal based on the target noise correlation matrix to obtain a demodulated signal.

[0006] Another embodiment of this application proposes an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the signal processing method as described in the foregoing aspect.

[0007] Another embodiment of this application proposes a chip including an interface circuit and a processing circuit coupled to each other, the interface circuit being used to input or output signals, and the processing circuit being configured to perform the signal processing method as described in the foregoing aspect.

[0008] Another embodiment of this application proposes a non-transitory computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the signal processing method as described in the foregoing aspect.

[0009] Another embodiment of this application proposes a computer program product having a computer program stored thereon, which, when executed by a processor, implements the signal processing method as described in the foregoing aspect.

[0010] The signal processing method, apparatus, electronic device, chip, and storage medium proposed in this application obtain an initial noise correlation matrix by performing noise estimation on the received signal, and then corrects the initial noise correlation matrix according to the interference scaling factor corresponding to the initial noise correlation matrix to obtain a target noise correlation matrix. This can effectively suppress estimation bias caused by limited samples or non-ideal environments, significantly reduce the difference between the estimated noise correlation matrix and the true noise correlation matrix, thereby improving the accuracy of noise correlation matrix estimation. On this basis, noise and interference suppression of the received signal based on the accurate noise correlation matrix can make the final demodulated signal closer to the real transmitted signal and better adapt to the complex and ever-changing noise and interference environment in reality.

[0011] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0012] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A schematic flowchart of a signal processing method provided for an exemplary embodiment of this application; Figure 2 A schematic flowchart of another signal processing method provided for an exemplary embodiment of this application; Figure 3 A schematic flowchart of yet another signal processing method provided for an exemplary embodiment of this application; Figure 4 A schematic flowchart of another signal processing method provided for an exemplary embodiment of this application; Figure 5 A schematic diagram of the structure of a signal processing apparatus provided for an exemplary embodiment of this application; Figure 6 A schematic diagram of the structure of an electronic device provided for an exemplary embodiment of this application; Figure 7 This is a schematic diagram of the structure of a chip proposed as an exemplary embodiment of this application. Detailed Implementation

[0013] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0014] In MIMO systems, the received signal model is typically represented as: (1) in, y represents the transmitted signal vector, and y represents the received signal vector. Let represent the MIMO channel matrix or channel vector, and n represent the received noise vector. It is usually assumed that the noise is additive white Gaussian noise (AWGN) with a noise power of . Then we have: (2) Where I is the identity matrix. In this case, the noise is independent and identically distributed across all dimensions, making analysis relatively convenient. However, in practical systems, the receiver's RF link and hardware suffer from correlation, mutual coupling, and filter non-ideality; simultaneously, noise and interference are usually processed together, which means the noise correlation matrix (or noise covariance matrix) is not a diagonal matrix, but rather: (3) That is, the noise is correlated across different antennas.

[0015] In related technologies, noise estimation of the received signal refers to the process of estimating the aforementioned noise correlation matrix (noise covariance matrix) and whitening it into an identity matrix. Specifically: (1) Noise correlation matrix (noise covariance matrix) estimation: For N samples (i.e., N noise estimations of the received signal), the received noise vector of the receiving antenna for each sample (each noise estimation) is estimated as follows: Then the noise correlation matrix (noise covariance matrix) can be estimated as: (4) (2) Noise whitening: Noise whitening can significantly reduce the complexity of subsequent steps. For example, many optimal detectors (such as maximum likelihood (ML) detection) assume that the noise is independent and identically distributed Gaussian noise. Without noise whitening, the ML criterion would need to adopt a complex weighting form. At the same time, noise whitening can simplify subsequent equalization, channel capacity analysis and performance derivation.

[0016] In the estimated Afterwards, noise whitening aims to find a linear transformation matrix W such that, after transformation, the following conditions are met: (5) Corresponding noise satisfy: (6) Based on the above formula, we can derive: (7) (8) Therefore, noise whitening can be established using the following equivalent model: (9) (10) (11) (12) The above whitening matrix Cholesky decomposition or LL decomposition are typically used to obtain: (13) (14) However, after performing N noise estimations on the received signal, the noise correlation matrix of the received signal is obtained. In the process of estimating the noise covariance matrix, if the value of N is large (i.e., the number of samples N is large), then the estimated noise correlation matrix will be... The actual noise correlation matrix R n The differences between them are small, so this method is adopted. The noise and interference suppression effect on the received signal is better, but if the value of N is small (i.e., the number of samples is small), the estimated noise correlation matrix will be less effective. The actual noise correlation matrix R n The differences between them are significant, which will significantly affect the noise whitening performance. The effect of noise and interference suppression on the received signal is poor. Consider the following two extreme scenarios: (1) AWGN scene: At this time, R n Theoretically, it should be an identity matrix, but due to the small number of samples N, The off-diagonal elements are still large, meaning that the off-diagonal elements are overestimated.

[0017] (2) Pure interference scenario: Assuming that the interference is the same for all receiving antennas, then R n Theoretically, it is a completely identical matrix, meaning all elements in the noise correlation matrix are exactly the same, and this element reflects the interference power. A small number of samples N does not affect this. The accuracy.

[0018] However, actual noise falls between AWGN and pure interference, therefore, in small sample scenarios with a small number of samples N, the judgment... Optimizing the overestimation of off-diagonal elements is crucial for noise estimation performance.

[0019] In response to at least one of the aforementioned problems, this application proposes a signal processing method, apparatus, electronic device, chip, and storage medium.

[0020] The signal processing method, apparatus, electronic device, chip, and storage medium of this application are described below with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic flowchart of a signal processing method provided for an exemplary embodiment of this application.

[0022] The signal processing method of this application embodiment can be applied to a receiving device (or receiving end). The receiving device can be a personal computer, terminal, network device, etc.

[0023] In any embodiment of this application, the signal processing method can be executed by a chip, which can be integrated into the receiving device. The chip includes a Central Processing Unit (CPU), Image Signal Processing (ISP), Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), System-on-Chip (SOC), Reduced Instruction Set Computer, etc., which will not be listed individually here.

