A signal detection method, device and equipment of a multiple-input multiple-output system

Abstract

This disclosure relates to the field of multiple-input multiple-output (MIMO) systems, and proposes a signal detection method, apparatus, and device for MIMO systems. The method includes: determining an iterative weight matrix based on an iterative reference variance, a system channel matrix, and system noise power; performing Newton iteration on the iterative weight matrix to generate an iterative covariance matrix; determining the iterative distribution variance based on the iterative covariance matrix; determining the iterative distribution expectation based on the iterative covariance matrix, an iterative reference expectation, the system output signal, the channel matrix, and noise power; updating the iterative reference expectation and iterative reference variance according to the iterative distribution expectation and iterative distribution variance, and proceeding to the next iteration cycle; when the iteration reaches a preset cycle, determining the iterative distribution expectation as the target distribution expectation, which is used to estimate the system's input signal. The technical solutions provided by one or more embodiments of this disclosure can reduce the computational complexity of the detection process and ensure the accuracy of the signal detection results.

Description

Technical Field

[0001] This disclosure relates to the field of multiple-input multiple-output (MIMO) systems, and specifically to a signal detection method, apparatus, and device for a MIMO system. Background Technology

[0002] With the continuous advancement of technology, mobile communication technology and numerous terminal devices have been widely adopted in people's daily lives. This has led to a rapid increase in mobile data traffic and has also posed significant challenges to wireless communication networks. Massive MIMO (Multiple Input Multiple Output) technology incorporates many characteristics of large-scale antennas, enabling rapid improvements in system capacity, power efficiency, and spectral efficiency while maintaining constant additional bandwidth and power. However, when using MIMO technology, the receiving device needs to accurately separate the client's signal and employ detection techniques to recover it.

[0003] However, when applying MIMO technology to mobile communication, it is usually necessary to address the drawbacks brought about by the increased antenna size: due to the larger scale of the base station at the antenna end, the complexity of the spatial signal is high. Therefore, in mobile communication networks using MIMO technology, the base station needs to process a surge in data, and the spatial channel environment used for signal propagation becomes extremely harsh. In addition, some traditional signal detection techniques used in MIMO systems also experience a decline in SER (symbol error rate) performance in the network environment, and the computational complexity gradually increases. Summary of the Invention

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

[0005] This disclosure provides a signal detection method for a multiple-input multiple-output (MIMO) system. The method includes: determining an iterative weight matrix based on an iterative reference variance, the channel matrix of the MIMO, and the noise power of the MIMO for a target iteration period; performing at least one Newton iteration on the iterative weight matrix for the target iteration period to generate an iterative covariance matrix, the iterative covariance matrix being used to characterize the inverse of the iterative weight matrix; determining an iterative distribution variance based on the iterative covariance matrix for the target iteration period; determining an iterative distribution expectation based on the iterative covariance matrix, an iterative reference expectation, the output signal of the MIMO, the channel matrix, and the noise power for the target iteration period; updating the iterative reference expectation and the iterative reference variance according to the iterative distribution expectation and the iterative distribution variance for the target iteration period, and proceeding to the next iteration period of the target iteration period; when the target iteration period reaches a preset number of periods, determining the iterative distribution expectation as the target distribution expectation, the target distribution expectation being used to estimate the input signal of the MIMO.

[0006] This disclosure also provides a signal detection apparatus for a multiple-input multiple-output (MIMO) system, the apparatus comprising: a weight determination unit, configured to determine an iterative weight matrix based on an iterative reference variance, the channel matrix of the MIMO, and the noise power of the MIMO for a target iteration period; a Newton iteration unit, configured to perform at least one Newton iteration on the iterative weight matrix for the target iteration period to generate an iterative covariance matrix, the iterative covariance matrix being used to characterize the inverse of the iterative weight matrix; and a variance determination unit, configured to determine the iterative distribution variance based on the iterative covariance matrix for the target iteration period; and an expected variance determination unit. The system comprises: a determination unit, configured to determine the iterative distribution expectation based on the iterative covariance matrix, the iterative baseline expectation, the output signal of the multiple-input multiple-output system, the channel matrix, and the noise power for the target iterative period; an update unit, configured to update the iterative baseline expectation and the iterative baseline variance according to the iterative distribution expectation and the iterative distribution variance for the target iterative period, and then proceed to the next iterative period of the target iterative period; and an output unit, configured to determine the iterative distribution expectation as the target distribution expectation when the target iterative period reaches a preset number of periods, wherein the target distribution expectation is used to estimate the input signal of the multiple-input multiple-output system.

