Array signal compression transmission and recovery method in multi-source scene

Abstract

The invention discloses an array signal compression transmission and recovery method in a multi-source scene, and relates to the technical field of wireless communication. The method comprises the following steps: obtaining an observation signal received by an array matrix in a multi-source scene, compressing the observation signal, and carrying out transmission estimation to obtain a receiving signal of the array matrix; transmitting the received signal as a communication signal to a preset signal recovery model, wherein the signal recovery model comprises a signal iteration recovery module, a de-noising device module and an iteration noise reduction module; the signal iteration recovery module carries out signal iteration recovery on the communication signals, the de-noising device module carries out array parameter estimation on the communication signals in the iteration recovery process, and the iteration noise reduction module carries out parameter updating according to the estimated array parameters and outputs the communication signals after parameter updating. Through the method provided by the invention, the difficulty that the required calculation requirement is high due to excessive information sources is overcome, and meanwhile, the signal can be accurately recovered.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for array signal compression transmission and recovery in a multi-source scenario. Background Technology

[0002] Line spectrum estimation (spectral analysis), a core fundamental problem in signal and information processing, aims to reveal the inherent periodic structure of physical signals by decomposing them into a superposition of simple signals (harmonics) with different frequencies, amplitudes, and phases, and accurately estimating model parameters (frequency, amplitude, order). Its applications span numerous key fields, including sonar and radar angular spectrum estimation, wireless communication channel modeling, speech analysis, spectroscopy, and geophysical exploration. Although parametric high-resolution methods (such as MUSIC and ESPRIT) have become the mainstream choice due to their superior performance, their high computational complexity poses a significant challenge to array processing hardware in practical applications, especially in multi-source scenarios, limiting their deployment capabilities in resource-constrained environments.

[0003] Meanwhile, compressed sensing (CS) theory offers new insights into overcoming the limitations of traditional Shannon sampling, allowing the recovery of sparse signals from observations at rates far below the Nyquist rate. However, mainstream CS recovery algorithms based on approximate message passing (AMP) and its variants often experience a significant decrease in recovery performance or even fail when dealing with communication signals that have intrinsic structure or strong correlations.

[0004] Therefore, under the constraints of numerous signal sources and limited local hardware computing resources, designing a robust signal detection and recovery method to overcome both the computational bottleneck of high-resolution line spectrum estimation and the sensitivity of traditional CS algorithms to related signals has become a critical problem that urgently needs to be solved. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the present invention aims to provide a method for array signal compression transmission and recovery in multi-source scenarios, which can effectively complete parameter estimation under the real constraints of numerous source numbers and limited local hardware computing resources by modeling the acquired data features and partial parameters, and simultaneously recover the signal.

[0006] To achieve the objectives of this invention, the following technical solution is adopted: A method for compressed transmission and recovery of array signals in a multi-source scenario, the method comprising the following steps: The observation signal received by the array matrix in a multi-source scenario is acquired, and the observation signal is compressed and then transmission estimation is performed to obtain the received signal of the array matrix. The received signal is transmitted as a communication signal to a preset signal recovery model, which includes a signal iterative recovery module, a denoiser module, and an iterative noise reduction module. The signal iterative recovery module performs iterative recovery on the communication signal, the denoising module estimates the array parameters of the communication signal during the iterative recovery process, the iterative denoising module updates the parameters according to the estimated array parameters, and outputs the communication signal with updated parameters.

[0007] In the above technical solution, by using a preset signal recovery model to perform iterative signal recovery on the received signal, it is possible to compress and transmit communication signals that cannot be processed under multiple signal sources; by estimating the array parameters of the communication signal during the iterative recovery process, the influence of structural and unknown parameters can be avoided, thus improving applicability; and by updating the parameters based on the estimated array parameters, more accurate information can be obtained.

[0008] Furthermore, the process of acquiring the observation signals received by the array matrix in a multi-source scenario includes: The array matrix is ​​used to monitor communication signals with frequency and amplitude information in a multi-source scenario. First Gaussian white noise is then superimposed on the communication signals to obtain the observed signal, expressed as:

[0009] in, Indicates the amplitude information of the communication signal. Represents an array matrix. Represents communication signals, Represents the channel matrix. This indicates superimposed Gaussian white noise. This indicates the observation signal received through the array.