[0024] In this context, a terminal is a user-side entity used to receive or transmit signals, such as a mobile phone. A terminal can also be called a terminal device (terminal), user equipment (UE), mobile station (MS), mobile terminal device (MT), etc. Terminals can be communication-enabled vehicles, smart cars, mobile phones, wearable devices, tablets, computers with wireless transceiver capabilities, virtual reality (VR) terminals, augmented reality (AR) terminals, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, and so on. The embodiments in this application do not limit the specific technology or device form used in the terminal.

[0025] In this context, a network device is an entity on the network side used to transmit or receive signals. For example, a network device can be an evolved NodeB (eNB), a transmission reception point (TRP), a next-generation NodeB (gNB) in a 5G new radio (NR) system, a base station in other future mobile communication systems, or an access node in a wireless fidelity (WiFi) system. The embodiments of this application do not limit the specific technology or device form used in the network device. The network device provided in the embodiments of this application can be composed of a central unit (CU) and a distributed unit (DU). The CU can also be called a control unit. Using a CU-DU structure allows the protocol layer of a network device, such as a base station, to be separated. Some protocol layer functions are centrally controlled by the CU, while the remaining part or all protocol layer functions are distributed in the DU, which is centrally controlled by the CU.

[0026] like Figure 1 As shown, the signal processing method may include the following steps S101 to S104: Step S101: Perform noise estimation on the received signal received by at least one receiving antenna to obtain the initial noise correlation matrix of the received signal.

[0027] The noise correlation matrix, also known as the noise covariance matrix, is obtained by estimating the noise in the received signal.

[0028] In the embodiments of this application, a noise estimation algorithm can be used to estimate the noise of the received signal received by at least one receiving antenna in order to obtain the initial noise correlation matrix of the received signal.

[0029] In any embodiment of this application, N noise estimations can be performed on the received signals received by each receiving antenna to obtain N received noise vectors. And based on the N received noise vectors, an initial noise correlation matrix is ​​generated. Where N is a positive integer. For example, the initial noise correlation matrix... The representation of can be shown in formula (4).

[0030] Understandably, when N is greater than 1, performing multiple noise estimations on the received signals received by each receiving antenna can improve the accuracy and reliability of the initial noise correlation matrix estimation.

[0031] Step S102: Determine the interference scaling factor based on the initial noise correlation matrix; wherein, the interference scaling factor is used to characterize the ratio of white noise to interference in the received signal.

[0032] In this embodiment, an interference scaling factor, characterizing the ratio of white noise to interference in the received signal, can be calculated based on an initial noise correlation matrix. For example, the interference scaling factor is labeled as... .

[0033] As one possible implementation, the initial noise correlation matrix can be calculated using an eigenvalue decomposition algorithm. The minimum eigenvalue is determined, and the interference scaling factor is determined based on the minimum eigenvalue. Among them, the interference scaling factor It is positively correlated with the smallest eigenvalue; that is, the smaller the smallest eigenvalue, the smaller the interference scaling factor. The smaller the value, the larger the minimum eigenvalue, and vice versa; the greater the interference scaling factor. The larger.

[0034] For example, the minimum eigenvalue can be calculated by using the Givens transform or Householder transform algorithm. Perform precise or fuzzy diagonalization (here, fuzzy diagonalization means that the above transformation is performed a finite number of times, and the final matrix is ​​not a diagonal matrix), and extract the minimum value of the diagonal elements in the diagonalized matrix to approximate the minimum eigenvalue. Alternatively, other algorithms can be used to calculate the initial noise correlation matrix. The minimum eigenvalue is not limited in this embodiment.

[0035] It should be understood that the above-described method for calculating the interference scaling factor is merely an illustrative example, but this application is not limited thereto. In practical applications, other algorithms can also be used to calculate the interference scaling factor based on the initial noise correlation matrix, and this application does not impose any restrictions on this.

[0036] Step S103: Correct the initial noise correlation matrix according to the interference scaling factor to obtain the target noise correlation matrix.

[0037] In this embodiment of the application, the initial noise correlation matrix can be adjusted based on the interference scaling factor. Make corrections or optimizations to obtain the target noise correlation matrix.

[0038] Step S104: Based on the target noise correlation matrix, noise and interference suppression is performed on the received signal to obtain the demodulated signal.

[0039] In the embodiments of this application, a relevant signal processing algorithm can be used to suppress noise and interference in the received signal based on the target noise correlation matrix to obtain a demodulated signal.

[0040] The signal processing method of this application embodiment estimates noise in the received signal to obtain an initial noise correlation matrix, and then corrects the initial noise correlation matrix according to the interference scaling factor corresponding to the initial noise correlation matrix to obtain a target noise correlation matrix. This can effectively suppress estimation bias caused by limited samples or non-ideal environments, significantly reduce the difference between the estimated noise correlation matrix and the true noise correlation matrix, thereby improving the accuracy of noise correlation matrix estimation. On this basis, noise and interference suppression of the received signal based on the accurate noise correlation matrix can make the final demodulated signal closer to the real transmitted signal and better adapt to the complex and ever-changing noise and interference environment.

[0041] This application provides another signal processing method. Figure 2 A schematic flowchart of another signal processing method provided for an exemplary embodiment of this application.

[0042] It should be noted that the signal processing method can be executed alone, or it can be executed together with any embodiment or possible implementation in the embodiment of this application, or it can be executed together with any technical solution in the related technology. The embodiments of this application do not limit this.

[0043] like Figure 2 As shown, the signal processing method may include the following steps S201 to S206: Step S201: Noise estimation is performed on the received signal received by at least one receiving antenna to obtain the initial noise correlation matrix of the received signal.

[0044] Step S202: Determine the interference scaling factor based on the initial noise correlation matrix; wherein, the interference scaling factor is used to characterize the ratio of white noise to interference in the received signal.

