An ISAC system signal detection method based on block-diagonal expectation propagation

Abstract

The application provides an ISAC system signal detection method based on block diagonal expectation propagation, wherein a communication signal and a target echo signal sent by a multi-antenna user equipment are acquired by a receiving end, the received signals are subjected to noise whitening processing, and interference of radar on communication is minimized; the optimized complex signals are converted into real value system models; mean vector and covariance matrix parameters of an expectation propagation algorithm are initialized, and an iteration number is set; diagonal line elements of the covariance matrix are calculated by using a block diagonal Neumann series approximation method; the mean vector is updated, and an iteration step is adjusted by using a dynamic residual factor; and finally, a final detection signal is output after a preset number of iterations. The diagonal line elements of the matrix inverse are calculated to replace direct solution of the matrix inverse, so that the complexity is reduced, and a special mean calculation method is designed for the matrix diagonalization characteristics with similar channel hardening characteristics, so that the precision is optimized.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, specifically to a signal detection method for an ISAC system based on block diagonal expected propagation. Background Technology

[0002] With the development of 6G technology, Integrated Sensing and Communication (ISAC) systems have become a research hotspot. However, existing ISAC signal detection technologies still face significant challenges. Traditional methods typically model radar target echoes simply as additive white Gaussian noise, a simplification that leads to severe performance loss in practical applications. Studies show that in typical application scenarios, this approach can cause a 5-8 dB loss in signal-to-noise ratio, increasing the system's bit error rate by more than an order of magnitude.

[0003] In multi-antenna user equipment (MAUE) scenarios, signal detection faces even greater technical challenges. Due to the size limitations of terminal devices, the antenna spacing is often compressed to 0.3-0.5 times the wavelength, resulting in a spatial correlation coefficient between antennas as high as 0.6-0.8. This strong correlation significantly weakens the channel hardening effect, causing a sharp decline in the performance of traditional detection algorithms based on the assumption of independent and identically distributed channels. Real-world data shows that under strongly correlated channel conditions, the bit error rate of existing detection algorithms can deteriorate by 10-100 times.

[0004] Furthermore, computational complexity also hinders the practical deployment of ISAC systems. Traditional precise matrix inversion operations have a complexity as high as O(N³), resulting in a single detection delay exceeding 10ms in a 64-antenna system, which is insufficient to meet real-time requirements. Although various approximation algorithms have been proposed, these methods either suffer from excessive accuracy loss or exhibit unstable performance under correlated channel conditions. Especially in edge computing scenarios, the computational resource requirements of existing algorithms often exceed the processing capabilities of terminal devices, severely limiting the widespread application of ISAC technology. Summary of the Invention

[0005] To address the issue of high algorithm complexity, this invention proposes a signal detection method for ISAC systems based on block diagonal expectation propagation. This scheme reduces complexity by calculating the diagonal elements of the matrix inverse instead of directly solving for the matrix inverse. Furthermore, a specialized averaging method is designed to optimize accuracy, taking into account the matrix diagonalization characteristic, which exhibits similar channel hardening properties. The technical solution provided by this invention is as follows:

[0006] A signal detection method for an ISAC system based on block diagonal expected propagation includes the following steps:

[0007] Step 1: Obtain the communication signal and target echo signal sent by the multi-antenna user equipment through the receiving end, and whiten the received signal and noise to minimize the interference of radar on communication;

[0008] Step 2: Convert the optimized complex signal into a real-valued system model;

[0009] Step 3: Initialize the mean vector and covariance matrix parameters of the expectation propagation algorithm, and set the number of iterations;

[0010] Step 4: In the first iteration, singular value decomposition is performed on the channel matrix to transform the complex matrix inversion operation into eigenvalue operation to reduce computational complexity.

[0011] Step 5: In subsequent iterations, the diagonal elements of the covariance matrix are calculated using the block diagonal Neumann series approximation method.

[0012] Step 6: Update the mean vector and adjust the iteration step size using the dynamic residual factor;

[0013] Step 7: After a preset number of iterations, output the final detection signal.

[0014] Preferably, a whitening matrix is ​​constructed using the Sherman-Morrison formula and eigenvalue decomposition method to whiten the received signal. The whitening matrix Q is expressed as:

[0015] ,

[0016] in It is the identity matrix. The standard deviation is a Gaussian distribution. The radar channel response matrix, For the transmit beamforming vector, .

