UAMP-based RHS-MIMO system symbol detection method

Abstract

The invention relates to the technical field of wireless communication, in particular to an RHS-MIMO system symbol detection method based on UAMP, which comprises the following steps: step A, modeling an RHS-MIMO uplink communication channel and system; step B, decomposing the high-dimensional received signal model into a plurality of low-dimensional sub-signal models by using the structural characteristics of the channel matrix; c, performing unitary transformation processing on the sub-signal model, performing factorization on the joint probability density distribution function of all variables, and constructing a corresponding factor graph model according to the factorization; step D, designing a symbol detection message passing algorithm based on UAMP; and E, repeating the step D until the algorithm converges. Compared with the existing algorithm, the algorithm disclosed by the invention has certain advantages in the aspects of bit error rate performance and robustness, and is high in convergence speed and good in stability.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a RHS-MIMO system symbol detection method based on UAMP. Background Art

[0002] Reconfigurable holographic surface (RHS), also known as active smart metasurface, provides a new solution for improving the performance of wireless communication systems with its advantages of easy deployment, low energy consumption and low cost. Completely different from the working mode of passive metasurface, RHS can act as a transmitter, receiver or relay processor in a wireless communication environment to achieve dynamic adjustment of the propagation path of electromagnetic waves. RHS usually installs a large number of radiating units on a two-dimensional smart metasurface of limited size. Its feed, RF link and signal processing unit are often highly integrated with the metasurface. It is the existence of these active components that makes RHS called active metasurface. The working principle of RHS is inspired by the principle of optical holographic interference. Specifically, the RF link will be connected to the feed on the RHS one by one. The electromagnetic wave carrying information (i.e., the reference wave) generated by the feed propagates on the metasurface and excites the RHS radiating units in turn. Subsequently, these radiating units construct holographic patterns on the metasurface and generate target beams by controlling the radiation amplitude of the electromagnetic wave. The specific implementation of RHS can be divided into two types: discrete aperture and continuous aperture. According to the definition in the relevant literature, when a large number of components are placed at equal intervals on an intelligent metasurface of a certain physical size, the spacing between adjacent antenna elements will gradually decrease as the number increases. Another implementation method is to densely arrange a large number of tiny antenna patches on the metasurface to achieve a continuous antenna aperture, which can be regarded as the asymptotic limit form of large-scale MIMO. It is worth noting that RHS, as a natural evolution of traditional large-scale MIMO, is significantly different from traditional MIMO. RHS usually does not require the use of expensive equipment such as amplifiers and phase shifters, which greatly reduces the complexity and cost of the system. Therefore, a MIMO system equipped with RHS can also be expressed as an RHS-MIMO system, or a holographic MIMO (HMIMO) system.

[0003] In recent years, RHS-MIMO systems have attracted widespread attention from academia and industry, and related research has shown explosive growth. These studies cover a variety of application scenarios of RHS in the system, but research on symbol detection is still relatively weak. Since the antenna aperture of the RHS-MIMO system is significantly larger than that of the traditional array antenna, and the introduction of high-frequency bands such as millimeter waves or terahertz, the Rayleigh communication distance has increased significantly. This means that most users in the RHS-MIMO system will work within the near-field communication range, which undoubtedly poses new challenges to traditional channel modeling and symbol detection methods. Compared with the traditional channel model, the latter is mainly applicable to the far-field region, and the electromagnetic waves can be approximated as plane waves. However, in the near-field region, electromagnetic waves exhibit spherical wave characteristics, which makes channel modeling more complicated. The channel function in the near-field range is often related to multiple parameters such as communication distance and angle, which makes its mathematical expression more complicated, thereby significantly increasing the non-ideal characteristics of the channel. Given that the relevant research on RHS is still in its infancy, the symbol detection of the RHS-MIMO system faces two core challenges: one is the complex full-polarization near-field channel characteristics. The high-dimensional near-field channel in the RHS system has an extremely complex expression. The full polarization characteristics brought by the spherical wave further increase the complexity of the channel. These factors together cause the channel to present structural characteristics that are difficult to predict. The second is the strong correlation of the fully polarized channel. The RHS is equipped with a large number of reconfigurable units, and the distance between adjacent units is usually less than half a wavelength. This densely arranged physical structure makes the near-field channel show extremely strong correlation in the spatial, temporal or frequency dimensions, which may lead to the deterioration of the channel matrix condition number, thereby increasing the difficulty of symbol detection. These problems caused by the complexity of the channel will be significantly reflected in the symbol detection process. Therefore, the symbol detection of the RHS-MIMO system requires the comprehensive use of advanced technologies in multiple fields such as signal processing and optimization theory to design a high-performance, low-complexity and highly robust symbol detection algorithm that can effectively handle the fully polarized near-field channel characteristics of the RHS-MIMO system. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a RHS-MIMO system symbol detection method based on UAMP. The present invention realizes effective detection of the symbol matrix while reducing the algorithm complexity, which not only improves the detection accuracy, but also has the advantages of low system overhead, fast convergence speed and high robustness.