[0007] This disclosure also provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program, which, when executed by the processor, implements the signal detection method of the multiple input multiple output system described above.

[0008] This disclosure also provides a computer-readable storage medium for storing a computer program that, when executed by a processor, implements the signal detection method of the multiple-input multiple-output system described above.

[0009] The technical solutions provided by one or more embodiments of this disclosure can, based on prior information such as the channel matrix, noise power, and output signal, set an iterative baseline variance and an iterative baseline expectation, and determine the iterative distribution expectation and iterative distribution variance by determining the iterative weight matrix and the iterative distribution variance. The iterative baseline expectation and iterative distribution variance can then be updated using these, entering the next iteration cycle. Thus, after a preset number of iterations, the iterative distribution expectation can be determined as the target distribution expectation. The target distribution expectation can reflect an approximation of the marginal posterior probability distribution of each input signal in a multi-input multi-output system, thereby achieving accurate detection of signals in the multi-input multi-output system.

[0010] Furthermore, in one or more embodiments of the present disclosure, the intermediate variable iterative weight matrix is ​​generated using Newton's iteration method to produce an iterative covariance matrix, such that the iterative covariance matrix can represent an approximate solution of the inverse matrix of the iterative weight matrix. This reduces the complexity of matrix inversion. In particular, in each iteration cycle, one inversion operation of the iterative weight matrix can be avoided, significantly reducing the overall computational complexity of the method. Attached Figure Description

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

[0012] Figure 1 A schematic diagram illustrating the steps of a signal detection method for a multiple-input multiple-output system according to one embodiment of this disclosure is shown.

[0013] Figure 2 A comparison chart of signal detection performance using one Newton iteration and two Newton iterations is shown in one embodiment of this disclosure;

[0014] Figure 3 A comparison graph showing the signal detection performance using one Newton iteration and two Newton iterations in one embodiment of this disclosure is shown.

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

[0016] Figure 5This invention provides a comparison chart of signal detection performance using one Newton iteration and two Newton iterations in one embodiment of the present disclosure.

[0017] Figure 6 A schematic diagram of the functional modules of a signal detection device for a multiple-input multiple-output system according to one embodiment of the present disclosure is shown.

[0018] Figure 7 A schematic diagram of the structure of an electronic device according to one embodiment of the present disclosure is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0020] In related technologies, EP (Expectation Propagation) is a message-passing algorithm that plays 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 of antennas in MIMO systems continues to increase, the complexity of the algorithm also gradually increases, and the processing time becomes longer.

[0021] In related technologies, the EP algorithm requires inverting a high-dimensional matrix to obtain the latest approximate posterior probability, and this inversion needs to be performed again each time an update is performed. This algorithmic operation consumes excessive computational resources when the matrix to be calculated is large.

[0022] In view of this, the signal detection method for multiple input multiple output systems provided in this disclosure can avoid some of the inversion processes in the expectation propagation detection algorithm by using Newton's iteration method, thereby minimizing the complexity of inverting high-dimensional matrices and saving computational resources while minimizing the performance loss in bit error rate.

[0023] Please see Figure 1 The present disclosure provides a signal detection method for a multiple-input multiple-output system according to one embodiment, which may include the following steps.

[0024] S1: For the target iteration period, determine the iteration weight matrix based on the iteration baseline variance, the channel matrix of the multiple input multiple output system, and the noise power of the multiple input multiple output system.

