Signal detection method, device and equipment for multi-input multi-output system

Abstract

The invention relates to the technical field of multiple-input-multiple-output systems, and provides a signal detection method, device and equipment for a multiple-input-multiple-output system, and the method comprises the steps: determining an iteration weight matrix based on an iteration reference variance, a system channel matrix and system noise power; performing Newton iteration on the iteration weight matrix to generate an iteration covariance matrix; determining an iteration distribution variance based on the iteration covariance matrix; determining an iteration distribution expectation based on the iteration covariance matrix, the iteration reference expectation, the system output signal, the channel matrix and the noise power; updating the iteration reference expectation and the iteration reference variance according to the iteration distribution expectation and the iteration distribution variance, and turning to the next iteration period; and when iteration reaches a preset period, determining the iteration distribution expectation as a target distribution expectation for estimating an input signal of the system. According to the technical scheme provided by one or more embodiments of the invention, the calculation complexity of the detection process can be reduced, and the accuracy of the signal detection result is ensured.

Description

Technical Field

[0001] The present disclosure relates to the technical field of multi-input multi-output systems, and in particular to a signal detection method, device and equipment for a multi-input multi-output system. Background Art

[0002] With the continuous progress of the times, mobile communication technology and numerous terminal devices have been widely used in people's daily lives, which has led to a rapid increase in mobile data traffic and has also brought huge challenges to wireless communication networks. Massive MIMO (Multiple Input Multiple Output) technology covers the characteristics of many large antennas and can quickly improve the system's capacity, power efficiency, and spectrum efficiency while maintaining the additional bandwidth and power. However, when using MIMO technology, the receiving device needs to accurately separate the client's signal and use detection technology to complete the recovery.

[0003] However, when MIMO technology is applied to mobile communication technology, it is usually necessary to deal with the defects caused by the increase in antenna size: due to the large size of the base station at the antenna end, the complexity of the spatial signal is high. Therefore, in the mobile communication network using MIMO technology, the base station needs to process a surge in the amount of data, and the spatial channel environment used to propagate the signal will also become very bad. In addition, some traditional signal detection technologies used in MIMO systems will also experience a decrease in SER (symbol error rate) performance in the network environment, and the complexity of the calculation will gradually increase. Summary of the invention

[0004] In view of this, one or more embodiments of the present disclosure provide a signal detection method, apparatus and device for a multi-input multi-output system, which can reduce the computational complexity of the detection process and ensure the accuracy of the signal detection results.

[0005] On the one hand, the present disclosure provides a signal detection method for a multi-input multi-output system. The method includes: for a target iteration period, determining an iteration weight matrix based on an iteration reference variance, a channel matrix of the multi-input multi-output system, and a noise power of the multi-input multi-output system; for the target iteration period, performing at least one Newton iteration on the iteration weight matrix to generate an iteration covariance matrix, wherein the iteration covariance matrix is ​​used to characterize the inverse matrix of the iteration weight matrix; for the target iteration period, determining an iteration distribution variance based on the iteration covariance matrix; for the target iteration period, determining an iteration distribution expectation based on the iteration covariance matrix, an iteration reference expectation, an output signal of the multi-input multi-output system, the channel matrix, and the noise power; for the target iteration period, updating the iteration reference expectation and the iteration reference variance according to the iteration distribution expectation and the iteration distribution variance, and transferring to the next iteration period of the target iteration period; when the target iteration period reaches a preset number of periods, determining the iteration distribution expectation as a target distribution expectation, wherein the target distribution expectation is used to estimate the input signal of the multi-input multi-output system.

[0006] On the other hand, the present disclosure also provides a signal detection device for a multi-input multi-output system, the device comprising: a weight determination unit, for determining an iteration weight matrix based on an iteration reference variance, a channel matrix of the multi-input multi-output system, and a noise power of the multi-input multi-output system for a target iteration period; a Newton iteration unit, for performing at least one Newton iteration on the iteration weight matrix for the target iteration period to generate an iteration covariance matrix, the iteration covariance matrix being used to characterize an inverse matrix of the iteration weight matrix; a variance determination unit, for determining an iteration distribution variance based on the iteration covariance matrix for the target iteration period; and an expected determination unit. A determination unit is used to determine the iteration distribution expectation for the target iteration period based on the iteration covariance matrix, the iteration benchmark expectation, the output signal of the multi-input multi-output system, the channel matrix, and the noise power; an updating unit is used to update the iteration benchmark expectation and the iteration benchmark variance for the target iteration period according to the iteration distribution expectation and the iteration distribution variance, and transfer to the next iteration period of the target iteration period; an output unit is used to determine the iteration distribution expectation as the target distribution expectation when the target iteration period reaches a preset number of periods, and the target distribution expectation is used to estimate the input signal of the multi-input multi-output system.

[0007] On the other hand, the present disclosure further provides an electronic device, which includes a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the signal detection method of the multi-input multi-output system is implemented.