[0010] Further, the process of compressing the observed signal and performing transmission estimation to obtain the received signal of the array matrix includes: the communication signal After the channel matrix The received signal of the array matrix is ​​obtained by superimposing a second Gaussian white noise, and the expression is:

[0011] in, This represents the amplitude information of the communication signal to be estimated. Represents an array matrix. Represents communication signals, Represents the channel matrix. This represents the second superimposed Gaussian white noise. This represents the received signal, where m, n, k, and T are all positive integers.

[0012] Furthermore, in the signal recovery model: Set up a signal iteration process with several rounds, where: The iterative recovery module performs iterative signal recovery on the communication signal based on a series of signal iterations. The denoising module estimates the array parameters of the communication signal in each iteration and outputs the parameter estimation results, wherein the parameter estimation results include array matrix parameters and amplitude information. The iterative noise reduction module performs iterative noise reduction on the communication signal based on the estimated column matrix parameters and amplitude information, so as to iteratively update the array matrix parameters and amplitude information in the communication signal. The loss function is set to optimize the iterative update process of array matrix parameters and amplitude information. When the number of iterations is reached or the loss function converges, the iteration ends and the communication signal is reconstructed based on the final array matrix parameters and amplitude information.

[0013] Furthermore, the iterative recovery module performs iterative signal recovery on the communication signal based on a series of signal iterations, the expression of which includes:

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[0023] in, , Both represent intermediate variables in the linear operations during the iteration process. Let Variance be the noise variance. These represent solving for the Hadama product and Hadama quotient, respectively. This represents the operation of calculating its divergence, where t represents the number of iterations, and the function... and Find the distribution of . The mean and variance, and Let these represent the mean and variance of the communication signal distribution, respectively. This indicates the operation of the noise reduction module.

[0024] Furthermore, the process by which the denoiser module estimates the array parameters of the communication signal in each iteration includes: The array matrix parameters and amplitude information are updated and estimated using the following expression:

[0025] in, express There are many corresponding variables in the middle; Indicates the amplitude information of the communication signal; Let a function be a sequence of elements with an expected value of 0 and a variance of 0. Gaussian noise; This represents a Gaussian distribution.

[0026] Furthermore, the process of iteratively updating the array matrix parameters and amplitude information in the communication signal includes: The iterative noise reduction module iteratively updates the amplitude information based on the updated estimates of the array matrix parameters and amplitude information. The expression includes:

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[0045] in, , , , , , , , , , , , , , , , , These are all intermediate variables in the linear operations during the iteration process. These represent solving for the Hadama product and Hadama quotient, respectively. , These are respectively represented as the solution distributions. The expected value and variance, where Defined as , , They represent the solution distributions respectively. Expectation and variance To convert a vector into a diagonal matrix, To extract the diagonal elements of the matrix; When the number of iterations is reached or the loss function converges, the updated amplitude information estimate is obtained. .

[0046] Furthermore, the iterative noise reduction module uses the Generalized Expectation Consistent Signal Recovery (GEC-SR) algorithm to iteratively update the amplitude information of the communication signal.

[0047] Furthermore, estimation is based on the updated amplitude information. Obtain the updated array matrix parameter information and reconstructed array matrix ; Based on the updated amplitude information, the estimation and reconstructed array matrix The final estimate of the communication signal is obtained. .

[0048] Furthermore, the optimizer Adam is used to optimize the array matrix parameters and amplitude information during the iterative update process, and the learning rate is set to 0.0001.

[0049] In the above technical solution, the iterative recovery module compresses and recovers the received observation signal, and iterates to obtain the input of the denoiser module; the parameter estimation module in the denoiser module estimates the parameters and obtains the input of the iterative noise reduction module; the iterative noise reduction module obtains a more accurate signal estimate through iterative updates; finally, the iterative recovery module completes the iteration until it converges to the optimal solution, and finally obtains the estimated communication signal.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method for compressed transmission and recovery of array signals in multi-source scenarios. By employing a preset signal recovery model to perform iterative signal recovery on the received signal, it is possible to compress and transmit communication signals that cannot be processed under multi-source conditions. By estimating the array parameters of the communication signal during the iterative recovery process, the influence of structural and unknown parameters can be avoided, thus improving applicability. By updating the parameters based on the estimated array parameters, more accurate information can be obtained. Attached Figure Description