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

[0026] In this embodiment, noise power is a crucial parameter of the MIMO system, affecting its signal-to-noise ratio (SNR) and channel capacity. Noise power typically refers to the power of noise received at the receiver, and it is closely related to the system's channel capacity and performance. Noise power can be known in advance and can be expressed as σ. n 2 .

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

[0028] In some implementations, the target Gram matrix can be determined based on the channel matrix and the noise power. An iterative diagonal matrix is ​​determined based on the iterative baseline variance, the main diagonal elements of which are composed of the elements of the iterative baseline variance. The iterative weight matrix is ​​determined by summing the target Gram matrix and the iterative diagonal matrix.

[0029] The Gram matrix can be calculated from the channel matrix H. In MIMO systems, if the ratio of transmit to receive antennas remains constant, increasing the number of transmit and receive antennas to a sufficiently large extent results in a significant increase in the magnitude of the main diagonal elements of the Gram matrix compared to other elements, exhibiting a continuously significant magnitude dominance. Therefore, when analyzing the Gram matrix, the matrix composed of its diagonal elements can be used to calculate channel estimation. This greatly reduces computational complexity while ensuring the accuracy of channel performance analysis.

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

[0031]

[0032] in, It is a Gram matrix, belonging to the Hermitian positive definite matrix; diag(Λ) is a matrix whose main diagonal elements are composed of the elements of the variance vector Λ. Because diag(Λ) is a diagonal matrix and also belongs to the 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 iterative weight matrix to generate an iterative covariance matrix, which is used to characterize an approximate solution of the inverse matrix of the iterative weight matrix.

[0034] In this embodiment, the iterative covariance matrix is ​​obtained by inverting the iterative weight matrix. Using the iterative covariance matrix, the iterative distribution expectation and iterative distribution variance of the approximate posterior probability distribution can be calculated by combining the iterative baseline expectation and iterative baseline variance.

[0035] In a practical application example, the formula for inverting the iterative weight matrix W to obtain the iterative covariance matrix Σ can be expressed as Σ = W. -1 The formulas for calculating the expected value μ and the variance σ of the iterative distribution can be as follows:

[0036] σ=diag(Σ),

[0037] μ=Σ(b+γ),

[0038] in, Let y represent the output signal vector of the MIMO system, and γ represent the iterative baseline expectation. Within each iteration cycle, the process of updating the iterative distribution expectation μ and the iterative distribution variance σ uses the inverse matrix of the iterative weight matrix, i.e., the iterative covariance matrix Σ, and this inverse matrix needs to be recalculated after each iteration. Therefore, the computational complexity of matrix inversion in the above detection algorithm follows O(K) time complexity. 3 As the matrix size of MIMO systems gradually increases, the complexity of matrix inversion during signal detection increases, placing higher demands on hardware and increasing latency, which negatively impacts the performance of MIMO systems. Therefore, the signal detection method proposed in this disclosure avoids directly inverting the iterative weight matrix. Instead, it uses Newton's iteration method to obtain an approximate solution for the inverse of the iterative weight matrix. This allows for approximate inversion of the iterative weight matrix while maintaining relatively small performance differences, thus reducing computational complexity.

[0039] In some implementations, performing at least one Newton iteration on the iterative weight matrix to generate the iterative covariance matrix includes: extracting the diagonal elements of the iterative weight matrix to generate a weight diagonal matrix; and 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] Where I is the identity matrix, X is the inverse of the weight diagonal matrix, W is the iterative weight matrix, l is the period value of the target iterative 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 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 baseline 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, it can be determined using the formula... Let y represent the output signal vector of the MIMO system. Let H represent the noise power of the MIMO system, and let H represent the channel matrix of the MIMO system. Then, the matched filter vector b and the iterative baseline expectation γ can be summed. Multiplying the summation result by the iterative covariance matrix Σ generates the iterative distribution expectation.

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

[0048] μ=Σ(b+γ).