[0008] On the other hand, the present disclosure further provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the signal detection method of the multi-input multi-output system is implemented.

[0009] The technical solution provided by one or more embodiments of the present disclosure can set the iteration benchmark variance and the iteration benchmark expectation on the basis of obtaining the prior information such as the channel matrix, the noise power, and the output signal, and determine the iteration distribution expectation and the iteration distribution variance by determining the iteration weight matrix and the iteration weight matrix. Through the iteration distribution expectation and the iteration distribution variance, the iteration benchmark expectation and the iteration benchmark variance can be updated to enter the next iteration cycle. In this way, after the number of iterations reaches the preset cycle, the iteration distribution expectation can be determined as the target distribution expectation. The target distribution expectation can reflect the approximation of the marginal posterior probability distribution of each input signal in the multi-input multi-output system, thereby realizing accurate detection of the signals of the multi-input multi-output system.

[0010] In addition, in the technical solution provided by one or more embodiments of the present disclosure, for the intermediate variable iterative weight matrix, a Newton iteration method is used to generate an iterative covariance matrix, so that the iterative covariance matrix can represent the approximate solution of the inverse matrix of the iterative weight matrix. In this way, the complex matrix inversion process can be reduced. In particular, in each iteration cycle, the inversion operation of the iterative weight matrix can be avoided once, which greatly reduces the computational complexity of the overall method. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The features and advantages of the embodiments of the present disclosure may be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present disclosure in any way. In the accompanying drawings:

[0012] Figure 1 A schematic diagram showing the steps of a signal detection method for a multi-input multi-output system in one embodiment of the present disclosure is shown;

[0013] Figure 2 A comparison diagram of signal detection effects using one Newton iteration and two Newton iterations in one embodiment of the present disclosure is shown;

[0014] Figure 3 Another comparison diagram of signal detection effects using one Newton iteration and using two Newton iterations in one embodiment of the present disclosure is shown;

[0015] Figure 4 A comparison diagram of signal detection effects using one Newton iteration and two Newton iterations in one embodiment of the present disclosure is shown;

[0016] Figure 5A comparison diagram of signal detection effects using one Newton iteration and two Newton iterations in one embodiment of the present disclosure is shown;

[0017] Figure 6 A schematic diagram of functional modules of a signal detection device of a multi-input multi-output system in one embodiment of the present disclosure is shown;

[0018] Figure 7 A schematic structural diagram of an electronic device in one embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0020] In the related art, EP (Expectation Propagation) is a message passing algorithm that can play an important role in MIMO communication systems. When used for MIMO detection, the EP algorithm has been proven to achieve Bayesian optimal performance. Although the expectation propagation detection algorithm has good detection performance, as the size of the high-dimensional matrix composed of antennas in the MIMO system continues to increase, the complexity of the algorithm also gradually increases, and the processing time will also become longer.

[0021] In the related art, the EP algorithm needs to invert a high-dimensional matrix to obtain the latest approximate posterior probability, and the inversion needs to be re-calculated each time an update calculation is performed. This algorithm operation consumes excessive computing resources when the matrix to be calculated is large.

[0022] In view of this, the signal detection method for a multi-input multi-output system provided by the present invention can use the Newton iteration method to avoid some inversion processes in the expected propagation detection algorithm, thereby reducing the complexity of the inversion of the high-dimensional matrix as much as possible and saving computing resources under the premise of small bit error rate performance loss.

[0023] See also Figure 1 , a signal detection method for a multi-input multi-output system provided in one embodiment of the present disclosure may include the following steps.

[0024] S1: for a target iteration period, determining an iteration weight matrix based on an iteration reference variance, a channel matrix of the MIMO system, and a noise power of the MIMO system.

[0025] In this embodiment, the transmitting end of the MIMO system can be composed of K antennas, the receiving end can be composed of N antennas, and the channel matrix can be expressed as H. The transmitting signal vector of the MIMO system can be x=[x1, x2..., xK], and the receiving signal vector can be y=[y1, y2..., yN]. Where xK represents the transmitting signal of the Kth transmitting antenna, and yN represents the receiving signal of the Nth receiving antenna. The system model of the MIMO system can be expressed as y=Hx+n, where H is the channel matrix and n represents additive Gaussian white noise with a mean of 0. The channel matrix H can describe the characteristics of the wireless channel from the transmitting end to the receiving end. All elements in the channel matrix H are independent of each other and do not interfere with each other.

[0026] In this embodiment, noise power is an important parameter of the MIMO system, which affects the signal-to-noise ratio (SNR) and channel capacity of the system. Noise power generally refers to the power of the noise received at the receiving end, which is closely related to the channel capacity and performance of the system. The noise power can be known in advance and can be expressed as σ n 2 .

[0027] In this embodiment, the iterative reference variance can be represented by Λ, which can be obtained by the a priori average symbol power E s Perform initial assignment.