[0017] Preferably, through joint optimization To minimize radar interference with communications:

[0018]

[0019] in, For complex domain communication channel response, P is the guide vector. max That is the maximum transmission power. This is the minimum radar gain threshold.

[0020] Preferably, the real-valued expression of the receive vector is:

[0021]

[0022] in The mean is zero and the variance is... The additive white Gaussian noise vector is and This is the spatial correlation matrix between the receiver and transmitter. , , , and These are the base station received signal and the communication transmitted signal, respectively, in the complex domain. Indicates the real part, Indicates the imaginary part.

[0023] Preferably, the initialization parameters in step 3 are: and ,in Represents average symbolic energy. and These are the parameters that determine the characteristics of the alternative Gaussian function for each dimension.

[0024] Preferably, the large-scale channel matrix is ​​divided into several sub-block matrices of the same size according to user antenna groups, with each sub-block corresponding to an antenna set of a user equipment; the diagonal elements of each sub-block matrix are extracted to form a diagonal matrix, and the inverse matrix of the diagonal matrix is ​​calculated as an initial approximation; the Neumann series expansion method is used to gradually approximate the true inverse matrix of each sub-block matrix through a finite number of matrix multiplications and additions, wherein the number of terms in the series expansion is dynamically adjusted according to the required accuracy; after obtaining the approximate inverse matrix of each sub-block matrix, the approximate inverse matrix is ​​recombined according to the block structure of the original matrix to form a complete approximate covariance matrix.

[0025] Preferably, the mean vector update optimization method specifically includes: in each iteration, firstly, a system of linear equations is constructed using the currently estimated covariance matrix and the received signal vector; then, the preprocessing Jacobi iterative method is used to solve the system of equations by decomposing the coefficient matrix into two parts, a diagonal matrix and an off-diagonal matrix, and retaining only the inverse of the diagonal matrix for calculation; during the iterative solution process, the relaxation factor is dynamically adjusted according to the channel hardening degree to accelerate the convergence speed; at the same time, the statistical characteristics of channel state information are introduced to perform weighted averaging on each dimension of the mean vector, where the weight coefficients are proportional to the quality of the corresponding antenna channel.

[0026] Preferably, the dynamic residual factor adjustment mechanism includes the following process: during the algorithm initialization phase, the residual factors of all iteration layers are set to a uniform empirical value; during each iteration, the residual factor of each layer is dynamically updated according to the condition number of the current channel matrix and the signal-to-noise ratio parameter through a preset adjustment rule; a momentum term is introduced during the update process to smooth parameter changes; at the same time, upper and lower limit thresholds of the residual factor are set, and truncation is performed when the calculated updated value exceeds the range.

[0027] Compared with the prior art, the beneficial effects achieved by this invention are: improving the block matrix Neumann method by replacing the original calculation of the matrix inverse with the calculation of the diagonal elements of the matrix inverse to reduce complexity; designing a method for calculating the mean value for constructing a matrix diagonalization feature similar to channel hardening; and optimizing the method to solve the accuracy problem. Attached Figure Description

[0028] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0029] Figure 1 This is a system model diagram of the present invention;

[0030] Figure 2 This is a schematic diagram illustrating the solution of matrix C using the block diagonal method in the embodiment;

[0031] Figure 3 This is a schematic diagram illustrating the solution of matrix D using the improved block diagonal method in the embodiment;

[0032] Figure 4 This is a network structure diagram of the improved block diagonal expectation propagation algorithm of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] To make the above-mentioned objectives, features and effects of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Example 1: A signal detection method for an ISAC system based on block diagonal expected propagation. This invention is based on an ISAC system, such as... Figure 1 As shown, one of the multi-antenna base stations receives communication signals from multiple users while performing target detection. The present invention specifically includes the following steps.

[0036] Step 1: Obtain the communication signals and target echo signals sent by the multi-antenna user equipment through the receiving end, and perform whitening processing on the received signals and noise to minimize radar interference to communication.

[0037] In the ISAC system, the received signal can be represented as:

[0038]

[0039] in, , , , , and These represent the base station received signal, communication channel response, communication transmitted signal, radar channel response matrix, transmitted beamforming vector, and system noise in the complex domain, respectively. and This indicates that it has not undergone whitening treatment. , This is the guide vector. Let be the reflection coefficient of the target, and s be the radar signal transmitted by the base station. and These correspond to the number of antennas at the base station receiver and the communication user transmitter, respectively.