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

[0050] In this embodiment, the iterative baseline variance can be updated based on the iterative distribution variance and the iterative baseline variance. The iterative baseline expectation can be updated based on the iterative distribution variance, the iterative baseline variance, the iterative distribution expectation, and the iterative baseline expectation. The processes of updating the iterative baseline expectation and updating the iterative baseline variance include at least updating the cavity probability distribution and the refinement probability distribution.

[0051] In a practical application example, assume the MIMO system has a channel matrix H, a received signal vector y, and an average symbol power E. s and noise power It is known that, firstly, we can initialize the iterative baseline expectation γ and iterative baseline variance Λ corresponding to the transmitted signal with vector dimension i among the K transmitted signal vectors:

[0052]

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

[0054]

[0055] Next, for a transmitted signal with vector dimension i, the expected value μ and variance σ of its approximate posterior probability iterative distribution 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] Once initialization is complete, the iteration process can begin.

[0061] For example, when updating the iterative baseline expectation and iterative baseline variance of a transmitted signal with vector dimension i after l iterations...

[0062] The cavity probability distribution can be updated and iterated using the following formula:

[0063]

[0064] The refining probability distribution can be updated and iterated using the following formula:

[0065]

[0066] The iterative updates of the benchmark expectation and benchmark variance can be achieved using the following formulas:

[0067]

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

[0069] First, we obtain the iterative weight matrix W after iteration l. (l) :

[0070] W (l) =A+diag(Λ) (l) ).

[0071] Next, for matrix W (l) The diagonal matrix D is calculated, and the diag function is used to calculate the required W. (l) By taking the matrix from its diagonal elements and outputting the matrix formed by the diagonal elements, we can obtain the inverse D of the diagonal matrix. -1 That is, the initial estimate X obtained by approximate matrix 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 we use the Newton-Raphson algorithm for k iterations, we need to initialize the estimated value X. (0) After k iterations, the result is:

[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 inversion, i.e., W -1(l) =X (k) Based on this, the expected value and variance of the iterative distribution of the approximate posterior probability distribution can be calculated. The reference formulas for this process are as follows:

[0077] μ (l) =W -1(l) (b+γ (l) ),

[0078] σ(l) =diag(W -1(l) ).

[0079] The above process describes a signal detection method for a multiple-input multiple-output (MIMO) system provided in one embodiment of this disclosure, specifically the algorithm flow for the l-th iteration period for a transmitted signal with vector dimension i. This signal detection method for an MIMO system can continuously repeat the above process until the iteration period l reaches a preset number of periods L. After completing L iterations, the desired vector obtained is the solution vector.

[0080] S6: When the target iteration period reaches a preset number of periods, the expected iteration distribution is determined as the expected target distribution, which is used to estimate the input signal of the multiple input multiple output system.

[0081] In this embodiment, for each transmitted signal in the MIMO system, the log-likelihood ratio (LLR) can be calculated based on the expected target distribution of its approximate posterior distribution, thereby enabling the detection and identification of the transmitted signal. The LLR is crucial for binary symbol decision-making; it compares the posterior probabilities when the symbol is 1 and when it is 0. Based on the LLR value, hard decision-making can be performed, directly determining the value of each symbol bit. Alternatively, soft decision-making can be performed, determining the relative probability that each bit of the signal is 0 or 1.

[0082] It should be noted that, in one embodiment of the signal detection method for a multi-input multi-output system provided by this disclosure, the step of "performing at least one Newton iteration on the iterative weight matrix to generate an iterative covariance matrix" can be performed by selecting 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 multiple-input multiple-output system is in a first numerical range, and the total number of antennas of the multiple-input multiple-output system is less than a first reference value, if the signal-to-noise ratio of the multiple-input multiple-output system is less than a first threshold, then two Newton iterations are performed on the iterative weight matrix; if the signal-to-noise ratio of the multiple-input multiple-output system is greater than the first threshold, then 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 multiple-input multiple-output system is within the first numerical range, and the total number of antennas of the multiple-input multiple-output system is greater than the second reference value, a Newton iteration is performed on the iterative weight matrix.