[0005] The object of the present invention is achieved by the following measures: A RHS-MIMO system symbol detection method based on UAMP comprises the following steps:

[0006] Step A, modeling the RHS-MIMO uplink communication channel and system;

[0007] Step B: utilizing the structural characteristics of the channel matrix to decompose the high-dimensional received signal model into multiple low-dimensional sub-signal models;

[0008] Step C, performing unitary transformation on the sub-signal model, factorizing the joint probability density distribution function of all variables, and constructing a corresponding factor graph model accordingly;

[0009] Step D: design a symbol detection message passing algorithm based on UAMP;

[0010] Step E: Repeat step D until the algorithm converges.

[0011] In the above-mentioned RHS-MIMO system symbol detection method based on UAMP, in step A, a RHS-MIMO uplink communication system, in which both the base station and the user use RHS for signal transmission, comprises M and N antenna patches respectively, and the size of each antenna patch is and They represent the antenna patch sizes of the base station and the user in the horizontal and vertical directions, respectively. At the same time, the antenna patches on each RHS are counted row by row. The physical planes where the mth antenna patch of the base station and the nth antenna patch of the user are located are represented as and And use and Respectively represent the center coordinates of the corresponding antenna patches, and the channel between the two can be expressed as:

[0012]

[0013] in, r mn represents the distance between the center coordinates of the mth antenna patch and the nth antenna patch of the user, I represents the 3′3 unit matrix, k0 represents the wave number, and the normalized vector represents the unit direction vector, w and m are the dielectric constant and magnetic permeability respectively, then the total channel matrix It can be defined as:

[0014]

[0015] In L consecutive time slots, the received signal model at the base station can be expressed as:

[0016]

[0017] in, represents the signal matrix, Represent the signal matrix in each polarization direction, Then it represents the noise matrix, in which each element satisfies the complex Gaussian distribution with mean 0 and variance g.

[0018] In the above-mentioned RHS-MIMO system symbol detection method based on UAMP, in step B, the channel matrix Re-expressed as:

[0019]

[0020] Using the symmetry of the channel matrix, the received signal model Split into multiple sub-matrices and multiply them:

[0021]

[0022] The decomposition transformation can be further performed to define the following intermediate variables:

[0023]

[0024] According to the above definition, It can be re-expressed as a 3-sub-signal model:

[0025]

[0026] According to the above definition and transformation, the original signal model can be finally split into 9 sub-signal models.