[0045] It should be noted that the explanations of steps S201 to S202 can be found in the relevant descriptions in any embodiment of this application, and will not be repeated here.

[0046] Step S203: Determine the weighting weights associated with the initial noise correlation matrix based on the interference scaling factor; wherein the weighting weights are positively correlated with the interference scaling factor.

[0047] In this embodiment of the application, the initial noise correlation matrix can be used as a reference. Corresponding interference scaling factor Determine the correlation matrix with the initial noise. Associated weighted weights Among them, weighted weight With interference scaling factor There is a positive correlation, that is, the interference scaling factor The larger the weight, the higher the weight. The larger the value, the greater the interference scaling factor. The smaller the weight, the higher the weight. The smaller.

[0048] In any embodiment of this application, the weighted weight The determination method is, for example, based on the initial noise correlation matrix. Corresponding interference scaling factor The initial weight (referred to as the third initial weight in this application) is determined by the ratio of the average noise power on each receiving antenna, and the weighted weight is determined by the product of the set hyperparameter and the third initial weight (referred to as the fifth product in this application). .

[0049] The average noise power is based on the initial noise correlation matrix. The number of traces and receiving antennas is determined.

[0050] For example, the number of marked receiving antennas is N. R The weighted weights can be calculated using the following formula. : (15) Where k refers to the hyperparameter. This refers to the initial noise correlation matrix. traces (i.e.) (sum of the elements on the middle diagonal) This refers to the average noise power across all receiving antennas.

[0051] It should be understood that the above-described method for calculating weighting is merely illustrative, and this application is not limited thereto; other methods may also be used to calculate the interference scaling factor. Positive correlation weighted weight However, the embodiments in this application do not impose any limitations on this.

[0052] Step S204: Generate the target diagonal matrix based on the initial noise correlation matrix.

[0053] In any embodiment of this application, N can be determined based on the initial noise correlation matrix. R The average noise power on the root receiving antenna is used to generate the target diagonal matrix.

[0054] As one possible implementation, the target diagonal matrix can be calculated, for example, by using the initial noise correlation matrix. The number of traces and receiving antennas N R The average noise power on each receiving antenna is determined, and the target diagonal matrix is ​​determined based on the product of the average noise power and the identity matrix. For example, the target diagonal matrix is ​​as follows: .

[0055] Step S205: Based on the weighted weights, the initial noise correlation matrix and the target diagonal matrix are fused to obtain the target noise correlation matrix.

[0056] In this embodiment of the application, it can be based on the initial noise correlation matrix. Associated weighted weights For the initial noise correlation matrix The target noise correlation matrix is ​​obtained by fusing it with the target diagonal matrix. For example, the target noise correlation matrix is ​​labeled as... Then we have: (16) Step S206: Based on the target noise correlation matrix, noise and interference suppression is performed on the received signal to obtain the demodulated signal.

[0057] It should be noted that the explanation of step S206 can be found in the relevant description in any embodiment of this application, and will not be repeated here.

[0058] The signal processing method of this application fuses the initial noise correlation matrix and the target diagonal matrix using weighted weights that are positively correlated with the interference scaling factor corresponding to the initial noise correlation matrix, to obtain the target noise correlation matrix. The interference scaling factor sensitively reflects the matrix characteristics, and the positively correlated weighted weights can be dynamically adjusted based on the matrix's inherent features, making the fusion process more scientific and adaptable. In complex scenarios such as small sample sizes (i.e., small N), this weighted fusion method can fully leverage the advantages of both matrices, effectively correct the estimation bias of the initial noise correlation matrix, improve the accuracy of the target noise correlation matrix, thereby significantly optimizing the demodulated signal quality and enhancing the performance of the communication system in diverse noise environments.

[0059] This application provides another signal processing method. Figure 3 This is a schematic flowchart of yet another signal processing method provided for an exemplary embodiment of this application.

[0060] It should be noted that the signal processing method can be executed alone, or it can be executed together with any embodiment or possible implementation in the embodiment of this application, or it can be executed together with any technical solution in the related technology. The embodiments of this application do not limit this.

[0061] like Figure 3 As shown, the signal processing method may include the following steps S301 to S306: Step S301: Perform N noise estimations on the received signal received by at least one receiving antenna to obtain the initial noise correlation matrix of the received signal.

[0062] Step S302: Determine the interference scaling factor based on the initial noise correlation matrix; wherein, the interference scaling factor is used to characterize the ratio of white noise to interference in the received signal.

[0063] It should be noted that the explanations of steps S301 to S302 can be found in the relevant descriptions in any embodiment of this application, and will not be repeated here.

[0064] Step S303: Determine the weighting weights associated with the initial noise correlation matrix based on the interference scaling factor and N.

[0065] Among them, weighted weight Correlation matrix with initial noise Corresponding interference scaling factor There is a positive correlation, that is, the interference scaling factor The larger the weight, the higher the weight. The larger the value, the greater the interference scaling factor. The smaller the weight, the higher the weight. The smaller, Among them, weighted weight It is negatively correlated with N, that is, the larger N is, the higher the weighting weight. The smaller N is, the greater the weighting weight. The larger.

[0066] It should be noted that the core of formula (16) lies in: how to determine the weighting weights. ;in, Indicates will Forced to be a diagonal matrix; Indicates retention of the original Unchanged. As above, in scenarios with small samples (i.e., N is small), the judgment... The overestimation of the diagonal elements in the Central African Republic can be intuitively understood as an analysis of AWGN and interference. The proportion within; where, the theoretical R of AWGN n It is the identity matrix; when estimating The smaller the number of samples used (i.e., the number of noise estimation attempts) N, the better. The greater the overestimation of the diagonal elements in the Central African Republic, the more significant the theoretical overestimation. Theoretical R based on pure interference. n and estimates All are identical matrices, and their corresponding interference scaling factors It is 0. This shows that: (1) When the sample size N is extremely small, The AWGN portion is somewhat inaccurate, therefore It should be designed to have a larger value. Therefore, The choice of [aspect] should be inversely correlated with the sample size N; that is, the larger the sample size N, the better. The smaller.

