Method for detecting communication signal of multi-mode analog-to-digital converter branch terahertz single-bit quantization

Abstract

The application provides a multi-analog-to-digital converter branch terahertz single-bit quantization communication signal detection method, relates to the technical field of communication, and comprises the following steps: S1, a terahertz single-bit quantization communication system with a plurality of analog-to-digital converter branches is constructed; the analog-to-digital converter branch comprises a phase rotation module and an analog-to-digital converter connected in sequence; and the phase rotation modules of different analog-to-digital converter branches have different phase rotation angles; S2, a vector-valued factor graph is used to represent the communication system; S3, vector approximation message passing is performed on the vector-valued factor graph, and estimation of a noiseless received signal is iteratively calibrated, so that detection of a transmitted symbol is finally realized. The application can realize reliable signal detection under the influence of single-bit quantization distortion and effectively improve detection performance.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a communication signal detection method using terahertz single-bit quantization in a multi-analog-to-digital converter branch. Background Technology

[0002] With the development of wireless communication, the number of users accessing the network and the data transmission required for wireless communication services are growing exponentially. Statistics show that the demand for wireless communication bandwidth doubles every 18 months. However, in the lower frequency ranges commonly used in current mobile communication technologies, bandwidth limitations make it difficult to achieve data transmission rates of tens of Gbps or higher, even with the application of various complex modulation and multiplexing techniques. Therefore, to meet the higher demands of mobile communication development for wireless communication speeds, more spectrum resources in higher frequency ranges must be developed and utilized.

[0003] Terahertz (THz) communication technology utilizes the abundant bandwidth resources offered by the high-frequency band to support frequencies exceeding 10 GHz. 12 The technology demands information transmission at the bit-per-second level. However, one of the major bottlenecks facing this technology is the complexity and power consumption of the receiver. In wireless communication systems, especially at the receiver end, particularly the analog-to-digital converter (ADC) module, high-precision ADCs in the THz band introduce unacceptable complexity and power consumption. To address this issue, researchers are exploring the use of low-precision quantization ADCs in THz communication systems. This requires THz systems to achieve reliable signal detection even under the influence of severe nonlinear quantization distortion. Summary of the Invention

[0004] The main objective of this invention is to propose a communication signal detection method using terahertz single-bit quantization in a multi-analog-to-digital converter branch, which can achieve reliable signal detection under the influence of single-bit quantization distortion and effectively improve detection performance.

[0005] This invention is achieved through the following technical solution:

[0006] A method for detecting communication signals using terahertz single-bit quantization in a multi-mode-to-digital converter branch includes the following steps:

[0007] Step S1: Construct a terahertz single-bit quantization communication system with multiple analog-to-digital converter branches. Each analog-to-digital converter branch includes a phase rotation module and an analog-to-digital converter connected in sequence. The phase rotation modules of different analog-to-digital converter branches have different phase rotation angles. The communication system is a multi-user multiple-input multiple-output system.

[0008] Step S2: Represent the communication system using a vector value factor graph;

[0009] Step S3: Perform vector approximation message passing on the vector value factor graph, iteratively calibrate the estimation of the noiseless received signal, and finally realize the detection of the transmitted symbol. During the message passing process, the single-bit sampling information of different analog-to-digital converter branches is aggregated.

[0010] Furthermore, in step S1, the received signal model in the communication system is represented as follows: The vector composed of the noiseless received signals at all receiving antennas is represented as ,in, The single-bit quantization process implemented in the analog-to-digital converter branch. This is a vector composed of the phase rotation angles of the phase rotation modules in each analog-to-digital converter branch. It is the identity matrix. The equivalent channel matrix for terahertz block fading baseband is derived from the center frequency of the communication system, the locational relationship between the base station and each user, and the surrounding scattering environment. Symbols are transmitted to all users, and these transmitted symbols satisfy a discrete uniform distribution on the modulation constellation diagram used by the communication system. It is a zero-mean Gaussian white noise matrix.