[0051] Figure 1 A flowchart illustrating the steps of a method for array signal compression transmission and recovery in a multi-source scenario provided in this application embodiment; Figure 2 This is a schematic diagram of the signal recovery model provided in an embodiment of this application. Detailed Implementation

[0052] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0054] Example: This embodiment provides a method for array signal compression transmission and recovery in a multi-source scenario. (See also...) Figure 1 and Figure 2 The method includes the following steps: Step S1: Obtain the observation signal received by the array matrix in a multi-source scenario, compress the observation signal and perform transmission estimation to obtain the received signal of the array matrix; Step S2: The received signal is transmitted as a communication signal to a preset signal recovery model, the signal recovery model including a signal iterative recovery module, a denoiser module and an iterative noise reduction module; Step S3: The signal iterative recovery module performs iterative recovery on the communication signal, the denoising module estimates the array parameters of the communication signal during the iterative recovery process, and the iterative denoising module updates the parameters according to the estimated array parameters and outputs the updated communication signal.

[0055] In a preferred embodiment, step S1, the process of acquiring the observation signal received by the array matrix in a multi-source scenario, includes: The array matrix is ​​used to monitor communication signals with frequency and amplitude information in a multi-source scenario. First Gaussian white noise is then superimposed on the communication signals to obtain the observed signal, expressed as:

[0056] in, Indicates the amplitude information of the communication signal. Represents an array matrix. Represents communication signals, Represents the channel matrix. This indicates superimposed Gaussian white noise. This indicates the observation signal received through the array.

[0057] Further, the process of compressing the observed signal and performing transmission estimation to obtain the received signal of the array matrix includes: the communication signal After the channel matrix The received signal of the array matrix is ​​obtained by superimposing a second Gaussian white noise, and the expression is:

[0058] in, This represents the amplitude information of the communication signal to be estimated. Represents an array matrix. Represents communication signals, Represents the channel matrix. This represents the second superimposed Gaussian white noise. This represents the received signal, where m, n, k, and T are all positive integers.

[0059] Specifically, in a multi-source scenario, a uniform array is used to monitor communication signals carrying frequency and amplitude information. After adding Gaussian white noise Afterwards, we obtained our observation signal; communication signal. After the channel matrix Then, Gaussian white noise is superimposed. Received signal .

[0060] In a preferred embodiment, in step S3, in the signal recovery model: Set up a signal iteration process with several rounds, where: The iterative recovery module described in S31 performs iterative signal recovery on the communication signal according to a series of signal iteration processes. S32: The denoising module estimates the array parameters of the communication signal in each iteration and outputs the parameter estimation results, wherein the parameter estimation results include array matrix parameters and amplitude information; S33: The iterative noise reduction module performs iterative noise reduction on the communication signal based on the estimated column matrix parameters and amplitude information, so as to iteratively update the array matrix parameters and amplitude information in the communication signal; S34: Set the loss function to optimize the parameters of the iterative update process of the array matrix parameters and amplitude information. When the number of iterations is reached or the loss function converges, the iteration ends and the communication signal is reconstructed based on the final array matrix parameters and amplitude information.

[0061] In step S31, the iterative recovery module performs iterative signal recovery on the communication signal according to a series of signal iterations, the expression of which includes:

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[0071] in, , Both represent intermediate variables in the linear operations during the iteration process. Let Variance be the noise variance. These represent solving for the Hadama product and Hadama quotient, respectively. This represents the operation of calculating its divergence, where t represents the number of iterations, and the function... and Find the distribution of . The mean and variance, and Let these represent the mean and variance of the communication signal distribution, respectively. This indicates the operation of the noise reduction module.

[0072] In step S32, the process by which the denoiser module estimates the array parameters of the communication signal in each iteration includes: The array matrix parameters and amplitude information are updated and estimated using the following expression:

[0073] in, express There are many corresponding variables in the middle; Indicates the amplitude information of the communication signal; Let a function be a sequence of elements with an expected value of 0 and a variance of 0. Gaussian noise; This represents a Gaussian distribution.

[0074] Specifically, in the iterative recovery module, the above parameter expressions and amplitude prior information are used as inputs to the denoising module, where... and These are intermediate variables for linear operations during the iteration process.