[0027] In the above-mentioned RHS-MIMO system symbol detection method based on UAMP, in step C, a unitary transformation is performed on the sub-signal model. First of all Do SVD decomposition, Multiply both sides of the equation by We can get:

[0028]

[0029] in, According to the above method, we can obtain and The signal model after unitary transformation is defined as k∈{xx,xy,xz,yy,yz,zz}, k1∈{x,y,z}. The factorization process of the joint probability density distribution function of each variable can be expressed as:

[0030]

[0031] in, express and The relationship between , I represents the unit matrix, express and relationship, express function, a represents the modulated discrete data, express The probability is equal to a and satisfies

[0032] The above-mentioned UAMP-based RHS-MIMO system symbol detection method constructs a factor graph model containing variable nodes and factor nodes based on the results of factor decomposition, where the variable nodes represent the transmission symbols to be detected, noise variance and intermediate variables, and the factor nodes reflect the probabilistic relationship between the observed signal, noise and symbols.

[0033] In the above-mentioned RHS-MIMO system symbol detection method based on UAMP, the step D specifically comprises the following steps:

[0034] Step D1: Assumption and is a variable Initialize the mean matrix and variance matrix of , and use the mean field rule to calculate the auxiliary variables on the factor graph of the joint posterior probability density function distribution and Mean With variance and the confidence b(g) and estimate of the noise variance

[0035] Step D2: Use the UAMP algorithm to calculate the variables on the factor graph of the joint posterior probability density function distribution and and and

[0036] Step D3: Use discrete priors of symbolic variables to compute variables on the factor graph of the joint posterior probability density function distribution Confidence Mean and variance

[0037] The above-mentioned RHS-MIMO system symbol detection method based on UAMP specifically includes the following steps in step D1:

[0038] Assumptions and is a variable Initialize the mean matrix and variance matrix of , and use the mean field rule to calculate the auxiliary variables on the factor graph of the joint posterior probability density function distribution and Mean With variance and the confidence b(g) and estimate of the noise variance They are;

[0039]

[0040] The above-mentioned RHS-MIMO system symbol detection method based on UAMP specifically includes the following steps in step D2:

[0041] The UAMP algorithm is used to calculate the variables on the factor graph of the joint posterior probability density function distribution. and and and They are:

[0042]

[0043]

[0044] The above-mentioned RHS-MIMO system symbol detection method based on UAMP specifically includes the following steps in step D3:

[0045] Using discrete priors on symbolic variables to compute variables on the factor graph of the joint posterior probability density function distribution Confidence Mean and variance They are:

[0046]

[0047] Beneficial effects of the present invention: The present invention targets the RHS-MIMO uplink communication system, performs channel and system modeling based on the dyadic Green's function, utilizes the block symmetry of the fully polarized near-field channel matrix, decomposes the high-dimensional signal model into multiple low-dimensional sub-signal models, and on this basis, places the symbol detection problem in a probabilistic framework for modeling, and constructs a corresponding factor graph. Combining the factor graph with the UAMP technology, an efficient Bayesian symbol detection algorithm is proposed, which can significantly alleviate the adverse effects of the non-ideal characteristics of the RHS-MIMO near-field polarization channel matrix. Simulation results show that compared with traditional symbol detectors, the proposed algorithm effectively reduces the computational complexity of channel matrix preprocessing, has significant advantages in performance, and improves the robustness of the detection algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of the present invention;

[0049] Figure 2 It is a schematic diagram of the factor graph model;

[0050] Figure 3 Schematic diagram of the curve of BER performance changing with the number of iterations;

[0051] Figure 4 The diagram is a curve diagram of BER performance changing with SNR at different communication distances;

[0052] Figure 5 The diagram is a curve showing the variation of BER performance with SNR when the number of base station antenna patches is different;

[0053] Figure 6 The diagram is a curve diagram of BER changing with SNR when the number of RF links is different;

[0054] Figure 7 The diagram is a diagram of the BER versus SNR curve for different modulation modes. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example 1: Figure 1 As shown, a RHS-MIMO system symbol detection method based on UAMP includes the following steps:

[0057] Step A, modeling the RHS-MIMO uplink communication channel and system;

[0058] Step B: utilizing the structural characteristics of the channel matrix to decompose the high-dimensional received signal model into multiple low-dimensional sub-signal models;

[0059] Step C, performing unitary transformation on the sub-signal model, factorizing the joint probability density distribution function of all variables, and constructing a corresponding factor graph model accordingly;

[0060] Step D: design a symbol detection message passing algorithm based on UAMP;

[0061] Step E: Repeat step D until the algorithm converges.