[0028] In some implementations, a target Gram matrix may be determined based on the channel matrix and the noise power. An iterative diagonal matrix may be determined based on the iterative reference variance, wherein the main diagonal elements of the iterative diagonal matrix are composed of elements of the iterative reference variance. The iterative weight matrix may be determined by summing the target Gram matrix and the iterative diagonal matrix.

[0029] The Gram matrix can be calculated through the channel matrix H. In the MIMO system, if the ratio of the transmit and receive antennas remains unchanged and the number of transmit and receive antennas is increased to a large enough level, the main diagonal elements of the Gram matrix will have a significantly larger increase than other elements, and have a characteristic of continuous and significant amplitude dominance. Therefore, when analyzing the Gram matrix, the matrix composed of its diagonal elements can be used to calculate the channel estimation, which can greatly reduce the computational complexity while ensuring the accuracy of the channel performance analysis.

[0030] In a practical application example, the Gram matrix and the iterative weight matrix can be constructed by the following formula:

[0031]

[0032] in, is a Gram matrix, which is a Hermitian positive definite matrix; diag(Λ) is a matrix whose main diagonal elements are composed of the elements of the variance vector Λ. Since diag(Λ) is a diagonal matrix and a Hermitian positive definite matrix, the iterative weight matrix W is also a Hermitian positive definite matrix.

[0033] S2: for the target iteration period, perform at least one Newton iteration on the iteration weight matrix to generate an iteration covariance matrix, where the iteration covariance matrix is ​​used to characterize an approximate solution of an inverse matrix of the iteration weight matrix.

[0034] In this embodiment, the iteration weight matrix is ​​inverted to obtain the iteration covariance matrix. The iteration covariance matrix can be used to combine the iteration base expectation and the iteration base variance to calculate the iteration distribution expectation and the iteration distribution variance of the approximate posterior probability distribution.

[0035] In a practical application example, the iterative weight matrix W is inverted to obtain the formula of the iterative covariance matrix Σ, which can be Σ=W -1 The formula for calculating the iterative distribution expectation μ and the iterative distribution variance σ can be as follows:

[0036] σ=diag(Σ),

[0037] μ=Σ(b+γ),

[0038] in, y represents the output signal vector of the MIMO system, and γ represents the iterative benchmark expectation. In each iteration cycle, the above process of updating the iterative distribution expectation μ and the iterative distribution variance σ uses the inverse matrix of the iterative weight matrix, that is, the iterative covariance matrix Σ, and the inverse matrix needs to be recalculated after each iteration. Therefore, the computational complexity of the above detection algorithm for matrix inversion obeys O(K 3 ). As the matrix size of the MIMO system gradually increases, when performing signal detection, the more complex the matrix inversion is, the higher the requirements for hardware equipment will be, and the longer the delay will be, which will have a huge negative impact on the performance of the MIMO system. Therefore, the signal detection method proposed in the present disclosure can no longer directly invert the iterative weight matrix, but instead use the Newton iteration method to obtain an approximate solution of the inverse matrix of the iterative weight matrix. In this way, while ensuring that the performance difference is small, the iterative weight matrix can be approximately inverted to reduce the computational complexity.

[0039] In some embodiments, performing at least one Newton iteration on the iterative weight matrix to generate an iterative covariance matrix includes: extracting the diagonal elements of the iterative weight matrix to generate a weight diagonal matrix; performing at least one Newton iteration on the weight diagonal matrix according to a preset Newton iteration formula to generate the iterative covariance matrix.

[0040] In a practical application example, the preset Newton iteration formula includes:

[0041] X (k) =X (k-1) (2I-W (l) X (k-1) ),

[0042] Wherein, I is the unit matrix, X is the inverse matrix of the weight diagonal matrix, W is the iterative weight matrix, l is the period value of the target iteration period, and k is the number of iterations of the Newton iteration.

[0043] S3: For the target iteration period, determine the iteration distribution variance based on the iteration covariance matrix.

[0044] In this embodiment, the iterative distribution variance σ can be calculated from the iterative covariance matrix Σ by using the formula σ=diag(Σ).

[0045] S4: For the target iteration period, determine the iteration distribution expectation based on the iteration covariance matrix, the iteration reference expectation, the output signal of the multiple-input multiple-output system, the channel matrix, and the noise power.

[0046] In this embodiment, the matched filter vector b can be determined based on the output signal, the channel matrix and the noise power. For example, the matched filter vector b can be determined by the formula Determine, y represents the output signal vector of the MIMO system, represents the noise power of the MIMO system, and H represents the channel matrix of the MIMO system. Subsequently, the matched filter vector b and the iterative reference expectation γ may be summed. The summation result is multiplied by the iterative covariance matrix Σ to generate the iterative distribution expectation.

[0047] In a practical application example, the formula for determining the expected iterative distribution can be specifically:

[0048] μ=Σ(b+γ).