[0067] (2) When interference is dominant, More accurate, therefore It should be designed to have a small value. The interference scaling factor is affected by the dominance of interference. Smaller The selection should be related to the interference scaling factor. Positively correlated, i.e., interference scaling factor The smaller the value, the more likely interference is to dominate. The smaller.

[0068] Therefore, in any embodiment of this application, the weighted weight The determination method is, for example, to substitute N into the first positive correlation function to obtain the first function value, and then add the interference scaling factor. Substitute the second positive correlation function to obtain the second function value; determine the initial weights based on the ratio of the second function value to the first function value; determine the weighted weights based on the product of the initial weights and the set hyperparameters. For example, weighted weights The following formula can be used to calculate it: (17) Where k is a hyperparameter, and It is represented as a positively correlated function.

[0069] In any embodiment of this application, due to the interference scaling factor and The range of values ​​for elements in the middle is related, therefore it is possible to... Normalization is performed, at which point the weighted weights are... The determination method is, for example, to substitute N into the first positive correlation function to obtain the first function value, and then add the interference scaling factor. Substituting the second positive correlation function, we obtain the second function value; we then determine the first product of the first function value and the average noise power on each receiving antenna; where the average noise power is determined based on the initial noise correlation matrix. The number of traces and receiving antennas N R The first initial weight is determined based on the ratio of the second function value to the first product; the weighted weight is determined based on the second product of the first initial weight and the set hyperparameters. For example, weighted weights The following formula can be used to calculate it: (18) In any embodiment of this application, the weighted weight The method for determining N could be, for example, by multiplying N by the third product of the average noise power on each receiving antenna; where the average noise power is determined based on the initial noise correlation matrix. The number of traces and receiving antennas N R Determined; based on the interference scaling factor The ratio of the product with the third product determines the second initial weight; the fourth product of the second initial weight and the set hyperparameters determines the weighted weight. For example, weighted weights The following formula can be used to calculate it: (19) In summary, different methods can be used to calculate the correlation matrix with the initial noise. Associated weighted weights This can improve the flexibility and applicability of the method.

[0070] Step S304: Generate the target diagonal matrix based on the initial noise correlation matrix.

[0071] Step S305: Based on the weighted weights, the initial noise correlation matrix and the target diagonal matrix are fused to obtain the target noise correlation matrix.

[0072] Step S306: Based on the target noise correlation matrix, noise and interference suppression is performed on the received signal to obtain the demodulated signal.

[0073] It should be noted that the explanations of steps S304 to S306 can be found in the relevant descriptions in any embodiment of this application, and will not be repeated here.

[0074] The signal processing method of this application embodiment uses an interference scaling factor that reflects the essential characteristics of the initial noise correlation matrix. A positive correlation with this factor allows the weighting weights to change reasonably according to the matrix characteristics. A negative correlation between the weighting weights and the number of samples N enables the weighting weights to be increased in scenarios with small samples, correcting the estimation bias of the initial noise correlation matrix using the target diagonal matrix, while decreasing the weighting weights in scenarios with sufficient samples, preserving the effective information of the initial noise correlation matrix. This dynamically adjusted weighting weight determination mechanism improves the accuracy of the target noise correlation matrix, thereby optimizing noise and interference suppression effects and enhancing the adaptability of the communication system in complex environments.

[0075] This application provides another signal processing method. Figure 4 A schematic flowchart of another signal processing method provided for an exemplary embodiment of this application.

[0076] It should be noted that the signal processing method can be executed alone, or it can be executed together with any embodiment or possible implementation in the embodiment of this application, or it can be executed together with any technical solution in the related technology. The embodiments of this application do not limit this.

[0077] like Figure 4 As shown, the signal processing method may include the following steps S401 to S404: Step S401: Perform noise estimation on the received signal received by at least one receiving antenna to obtain the initial noise correlation matrix of the received signal.

[0078] It should be noted that the explanation of step S401 can be found in the relevant description in any embodiment of this application, and will not be repeated here.

[0079] Step S402, based on the initial noise correlation matrix and the number of receiving antennas N R The interference scaling factor is determined; whereby the interference scaling factor is used to characterize the ratio of white noise to interference in the received signal.

[0080] In any embodiment of this application, the initial noise correlation matrix Corresponding interference scaling factor The determination method is, for example, based on the sum of all elements of the initial noise correlation matrix and the number of receiving antennas N. R The first factor term is determined, and the second factor term is determined based on the mean of the diagonal elements of the initial noise correlation matrix; the second factor term is determined based on the sum of the off-diagonal elements of the initial noise correlation matrix and the number of receiving antennas N. R First, determine the third factor term, and then determine the fourth factor term based on the difference between the second and third factor terms. Next, determine the minimum value among the first and fourth factor terms, and then determine the interference ratio factor based on the minimum value and the maximum value among the set values. For example, taking a value of 0 as an example, the interference scaling factor... The following formula can be used to approximate the result: (20) Therefore, there is no need to modify the initial noise correlation matrix. Multiple rounds of transformations are performed to solve for the interference scaling factor. This can reduce computational complexity, thereby reducing computational overhead.

[0081] In any embodiment of this application, the initial noise correlation matrix (N) R ×N R The matrix, N R Interference scaling factor (corresponding to the number of receiving antennas) The determination method is, for example, to determine the Nth element in the initial noise correlation matrix. R row N R The column element (i.e., the bottom right element) serves as the initial lower and upper bounds for the interference scaling factor; based on the initial noise correlation matrix, N is applied to the initial lower and upper bounds. R - Two rounds of iterative update process; based on the lower limit and upper limit of the value obtained from the last round of iterative update process, the interference ratio factor is determined.