[0040] Following the Swerling II target model, assuming that the target's reflection coefficient varies with the pulse, with an amplitude following a Rayleigh / exponential distribution, in this case... The inner diameter follows a complex Gaussian distribution. For ease of labeling and without loss of generality, noise power is used. To normalize The variance, in this case, is obtained. .

[0041] To detect communication signals, other signals, including target echoes and noise, are treated as unwanted signals. Therefore, the signal model can be represented as:

[0042]

[0043] in Due to random variables sum vector They are all white Gaussian distributions and independent of each other, therefore they are linear combinations. It is also a Gaussian distribution with zero mean. Its covariance matrix is:

[0044]

[0045] This indicates The signal follows a complex Gaussian distribution, leading to correlation between communication subchannels. With this known covariance matrix, the received signal can be whitened and the communication channel decomposed into several independent subchannels.

[0046]

[0047] in It is a whitening matrix.

[0048] Calculate R using the Sherman-Morrison formula -1 Because of R -1 It is the identity matrix plus a rank-1 matrix, and its inverse matrix can be analytically represented as:

[0049]

[0050] make ,but .

[0051] The whitening matrix Q must satisfy the following conditions: Right now Solving using eigenvalue decomposition The eigenvalues ​​of R are decomposed into: ,in , ,the remaining (because U is a rank-1 matrix that contributes only one non-zero eigenvalue, and U is a unitary matrix.

[0052] calculate ,in Therefore, the whitening matrix Q is expressed as:

[0053] .

[0054] in This can be expressed through joint optimization, which means minimizing radar interference with communication while ensuring basic sensing capabilities:

[0055]

[0056] Where P max That is the maximum transmission power. To find the minimum radar gain threshold, this problem can be solved using the Lagrange multiplier method or semidefinite programming (SDP).

[0057] Step 2: Convert the optimized complex signal into a real-valued system model.

[0058] At this moment, the system model formula can be expressed as: ,in This represents the whitened complex domain base station received signal. This represents the whitened complex-domain communication channel response. It is a standard Gaussian white noise matrix.

[0059] To facilitate the implementation of the signal detection algorithm, the system model in the complex field is converted into an equivalent real-field representation, as follows:

[0060] in, , , .

[0061] like Figure 1 As shown, this invention considers an ISAC system, in which the base station BS is configured with N r One antenna communicates with m multi-antenna users, each user is equipped with m UE One antenna and one radar for target detection. In this architecture: transmitting antennas within the same user exhibit significant spatial correlation due to their spacing, while antenna channel correlation between different user devices is generally negligible. The total number of transmitting antennas in the system is... The transmission vector is represented as , the elements for , millimeter-wave channel matrix , . Let N represent the uplink from the j-th antenna of the i-th UE to the BS. Let N = 2N. r M=2N t The vector received by BS Then the real-valued expression of the receiving vector is:

[0062]

[0063] in The mean is zero and the variance is... The additive white Gaussian noise vector. The spatial correlation matrices of the receiver and transmitter are respectively... and .

[0064]

[0065]

[0066] in It is the value in the p-th row and q-th column of the receiving antenna correlation matrix. It is the value in the p-th row and q-th column of the transmit antenna correlation matrix. and Indicates its complex conjugate. and These are defined as the correlation factors of the receiving antenna and the transmitting antenna from the same terminal, respectively.

[0067] In multi-antenna user equipment systems, multiple antennas of the same user equipment are typically arranged in a uniform linear array, with an adjacent antenna spacing of d. iDue to UE size limitations, the antenna spacing is typically less than half a wavelength. This leads to enhanced spatial correlation. Assume the antenna spacing of the i-th UE is d. i Its transmitter spatial correlation matrix The element in the m-th row and n-th column can be modeled as: ;

[0068] in This is the inter-antenna correlation factor, which is inversely proportional to the antenna spacing. When hour, At this point, the antennas are approximately independent; when hour, The value increases as the spacing decreases, which has two main effects: a significant enhancement of the off-diagonal elements of the channel matrix and a reduction in channel hardening.

[0069] Based on the constructed system model, the posterior probability distribution of the transmitted symbol vector can be expressed as:

[0070]

[0071] in It is an indicator function, if If the condition is met, return 1; otherwise, return 0.

