Single carrier signal detection method and device based on expansion factor graph and Gaussian interference

Abstract

The invention discloses a single-carrier signal detection method and device based on an expansion factor graph and Gaussian interference, relates to the field of digital signal processing, solves the problem that a traditional single-carrier frequency domain equalization technology and a message passing algorithm are invalid in an SCMA system, and is characterized in that a signal model is established according to a received signal; modeling the interference term as a Gaussian random variable distribution model to obtain Gaussian information; constructing an expansion factor graph; performing function node updating to obtain transmission information; the transmission message is transmitted between the function node and the variable node according to the expansion factor graph; performing variable node updating on the transmitted information to obtain a normalized variable node message; returning the variable node message to the function node for iterative updating until a preset number of times is reached; after the last iteration is completed, calculating and outputting the log-likelihood ratio of the bit stream according to the variable node information obtained after the iteration update; and the effects of remarkably reducing the calculation burden and remarkably improving the detection performance are achieved.

Description

Technical Field

[0001] The present invention relates to the field of digital signal processing, and more particularly to a single carrier signal detection method and device based on an expansion factor graph and Gaussian interference. Background Art

[0002] Future wireless communication systems will require higher system capacity to meet the demands of massive access and high-speed transmission. Non-orthogonal Multiple Access (NOMA) has become a promising candidate. Sparse Code Multiple Access (SCMA), a representative technology of code-domain NOMA, directly maps the original bit stream into multidimensional codewords to achieve significant shaping gain. Receiver design is a key issue in SCMA systems and crucial for meeting the massive access and high-speed transmission requirements of future communication systems.

[0003] SCMA systems perform signal detection based on the Belief Propagation (BP) principle. A representative example is the Message Passing Algorithm (MPA), which implements Bayesian posterior probability inference through iterative updates and can approach maximum likelihood (ML) detection performance. Given that the high computational complexity of MPA exceeds the processing capabilities of practical hardware, the Approximate Expectation Propagation Algorithm (AEPA) implements SCMA detection with only a slight performance loss. Such methods can reduce complexity from exponential to linear.

[0004] For complex urban communication scenarios, frequency selective fading caused by multipath effects cannot be ignored, especially in broadband signal transmission, which will cause severe inter-symbol interference. Orthogonal Frequency Division Multiplexing (OFDM) systems are naturally resistant to ISI. However, the high Peak to Average Power Ratio (PAPR) caused by multi-carrier technology restricts the application of SCMA in long-distance transmission and low-cost sensor networks. Single-carrier solutions face the technical bottleneck of severe inter-symbol interference (ISI) and multi-user interference (MUI) superposition in frequency selective channels. Traditional single-carrier frequency domain equalization (SC-FDE) technology can compensate for the signal distortion caused by multipath fading channels, but this technology is ineffective for SC-SCMA. This is because different user symbols are non-orthogonally superimposed on the same resource unit, and each user signal experiences different channel characteristics, making it impossible to complete multi-channel joint equalization through a single tap coefficient.

[0005] Therefore, for the frequency selective channel uplink, in order to address the problem that the traditional single-carrier frequency domain equalization technology SC-FDE and the message passing algorithm MPA fail in the SCMA system, a reliable and low-complexity SC-SCMA signal detection solution is urgently needed to solve the above technical problems. Summary of the Invention

[0006] In order to address the deficiencies in the prior art, the purpose of the present invention is to provide a single-carrier signal detection method and device based on an extended factor graph and Gaussian interference. By targeting the intra-symbol interference, inter-symbol interference and non-orthogonal superposition interference of the SC-SCMA system, a self-interference analysis model is proposed to accurately characterize the signal composition. The factor graph is expanded in dimension through the maximum multipath delay to form an extended factor graph mapping characterization method, which accurately characterizes the multi-user detection process. By modeling the multipath interference signal as a Gaussian random variable, the detector complexity increases linearly with the number of multipath signals, significantly reducing the computational burden and significantly improving the detection performance.