[0062] Step A specifically includes:

[0063] Consider an RHS-MIMO uplink communication system, where both the base station and the user use RHS for signal transmission, which contains M and N antenna patches respectively, and the size of each antenna patch is and They represent the antenna patch sizes in the horizontal and vertical directions of the base station and user, respectively. At the same time, the antenna patches on each RHS are counted row by row. Assuming that the receiving plane is in the xy plane and the center coordinates of the first base station antenna patch are at the origin, the center coordinates of the first unit of the transmitting surface are expressed as The physical planes where the mth antenna patch of the base station and the nth antenna patch of the user are located are represented as and And use and They represent the center coordinates of the corresponding antenna patches respectively. The relationship between the center coordinates of the antenna patches on the RHS of the base station and the user can be expressed as:

[0064]

[0065] in, Then the channel between the mth antenna patch of the base station and the nth antenna patch of the user can be expressed as:

[0066]

[0067] in,

[0068]

[0069] r mn represents the distance between the center coordinates of the mth antenna patch and the nth antenna patch of the user, I represents the 3′3 unit matrix, and l represents the wavelength, the normalized vector represents the unit direction vector, w and m are the dielectric constant and magnetic permeability respectively, then the total channel matrix It can be defined as:

[0070]

[0071] Therefore, in L consecutive time slots, the received signal model at the base station can be expressed as:

[0072]

[0073] in, represents the signal matrix, Represent the sub-signal matrix in each polarization direction, Then it represents the noise matrix, Each element satisfies the complex Gaussian distribution with mean 0 and variance g;

[0074] Step B specifically includes:

[0075] In order to simplify the model, the channel matrix Re-expressed as:

[0076]

[0077] Using the block symmetry of the channel matrix, the received signal model Split into multiple sub-matrices and multiply them:

[0078]

[0079] by As an example, we can further decompose and transform, and define the following intermediate variables respectively:

[0080]

[0081] According to the above definition, It can be re-expressed as a 3-sub-signal model:

[0082]

[0083] According to similar definitions and transformations, we can obtain Corresponding sub-signal models, so the original signal model can be finally split into 9 sub-signal models;

[0084] Step C specifically includes:

[0085] The sub-signal model is unitarily transformed to For example, first Do SVD decomposition, that is Multiply both sides of the equation by We can get:

[0086]

[0087] in, Similarly, and Signal model after unitary transformation:

[0088]

[0089] According to the above process, the expressions of other sub-signal models after unitary transformation can also be obtained, which will not be repeated here. For k∈{xx,xy,xz,yy,yz,zz}, k1∈{x,y,z}, the joint probability density distribution function of all variables can be decomposed into:

[0090]

[0091] in, express and The relationship between , I represents the unit matrix, express and relationship, express The specific function form of a represents the modulated discrete data. express The probability is equal to a and satisfies

[0092] like Figure 2 As shown, according to the factor graph model construction rules, the factor graph model corresponding to the RHS-MIMO communication system model under this problem is obtained.