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

[0086] In a practical application example, please refer to Figure 2 A MIMO system can have 128 receiving antennas and 32 transmitting antennas. Figure 2 In the diagram, the bit error rate curves, with signal-to-noise ratio (SNR) as the abscissa, are shown for the expectation propagation detection algorithm using one Newton iteration and two Newton iterations. The first and second Newton iteration methods are denoted as EP-NT1 and EP-NT2, respectively. From... 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 minimal loss. Furthermore, when the signal-to-noise ratio (SNR) is greater than 12 dB, the detection performance of EP-NT1 (using one Newton iteration) and EP-NT2 (using two Newton iterations) is almost identical, with simulation curves nearly overlapping. When the SNR is less than 12 dB, there is a slight difference in detection performance between EP-NT1 (using one Newton iteration) and EP-NT2 (using two Newton iterations). The performance loss of EP-NT1 (using one Newton iteration) is approximately 0.4 dB, while the performance loss of EP-NT2 (using two Newton iterations) is almost less than 0.1 dB. Therefore, it can be assumed that when the signal-to-noise ratio is small, i.e. 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 matrix W. Meanwhile, in order to minimize the computational complexity, when the signal-to-noise ratio is large, i.e. 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 matrix W.

[0087] In a practical application example, please refer to Figure 3 The number of receiving antennas can be set to 256 and the number of transmitting antennas to 64, representing a simulation of the MIMO system with an increased matrix size and a constant ratio of receiving to transmitting antennas. Figure 3In the simulation, we still used one Newton iteration and two Newton iterations for comparison. Analysis of the simulation curves shows that when the signal-to-noise ratio (SNR) is greater than 12 dB, the performance difference between the detection algorithm EP-NT1 using one Newton iteration and the detection algorithm EP-NT2 using two Newton iterations is negligible. However, when the SNR is less than 12 dB, the performance loss of the detection algorithm EP-NT1 using one Newton iteration is relatively small compared to... Figure 1 The performance loss is relatively small. Therefore, it can be assumed that when the transmit / receive antenna ratio remains constant and the matrix size gradually increases, the performance loss of the detection algorithm EP-NT1 with 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 computational complexity, the channel estimation detection can be completed by using the detection algorithm EP-NT1 with one Newton iteration.

[0088] In a practical application example, please refer to Figure 4 The number of transmitting antennas can remain at 32, while the number of receiving antennas can be set to 256. This means that the number of transmitting antennas in the MIMO system remains unchanged, and the detection performance simulation is performed when the number of receiving antennas is increased, i.e., the transmit / receive antenna ratio is increased. Figure 4 In the simulation, we still used one Newton iteration and two Newton iterations for comparison. Analysis of the simulation curves shows that as the signal-to-noise ratio (SNR) 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. Furthermore, when the SNR is low, the performance loss of the detection algorithm EP-NT1 using one Newton iteration is relatively smaller compared to... Figure 1 The performance loss remains small. Therefore, it can be assumed that when the transmit / receive antenna ratio increases, the channel estimation of the channel matrix can be achieved using the detection algorithm EP-NT1 with one Newton iteration to reduce computational complexity. However, if the transmit / receive antenna ratio 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 transmit / receive antenna ratio. The channel estimation of the channel matrix can be completed using the detection algorithm EP-NT2 with two Newton iterations.