[0082] As an example, the first round of iterative update process includes: based on the element in the first row and first column of the initial noise correlation matrix (i.e., the element in the upper left corner), the 2×2 matrix in the lower right corner of the initial noise correlation matrix is ​​divided into blocks to obtain multiple sub-matrices for the first round of iterative update process, and the initial lower limit and upper limit of values ​​are updated according to the multiple sub-matrices for the first round of iterative update process to obtain the updated lower limit and upper limit of values ​​for the first round of iterative update process.

[0083] As an example, in a round other than the first round, such as the j-th round (j is greater than 1 and less than or equal to N)... R The iterative update process (positive integers of -2) includes: based on the j×j matrix in the lower right corner of the initial noise correlation matrix, the (j+1)×(j+1) matrix in the lower right corner of the initial noise correlation matrix is ​​divided into blocks to obtain multiple sub-matrices for the j-th iteration update process. Based on the multiple sub-matrices for the j-th iteration update process, the lower limit and upper limit of the values ​​obtained in the (j-1)-th iteration update process are updated to obtain the lower limit and upper limit of the values ​​obtained in the j-th iteration update process.

[0084] For example, a low-complexity interference scaling factor The approximate calculation process may include the following steps: (1) Take out The bottom right element (i.e., the 1×1 matrix in the lower right corner), which is used as the initial lower limit of the value corresponding to the interference scaling factor. and upper limit of value .

[0085] (2) Perform the following iterative update process: For i=2:N R -1: Remove The bottom right i×i matrix It can be broken down into the following block matrix form: Where i=j+1; i=2 refers to the first round of iterative update process, and i=N R -1 refers to the last round of iteration and update process.

[0086] Estimate according to the following formula The lower limit of the corresponding interference scaling factor and upper limit of value : ;(twenty one) (3) It can be split into the following blocks: ,but Corresponding interference scaling factor It can be approximated by any value within the following range: ;(twenty two) That is, in any embodiment of this application, any value between the lower limit and the upper limit obtained from the last round of iterative update can be used as the interference scaling factor. .

[0087] In any embodiment of this application, the lower limit of the value obtained from the last round of iterative update and the maximum value among the set values ​​can also be used as the interference scaling factor. For example, let's take a value of 0 as an example. Corresponding interference scaling factor It can also be approximated as: ;(twenty three) Step S403: Correct the initial noise correlation matrix according to the interference scaling factor to obtain the target noise correlation matrix.

[0088] Step S404: Based on the target noise correlation matrix, noise and interference suppression is performed on the received signal to obtain the demodulated signal.

[0089] It should be noted that the explanations of steps S403 to S404 can be found in the relevant descriptions in any embodiment of this application, and will not be repeated here.

[0090] The signal processing method in this application embodiment can calculate the initial noise correlation matrix in different ways. Corresponding interference scaling factor This improves the flexibility and applicability of the method.

[0091] In any embodiment of this application, the signal processing method provided in this application can be applied to the field of mobile communication, including but not limited to: 2G, 3G, 4G, 5G, 6G, Bluetooth, WiFi (Wireless Fidelity), satellite and other wireless communication fields.

[0092] This application proposes a low-complexity optimization method for small-sample noise estimation. The method, namely, to compensate for the overestimation of off-diagonal elements in small sample scenarios, is to... The diagonal and off-diagonal elements are corrected using a formula, for example: ; where N R for The dimension, i.e., the number of receiving antennas. express traces (i.e.) (sum of the elements on the middle diagonal) This refers to the average noise power across all receiving antennas, which makes the correction magnitude of the diagonal elements similar to... comparable.

[0093] in, It can be represented in the following form: Where k is a hyperparameter; for The corresponding interference scaling factor; and This is expressed as a positively correlated function. Due to the interference scaling factor... and The range of values ​​for elements in the middle is related, therefore it is necessary to... Normalization is performed at this point. It can be represented as: .

[0094] This application provides a low-complexity approximation perturbation scaling factor. The method will It can be split into the following blocks: ;(twenty four) Where Q is (N R -1) order square matrix, b is a matrix of length (N) R The column vector is -1). Corresponding maximum interference ratio and interference scaling factor The following conditions must be met: (25) in, and This indicates the lower limit of the value of the interference scaling factor corresponding to matrix Q. Less than or equal to the interference scaling factor ) and upper limit of value ( Greater than or equal to the interference scaling factor ).

[0095] The following section uses specific formulas to explain the above-mentioned formulas based on the number of samples N and the interference ratio factor. right The optimization process is explained in detail to effectively improve noise estimation performance.

[0096] Regarding formula (16). Features include one or more of the following: with estimation The sample size N is inversely correlated with; Corresponding interference scaling factor They are positively correlated. Among them, The calculation forms include, but are not limited to, the above formula (18) and the following two: and .

[0097] As an example, a low-complexity interference scaling factor It can be approximated as: (20) As another example, low-complexity interference scaling factor It can be approximated as: (1) Take out The bottom right element (i.e., the 1×1 matrix in the lower right corner), which is used as the initial lower limit of the value corresponding to the interference scaling factor. and upper limit of value .

[0098] (2) Perform the following iterative update process: For i=2:N R -1: Remove The bottom right i×i matrix It can be broken down into the following block matrix form: .

[0099] Estimate according to the following formula The lower limit of the corresponding interference scaling factor and upper limit of value : ;(twenty one) (3) It can be split into the following blocks: ,but Corresponding interference scaling factor It can be approximated by any value within the following range: ;(twenty two) As yet another example Corresponding interference scaling factor It can also be approximated as: ;(twenty three) In summary, for MIMO noise estimation in small sample scenarios, it is possible to effectively improve noise estimation performance with low computational complexity.

[0100] To implement the above embodiments, this application also proposes a signal processing device. Figure 5 This is a schematic diagram of the structure of a signal processing apparatus provided for an exemplary embodiment of this application.

[0101] like Figure 5 As shown, the signal processing device 500 may include: an estimation module 510, a determination module 520, a correction module 530, and a suppression module 540.