[0011] Furthermore, in step S2, in the vector value factor graph, all users send symbols The vector consisting of the noise-free received signals at all receiving antennas Let and represent the two variable nodes of the vector-valued factor graph. Each factor obtained by decomposing the joint probability density function of the communication system according to different vector parameters using Bayes' theorem is represented as a factor node in the vector-valued factor graph. The factor nodes include the conditional probability distribution of the noise-free received signal relative to the transmitted symbol and the channel matrix. The conditional probability distribution of single-bit sampled information of the analog-to-digital converter branch relative to the noise-free received signal. and the prior distribution of transmitted symbols .

[0012] Furthermore, in step S2, in Corresponding factor nodes and When the corresponding variable nodes pass messages, a phase alignment operation is set up with the phase difference between different analog-to-digital converter branches as a parameter to aggregate single-bit sampling information from different analog-to-digital converter branches.

[0013] Furthermore, in step S3, the vector approximation message passing on the vector value factor graph specifically includes the following steps:

[0014] Step S31, Initialization: Initialize the two variable nodes and the messages between factor nodes in the vector value factor graph;

[0015] Step S32, Linearity Detection: Based on the initialization or the two variable nodes from the previous iteration, send the data to the factor node. The message, using the product formula of the Gaussian distribution of vector values, calculates... A linear posterior estimate and A linear posterior estimate ;

[0016] Step S33: Calculate the likelihood message based on the message passing rules on the factor graph: The result obtained in step S32... A linear posterior estimate Divide by The prior distribution of the linear detection in this round is used to obtain the likelihood message of the transmitted symbol, and this likelihood message is used as the linear detection pair for the transmitted symbol. The basis for iterative correction of the posterior estimate; using the calculation rule of division of complex Gaussian distribution, the result obtained in step S32 is... A linear posterior estimate Divide by Prior distribution of linear detection in this round of iteration This yields the likelihood message used to calibrate the estimate of the noiseless received signal. The likely message This serves as the basis for iterative calibration of the estimation of the noiseless received signal using linear detection;

[0017] Step S34, Update Posterior estimation: Using a scalar Gaussian denoiser, prior information about the transmitted symbol modulation constellation is incorporated to calculate... Posterior estimated mean and the corresponding posterior estimate of scalar variance and will Likelihood messages are removed according to the calculation rules of the complex Gaussian distribution. To obtain the variable node corresponding to the sent symbol The corresponding factor node message, i.e., the next round of iterative linear detection. The prior distribution;

[0018] Step S35, Update Posterior estimation: Sampling information from each analog-to-digital converter branch is aggregated through phase alignment, and Bayes' theorem is applied to update the linear detection for the next iteration. The prior distribution;

[0019] Step S36: Repeat steps S32 to S35 until the number of iterations reaches the set maximum, or the user sends a symbol. If the estimated variance is below a set threshold, then vector approximation message passing stops. posterior estimation For output, based on Hard decision is made using the Euclidean distances between the points in the modulation constellation and the signal detection results.

[0020] Furthermore, in step S32, the linear posterior estimation middle, Let be the mean of this linear posterior estimate. Let be the mean of the variance of this linear posterior estimate, where Describes the Gaussian distribution in the complex field. Let X represent the mean of the prior X used for linear detection. Let X represent the variance of the prior X used for linear detection. This represents the mean of the likelihood estimate of the noiseless received signal used for linear detection. This represents the variance of the likelihood estimate for the noiseless received signal. express The conjugate transpose of . Represents the identity matrix. This represents the mean of the diagonal elements of the matrix; the linear posterior estimate middle, Let be the mean of this linear posterior estimate. Let be the mean of the variance of this linear posterior estimate, where This indicates the number of users per antenna transmitting signals. Indicates the number of receiving antennas of the base station; in step S33, the likelihood message middle, , .