[0075] Furthermore, the specific steps for updating and estimating the array matrix parameters and amplitude information are as follows: A: Parameters for the input noise filter We obtain its covariance matrix. ,in for transpose; B: Regarding the income The eigenvalue decomposition is expressed as follows: The eigenvector matrix is ​​arranged in descending order of its eigenvalues. Divided into , which respectively represent the signal subspace and the noise subspace; The obtained signal subspace Divide into two subarrays of the same dimension, for example for Line 1 to line 2 OK, for Line 2 to line 1 The two subarrays obtained have the following relationship:

[0076] in, It is a diagonal matrix containing the parameters we need to solve for.

[0077] C: Construct an augmented matrix using the two obtained subarrays: Perform singular value decomposition on matrix C: ; Will Divided into 4 sub-blocks as shown below: =

[0078] Obtained by using sub-blocks The specific expression is as follows:

[0079] in To find the inverse process.

[0080] For the obtained matrix The eigenvalues ​​are calculated, and the elements of the diagonal matrix are extracted. These elements are the parameters that need to be estimated and updated. D: Reconstruct a new array matrix using the obtained new parameters. Its structure:

[0081] in These are the parameters estimated in the steps above.

[0082] In step S33, the process of iteratively updating the array matrix parameters and amplitude information in the communication signal includes: The iterative noise reduction module iteratively updates the amplitude information based on the updated estimates of the array matrix parameters and amplitude information. The expression includes:

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[0101] in, , , , , , , , , , , , , , , , , These are all intermediate variables in the linear operations during the iteration process. These represent solving for the Hadama product and Hadama quotient, respectively. , These are respectively represented as the solution distributions. The expected value and variance, where Defined as , , They represent the solution distributions respectively. Expectation and variance To convert a vector into a diagonal matrix, To extract the diagonal elements of the matrix; When the number of iterations is reached or the loss function converges, the updated amplitude information estimate is obtained. ; Based on the updated amplitude information, the estimation is... Obtain the updated array matrix parameter information and reconstructed array matrix ; Based on the updated amplitude information, the estimation and reconstructed array matrix The final estimate of the communication signal is obtained. .

[0102] In a preferred embodiment, the iterative noise reduction module uses the Generalized Expectation Consistent Signal Recovery (GEC-SR) algorithm to iteratively update the amplitude information of the communication signal.

[0103] In a preferred embodiment, the optimizer Adam is used to optimize the array matrix parameters and amplitude information during the iterative update process, and the learning rate is set to 0.0001.

[0104] Understandably, the iterative recovery module compresses and recovers the received observed signal, and iterates to obtain the input to the denoiser module; the parameter estimation module in the denoiser module estimates the parameters and obtains the input to the iterative denoising module; the iterative denoising module obtains a more accurate signal estimate through iterative updates; finally, the iterative recovery module completes the iteration until it converges to the optimal solution, and finally obtains the estimated communication signal.

[0105] In this embodiment, by using a preset signal recovery model to perform iterative signal recovery on the received signal, the communication signal that cannot be processed under multiple signal sources can be compressed and transmitted. By estimating the array parameters of the communication signal during the iterative recovery process, the influence of structural and unknown parameters can be avoided, thus improving applicability. By updating the parameters based on the estimated array parameters, more accurate information can be obtained.

[0106] In this embodiment, the iterative recovery module is used as a whole framework to handle the problem of data from multiple sources being too large to process; by embedding the denoising module into the iterative recovery module, the problem of unknown parameters in the array matrix is ​​solved; and by embedding the iterative denoising module into the denoising module, more accurate amplitude information can be obtained.

[0107] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for array signal compression transmission and recovery in a multi-source scenario, characterized in that, The method includes the following steps: The observation signal received by the array matrix in a multi-source scenario is acquired, and the observation signal is compressed and then transmission estimation is performed to obtain the received signal of the array matrix. The received signal is transmitted as a communication signal to a preset signal recovery model, which includes a signal iterative recovery module, a denoiser module, and an iterative noise reduction module. The signal iterative recovery module performs iterative recovery on the communication signal, the denoising module estimates the array parameters of the communication signal during the iterative recovery process, the iterative denoising module updates the parameters according to the estimated array parameters, and outputs the communication signal with updated parameters.