[0049] S5: For the target iteration cycle, update the iteration benchmark expectation and the iteration benchmark variance according to the iteration distribution expectation and the iteration distribution variance, and enter the next iteration cycle of the target iteration cycle.

[0050] In this embodiment, the iteration reference variance can be updated based on the iteration distribution variance and the iteration reference variance. The iteration reference expectation can be updated based on the iteration distribution variance, the iteration reference variance, the iteration distribution expectation, and the iteration reference expectation. The process of updating the iteration reference expectation and updating the iteration reference variance at least includes updating the cavity probability distribution and updating the refined probability distribution.

[0051] In a practical application example, assume that the channel matrix H, the received signal vector y, and the average symbol power E of the MIMO system s and noise power It is known that, first, the iterative benchmark expectation γ and iterative benchmark variance Λ corresponding to the transmitted signal with vector dimension i among the K transmitted signal vectors can be initialized:

[0052]

[0053] Where i = 1, 2, ..., K. Then, the Gram matrix A and the matched filter vector b can be calculated:

[0054]

[0055] Next, for the transmitted signal with vector dimension i, the iterative distribution expectation μ and iterative distribution variance σ of its approximate posterior probability can be initialized:

[0056] W (0) =A+diag(Λ (0) ),

[0057] Σ (0) =(W (0) ) -1 ,

[0058] σ (0) =diag(Σ (0) ),

[0059] μ (0) =Σ (0) (b (0) +γ (0) ).

[0060] After initialization is complete, the iteration process can begin.

[0061] For example, when the iteration benchmark expectation and iteration benchmark variance corresponding to the transmitted signal with vector dimension i are updated for l iterations,

[0062] The updated iteration of cavity probability distribution can be obtained by the following formula:

[0063]

[0064] The update iteration of the refined probability distribution can be achieved through the following formula:

[0065]

[0066] The update iteration of the iterative benchmark expectation and iterative benchmark variance can be obtained by the following formula:

[0067]

[0068] After the above operations are completed, the updated calculation results of the iterative benchmark expectation and the iterative benchmark variance can be used to calculate the approximate posterior probability to determine the iterative distribution expectation and the iterative distribution variance of the transmitted signal with a vector dimension of i.

[0069] First, we get the iterative weight matrix W for iteration l (l) :

[0070] W (l) =A+diag(Λ (l) ).

[0071] Next, for the matrix W (l) The diagonal matrix D is calculated, and the W to be calculated is calculated through the diag function. (l) The matrix outputs the matrix composed of diagonal elements, and the inverse D of the diagonal matrix can be obtained. -1 , which is the initialization estimate X for matrix approximate inversion using Newton's iterative algorithm (0) The reference formula for this process is as follows:

[0072] D=diag(diag(W (l) )),

[0073] X (0) =D -1 .

[0074] Assuming that k Newton iterations are used, the initial estimate X is required. (0) After k iterations, the iteration results are:

[0075] X (k) =X (k-1) (2I-W (l) X (k-1) ).

[0076] The matrix obtained by k Newton iterations can be determined as the matrix W (l) The approximate result of the inverse, that is, W -1(l) =X (k) On this basis, the iterative distribution expectation and iterative distribution variance of the approximate posterior probability distribution can be calculated. The reference formula for this process is as follows:

[0077] μ (l) =W -1(l) (b+γ (l) ),

[0078] σ(l) =diag(W -1(l) ).

[0079] The above process is a signal detection method for a multi-input multi-output system provided by an embodiment of the present disclosure, and is an algorithm flow of the lth iteration cycle for a transmission signal with a vector dimension of i. The signal detection method for a multi-input multi-output system provided by an embodiment of the present disclosure can continuously repeat the above process until the iteration cycle l reaches a preset number of cycles L. After completing the L iteration process, the expected vector obtained is the solution vector.

[0080] S6: When the target iteration period reaches a preset number of periods, the iteration distribution expectation is determined as a target distribution expectation, where the target distribution expectation is used to estimate an input signal of the MIMO system.

[0081] In this embodiment, for each transmitted signal of the MIMO system, based on the target distribution expectation of its approximate posterior distribution, the log likelihood ratio (LLR) can be calculated to detect and identify the transmitted signal. LLR is the key to binary symbol decision, which compares the posterior probability when the symbol is 1 and the symbol is 0. According to the value of LLR, a hard decision of the signal can be made, that is, the value of each symbol bit of the signal can be directly determined. According to the value of LLR, a soft decision of the signal can also be made to determine the relative probability that each bit of the signal is 0 or 1.

[0082] It should be additionally explained that, in the signal detection method for a multi-input multi-output system provided by an embodiment of the present disclosure, the step of "performing at least one Newton iteration on the iterative weight matrix to generate an iterative covariance matrix" can select different numbers of Newton iterations according to preset rules.