[0021] Furthermore, in step S34, the... middle, , , ,in, This indicates calculating the mean. Indicates to The mean of the messages from the nodes, i.e., the pair given by linear detection. The mean of the likelihood estimate, Indicates to The variance of the node's messages, i.e., the variance given by linear detection. The variance of the likelihood estimate, This indicates the calculation of variance. This indicates a proportional relationship, meaning that the constant coefficients other than the exponent term e are omitted from the expression on the right side of the symbol.

[0022] Furthermore, in step S35, the Bayesian formula applied is: , This represents the posterior distribution of the noiseless received signal relative to the single-bit sampled information. This represents the likelihood distribution of the noiseless received signal relative to the single-bit sampled information. This represents the prior distribution of the noiseless received signal. This represents the single-bit sampled information of each analog-to-digital converter branch on all receiving antennas. This represents the joint probability distribution of single-bit sampled information from each analog-to-digital converter branch.

[0023] Furthermore, in step S35, the aggregation of sampling information from each analog-to-digital converter branch through phase alignment includes the following steps:

[0024] Step S351: First calculate the posterior distribution corresponding to the first analog-to-digital converter branch. If the phase rotation angle of the first analog-to-digital converter branch is Then the mean of the posterior distribution is and variance Using the formulas respectively and Calculate, where, , When the quantization sampling result is less than 0, , When the quantization sampling result is greater than 0, , , This represents the single-bit sampled information of the first analog-to-digital converter branch on all receiving antennas. This represents the variance of the zero-mean Gaussian white noise at the receiving end of the communication system. The standard Gaussian probability density function is represented in The value at that location, The standard Gaussian cumulative distribution function is represented in The value at;

[0025] Step S352, Introduce the first One analog-to-digital converter branch, considering phase rotation When obtaining subsequent sampling information, it is necessary to update the posterior distribution. The mean is first multiplied by the phase alignment factor. , then calculate and Then multiply by the coefficient. To eliminate the influence of the phase alignment coefficient, i.e., to obtain up to the [number]th ... The posterior distribution of each analog-to-digital converter branch The mean and variance, This represents the single-bit sampled information of the k-th analog-to-digital converter branch on all receiving antennas;

[0026] Step S353: Repeat step S352 until the sampling information of all analog-to-digital converter branches is taken into account, thus obtaining the posterior distribution of the noiseless received signal in the current iteration. mean and variance .

[0027] As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following beneficial effects:

[0028] This invention first constructs a terahertz single-bit quantization communication system with multiple analog-to-digital converter (ADC) branches. Then, it uses a vector value factor graph to represent the communication system and aggregates single-bit samples from different ADC branches. Finally, it performs vector approximation message passing on the vector value factor graph to iteratively calibrate the estimation of the noiseless received signal, thereby achieving reliable detection of the transmitted symbol. The configuration of multiple ADC branches and the phase rotation modules of different ADC branches, which use information with different phase offsets for signal detection, increase the amount of information available for signal detection in the communication system. Furthermore, by utilizing factor graphs and vector approximation message passing, the convergence of the detector is guaranteed through a progressively converging iterative process, thus overcoming quantization distortion. The combination of these two methods enables reliable signal detection even under the influence of single-bit quantization distortion, effectively improving detection performance. Attached Figure Description

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] Figure 1 This is a flowchart of the present invention.

[0031] Figure 2 This is a structural block diagram of the communication system of the present invention.

[0032] Figure 3 This is a schematic diagram of the vector value factor graph model corresponding to the communication system of the present invention.

[0033] Figure 4 This is a flowchart of vector approximation message passing on the vector value factor graph of the present invention.

[0034] Figure 5 This is a flowchart illustrating the phase alignment and aggregation of sampling information from each analog-to-digital converter branch in this invention.

[0035] Figure 6 This is a simulation diagram comparing the detection performance of the present invention and the comparative scheme.