[0089] In a practical application example, please refer to Figure 5 The graph represents the simulation curve of the expectation propagation detection algorithm using Newton's hybrid iteration. Simulation analysis shows that when the signal-to-noise ratio is low, the detection algorithm EP-NT2 with two Newton iterations can be used to approximate the inversion of matrix W. Meanwhile, to minimize the computational complexity, when the signal-to-noise ratio is high, the detection algorithm EP-NT1 with one Newton iteration can be used to approximate the inversion of matrix W. Figure 5The number of receiving antennas is still set to 128, and the number of transmitting antennas to 32. A signal-to-noise ratio (SNR) of 12 dB is used as the dividing point. When the SNR is less than 12 dB, the detection algorithm EP-NT2 with two Newton iterations is used to approximate the inversion of matrix W. When the SNR is greater than 12 dB, the detection algorithm EP-NT1 with one Newton iteration can be used to approximate the inversion of matrix W, forming a result as follows: Figure 5 The simulation diagram of the channel detection algorithm is shown. This Newtonian hybrid iterative expectation propagation detection algorithm can significantly reduce computational complexity while ensuring almost zero performance loss.

[0090] The technical solutions provided by one or more embodiments of this disclosure can, based on prior information such as the channel matrix, noise power, and output signal, set the iterative reference variance and iterative reference expectation of the transmitted signal, and determine the iterative distribution expectation and iterative distribution variance of the transmitted signal by determining the iterative weight matrix. The iterative reference expectation and iterative reference variance can then be updated using the iterative distribution expectation and iterative distribution variance, entering the next iteration cycle. Thus, after the number of iterations reaches a preset period, the iterative distribution expectation can be determined as the target distribution expectation. The target distribution expectation can reflect an approximation of the marginal posterior probability distribution of each transmitted signal in the multiple-input multiple-output system, thereby achieving accurate detection of signals in the multiple-input multiple-output system.

[0091] Furthermore, in one or more embodiments of the present disclosure, the intermediate variable iterative weight matrix is ​​generated using Newton's iteration method to produce an iterative covariance matrix, such that the iterative covariance matrix can represent an approximate solution of the inverse matrix of the iterative weight matrix. This reduces the complexity of matrix inversion. In particular, in each iteration cycle, one inversion operation of the iterative weight matrix can be avoided, significantly reducing the overall computational complexity of the method.

[0092] In some embodiments of the present disclosure, when the number of antennas in a MIMO system is small, the transmit / receive antenna ratio is small, and the signal-to-noise ratio is small, the expectation propagation detection algorithm using a second-order Newton iteration has a significantly improved performance compared to the expectation propagation detection algorithm using a single-order Newton iteration. However, when the number of antennas in a MIMO system is large, the transmit / receive antenna ratio is large, and the signal-to-noise ratio is large, the performance of the expectation propagation detection algorithm using a single-order Newton iteration is almost the same as that using a second-order Newton iteration, and the algorithm complexity is small.

[0093] When the signal-to-noise ratio is low, the detection algorithm EP-NT2, which involves two Newton iterations, can be used to approximate the matrix inversion, and its curve almost perfectly coincides with that of the expectation propagation EP detection algorithm. Meanwhile, to minimize computational complexity, when the signal-to-noise ratio is high, the detection algorithm EP-NT1, which involves one Newton iteration, can be used to approximate the matrix inversion, and its curve still almost perfectly coincides with that of the expectation propagation EP detection algorithm. Therefore, by using this Newton hybrid iteration expectation propagation detection algorithm, we can achieve a significant reduction in computational complexity while ensuring almost zero performance loss.

[0094] Please see Figure 6 This disclosure also provides a signal detection device for a multiple-input multiple-output system, the device comprising:

[0095] The weight determination unit 100 is used to determine the iterative weight matrix based on the iterative reference variance, the channel matrix of the multiple input multiple output system, and the noise power of the multiple input multiple output system for the target iterative period.

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

[0097] The variance determination unit 300 is used to determine the iterative distribution variance based on the iterative covariance matrix for the target iterative period.

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

[0099] The update unit 500 is used to update the iteration baseline expectation and the iteration baseline variance according to the iteration distribution expectation and the iteration distribution variance for the target iteration cycle, and then proceed to the next iteration cycle of the target iteration cycle.

[0100] The output unit 600 is used to determine the expected iteration distribution as the expected target distribution when the target iteration period reaches a preset number of periods. The expected target distribution is used to estimate the input signal of the multiple input multiple 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 the 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; and to perform at least one Newton iteration on the weight diagonal matrix according to a preset Newton iteration formula to generate the iterative covariance matrix.