[0083] Specifically, when the ratio of the number of receiving antennas to the number of transmitting antennas of the multi-input multi-output system is within a first numerical interval and the total number of antennas of the multi-input multi-output system is less than a first reference value, if the signal-to-noise ratio of the multi-input multi-output system is less than a first threshold, two Newton iterations are performed on the iterative weight matrix; if the signal-to-noise ratio of the multi-input multi-output system is greater than the first threshold, one Newton iteration is performed on the iterative weight matrix.

[0084] When the ratio of the number of receiving antennas to the number of transmitting antennas of the MIMO system is within the first value interval and the total number of antennas of the MIMO system is greater than a second reference value, a Newton iteration is performed on the iterative weight matrix.

[0085] When the total number of antennas of the multiple-input multiple-output system is less than the first reference value, if the ratio of the number of receiving antennas to the number of transmitting antennas of the multiple-input multiple-output system is higher than the first numerical interval, a Newton iteration is performed on the iterative weight matrix; if the ratio of the number of receiving antennas to the number of transmitting antennas of the multiple-input multiple-output system is lower than the first numerical interval, two Newton iterations are performed on the iterative weight matrix.

[0086] For a practical example, see Figure 2 , the number of receiving antennas in the MIMO system can be 128, and the number of transmitting antennas can be 32. Figure 2 In the figure, the bit error rate curves with the signal-to-noise ratio (SNR) as the horizontal axis of the expected propagation detection algorithm using one Newton iteration and two Newton iterations are shown. The first Newton iteration and the second Newton iteration are respectively denoted as EP-NT1 and EP-NT2. Figure 2 It can be seen that the detection performance of EP-NT1 and EP-NT2 is very close to that of the EP detection algorithm, with little loss. When the signal-to-noise ratio is greater than 12dB, there is almost no difference in detection performance between the detection algorithm EP-NT1 using one Newton iteration and the detection algorithm EP-NT2 using two Newton iterations, and the simulation curves almost completely overlap. When the signal-to-noise ratio is less than 12dB, there is a small difference in detection performance between the detection algorithm EP-NT1 using one Newton iteration and the detection algorithm EP-NT2 using two Newton iterations. The performance loss of the detection algorithm EP-NT1 using one Newton iteration is about 0.4dB, and the performance loss of the detection algorithm EP-NT2 using two Newton iterations is almost less than 0.1dB. Therefore, it can be considered that when the signal-to-noise ratio is small, that is, when the signal-to-noise ratio is less than 12dB, the detection algorithm EP-NT2 with two Newton iterations can be used to approximate the inversion of the matrix W. At the same time, in order to make the calculation complexity as small as possible, when the signal-to-noise ratio is large, that is, when the signal-to-noise ratio is greater than 12dB, the detection algorithm EP-NT1 with one Newton iteration can be used to approximate the inversion of the matrix W.

[0087] For a practical example, see Figure 3 , the number of receiving antennas can be set to 256, and the number of transmitting antennas can be set to 64, which represents the simulation when the matrix scale of the MIMO system increases and the ratio of receiving antennas to transmitting antennas remains unchanged. Figure 3In the simulation, we still use one Newton iteration and two Newton iterations for comparative simulation. Through the curve analysis obtained by simulation, we know that when the signal-to-noise ratio is greater than 12dB, the difference in detection performance between the detection algorithm EP-NT1 using one Newton iteration and the detection algorithm EP-NT2 using two Newton iterations can still be ignored. When the signal-to-noise ratio is less than 12dB, the performance loss of the detection algorithm EP-NT1 using one Newton iteration is less than that of Figure 1 Therefore, it can be considered that when the ratio of the transmit and receive antennas remains unchanged and the matrix size gradually increases, the performance loss of the detection algorithm EP-NT1 using one Newton iteration will gradually decrease. That is, when the matrix size is very large and the signal-to-noise ratio is not small, in order to reduce the complexity of the calculation, the detection algorithm EP-NT1 using one Newton iteration can complete the channel estimation detection.

[0088] For a practical example, see Figure 4 , the number of transmitting antennas can be kept at 32, and the number of receiving antennas can be set to 256, indicating that the number of transmitting antennas of the MIMO system remains unchanged, and the detection performance simulation is performed when the number of receiving antennas is increased, that is, the ratio of transmitting and receiving antennas is increased. Figure 4 In the simulation, we still use one Newton iteration and two Newton iterations for comparative simulation. Through the curve analysis obtained by simulation, we know that as the signal-to-noise ratio gradually increases, the simulation curves of the detection algorithm EP-NT1 using one Newton iteration and the detection algorithm EP-NT2 using two Newton iterations gradually overlap, and when the signal-to-noise ratio is small, the performance loss of the detection algorithm EP-NT1 using one Newton iteration is smaller than that of Figure 1 The performance loss is still getting smaller. Therefore, it can be considered that when the ratio of the transmit and receive antennas becomes larger, in order to reduce the complexity of the calculation, the detection algorithm EP-NT1 with one Newton iteration can be used to achieve channel estimation of the channel matrix. At the same time, if the ratio of the transmit and receive antennas is small, the performance loss of the detection algorithm EP-NT1 with one Newton iteration is large, that is, EP-NT1 is not suitable for channel estimation with a small ratio of the transmit and receive antennas. The detection algorithm EP-NT2 with two Newton iterations can be used to complete the channel estimation of the channel matrix.