[0036] Figure 7 This is a simulation diagram comparing the detection performance of the present invention under different parameter configurations. Detailed Implementation

[0037] like Figure 1 As shown, the communication signal detection method using terahertz single-bit quantization in a multi-analog-to-digital converter branch includes the following steps:

[0038] Step S1: Construct a terahertz single-bit quantization communication system with multiple analog-to-digital converter branches. Each analog-to-digital converter branch includes a phase rotation module and an analog-to-digital converter connected in sequence. The phase rotation modules of different analog-to-digital converter branches have different phase rotation angles. The communication system is a multi-user multiple-input multiple-output system.

[0039] like Figure 2 As shown, the communication system includes N t A single-antenna user and a base station with multiple receiving antennas are configured, each antenna connected to a corresponding down-conversion low-pass filter module, each down-conversion low-pass filter module connected to a corresponding analog-to-digital converter (ADC) branch, and each ADC branch connected to a vector message passing detector. The down-conversion low-pass filter module is existing technology. In the communication system, the received signal model is represented as... The vector composed of the noiseless received signals at all receiving antennas is represented as ,in, The single-bit quantization process implemented in the analog-to-digital converter branch. The phase rotation angle of the phase rotation module in each analog-to-digital converter branch. The vector formed Given an identity matrix, according to the central limit theorem, the phase of the received signal in a practical system generally exhibits a uniform distribution. Two analog-to-digital converter (ADC) branches can be used, with phase rotation angles of 0° and π / 4 respectively. Alternatively, multiple ADC branches can be used. When there are K ADC branches, the... The phase rotation angle of each analog-to-digital converter branch is ; The equivalent channel matrix for terahertz (THz) block fading baseband is derived from the center frequency of the communication system, the locational relationships between the base station and each user, and the surrounding scattering environment, and is represented as follows: , The exponential term represents the path loss corresponding to different signal propagation paths. The phase shift caused by the delay of different transmission paths to the transmitted symbols. The complex gain is the result of the combined effects of the angle of arrival, departure angle, and antenna gain for different paths. Symbols are transmitted to all users, and these transmitted symbols are in the modulation constellation diagram used by the communication system. The above satisfies a discrete uniform distribution. The matrix is ​​a zero-mean Gaussian white noise matrix with variance . .

[0040] Step S2: Represent the communication system using a vector value factor graph;

[0041] The constructed multi-user multiple-input multiple-output system has a joint probability distribution. The joint probability distribution can be decomposed using Bayes' theorem as follows: Thus establishing Figure 3 The vector-valued factor graph model shown is designed to facilitate the derivation of the vector-valued approximation message passing algorithm. On the vector-valued factor graph, they are represented by two identical variable nodes, namely And distributed by the Dirac delta distribution To ensure the equality of the two values, the joint probability density function of the communication system is decomposed using Bayes' theorem according to different vector parameters. Each factor is represented as a factor node in a vector-valued factor graph. Each factor node includes the conditional probability distribution of the noise-free received signal relative to the user-transmitted symbols and the channel matrix. The conditional probability distribution of single-bit sampled information of the analog-to-digital converter branch relative to the noise-free received signal. Prior distribution of symbols sent by the user .in, This represents the single-bit sampled information of each analog-to-digital converter branch on all receiving antennas. For all receiving antennas, the first Single-bit sampled information of each analog-to-digital converter branch.

[0042] exist Corresponding factor nodes and When the corresponding variable nodes pass messages, a set is used. Phase alignment operations are performed for the parameters, thereby aggregating single-bit sampled information from different analog-to-digital converter branches during message passing.

[0043] Step S3: Perform vector approximation message passing on the vector value factor graph, iteratively calibrate the estimation of the noiseless received signal, and finally realize the detection of the transmitted symbol. During the message passing process, the single-bit sampling information of different analog-to-digital converter branches is aggregated.