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

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

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

[0106] In one embodiment, the expectation determination unit 400 is specifically configured 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 baseline expectation; and multiply the summation result by the iterative covariance matrix to generate the iterative distribution expectation.

[0107] In one embodiment, the updating unit 500 is specifically used to update the iterative baseline variance based on the iterative distribution variance and the iterative baseline variance; and to update the iterative baseline expectation based on the iterative distribution variance, the iterative baseline variance, the iterative distribution expectation, and the iterative baseline expectation; wherein the process of updating the iterative baseline expectation and updating the iterative baseline variance includes at least updating the cavity probability distribution and updating the refinement 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 multiple-input multiple-output system is in a first numerical range and the total number of antennas of the multiple-input multiple-output system is less than a first reference value, if the signal-to-noise ratio of the multiple-input multiple-output system is less than a first threshold, then performing two Newton iterations on the iterative weight matrix; if the signal-to-noise ratio of the multiple-input multiple-output system is greater than the first threshold, then 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 multiple-input multiple-output system is in the first numerical range and the total number of antennas of the multiple-input multiple-output system is greater than a second reference value, then performing one 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 range, then 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 multiple-input multiple-output system is lower than the first numerical range, then performing two Newton iterations on the iterative weight matrix.

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

[0110] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0111] Please see Figure 7 This disclosure also provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program, and when the computer program is executed by the processor, it implements the above-described method for judging the degradation of landing pages.

[0112] This disclosure also provides a computer-readable storage medium for storing a computer program that, when executed by a processor, implements the above-described method for determining the degradation of landing pages.

[0113] The processor can be a central processing unit (CPU). It can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof.

[0114] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the methods in the above-described embodiments.

[0115] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

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

[0117] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, embodiments of apparatus, devices, and storage media are basically similar to method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0118] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0119] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall 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 includes: For the target iteration period, the iteration weight matrix is ​​determined based on the iteration reference variance, the channel matrix of the multiple input multiple output system, and the noise power of the multiple input multiple output system. For the target iteration period, the iterative weight matrix is ​​subjected to at least one Newton iteration to generate an iterative covariance matrix, which is used to characterize an approximate solution of the inverse matrix of the iterative weight matrix. For the target iteration period, the iteration distribution variance is determined based on the iteration covariance matrix; For the target iteration period, the iteration distribution expectation is determined based on the iteration covariance matrix, the iteration baseline expectation, the output signal of the multiple input multiple output system, the channel matrix, and the noise power. For the target iteration cycle, based on the expected iteration distribution and the variance of the iteration distribution, update the expected iteration base and the variance of the iteration base, and then proceed to the next iteration cycle of the target iteration cycle; When the target iteration period reaches a preset number of periods, the expected iteration distribution is determined as the expected target distribution, which is used to estimate the input signal of the multiple input multiple output system. 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 multiple input multiple output system is in a first value range, and the total number of antennas of the multiple input multiple output system is less than a first reference value, if the signal-to-noise ratio of the multiple input multiple output system is less than a first threshold, then two Newton iterations are performed on the iterative weight matrix; if the signal-to-noise ratio of the multiple input multiple output system is greater than the first threshold, then one Newton iteration is performed on the iterative weight matrix. When the ratio of the number of receiving antennas to the number of transmitting antennas of the multiple input multiple output system is within the first numerical range, and the total number of antennas of the multiple input multiple output system is greater than the second reference value, a Newton iteration is performed on the iterative weight matrix. When the total number of antennas in 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 in the multiple-input multiple-output system is higher than the first numerical range, then one Newton iteration is performed on the iterative weight matrix; if the ratio of the number of receiving antennas to the number of transmitting antennas in the multiple-input multiple-output system is lower than the first numerical range, then two Newton iterations are performed on the iterative weight matrix.