[0089] For a practical example, see Figure 5 , which can represent the simulation curve of the expected propagation detection algorithm using Newton mixing iteration. Through simulation analysis, it can be seen that when the signal-to-noise ratio is small, the detection algorithm EP-NT2 with two Newton iterations can be used to approximate the inversion of the matrix W. At the same time, in order to make the calculation complexity as small as possible, when the signal-to-noise ratio is large, the detection algorithm EP-NT1 with one Newton iteration can be used to approximate the inversion of the matrix W. Figure 5In the above example, the number of receiving antennas is still set to 128, the number of transmitting antennas is set to 32, and the signal-to-noise ratio is 12 dB as the demarcation point. When the signal-to-noise ratio is less than 12 dB, the detection algorithm EP-NT2 with two Newton iterations is used to approximate the inversion of the matrix W. When the signal-to-noise ratio is greater than 12 dB, the detection algorithm EP-NT1 with one Newton iteration can be used to approximate the inversion of the matrix W, forming the following: Figure 5 The simulation diagram of the channel detection algorithm is shown in FIG. Through this Newton mixing iterative expected propagation detection algorithm, it is possible to achieve a significant reduction in computational complexity while ensuring that the performance loss is almost zero.

[0090] The technical solution provided by one or more embodiments of the present disclosure can set the iteration benchmark variance and iteration benchmark expectation of the transmitted signal on the basis of knowing the prior information such as the channel matrix, noise power, and output signal, and determine the iteration distribution expectation and iteration distribution variance of the transmitted signal by determining the iteration weight matrix and the iteration weight matrix. Through the iteration distribution expectation and iteration distribution variance, the iteration benchmark expectation and iteration benchmark variance can be updated to enter the next iteration cycle. In this way, after the number of iterations reaches a preset cycle, the iteration distribution expectation can be determined as the target distribution expectation. The target distribution expectation can reflect the approximation of the marginal posterior probability distribution of each transmitted signal in the multi-input multi-output system, thereby realizing accurate detection of the signals of the multi-input multi-output system.

[0091] In addition, in the technical solution provided by one or more embodiments of the present disclosure, for the intermediate variable iterative weight matrix, a Newton iteration method is used to generate an iterative covariance matrix, so that the iterative covariance matrix can represent the approximate solution of the inverse matrix of the iterative weight matrix. In this way, the complex matrix inversion process can be reduced. In particular, in each iteration cycle, the inversion operation of the iterative weight matrix can be avoided once, which greatly reduces the computational complexity of the overall method.

[0092] In the technical solutions provided by some embodiments of the present disclosure, when the number of antennas in the MIMO system is small, the ratio of transmit-receive antennas is small, and the signal-to-noise ratio is small, the performance of the expected propagation detection algorithm using the second Newton iteration is greatly improved compared with the expected propagation detection algorithm using the first Newton iteration. When the number of antennas in the MIMO system is large, the ratio of transmit-receive antennas is large, and the signal-to-noise ratio is large, the performance of the expected propagation detection algorithm using the first Newton iteration is almost the same as that using the second Newton iteration, and the algorithm complexity is relatively small.

[0093] When the signal-to-noise ratio is small, the detection algorithm EP-NT2 with two Newton iterations can be used to approximate the inversion of the matrix, which almost completely overlaps with the curve of the expected propagation EP detection algorithm. At the same time, in order to minimize the complexity of the calculation, when the signal-to-noise ratio is large, the detection algorithm EP-NT1 with one Newton iteration can be used to approximate the inversion of the matrix, which can still almost completely overlap with the curve of the expected propagation EP detection algorithm. Therefore, using this Newton hybrid iterative expected propagation detection algorithm, it is possible to achieve a guarantee that the performance loss is almost zero while greatly reducing the computational complexity.

[0094] See also Figure 6 The present disclosure also provides a signal detection device for a multi-input multi-output system, the device comprising:

[0095] The weight determination unit 100 is used to determine an iteration weight matrix based on an iteration reference variance, a channel matrix of the MIMO system, and a noise power of the MIMO system for a target iteration period;

[0096] A Newton iteration unit 200, configured to perform at least one Newton iteration on the iteration weight matrix for the target iteration period to generate an iteration covariance matrix, wherein the iteration covariance matrix is ​​used to characterize an approximate solution of an inverse matrix of the iteration weight matrix;

[0097] A variance determination unit 300, configured to determine an iteration distribution variance based on the iteration covariance matrix for the target iteration period;

[0098] An expectation determination unit 400 is used to determine an iteration distribution expectation for the target iteration period based on the iteration covariance matrix, the iteration reference expectation, the output signal of the multiple-input multiple-output system, the channel matrix, and the noise power;

[0099] An updating unit 500, configured to update the iteration reference expectation and the iteration reference variance according to the iteration distribution expectation and the iteration distribution variance for the target iteration cycle, and to transfer to the next iteration cycle of the target iteration cycle;

[0100] The output unit 600 is used to determine the iteration distribution expectation as a target distribution expectation when the target iteration period reaches a preset number of periods, and the target distribution expectation is used to estimate the input signal of the multi-input multi-output system.