[0044] Specifically, implementing vector approximation message passing on the vector value factor graph includes, for example: Figure 4 The steps shown are as follows:

[0045] Step S31, Initialization: Initialize the two variable nodes and the messages between factor nodes in the vector value factor graph;

[0046] Specifically, in the vector-valued factor graph, the distribution of messages between nodes is characterized by a mean vector and a scalar variance value. In this embodiment, the probability distribution of the variable nodes corresponding to the transmitted symbols on the factor graph is initialized using a vector with a mean of all zeros and a variance of 2. The probability distribution of the variable nodes corresponding to the noiseless received signals on the factor graph is also initialized using a vector with a mean of all zeros and a variance of 2. For messages between vector-valued nodes, a probability distribution with a mean of all zeros and a variance of 1 is used for initialization.

[0047] Step S32, Linearity Detection: Based on the initialization or the two variable nodes from the previous iteration, send the data to the factor node. The message, using the product formula of the Gaussian distribution of vector values, calculates... A linear posterior estimate and A linear posterior estimate ;

[0048] Specifically, linear posterior estimation middle, Let be the mean of this linear posterior estimate. Let be the mean of the variance of this linear posterior estimate, where Describes the Gaussian distribution in the complex field. Indicates to The mean of the messages from the node, which is the mean of the X prior used for linear detection. This represents the variance of the message, specifically the variance of the X prior used for linear detection. Indicates to The node's message is the mean of the likelihood estimate of the noiseless received signal used for linear detection. Indicates to The variance of a node's messages represents the variance of the likelihood estimate of the noiseless received signal. express The conjugate transpose of . Represents the identity matrix. This indicates that the mean of the diagonal elements of the matrix is ​​calculated.

[0049] Linear posterior estimation middle, Let be the mean of this linear posterior estimate. Let be the mean of the variance of this linear posterior estimate, where This indicates the number of users per antenna transmitting signals. This indicates the number of receiving antennas at the base station.

[0050] Step S33: Calculate the likelihood message based on the message passing rules on the factor graph: The result obtained in step S32... A linear posterior estimate Divide by The prior distribution of the linear detection in this round is used to obtain the likelihood message of the transmitted symbol. The likely message As a linear detection pair for transmitted symbols The basis for iterative correction of the posterior estimate; using the calculation rule of division of complex Gaussian distribution, the result obtained in step S32 is... A linear posterior estimate Divide by Prior distribution of linear detection in this round of iteration This yields the likelihood message used to calibrate the estimate of the noiseless received signal. The likely message This serves as the basis for iterative calibration of the estimation of the noiseless received signal using linear detection;

[0051] Specifically, likelihood messages middle, , ;

[0052] Step S34, Update Posterior estimation: Based on the likelihood estimation results given by linear detection, a scalar Gaussian denoiser is used to incorporate prior information about the transmitted symbol modulation constellation to calculate... Posterior estimated mean and the corresponding posterior estimate of scalar variance and will Likelihood messages are removed according to the calculation rules of the complex Gaussian distribution. To obtain the variable node corresponding to the sent symbol The corresponding factor node message, i.e., the next round of iterative linear detection. prior distribution ;

[0053] Specifically, middle, , , ,in, The expression within parentheses represents the mean of the likelihood distribution relative to the prior distribution of the transmitted symbol. Indicates to The mean of the messages from the nodes, i.e., the pair given by linear detection. The mean of the likelihood estimate, Indicates to The variance of the node's messages, i.e., the variance given by linear detection. The variance of the likelihood estimate, The variance of the likelihood distribution within the parentheses is relative to the prior distribution of the transmitted symbol. This indicates a proportional relationship, meaning that the constant coefficients other than the exponent term e are omitted from the expression on the right side of the symbol.