[0072] Step 3: Initialize the mean vector and covariance matrix of the expectation propagation algorithm, and set the number of iterations.

[0073] The expectation propagation algorithm is a technique in Bayesian machine learning used to approximate posterior beliefs using an exponential family distribution. When applied to signal detection, the first step is to construct a Gaussian approximation. To replace the posterior distribution of the transmitted symbol vector Then, the mean of the Gaussian approximation is updated iteratively using polynomial time complexity. and variance The formula for EP iterative detection is given in detail below.

[0074] Given the posterior distribution, it is decomposed into Gaussian and non-Gaussian parts:

[0075]

[0076] in and These are the parameters that determine the characteristics of the surrogate Gaussian function for each dimension. Let... For any vector and covariance matrix and average vector The calculation is as follows:

[0077]

[0078]

[0079] Clearly, recursive updates are effective for... Equivalent to refreshing the Gaussian function average vector Covariance Matrix As the number of iterations increases, the approximate accuracy gradually improves. Setting an initial solution... and ,in This represents the average symbol energy. (For) The iterative update process is as follows:

[0080] Calculate the cavity edge: ;

[0081] in, , ;

[0082] Accurate non-Gaussian factor Introduction In the middle, we get:

[0083]

[0084]

[0085]

[0086] Update to This makes the following non-normalized Gaussian distribution: With mean and variance The calculation formula is as follows:

[0087]

[0088]

[0089] At this point, one iteration is basically complete.

[0090] The refined and Substituting the covariance matrix and mean vector into the calculations, we proceed with the next iteration, gradually increasing the approximation accuracy until the desired number of iterations is reached. In each iteration, all... All are in the corresponding input dimensions It is updated in parallel.

[0091] To further improve the convergence speed of the EP algorithm, a commonly used strategy in message passing is:

[0092]

[0093]

[0094] in The high complexity of the expectation propagation algorithm, where the convergence coefficient is used, is mainly due to the calculation of the matrix covariance and the mean vector. In particular, the matrix inverse operation of the covariance has a computational complexity reaching [value missing]. This would lead to excessively high computational overhead in signal detection in high-dimensional MIMO systems.

[0095] Step 4: In the first iteration, singular value decomposition is performed on the channel matrix to transform the complex matrix inversion operation into eigenvalue operation to reduce computational complexity.

[0096] Step 5: In subsequent iterations, the diagonal elements of the covariance matrix are calculated using the block diagonal Neumann series approximation method.

[0097] The large-scale channel matrix is ​​divided into several sub-block matrices of the same size according to user antenna groups, with each sub-block corresponding to the antenna set of a user equipment. The diagonal elements of each sub-block matrix are extracted to form a diagonal matrix, and the inverse matrix of the diagonal matrix is ​​calculated as an initial approximation. The Neumann series expansion method is used to gradually approximate the true inverse matrix of each sub-block matrix through a finite number of matrix multiplications and additions, where the number of terms in the series expansion is dynamically adjusted according to the required accuracy. After obtaining the approximate inverse matrix of each sub-block matrix, these approximate inverse matrices are recombined according to the block structure of the original matrix to form a complete approximate covariance matrix.

[0098] Specifically, firstly, the matrix Divided into 4 matrix blocks, with the upper left and lower right matrices set as follows: Then the matrix The approximate diagonal elements are divided into There are n small matrices, each of size n. Set them in order To ensure the convergence of the Neumann method, parameters are introduced. Operations on all small matrices Find the inverse of each smaller matrix. . The complexity of solving directly is Since the matrix contains both diagonal and off-diagonal elements, to reduce the complexity of inversion, the Neumann block matrix method is used for the following approximation:

[0099] ,

[0100] take out In : , ;

[0101] Perform block matrix Iteration of the Neumann method for the secondary block matrix ;

[0102] The same can be obtained Then and Put them together: ;

[0103] Get what you need Repeat the above method: , , ;

[0104] Perform block matrix Neumann method iteration for sub-block matrices: ,get .

[0105] Only need Only the diagonal elements are needed; there is no need to calculate the complete set. .according to , Rewritten in the following form: ;

[0106] After calculation:

[0107]

[0108]

[0109] Multiple were discovered Multiplying them reveals a pattern of off-diagonal and diagonal segments with a period of 2. Therefore, we get:

[0110]

[0111]

[0112] Since the above formula does not include diagonal elements, no calculation is needed. Only calculation is needed and :

[0113] .