[0093] Based on the above, the step D specifically includes the following three steps:

[0094] Step D1: Use the mean field rule to calculate auxiliary variables on the factor graph of the joint posterior probability density function distribution and Mean With variance and the confidence b(g) and estimate of the noise variance

[0095] Step D2: Use the UAMP algorithm to calculate the variables on the factor graph of the joint posterior probability density function distribution and and and

[0096] Step D3: Use discrete priors of symbolic variables to compute variables on the factor graph of the joint posterior probability density function distribution Confidence Mean and variance

[0097] Step D1 specifically includes:

[0098] Assumptions and is a variable Initialize the mean matrix and variance matrix of , and use the mean field rule to calculate the auxiliary variables on the factor graph of the joint posterior probability density function distribution and Mean With variance and the confidence b(g) and estimate of the noise variance First, calculate the auxiliary variables:

[0099]

[0100]

[0101] Secondly, according to the mean field rule, the variable The variance and mean of are expressed as:

[0102]

[0103] because use and Respectively represent matrices and The l-th column element of , then the confidence b(g) is:

[0104]

[0105] Estimation of the noise variance It can be expressed as:

[0106]

[0107] Step D2 specifically includes:

[0108] The UAMP algorithm is used to calculate the variables on the factor graph of the joint posterior probability density function distribution. and and and First calculate the intermediate variable and Right now:

[0109]

[0110] information Variance With the mean It can be expressed as:

[0111]

[0112] According to the above formula, we can get the message The variance and mean of , that is:

[0113]

[0114] Step D3 specifically includes:

[0115] Using discrete priors on symbolic variables to compute variables on the factor graph of the joint posterior probability density function distribution Confidence Mean and variance First assume and Respectively and For any element in the nth row and lth column, due to the symbol The prior is but The confidence can be expressed as:

[0116]

[0117] therefore, The mean and variance of can be expressed as:

[0118]

[0119] in,

[0120]

[0121] for Will and After merging into matrix form, we can get and

[0122] A comparison between the transmission scheme of the present invention and other existing transmission schemes is given below to make the advantages and features of the present invention more obvious.

[0123] Simulation parameter settings: Assume that the wavelength is 0.1m, the base station and the user are uniform arrays, the number of antenna patches is M and N respectively, the number of RF links is P, the proposed algorithm is referred to as "UAMP" in the simulation diagram, the comparison algorithm is referred to as "AMP", and the communication distance is represented by "Distance". Antenna patch size For the center coordinates of the user's first antenna patch Assumptions and It obeys the uniform distribution between [-1,1], It follows a uniform distribution between [20,40]. The maximum number of iterations is set to 50 times, and the threshold x = 10 -3 All simulations are averages obtained after 1000 Monte Carlo simulations.

[0124] Figure 3The curve of BER performance based on UAMP algorithm as a function of iteration number is given, where M=64, N=9, P=15, and the communication distance is 7m. As can be seen from the figure, for the iteration curves under different SNRs, the BER gradually decreases with the increase of iteration number and eventually stabilizes. In the initial iteration stage, the BER decreases rapidly, indicating that the UAMP algorithm can quickly improve the detection performance in the early iteration process. Subsequently, the BER decreases slowly, and after about 15 iterations, it finally converges to a stable value. The UAMP algorithm shows good BER performance under different SNR conditions, especially under high signal-to-noise ratio conditions, where the algorithm can converge to a lower BER faster, which once again illustrates the performance advantage of the proposed algorithm.

[0125] Figure 4 The curves of BER performance of different algorithms changing with SNR when the RHS communication distance between the base station and the user changes are depicted, where M=64, N=9, and P=15. Since the main communication range of the RHS-MIMO system is the near field area, changing the distance between the base station and the user RHS will have a greater impact on the performance of symbol detection. As the RHS communication distance between the base station and the user increases, the wireless signal will experience greater path loss and multipath effects during the propagation process, which weakens the received signal strength and increases signal distortion and noise interference, thereby causing an increase in BER. However, even at a longer communication distance, the UAMP algorithm can still maintain a low bit error rate, thanks to its superiority in signal processing and anti-interference capabilities. Compared with other algorithms, the UAMP algorithm is more stable in processing weak signals and high noise environments, and can more accurately restore the original signal. Therefore, even when the communication distance increases, the UAMP algorithm can still provide reliable symbol detection.