[0054] Step S35, Update Posterior estimation: Sampling information from each analog-to-digital converter branch is aggregated through phase alignment, and Bayes' theorem is applied to update the linear detection for the next iteration. The prior distribution;

[0055] The applied Bayesian formula is: , This represents the posterior distribution of the noiseless received signal relative to the single-bit sampled information. This represents the likelihood distribution of the noiseless received signal relative to the single-bit sampled information. This represents the prior distribution of the noiseless received signal. This represents the single-bit sampled information of each analog-to-digital converter branch on all receiving antennas. This represents the joint probability distribution of single-bit sampled information from each analog-to-digital converter branch.

[0056] like Figure 5 As shown, the specific steps for aggregating the sampling information of each analog-to-digital converter branch through phase alignment include the following:

[0057] Step S351: First calculate the posterior distribution corresponding to the first analog-to-digital converter branch. If the phase rotation angle of the first analog-to-digital converter branch is Then the mean of the posterior distribution is and variance Using the formulas respectively and Calculate, where, , In this invention, single-bit quantization is used. Therefore, when the quantization sampling result is less than 0, , When the quantization sampling result is greater than 0, , b=2, This represents the single-bit sampled information of the first analog-to-digital converter branch on all receiving antennas. This represents the variance of the zero-mean Gaussian white noise at the receiving end of the communication system. The standard Gaussian probability density function is represented in The value at that location, The standard Gaussian cumulative distribution function is represented in The value at;

[0058] Step S352, Introduce the first One analog-to-digital converter branch, considering phase rotation When obtaining subsequent sampling information, it is necessary to update the posterior distribution. The mean is first multiplied by the phase alignment factor. , then calculate and Then multiply by the coefficient. To eliminate the influence of the phase alignment coefficient, i.e., to obtain up to the [number]th ... The posterior distribution of each analog-to-digital converter branch The mean and variance;

[0059] Step S353: Repeat step S352 until the sampling information of all analog-to-digital converter branches is taken into account, thus obtaining the posterior distribution of the noiseless received signal in the current iteration. mean and variance .

[0060] Step S36: Repeat steps S32 to S35 until the number of iterations reaches the set maximum, or the user sends a symbol. If the estimated variance is below a set threshold, then vector approximation message passing stops. posterior estimation For output, based on Hard decision is made using the Euclidean distances between the points in the modulation constellation and the signal detection results.

[0061] exist Figure 6 In this invention, with a base station antenna count of 32 and 8 single-antenna users, the symbol error rate (BER) of the present invention is significantly better than the comparative scheme (LMMSE) across all signal-to-noise ratio (SNR) ranges. For QPSK modulated transmission signals, LMMSE is essentially unable to achieve high-reliability signal detection under single-bit quantization, while the present invention can achieve a BER of 11 dB. The level of symbol error rate.

[0062] exist Figure 7 In this invention, under the conditions of 32 base station antennas, 8 single-antenna users, and two analog-to-digital converter (ADC) branches, the central limit theorem ensures that the phase of the received signal is uniformly distributed. Therefore, the branches employ... When the phase rotation value is set to a certain value, optimal detection performance is achieved. If we further increase the number of quantization branches, we can achieve optimal detection performance with a suitable phase rotation setting, i.e., a phase rotation angle between each branch of _____. This allows for better detection performance; for example, three ADC branches can be observed, with a phase rotation angle of [missing information]. At that time, A 2dB performance improvement can be achieved at the symbol error rate level.

[0063] The present invention will be further described below through specific embodiments.

[0064] In this invention, the terms "first," "second," and "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. The use of terms such as "upper," "lower," "left," "right," "front," and "rear" to indicate orientation or positional relationships is based on the orientation or positional relationships shown in the accompanying drawings and is only for the convenience of describing the invention, not to indicate or imply that the device referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation on the scope of protection of this invention. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0065] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0066] The above are merely specific embodiments of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept shall be considered as infringing upon the protection scope of the present invention.