[0114] From this formula, we can know and Therefore, only calculation is needed. and Upon further observation, it was found that only one request was needed. Matrix multiplication.

[0115] As the correlation increases, the channel hardening phenomenon in the first layer becomes increasingly worse. Therefore, instead of approximating the first layer, the following method is adopted (i.e., for the first iteration): First, for... Perform singular value decomposition: U is a left singular matrix, and V is a right singular matrix. It is a singular value matrix;

[0116] get Because it was the first time I brought it in. Since the values ​​are all the same, we simplify it as follows:

[0117]

[0118] .

[0119] Step 6: Update the mean vector and adjust the iteration step size using the dynamic residual factor.

[0120] In each iteration, a system of linear equations is first constructed using the currently estimated covariance matrix and the received signal vector. Then, the preprocessing Jacobi iterative method is used to solve the system of equations by decomposing the coefficient matrix into two parts: a diagonal matrix D and a non-diagonal matrix L, and retaining only the inverse of the diagonal matrix for calculation. During the iterative solution process, the relaxation factor is dynamically adjusted according to the channel hardening degree to accelerate the convergence speed. At the same time, the statistical characteristics of channel state information are introduced to perform weighted averaging on each dimension of the mean vector, where the weight coefficients are proportional to the quality of the corresponding antenna channel.

[0121] The dynamic residual factor adjustment mechanism includes the following processing steps: During the algorithm initialization phase, the residual factors of all iteration layers are set to a unified empirical value; during each iteration, the residual factor of each layer is dynamically updated according to the condition number of the current channel matrix and the signal-to-noise ratio parameter through a preset adjustment rule; a momentum term is introduced during the specific update process to smooth parameter changes and prevent parameter oscillations caused by channel mutations; at the same time, upper and lower limit thresholds for the residual factor are set, and truncation is performed when the calculated updated value exceeds the range to ensure numerical stability.

[0122] Step 7: After a preset number of iterations, output the final detection signal.

[0123] Steps 3 to 7 constitute the improved block diagonal expectation propagation algorithm proposed in this invention. The specific algorithm network is as follows: In the first layer of the network (t=1), initialization processing is performed, unlike subsequent layers. Parameters are initialized based on the input received signal vector, channel matrix, noise power, and average signal energy. and Calculate the approximate covariance matrix and initial mean estimation .

[0124] The input to the second to t-th layers (t>=2) of the network is the mean vector from the previous layer. and parameters , First calculate the covariance matrix. The intermediate variable b is updated using the Jacobi method to estimate the mean. Then, the diagonal is extracted for error calculation. And calculate the mean without nonlinear mapping. Nonlinear mapping and residual factor Link Output the updated variance And update parameters and Finally, after iterative optimization using a T-layer network, the mean of the last layer's output is shifted... This is the final estimated value of the transmitted signal detected.

[0125] The above network structure is as follows Figure 4 As shown, the pseudocode of the algorithm is presented in Table 1.

[0126] Table 1 Improved Expectation Propagation Algorithm

[0127]

[0128] Example 2: The computer-readable storage medium of this example stores a computer program that, when executed by a processor, implements the steps of the ISAC system signal detection method based on block diagonal expected propagation in Example 1.

[0129] The computer-readable storage medium in this embodiment can be an internal storage unit of the terminal, such as the terminal's hard disk or memory; the computer-readable storage medium in this embodiment can also be an external storage device of the terminal, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc. equipped on the terminal; furthermore, the computer-readable storage medium can include both the terminal's internal storage unit and external storage devices.

[0130] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0131] Example 3 The computer device of this example includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the ISAC system signal detection method based on block diagonal expected propagation of Example 1.