[0126] Figure 5 The curves of the symbol detection algorithms based on UAMP and AMP with SNR changes when the number of user antenna patches N is different are described in , where M = 64, P = 15, and the communication distance is 7m. As can be seen from the figure, as the SNR increases, the BER of the symbol detection algorithm decreases. This is because when the number of user transmitting antennas decreases, the complexity of signal detection also decreases. For the receiver, fewer transmitting antennas means that the number of signals that need to be processed is reduced, which can simplify the signal processing algorithm, improve the detection accuracy, and thus reduce the BER. At the same time, as N increases, the symbol detection performance based on the AMP algorithm drops sharply, while the UAMP algorithm still has good performance, which reflects the strong robustness of the UAMP algorithm. In addition, the UAMP algorithm can obtain stable BER performance using less N. Therefore, the UAMP algorithm can effectively save the system's computing resources and reduce complexity.

[0127] Figure 6 The influence of the number of RF links P on the BER performance of the symbol detection algorithms based on UAMP and AMP is given, where M=64, N=9, and the communication distance is 7m. The figure shows that with the increase of SNR, the BER of both algorithms gradually decreases. This is because the increase in the number of RF links will reduce the BER by increasing diversity gain, improving spatial multiplexing, and enhancing signal processing capabilities. However, when the number of RF links is fixed, the UAMP algorithm can utilize these signal paths more efficiently and achieve more accurate symbol detection. This is fully reflected in the figure. When the base station uses the same number of RF links, the performance of the UAMP algorithm is always better than that of the AMP algorithm, especially in the high SNR area, the difference is more obvious. For example, when the SNR is about 22dB, the UAMP algorithm can reach 10 -5 The BER of the AMP algorithm can only reach 10 -2 Order of magnitude. At the same time, the BER obtained by the AMP algorithm when the number of RF links P = 15 is still higher than the BER obtained by the UAMP algorithm when P = 35. This is mainly because the fully polarized near-field channels of the RHS-MIMO system have strong correlation, and the channel matrix usually presents a low rank situation. The AMP algorithm often cannot guarantee good performance when facing such an imperfect channel matrix. The UAMP algorithm has the ability to achieve more accurate symbol detection due to its high robustness characteristics, so it also has better performance advantages. In addition, compared to the AMP algorithm, since the UAMP algorithm can still maintain excellent detection performance with fewer RF links, this means that the system can obtain higher communication quality and reliability without increasing hardware cost and complexity.

[0128] Figure 7 The curves of BER performance of different algorithms with SNR under different modulation modes are given, where M=64, N=9, P=15, and the communication distance is 7m. As can be seen from the figure, with the increase of SNR, the BER of various algorithms and modulation modes shows a downward trend, and the BER under QPSK modulation mode is significantly lower than that of 16QAM modulation mode. Under the same modulation mode, the BER of UAMP algorithm is always lower than that of AMP algorithm, which shows that the performance of UAMP algorithm in signal detection and bit error rate is better than that of AMP algorithm. Under 16QAM modulation mode, UAMP algorithm also shows its superiority. Although the complexity and anti-noise performance of 16QAM modulation are low, UAMP algorithm can still maintain a low BER under high SNR conditions, which further verifies the robustness and stability of UAMP algorithm under different modulation modes and SNR conditions. .

[0129] The specific initialization and symbol detection process in the UAMP-based RHS-MIMO system symbol detection method can be found in the above embodiment, and the embodiments of the present invention will not be described in detail here.

[0130] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A symbol detection method for a RHS-MIMO system based on UAMP, characterized in that: The steps include: Step A, modeling the RHS-MIMO uplink communication channel and system; Step B: utilizing the structural characteristics of the channel matrix to decompose the high-dimensional received signal model into multiple low-dimensional sub-signal models; Step C, performing unitary transformation on the sub-signal model, factorizing the joint probability density distribution function of all variables, and constructing a corresponding factor graph model accordingly; Step D: design a symbol detection message passing algorithm based on UAMP; Step E: Repeat step D until the algorithm converges.