Claims

1. A method for detecting a communication signal by multi-modulus-quantizer branch terahertz single-bit quantization, characterized in that: Includes the following steps: Step S1: Construct a terahertz single-bit quantization communication system with multiple analog-to-digital converter branches. Each analog-to-digital converter branch includes a phase rotation module and an analog-to-digital converter connected in sequence. The phase rotation modules of different analog-to-digital converter branches have different phase rotation angles. The communication system is a multi-user multiple-input multiple-output system. Step S2: Represent the communication system using a vector value factor graph; Step S3: Perform vector approximation message passing on the vector value factor graph, iteratively calibrate the estimation of the noiseless received signal, and finally realize the detection of the transmitted symbol. During the message passing process, the single-bit sampling information of different analog-to-digital converter branches is aggregated. In step S2, in the vector value factor graph, all users send symbols. The vector consisting of the noise-free received signals at all receiving antennas Let and represent the two variable nodes of the vector-valued factor graph. Each factor obtained by decomposing the joint probability density function of the communication system according to different vector parameters using Bayes' theorem is represented as a factor node in the vector-valued factor graph. The factor nodes include the conditional probability distribution of the noise-free received signal relative to the transmitted symbol and the channel matrix. The conditional probability distribution of single-bit sampled information of the analog-to-digital converter branch relative to the noise-free received signal. and the prior distribution of transmitted symbols ; In step S3, the vector approximation message passing on the vector value factor graph specifically includes the following steps: Step S31, Initialization: Initialize the two variable nodes and the messages between factor nodes in the vector value factor graph; Step S32, Linearity Detection: Based on the initialization or the two variable nodes from the previous iteration, send the data to the factor node. The message, using the product formula of the Gaussian distribution of vector values, calculates... A linear posterior estimate and A linear posterior estimate ; Step S33: Calculate the likelihood message based on the message passing rules on the factor graph: The result obtained in step S32... A linear posterior estimate Divide by The prior distribution of the linear detection in this round is used to obtain the likelihood message of the transmitted symbol, and this likelihood message is used as the linear detection pair for the transmitted symbol. The basis for iterative correction of the posterior estimate; using the calculation rule of division of complex Gaussian distribution, the result obtained in step S32 is... A linear posterior estimate Divide by Prior distribution of linear detection in this round of iteration This yields the likelihood message used to calibrate the estimate of the noiseless received signal. The likely message This serves as the basis for iterative calibration of the estimation of the noiseless received signal using linear detection; Step S34, Update Posterior estimation: Using a scalar Gaussian denoiser, prior information about the transmitted symbol modulation constellation is incorporated to calculate... Posterior estimated mean and the corresponding posterior estimate of scalar variance and will Likelihood messages are removed according to the calculation rules of the complex Gaussian distribution. To obtain the variable node corresponding to the sent symbol The corresponding factor node message, i.e., the next round of iterative linear detection. The prior distribution; Step S35, Update Posterior estimation: Sampling information from each analog-to-digital converter branch is aggregated through phase alignment, and Bayes' theorem is applied to update the linear detection for the next iteration. The prior distribution; Step S36: Repeat steps S32 to S35 until the number of iterations reaches the set maximum, or the user sends a symbol. If the estimated variance is below a set threshold, then vector approximation message passing stops. posterior estimation For output, based on Hard decision is made using the Euclidean distances between the points in the modulation constellation and the signal detection results.

2. The communication signal detection method using terahertz single-bit quantization in a multi-analog-to-digital converter branch according to claim 1, characterized in that: In step S1, the received signal model in the communication system is represented as follows: The vector composed of the noiseless received signals at all receiving antennas is represented as ,in, The single-bit quantization process implemented in the analog-to-digital converter branch. This is a vector composed of the phase rotation angles of the phase rotation modules in each analog-to-digital converter branch. It is the identity matrix. The equivalent channel matrix for terahertz block fading baseband is derived from the center frequency of the communication system, the locational relationship between the base station and each user, and the surrounding scattering environment. Symbols are transmitted to all users, and these transmitted symbols satisfy a discrete uniform distribution on the modulation constellation diagram used by the communication system. It is a zero-mean Gaussian white noise matrix.