[0102] The estimation module 510 is used to estimate the noise of the received signal received by at least one receiving antenna to obtain an initial noise correlation matrix of the received signal; the determination module 520 is used to determine the interference scaling factor based on the initial noise correlation matrix; wherein the interference scaling factor is used to characterize the ratio of white noise and interference in the received signal; the correction module 530 is used to correct the initial noise correlation matrix based on the interference scaling factor of the initial noise correlation matrix to obtain a target noise correlation matrix; and the suppression module 540 is used to suppress noise and interference in the received signal based on the target noise correlation matrix to obtain a demodulated signal.

[0103] In one implementation of this application, the correction module 530 is used to: determine the weighting weight associated with the initial noise correlation matrix based on the interference scaling factor; wherein the weighting weight is positively correlated with the interference scaling factor; generate a target diagonal matrix based on the initial noise correlation matrix; and fuse the initial noise correlation matrix and the target diagonal matrix based on the weighting weight to obtain the target noise correlation matrix.

[0104] In one implementation of this application, the correction module 530 is used to: determine the average noise power on each receiving antenna based on the trace of the initial noise correlation matrix and the number of receiving antennas; and determine the target diagonal matrix based on the product of the average noise power and the identity matrix.

[0105] In one implementation of this application, the estimation module 530 is used to: perform N noise estimations on the received signals received by each receiving antenna to obtain N received noise vectors; where N is a positive integer; and generate an initial noise correlation matrix based on the N received noise vectors.

[0106] In one implementation of this application, the correction module 530 is used to: determine the weighting weight associated with the initial noise correlation matrix based on the interference scaling factor and N; wherein the weighting weight is positively correlated with the interference scaling factor and negatively correlated with N.

[0107] In one implementation of this application, the correction module 530 is configured to: substitute N into a first positive correlation function to obtain a first function value, and substitute the interference scaling factor into a second positive correlation function to obtain a second function value; determine a first product of the first function value and the average noise power on each receiving antenna; wherein the average noise power is determined based on the trace of the initial noise correlation matrix and the number of receiving antennas; determine a first initial weight based on the ratio of the second function value to the first product; and determine a weighted weight based on a second product of the first initial weight and a set hyperparameter.

[0108] In one implementation of this application, the correction module 530 is used to: determine the third product of N and the average noise power on each receiving antenna; wherein the average noise power is determined based on the trace of the initial noise correlation matrix and the number of receiving antennas; determine the second initial weight based on the ratio of the interference scaling factor to the third product; and determine the weighted weight based on the fourth product of the second initial weight and the set hyperparameter.

[0109] In one implementation of this application, the correction module 530 is used to: determine a third initial weight based on the ratio of the interference scaling factor to the average noise power on each receiving antenna; wherein the average noise power is determined based on the trace of the initial noise correlation matrix and the number of receiving antennas; and determine a weighted weight based on the fifth product of the set hyperparameter and the third initial weight.

[0110] In one implementation of this application, the determining module 520 is configured to: determine a first factor term based on the sum of all elements of the initial noise correlation matrix and the number of receiving antennas; determine a second factor term based on the mean of the diagonal elements of the initial noise correlation matrix; determine a third factor term based on the sum and number of the off-diagonal elements in the initial noise correlation matrix; determine a fourth factor term based on the difference between the second and third factor terms; determine the minimum value among the first and fourth factor terms; and determine the interference scaling factor based on the minimum value and the maximum value among the set values.

[0111] In one implementation of this application, the initial noise correlation matrix is ​​N. R ×N R The matrix, N R The module 520, which determines the number of receiving antennas and the interference scaling factor, is used to: determine the Nth element in the initial noise correlation matrix. R row N R The column elements serve as the initial lower and upper bounds for the interference scaling factor; based on the initial noise correlation matrix, N is applied to the initial lower and upper bounds. R - Two rounds of iterative update process; based on the lower limit and upper limit of the value obtained from the last round of iterative update process, the interference ratio factor is determined.

[0112] In one implementation of this application, the first iteration update process includes: based on the element in the first row and first column of the initial noise correlation matrix, dividing the 2×2 matrix in the lower right corner of the initial noise correlation matrix into blocks to obtain multiple sub-matrices for the first iteration update process; and updating the initial lower limit and upper limit of values ​​according to the multiple sub-matrices for the first iteration update process to obtain the updated lower limit and upper limit of values ​​obtained in the first iteration update process; the j-th iteration update process includes: based on the j×j matrix in the lower right corner of the initial noise correlation matrix, dividing the (j+1)×(j+1) matrix in the lower right corner of the initial noise correlation matrix into blocks to obtain multiple sub-matrices for the j-th iteration update process; and updating the updated lower limit and upper limit of values ​​obtained in the (j-1)-th iteration update process according to the multiple sub-matrices for the j-th iteration update process to obtain the updated lower limit and upper limit of values ​​obtained in the j-th iteration update process; wherein, j is greater than 1 and less than or equal to N. R -2 is a positive integer.

[0113] In one implementation of this application, the determining module 520 is configured to perform any of the following: taking any value between the lower limit and the upper limit obtained from the last iteration update process as an interference scaling factor; taking the maximum value between the lower limit and the set value obtained from the last iteration update process as an interference scaling factor.

[0114] It should be noted that the foregoing explanation of the signal processing method embodiments also applies to the signal processing apparatus of this embodiment, and will not be repeated here.

[0115] In the signal processing apparatus of this application embodiment, an initial noise correlation matrix is ​​obtained by performing noise estimation on the received signal, and a target noise correlation matrix is ​​obtained by correcting the initial noise correlation matrix according to the interference scaling factor corresponding to the initial noise correlation matrix. This can effectively suppress the estimation deviation caused by limited samples or non-ideal environment, significantly reduce the difference between the estimated noise correlation matrix and the real noise correlation matrix, thereby improving the accuracy of noise correlation matrix estimation. On this basis, noise and interference suppression of the received signal based on the accurate noise correlation matrix can make the final demodulated signal closer to the real transmitted signal and better adapt to the actual complex and ever-changing noise and interference environment.