[0101] In one embodiment, the weight determination unit 100 is specifically used to determine a target Gram matrix based on the channel matrix and the noise power; determine an iterative diagonal matrix based on the iterative reference variance, wherein the main diagonal elements of the iterative diagonal matrix are composed of elements of the iterative reference variance; and sum the target Gram matrix and the iterative diagonal matrix to determine the iterative weight matrix.

[0102] In one embodiment, the Newton iteration unit 200 is specifically used to extract the diagonal elements of the iterative weight matrix to generate a weight diagonal matrix; according to a preset Newton iteration formula, perform at least one Newton iteration on the weight diagonal matrix to generate the iterative covariance matrix.

[0103] In one embodiment, the preset Newton iteration formula includes:

[0104] X (k) =X (k-1) (2I-W (l) X (k-1) )

[0105] Wherein, I is the unit matrix, X is the inverse matrix of the weight diagonal matrix, W is the iterative weight matrix, l is the period value of the target iteration period, and k is the number of iterations of the Newton iteration.

[0106] In one embodiment, the expectation determination unit 400 is specifically used to determine a matched filter vector based on the output signal, the channel matrix and the noise power; sum the matched filter vector and the iterative benchmark expectation; multiply the summation result with the iterative covariance matrix to generate the iterative distribution expectation.

[0107] In one embodiment, the updating unit 500 is specifically used to update the iteration reference variance based on the iteration distribution variance and the iteration reference variance; and to update the iteration reference expectation based on the iteration distribution variance, the iteration reference variance, the iteration distribution expectation, and the iteration reference expectation; wherein the process of updating the iteration reference expectation and updating the iteration reference variance at least includes updating the cavity probability distribution and updating the refined probability distribution.

[0108] In one embodiment, performing at least one Newton iteration on the iterative weight matrix includes: when the ratio of the number of receiving antennas to the number of transmitting antennas of the MIMO system is within a first numerical interval and the total number of antennas of the MIMO system is less than a first reference value, if the signal-to-noise ratio of the MIMO system is less than a first threshold, performing two Newton iterations on the iterative weight matrix; if the signal-to-noise ratio of the MIMO system is greater than the first threshold, performing one Newton iteration on the iterative weight matrix; when the ratio of the number of receiving antennas to the number of transmitting antennas of the MIMO system is within the first numerical interval and the total number of antennas of the MIMO system is greater than a second reference value, performing one Newton iteration on the iterative weight matrix; when the total number of antennas of the MIMO system is less than the first reference value, if the ratio of the number of receiving antennas to the number of transmitting antennas of the MIMO system is higher than the first numerical interval, performing one Newton iteration on the iterative weight matrix; if the ratio of the number of receiving antennas to the number of transmitting antennas of the MIMO system is lower than the first numerical interval, performing two Newton iterations on the iterative weight matrix.

[0109] Each unit described in the above embodiments may be implemented by a computer chip or a product having a certain function. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0110] For the convenience of description, the above devices are described in terms of functions and are described separately in various units. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0111] See also Figure 7 The present disclosure also provides an electronic device, which includes a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the above-mentioned method for determining the degradation of a landing page is implemented.

[0112] The present disclosure also provides a computer-readable storage medium, which is used to store a computer program. When the computer program is executed by a processor, the above-mentioned method for determining degradation of a landing page is implemented.

[0113] The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0114] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory, that is, implementing the method in the above method embodiment.

[0115] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0116] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0117] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0118] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0119] Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A signal detection method for a multiple-input multiple-output system, characterized in that: The method comprises: For a target iteration period, determining an iteration weight matrix based on an iteration reference variance, a channel matrix of the MIMO system, and a noise power of the MIMO system; For the target iteration period, performing at least one Newton iteration on the iteration weight matrix to generate an iteration covariance matrix, where the iteration covariance matrix is ​​used to characterize an approximate solution of an inverse matrix of the iteration weight matrix; For the target iteration period, determining an iteration distribution variance based on the iteration covariance matrix; For the target iteration period, determining an iteration distribution expectation based on the iteration covariance matrix, the iteration reference expectation, the output signal of the multiple-input multiple-output system, the channel matrix, and the noise power; For the target iteration cycle, according to the iteration distribution expectation and the iteration distribution variance, the iteration benchmark expectation and the iteration benchmark variance are updated, and the next iteration cycle of the target iteration cycle is entered; When the target iteration cycle reaches a preset number of cycles, the iteration distribution expectation is determined as a target distribution expectation, and the target distribution expectation is used to estimate the input signal of the multi-input multi-output system.