2. The method according to claim 1, characterized in that, The determination of the iterative weight matrix based on the iterative baseline variance, the channel matrix of the multiple-input multiple-output system, and the noise power of the multiple-input multiple-output system includes: Based on the channel matrix and the noise power, determine the target Gram matrix; Based on the iterative baseline variance, an iterative diagonal matrix is ​​determined, wherein the main diagonal elements of the iterative diagonal matrix are composed of the elements of the iterative baseline variance; The iterative weight matrix is ​​determined by summing the target Gram matrix and the iterative diagonal 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 the iterative covariance matrix includes: Extract the diagonal elements of the iterative weight matrix to generate a weight diagonal matrix; According to the preset Newton iteration formula, the weight diagonal matrix is ​​subjected to at least one Newton iteration 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) ) Where I is the identity matrix, X is the inverse of the weight diagonal matrix, W is the iterative weight matrix, l is the period value of the target iterative 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 baseline expectation, the output signal of the multi-input multi-output system, the channel matrix, and the noise power includes: Based on the output signal, the channel matrix, and the noise power, determine the matched filter vector; Summing the matched filter vector and the expected value of the iterative benchmark; The summation result is multiplied by the iterative covariance matrix to generate the expected iterative distribution.

6. The method according to claim 1, characterized in that, The step of updating the iterative baseline expectation and the iterative baseline variance based on the iterative distribution expectation and the iterative distribution variance includes: The iterative baseline variance is updated based on the iterative distribution variance and the iterative baseline variance; Based on the iterative distribution variance, the iterative baseline variance, the iterative distribution expectation, and the iterative baseline expectation, update the iterative baseline expectation; The process of updating the iterative baseline expectation and updating the iterative baseline variance includes at least updating the cavity probability distribution and updating the refinement probability distribution.

7. A signal detection device for a multiple-input multiple-output system, characterized in that, The device includes: The weight determination unit is used to determine the iterative weight matrix based on the iterative reference variance, the channel matrix of the multiple input multiple output system, and the noise power of the multiple input multiple output system for the target iterative period. A Newton iteration unit is used to perform at least one Newton iteration on the iterative weight matrix for the target iteration period to generate an iterative covariance matrix, wherein the iterative covariance matrix is ​​used to characterize an approximate solution of the inverse matrix of the iterative weight matrix. The variance determination unit is used to determine the iterative distribution variance based on the iterative covariance matrix for the target iterative period. The expectation determination unit is used to determine the iteration distribution expectation for the target iteration period based on the iteration covariance matrix, the iteration baseline expectation, the output signal of the multiple input multiple output system, the channel matrix, and the noise power. The update unit is used to update the iteration baseline expectation and the iteration baseline variance according to the iteration distribution expectation and the iteration distribution variance for the target iteration cycle, and then proceed to the next iteration cycle of the target iteration cycle. The output unit is used to determine the expected iteration distribution as the expected target distribution when the target iteration period reaches a preset number of periods. The expected target distribution is used to estimate the input signal of the multiple input multiple output system. Wherein, 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 multiple input multiple output system is in a first value range, and the total number of antennas of the multiple input multiple output system is less than a first reference value, if the signal-to-noise ratio of the multiple input multiple output system is less than a first threshold, then two Newton iterations are performed on the iterative weight matrix; if the signal-to-noise ratio of the multiple input multiple output system is greater than the first threshold, then one Newton iteration is performed on the iterative weight matrix. When the ratio of the number of receiving antennas to the number of transmitting antennas of the multiple input multiple output system is within the first numerical range, and the total number of antennas of the multiple input multiple output system is greater than the second reference value, a Newton iteration is performed on the iterative weight matrix. When the total number of antennas in 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 in the multiple-input multiple-output system is higher than the first numerical range, then one Newton iteration is performed on the iterative weight matrix; if the ratio of the number of receiving antennas to the number of transmitting antennas in the multiple-input multiple-output system is lower than the first numerical range, then two Newton iterations are performed on the iterative weight matrix.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory being used to store a computer program that, when executed by the processor, implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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