2. The method for array signal compression transmission and recovery in a multi-source scenario according to claim 1, characterized in that, The process of acquiring the observed signals received by the array matrix in a multi-source scenario includes: The array matrix is ​​used to monitor communication signals with frequency and amplitude information in a multi-source scenario. First Gaussian white noise is then superimposed on the communication signals to obtain the observed signal, expressed as: in, Indicates the amplitude information of the communication signal. Represents an array matrix. Represents communication signals, Represents the channel matrix. This indicates superimposed Gaussian white noise. This indicates the observation signal received through the array.

3. The method for array signal compression transmission and recovery in a multi-source scenario according to claim 2, characterized in that, The process of compressing the observed signal and then performing transmission estimation to obtain the received signal of the array matrix includes: the communication signal After the channel matrix The received signal of the array matrix is ​​obtained by superimposing a second Gaussian white noise, and the expression is: in, This represents the amplitude information of the communication signal to be estimated. Represents an array matrix. Represents communication signals, Represents the channel matrix. This represents the second superimposed Gaussian white noise. This represents the received signal, where m, n, k, and T are all positive integers.

4. The method for array signal compression transmission and recovery in a multi-source scenario according to claim 1, characterized in that, In the signal recovery model: Set up a signal iteration process with several rounds, where: The iterative recovery module performs iterative signal recovery on the communication signal based on a series of signal iterations. The denoising module estimates the array parameters of the communication signal in each iteration and outputs the parameter estimation results, wherein the parameter estimation results include array matrix parameters and amplitude information. The iterative noise reduction module performs iterative noise reduction on the communication signal based on the estimated column matrix parameters and amplitude information, so as to iteratively update the array matrix parameters and amplitude information in the communication signal. The loss function is set to optimize the iterative update process of array matrix parameters and amplitude information. When the number of iterations is reached or the loss function converges, the iteration ends and the communication signal is reconstructed based on the final array matrix parameters and amplitude information.

5. The method for array signal compression transmission and recovery in a multi-source scenario according to claim 4, characterized in that, The iterative recovery module performs iterative signal recovery on the communication signal based on a series of signal iterations, and the expression includes: in, , Both represent intermediate variables in the linear operations during the iteration process. Let Variance be the noise variance. These represent solving for the Hadama product and Hadama quotient, respectively. This represents the operation of calculating its divergence, where t represents the number of iterations, and the function... and Find the distribution of . The mean and variance, and Let these represent the mean and variance of the communication signal distribution, respectively. This indicates the operation of the noise reduction module.

6. The method for array signal compression transmission and recovery in a multi-source scenario according to claim 5, characterized in that, The process by which the denoiser module estimates the array parameters of the communication signal in each iteration includes: The array matrix parameters and amplitude information are updated and estimated using the following expression: in, express There are many corresponding variables in the middle; Indicates the amplitude information of the communication signal; Let a function be a sequence of elements with an expected value of 0 and a variance of 0. Gaussian noise; This represents a Gaussian distribution.

7. The method for array signal compression transmission and recovery in a multi-source scenario according to claim 6, characterized in that, The process of iteratively updating the array matrix parameters and amplitude information in the communication signal includes: The iterative noise reduction module iteratively updates the amplitude information based on the updated estimates of the array matrix parameters and amplitude information. The expression includes: in, , , , , , , , , , , , , , , , , These are all intermediate variables in the linear operations during the iteration process. These represent solving for the Hadama product and Hadama quotient, respectively. , These are respectively represented as the solution distributions. The expected value and variance, where Defined as , , They represent the solution distributions respectively. Expectation and variance To convert a vector into a diagonal matrix, To extract the diagonal elements of the matrix; When the number of iterations is reached or the loss function converges, the updated amplitude information estimate is obtained. .

8. The method for array signal compression transmission and recovery in a multi-source scenario according to claim 7, characterized in that, The iterative noise reduction module uses the Generalized Expectation Consistent Signal Recovery (GEC-SR) algorithm to iteratively update the amplitude information of the communication signal.

9. The method for array signal compression transmission and recovery in a multi-source scenario according to claim 7, characterized in that, Based on the updated amplitude information, the estimation is... Obtain the updated array matrix parameter information and reconstructed array matrix ; Based on the updated amplitude information, the estimation and reconstructed array matrix The final estimate of the communication signal is obtained. .

10. The method for array signal compression transmission and recovery in a multi-source scenario according to claim 5, characterized in that, The optimizer Adam is used to optimize the array matrix parameters and amplitude information during the iterative update process, and the learning rate is set to 0.0001.

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