2. The RHS-MIMO system symbol detection method based on UAMP according to claim 1, characterized in that: In step A, it is assumed that the base station and the user have M and N antenna patches respectively, and the physical planes where the mth antenna patch of the base station and the nth antenna patch of the user are located are respectively expressed as and And use and Respectively represent the center coordinates of the corresponding antenna patches, and the channel between the two can be expressed as: in, r mn represents the distance between the center coordinates of the mth antenna patch and the nth antenna patch of the user, I represents the 3′3 unit matrix, k0 represents the wave number, and the normalized vector represents the unit direction vector, w and m are the dielectric constant and magnetic permeability respectively, then the total channel matrix It can be defined as: In L consecutive time slots, the received signal model at the base station can be expressed as: in, represents the signal matrix, Represent the signal matrix in each polarization direction, Then it represents the noise matrix, in which each element satisfies the complex Gaussian distribution with mean 0 and variance g.

3. The RHS-MIMO system symbol detection method based on UAMP according to claim 2, characterized in that: In step B, the channel matrix Re-expressed as: Using the symmetry of the channel matrix, the received signal model Split into multiple sub-matrices and multiply them: The decomposition transformation can be further performed to define the following intermediate variables: According to the above definition, It can be re-expressed as a 3-sub-signal model: According to the above definition and transformation, the original signal model can be finally split into 9 sub-signal models.

4. The RHS-MIMO system symbol detection method based on UAMP according to claim 3, characterized in that: In step C, the sub-signal model is subjected to unitary transformation. First of all Do SVD decomposition, Multiply both sides of the equation by We can get: in, According to the above method, we can obtain and The signal model after unitary transformation is defined as k∈{xx,xy,xz,yy,yz,zz}, k1∈{x,y,z}. The factorization process of the joint probability density distribution function of each variable can be expressed as: in, express and The relationship between , I represents the unit matrix, express and relationship, express function, a represents the modulated discrete data, express The probability is equal to a and satisfies 5. The RHS-MIMO system symbol detection method based on UAMP according to claim 4, characterized in that: Based on the results of factor decomposition, a factor graph model consisting of variable nodes and factor nodes is constructed, in which the variable nodes represent the transmission symbols to be detected, the noise variance and the intermediate variables, and the factor nodes reflect the probability relationship between the observed signal, noise and symbols.

6. The RHS-MIMO system symbol detection method based on UAMP according to claim 1 or 5, characterized in that: The step D specifically comprises the following steps: Step D1: Use the mean field rule to calculate auxiliary variables on the factor graph of the joint posterior probability density function distribution and Mean With variance and the confidence b(g) and estimate of the noise variance Step D2: Use the UAMP algorithm to calculate the mean and variance variable matrix on the factor graph of the joint posterior probability density function distribution and and and Step D3: Use discrete priors of symbolic variables to compute variables on the factor graph of the joint posterior probability density function distribution Confidence Mean and variance 7. The RHS-MIMO system symbol detection method based on UAMP according to claim 6, characterized in that: The step D1 specifically includes the following: Assumptions and is a variable Initialize the mean matrix and variance matrix of , and use the mean field rule to calculate the auxiliary variables on the factor graph of the joint posterior probability density function distribution and Mean With variance and the confidence b(g) and estimate of the noise variance They are; 8. The RHS-MIMO system symbol detection method based on UAMP according to claim 6, characterized in that: The step D2 specifically includes the following: The UAMP algorithm is used to calculate the variables on the factor graph of the joint posterior probability density function distribution. and and and They are:

9. The RHS-MIMO system symbol detection method based on UAMP according to claim 6, characterized in that: The step D3 specifically includes the following: Using discrete priors on symbolic variables to compute variables on the factor graph of the joint posterior probability density function distribution Confidence Mean and variance They are:

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