[0116] To implement the above embodiments, this application also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the signal processing method as described in any of the foregoing embodiments.

[0117] Figure 6This is a schematic diagram of the structure of an electronic device provided for an exemplary embodiment of this application. For example, the electronic device 600 may be a vehicle, mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0118] Reference Figure 6 The electronic device 600 may include one or more of the following components: processing component 602, memory 604, power component 606, multimedia component 608, audio component 610, input / output (I / O) interface 612, sensor component 614, and communication component 616.

[0119] Processing component 602 typically controls the overall operation of electronic device 600, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 602 may include one or more processors 620 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 602 may include one or more modules to facilitate interaction between processing component 602 and other components. For example, processing component 602 may include a multimedia module to facilitate interaction between multimedia component 608 and processing component 602.

[0120] Memory 604 is configured to store various types of data to support the operation of electronic device 600. Examples of such data include instructions for any application or method operating on electronic device 600, contact data, phonebook data, messages, pictures, videos, etc. Memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0121] Power component 606 provides power to various components of electronic device 600. Power component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 600.

[0122] Multimedia component 608 includes a screen that provides an output interface between the electronic device 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 608 includes a front-facing camera and / or a rear-facing camera. When the electronic device 600 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0123] Audio component 610 is configured to output and / or input audio signals. For example, audio component 610 includes a microphone (MIC) configured to receive external audio signals when electronic device 600 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 604 or transmitted via communication component 616. In some embodiments, audio component 610 also includes a speaker for outputting audio signals.

[0124] I / O interface 612 provides an interface between processing component 602 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, start buttons, and lock buttons.

[0125] Sensor assembly 614 includes one or more sensors for providing state assessments of various aspects of electronic device 600. For example, sensor assembly 614 can detect the on / off state of electronic device 600, the relative positioning of components such as the display and keypad of electronic device 600, changes in position of electronic device 600 or a component of electronic device 600, the presence or absence of user contact with electronic device 600, orientation or acceleration / deceleration of electronic device 600, and temperature changes of electronic device 600. Sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 614 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0126] Communication component 616 is configured to facilitate wired or wireless communication between electronic device 600 and other devices. Electronic device 600 can access wireless networks based on communication standards, such as WiFi, 4G, or 5G, or combinations thereof. In one exemplary embodiment, communication component 616 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 616 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0127] In an exemplary embodiment, the electronic device 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0128] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 604 including instructions, which can be executed by a processor 620 of an electronic device 600 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0129] To implement the above embodiments, this application also proposes a chip, comprising: an interface circuit and a processing circuit coupled to each other; the interface circuit being used to input or output signals; and the processing circuit being configured to perform the signal processing method provided in any of the foregoing embodiments.

[0130] Figure 7 This is a schematic diagram of a chip structure proposed as an exemplary embodiment of this application. See also... Figure 7 The diagram shown is a schematic representation of the structure of chip 700, but is not limited thereto.

[0131] Chip 700 includes processing circuit 701, which is configured to perform any of the above signal processing methods.

[0132] In some embodiments, chip 700 further includes one or more interface circuits 702. Exemplarily, interface circuit 702 is connected to memory 703, and interface circuit 702 can be used to receive signals from memory 703 or other devices, and interface circuit 702 can be used to send signals to memory 703 or other devices. For example, interface circuit 702 can read instructions stored in memory 703 and send those instructions to processing circuit 701.

[0133] In some embodiments, the interface circuit 702 performs at least one of the communication steps such as sending and / or receiving in the above method, and the processing circuit 701 performs other steps.

[0134] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0135] In some embodiments, chip 700 further includes one or more memories 703 for storing instructions. Exemplarily, all or part of the memories 703 may be located outside of chip 700.

[0136] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the signal processing method as described in any of the foregoing method embodiments.

[0137] To implement the above embodiments, this application also proposes a computer program product having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the signal processing method as described in any of the foregoing method embodiments.

[0138] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0139] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0140] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0141] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0142] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0143] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0144] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0145] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A signal processing method, characterized in that, include: Noise estimation is performed on the received signal received by at least one receiving antenna to obtain the initial noise correlation matrix of the received signal; Based on the initial noise correlation matrix, an interference scaling factor is determined; wherein, the interference scaling factor is used to characterize the ratio of white noise to interference in the received signal; The initial noise correlation matrix is ​​corrected according to the interference scaling factor to obtain the target noise correlation matrix; Based on the target noise correlation matrix, noise and interference suppression are performed on the received signal to obtain the demodulated signal.

2. The method according to claim 1, characterized in that, The step of correcting the initial noise correlation matrix according to the interference scaling factor to obtain the target noise correlation matrix includes: Based on the interference scaling factor, a weighting weight associated with the initial noise correlation matrix is ​​determined; wherein the weighting weight is positively correlated with the interference scaling factor. Generate the target diagonal matrix based on the initial noise correlation matrix; Based on the weighted weights, the initial noise correlation matrix and the target diagonal matrix are fused to obtain the target noise correlation matrix.

3. The method according to claim 2, characterized in that, The step of generating the target diagonal matrix based on the initial noise correlation matrix includes: The average noise power on each of the receiving antennas is determined based on the trace of the initial noise correlation matrix and the number of receiving antennas. The target diagonal matrix is ​​determined based on the product of the average noise power and the identity matrix.

4. The method according to claim 2, characterized in that, The step of performing noise estimation on the received signal received by at least one receiving antenna to obtain the initial noise correlation matrix of the received signal includes: The received signals received by each of the aforementioned receiving antennas are subjected to N noise estimations to obtain N received noise vectors; where N is a positive integer; The initial noise correlation matrix is ​​generated based on the N received noise vectors.