[0132] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The memory can include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0133] Those skilled in the art will clearly understand that each implementation can be achieved using software plus the necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0134] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A signal detection method for an ISAC system based on block diagonal expected propagation, characterized in that, Includes the following steps: Step 1: Obtain the communication signal and target echo signal sent by the multi-antenna user equipment through the receiving end, and whiten the received signal and noise to minimize the interference of radar on communication; Step 2: Convert the optimized complex signal into a real-valued system model; Step 3: Initialize the mean vector and covariance matrix parameters of the expectation propagation algorithm, and set the number of iterations; Step 4: In the first iteration, singular value decomposition is performed on the channel matrix to transform the complex matrix inversion operation into eigenvalue operation to reduce computational complexity. Step 5: In subsequent iterations, the diagonal elements of the covariance matrix are calculated using the block diagonal Neumann series approximation method. Step 6: Update the mean vector and adjust the iteration step size using the dynamic residual factor; Step 7: After a preset number of iterations, output the final detection signal.

2. The signal detection method for an ISAC system based on block diagonal expected propagation according to claim 1, characterized in that, The whitening matrix Q is constructed using the Sherman-Morrison formula and eigenvalue decomposition method to whiten the received signal. ， in It is the identity matrix. The standard deviation is a Gaussian distribution. The radar channel response matrix, For the transmitted beamforming vector, superscript H Represents the conjugate transpose of a matrix. .

3. The method for signal detection in an ISAC system based on block diagonal expected propagation according to claim 2, characterized in that, Through joint optimization To minimize radar interference with communications: ； in, For complex domain communication channel response, As the guide vector, P represents the direction of the incoming signal wave. max That is the maximum transmission power. is the minimum radar gain threshold, min is the function for finding the minimum value, and st represents the constraint condition.

4. The method for signal detection in an ISAC system based on block diagonal expected propagation according to claim 3, characterized in that, The real-valued expression for the receive vector is: ； in The mean is zero and the variance is... The additive white Gaussian noise vector and This is the spatial correlation matrix between the receiver and transmitter. , , , and These are the base station received signal and the communication transmitted signal, respectively, in the complex domain. Indicates the real part, Indicates the imaginary part.

5. The method for signal detection in an ISAC system based on block diagonal expected propagation according to claim 1, characterized in that, Step 3 initializes the parameters as follows: and ,in Represents average symbolic energy. and These are the parameters that determine the characteristics of the alternative Gaussian function for each dimension.

6. The method for signal detection in an ISAC system based on block diagonal expected propagation according to claim 5, characterized in that, The large-scale channel matrix is ​​divided into several sub-block matrices of the same size according to user antenna groups, and each sub-block corresponds to the antenna set of a user equipment; the diagonal elements of each sub-block matrix are extracted to form a diagonal matrix, and the inverse matrix of the diagonal matrix is ​​calculated as an initial approximation; The Neumann series expansion method is adopted to gradually approximate the true inverse matrix of each sub-block matrix through a finite number of matrix multiplications and additions, wherein the number of terms in the series expansion is dynamically adjusted according to the required precision; After obtaining the approximate inverse matrix of each sub-block matrix, the approximate inverse matrix is ​​recombined according to the block structure of the original matrix to form a complete approximate covariance matrix.

7. The signal detection method for an ISAC system based on block diagonal expected propagation according to claim 1, characterized in that, The mean vector update optimization method specifically includes the following steps: In each iteration, a system of linear equations is first constructed using the currently estimated covariance matrix and the received signal vector; then, the preprocessing Jacobi iterative method is used to solve the system of equations by decomposing the coefficient matrix into two parts: a diagonal matrix and an off-diagonal matrix, retaining only the inverse of the diagonal matrix for calculation; during the iterative solution process, the relaxation factor is dynamically adjusted according to the channel hardening degree to accelerate the convergence speed; at the same time, the statistical characteristics of channel state information are introduced to perform weighted averaging on each dimension of the mean vector, where the weight coefficients are proportional to the quality of the corresponding antenna channel.

8. The method for signal detection in an ISAC system based on block diagonal expected propagation according to claim 7, characterized in that, The adjustment mechanism for the dynamic residual factor includes the following steps: During the algorithm initialization phase, the residual factors of all iteration layers are set to a uniform empirical value; During each iteration, the residual factor of each layer is dynamically updated according to the condition number and signal-to-noise ratio parameter of the current channel matrix through a preset adjustment rule; A momentum term is introduced during the update process to smooth parameter changes; at the same time, upper and lower limits are set for the residual factor, and truncation is performed when the calculated update value exceeds the range.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the ISAC system signal detection method based on block diagonal expected propagation as described in any one of claims 1-8.

10. A computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the ISAC system signal detection method based on block diagonal expected propagation as described in any one of claims 1-8.

Images