2. The method according to claim 1, characterized in that The step of determining an iterative weight matrix based on an iterative reference variance, a channel matrix of the MIMO system, and a noise power of the MIMO system comprises: Determining a target Gram matrix based on the channel matrix and the noise power; Based on the iteration reference variance, determining an iteration diagonal matrix, wherein the main diagonal elements of the iteration diagonal matrix are composed of elements of the iteration reference variance; The target Gram matrix and the iterative diagonal matrix are summed to determine the iterative weight matrix.

3. The method according to claim 1, characterized in that The step of performing at least one Newton iteration on the iterative weight matrix to generate an iterative covariance matrix comprises: Extracting diagonal elements of the iterative weight matrix to generate a weight diagonal matrix; According to a preset Newton iteration formula, at least one Newton iteration is performed on the weight diagonal matrix to generate the iterative covariance matrix.

4. The method according to claim 3, characterized in that The preset Newton iteration formula includes: X (k) =X (k-1) (2I-W (l) X (k-1) ) Wherein, I is the unit matrix, X is the inverse matrix of the weight diagonal matrix, W is the iterative weight matrix, l is the period value of the target iteration period, and k is the number of iterations of the Newton iteration.

5. The method according to claim 1, characterized in that The step of determining the iterative distribution expectation based on the iterative covariance matrix, the iterative reference expectation, the output signal of the multiple-input multiple-output system, the channel matrix, and the noise power comprises: determining a matched filter vector based on the output signal, the channel matrix and the noise power; Summing the matched filter vector and the iterative reference expectation; The summation result is multiplied by the iterative covariance matrix to generate the iterative distribution expectation.

6. The method according to claim 1, characterized in that The updating of the iteration reference expectation and the iteration reference variance according to the iteration distribution expectation and the iteration distribution variance comprises: Based on the iterative distribution variance and the iterative reference variance, updating the iterative reference variance; updating the iteration benchmark expectation based on the iteration distribution variance, the iteration benchmark variance, the iteration distribution expectation, and the iteration benchmark expectation; The process of updating the iterative benchmark expectation and updating the iterative benchmark variance at least includes updating the cavity probability distribution and updating the refined probability distribution.

7. The method according to any one of claims 1 to 6, characterized in that: The performing at least one Newton iteration on the iterative weight matrix comprises: When the ratio of the number of receiving antennas to the number of transmitting antennas of the MIMO system is within a first value interval and the total number of antennas of the MIMO system is less than a first reference value, if the signal-to-noise ratio of the MIMO system is less than a first threshold, performing two Newton iterations on the iterative weight matrix; if the signal-to-noise ratio of the MIMO system is greater than the first threshold, performing one Newton iteration on the iterative weight matrix; When the ratio of the number of receiving antennas to the number of transmitting antennas of the MIMO system is within the first value interval, and the total number of antennas of the MIMO system is greater than a second reference value, performing a Newton iteration on the iterative weight matrix; When the total number of antennas of the multiple-input multiple-output system is less than the first reference value, if the ratio of the number of receiving antennas to the number of transmitting antennas of the multiple-input multiple-output system is higher than the first numerical interval, a Newton iteration is performed on the iterative weight matrix; if the ratio of the number of receiving antennas to the number of transmitting antennas of the multiple-input multiple-output system is lower than the first numerical interval, two Newton iterations are performed on the iterative weight matrix.

8. A signal detection device for a multiple-input multiple-output system, characterized in that: The device comprises: A weight determination unit, configured to determine an iteration weight matrix for a target iteration period based on an iteration reference variance, a channel matrix of the MIMO system, and a noise power of the MIMO system; A Newton iteration unit, used for performing at least one Newton iteration on the iteration weight matrix for the target iteration period to generate an iteration covariance matrix, wherein the iteration covariance matrix is ​​used to characterize an approximate solution of an inverse matrix of the iteration weight matrix; A variance determination unit, configured to determine, for the target iteration period, an iteration distribution variance based on the iteration covariance matrix; an expectation determination unit, configured to determine, for the target iteration period, an iteration distribution expectation based on the iteration covariance matrix, the iteration reference expectation, the output signal of the multiple-input multiple-output system, the channel matrix, and the noise power; An updating unit, configured to update the iteration reference expectation and the iteration reference variance according to the iteration distribution expectation and the iteration distribution variance for the target iteration cycle, and to transfer to the next iteration cycle of the target iteration cycle; An output unit is used to determine the iteration distribution expectation as a target distribution expectation when the target iteration cycle reaches a preset number of cycles, and the target distribution expectation is used to estimate the input signal of the multi-input multi-output system.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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