5. The method according to claim 4, characterized in that, The step of determining the weighting weights associated with the initial noise correlation matrix based on the interference scaling factor includes: Based on the interference scaling factor and N, determine the weighting weights associated with the initial noise correlation matrix; The weighted weight is positively correlated with the interference ratio factor, and the weighted weight is negatively correlated with N.

6. The method according to claim 5, characterized in that, The step of determining the weighting weights associated with the initial noise correlation matrix based on the interference scaling factor and N includes: Substitute N into the first positive correlation function to obtain the first function value, and substitute the interference scaling factor into the second positive correlation function to obtain the second function value; Determine a first product between the first function value and the average noise power on each of the receiving antennas; wherein the average noise power is determined based on the trace of the initial noise correlation matrix and the number of receiving antennas; The first initial weight is determined based on the ratio of the second function value to the first product; The weighting weights are determined based on the second product of the first initial weights and the set hyperparameters.

7. The method according to claim 5, characterized in that, The step of determining the weighting weights associated with the initial noise correlation matrix based on the interference scaling factor and N includes: Determine the third product of N and the average noise power on each of the receiving antennas; wherein the average noise power is determined based on the trace of the initial noise correlation matrix and the number of receiving antennas; The second initial weight is determined based on the ratio of the interference scaling factor to the third product; The weighting weights are determined based on the fourth product of the second initial weights and the set hyperparameters.

8. The method according to claim 2, characterized in that, The step of determining the weighting weights associated with the initial noise correlation matrix based on the interference scaling factor includes: A third initial weight is determined based on the ratio of the interference scaling factor to the average noise power on each of the receiving antennas; wherein the average noise power is determined based on the trace of the initial noise correlation matrix and the number of receiving antennas; The weighting weights are determined based on the set hyperparameters and the fifth product of the third initial weights.

9. The method according to any one of claims 1-8, characterized in that, The step of determining the interference scaling factor based on the initial noise correlation matrix includes: The first factor term is determined based on the sum of all elements of the initial noise correlation matrix and the number of receiving antennas, and the second factor term is determined based on the mean of the diagonal elements of the initial noise correlation matrix. The third factor term is determined based on the sum of the off-diagonal elements in the initial noise correlation matrix and the number of elements thereon, and the fourth factor term is determined based on the difference between the second factor term and the third factor term. The minimum value among the first factor item and the fourth factor item is determined, and the interference ratio factor is determined based on the minimum value and the maximum value among the set values.

10. The method according to any one of claims 1-8, characterized in that, The initial noise correlation matrix is ​​N. R ×N R The matrix, N R The number of receiving antennas, and the determination of the interference scaling factor based on the initial noise correlation matrix, includes: The Nth element in the initial noise correlation matrix R row N R The column elements serve as the initial lower and upper limits of the values ​​corresponding to the interference scaling factor; Based on the initial noise correlation matrix, perform N operations on the initial lower and upper bounds of the values. R -2 rounds of iterative update process; The interference scaling factor is determined based on the lower and upper limits of the values ​​obtained from the last round of iteration.

11. The method according to claim 10, characterized in that, in, The first round of iterative update process includes: based on the element in the first row and first column of the initial noise correlation matrix, dividing the 2×2 matrix in the lower right corner of the initial noise correlation matrix into blocks to obtain multiple sub-matrices for the first round of iterative update process; and updating the initial lower limit and upper limit of values ​​according to the multiple sub-matrices for the first round of iterative update process to obtain the updated lower limit and upper limit of values ​​for the first round of iterative update process. The iterative update process in the j-th round includes: based on the j×j matrix in the lower right corner of the initial noise correlation matrix, dividing the (j+1)×(j+1) matrix in the lower right corner of the initial noise correlation matrix into blocks to obtain multiple sub-matrices for the iterative update process in the j-th round; and updating the lower limit and upper limit of the values ​​obtained by the iterative update process in the (j-1)-th round according to the multiple sub-matrices for the iterative update process in the j-th round to obtain the lower limit and upper limit of the values ​​obtained by the iterative update process in the j-th round. Where j is greater than 1 and less than or equal to N. R -2 is a positive integer.

12. The method according to claim 11, characterized in that, The determination of the interference scaling factor based on the lower and upper limits of the value obtained from the last round of iterative update process includes any one of the following: The value between the lower limit and the upper limit obtained from the last round of iterative update is used as the interference ratio factor; The lower limit of the value obtained from the last round of iterative update process and the maximum value among the set values ​​are used as the interference ratio factor.

13. A signal processing apparatus, characterized in that, include: An estimation module is used to perform noise estimation on the received signal received by at least one receiving antenna to obtain an initial noise correlation matrix of the received signal; The determination module is used to determine the interference scaling factor based on the initial noise correlation matrix; wherein the interference scaling factor is used to characterize the ratio of white noise to interference; The correction module is used to correct the initial noise correlation matrix according to the interference scaling factor to obtain the target noise correlation matrix; The suppression module is used to suppress noise and interference on the received signal based on the target noise correlation matrix to obtain a demodulated signal.

14. The apparatus according to claim 13, characterized in that, The correction module is used for: Based on the interference scaling factor, a weighting weight associated with the initial noise correlation matrix is ​​determined; wherein the weighting weight is positively correlated with the interference scaling factor. Generate the target diagonal matrix based on the initial noise correlation matrix; Based on the weighted weights, the initial noise correlation matrix and the target diagonal matrix are fused to obtain the target noise correlation matrix.

15. The apparatus according to claim 14, characterized in that, The correction module is used for: The average noise power on each of the receiving antennas is determined based on the trace of the initial noise correlation matrix and the number of receiving antennas. The target diagonal matrix is ​​determined based on the product of the average noise power and the identity matrix.

16. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 12.

17. A chip, characterized in that, The chip includes an interface circuit and a processing circuit that are coupled to each other. The interface circuit is used to input or output signals, and the processing circuit is used to implement the method of any one of claims 1 to 12.

18. A non-transitory computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by a processor, the program instructions implement the steps of the method described in any one of claims 1 to 12.

19. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 12.

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