3. The method of claim 2, wherein: In the step S3, in corresponding factor nodes and When message passing between the corresponding variable nodes, a phase alignment operation is set with the phase difference between different analog-to-digital converter branches as a parameter to aggregate single-bit sampling information of different analog-to-digital converter branches.

4. The method of claim 3, wherein: In step S32, the linear posterior estimation middle, Let be the mean of this linear posterior estimate. Let be the mean of the variance of this linear posterior estimate, where Describes the Gaussian distribution in the complex field. Let X represent the mean of the prior X used for linear detection. Let X represent the variance of the prior X used for linear detection. This represents the mean of the likelihood estimate of the noiseless received signal used for linear detection. This represents the variance of the likelihood estimate for the noiseless received signal. express The conjugate transpose of . Represents the identity matrix. This represents the mean of the diagonal elements of the matrix; the linear posterior estimate middle, Let be the mean of this linear posterior estimate. Let be the mean of the variance of this linear posterior estimate, where This indicates the number of users per antenna transmitting signals. Indicates the number of receiving antennas of the base station; in step S33, the likelihood message middle, , .

5. The method of claim 4, wherein: In step S34, the middle, , , ,in, This indicates calculating the mean. Indicates to The mean of the messages from the nodes, i.e., the pair given by linear detection. The mean of the likelihood estimate, Indicates to The variance of the node's messages, i.e., the variance given by linear detection. The variance of the likelihood estimate, This indicates the calculation of variance. This indicates a proportional relationship, meaning that the constant coefficients other than the exponent term e are omitted from the expression on the right side of the symbol.

6. The method of claim 5, wherein: In step S35, the Bayesian formula applied is: , This represents the posterior distribution of the noiseless received signal relative to the single-bit sampled information. This represents the likelihood distribution of the noiseless received signal relative to the single-bit sampled information. This represents the prior distribution of the noiseless received signal. This represents the single-bit sampled information of each analog-to-digital converter branch on all receiving antennas. This represents the joint probability distribution of single-bit sampled information from each analog-to-digital converter branch.

7. The communication signal detection method for terahertz single-bit quantization of a multi-analog-to-digital converter branch according to claim 6, characterized in that: In step S35, the step of aggregating the sampling information of each analog-to-digital converter branch through phase alignment includes the following steps: Step S351: First calculate the posterior distribution corresponding to the first analog-to-digital converter branch. If the phase rotation angle of the first analog-to-digital converter branch is Then the mean of the posterior distribution is and variance Using the formulas respectively and Calculate, where, , When the quantization sampling result is less than 0, , When the quantization sampling result is greater than 0, , , This represents the sampling information of the first analog-to-digital converter branch on all receiving antennas. This represents the variance of the zero-mean Gaussian white noise at the receiving end of the communication system. The standard Gaussian probability density function is represented in The value at that location, The standard Gaussian cumulative distribution function is represented in The value at; Step S352, Introduce the first One analog-to-digital converter branch, considering phase rotation When obtaining subsequent sampling information, it is necessary to update the posterior distribution. The mean is first multiplied by the phase alignment coefficient. , then calculate and Then multiply by the coefficient. To eliminate the influence of the phase alignment coefficient, i.e., to obtain up to the [number]th ... The posterior distribution of each analog-to-digital converter branch The mean and variance, This represents the single-bit sampled information of the k-th analog-to-digital converter branch on all receiving antennas; Step S353, repeating step S352 until the sampling information of all analog-to-digital converter branches is taken into account, i.e. the mean and variance of the posterior distribution in the current iteration of the noiseless received signal are obtained .

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