[0007] The above technical objectives of the present invention are achieved through the following technical solutions:

[0008] In a first aspect, a single carrier signal detection method based on an expansion factor graph and Gaussian interference is provided, comprising the following steps:

[0009] S1. Establish a signal model based on the received signal;

[0010] S2. Modeling the interference term in the signal model as a Gaussian random variable distribution model, and determining Gaussian information according to the Gaussian random variable distribution model;

[0011] S3. Constructing a spreading factor graph based on the maximum multipath delay spread of the actual channel;

[0012] S4. Update the function node according to the Gaussian information and the received signal, calculate and obtain transfer information, and transfer the transfer message between the function node and the variable node through the extended factor graph;

[0013] S5. Update the variable node according to the transferred information, aggregate all the transferred messages and complete normalization calculation to obtain a normalized variable node message;

[0014] S6. Return the variable node message to the function node for iterative update until a preset number of times is reached;

[0015] S7, during the iteration process, the variable node message is mixed with the variable node information in multiple iterations by using a damping factor;

[0016] S8. After the last iteration is completed, calculation is performed based on the variable node message obtained after the iterative update, and the log-likelihood ratio of the bit stream is output.

[0017] Furthermore, the calculation formula of the signal model in step S1 is:

[0018]

[0019] Among them, y j (n) is the received signal vector at the nth symbol position in the jth time slot; h j,d (n,p) is the channel response of the p-th signal of the d-th user at the n-th symbol position in the j-th time slot; x j,d (n-L+p) is the symbol at the n-L+pth symbol position of the dth user in the jth time slot; x j,k (n-L+1) is the symbol at the n-L+1th symbol position of the dth user in the jth time slot; Ω j is the set of users carried by the jth time slot; h j,k (n, l) represents the channel response of the lth signal of the kth user at the nth symbol position in the jth time slot; w j (n) is the nth noise in the jth time slot.

[0020] Furthermore, in step S2, the Gaussian information includes the interference term mean and the interference term variance;

[0021] The interference term mean is obtained by inputting the channel response estimation value and the symbol mean into the Gaussian random variable distribution model, and the calculation formula is:

[0022]

[0023] Among them, μ j,d (n,p) is the expected interference term of the p-th path signal of the d-th user at the n-th symbol position in the j-th time slot; x j,k (n-L+l) is the symbol at the n-L+lth symbol position of the kth user in the jth time slot; E[x j,k (n-L+l)] is the symbol x j,k The mean of (n-L+l); Ω represents the channel response estimate of the lth signal of the kth user at the nth symbol position in the jth time slot; j is the set of users carried by the j-th time slot.

[0024] The interference term variance is obtained by inputting the channel response estimation value, symbol variance and noise power into the Gaussian random variable distribution model, and the calculation formula is:

[0025]

[0026] in, is the variance of the interference term of the p-th path signal of the d-th user at the n-th symbol position in the j-th time slot; Var[x j，t (n-L+l)] is the symbol x j,k The variance of (n-L+l); N0 is the noise power.

[0027] Furthermore, in step S3, the maximum multipath delay is extended to a product of the first multiple and the number of symbol periods, and the dimension of the original factor graph is extended according to the first multiple.

[0028] Furthermore, in step S4, the transfer information calculation formula is:

[0029]

[0030] in, F j Passed to V d The m-th order information of the p-th path signal; V d is the dth variable node; F j is the jth functional node; The m-th order symbol of .

[0031] Furthermore, in step S5, the calculation formula of the variable node message is:

[0032]

[0033] in, V k Pass to F z The m-th order information of the p-th path signal; L is the number of multipath signals; M is the modulation order; F j Passed to V k The m-th order information of the l-th path signal; F j Passed to V k The m'th order information of the lth path signal.

[0034] Furthermore, in step S7, the calculation formula of the variable node message mix is:

[0035]

[0036] Where t is the number of iterations; α is the damping factor, α∈[0,1]; V after the tth iteration k Pass to F z Mixed variable node message.

[0037] Furthermore, in step S8, the log-likelihood ratio of the bit stream is calculated as follows:

[0038]

[0039] Among them, LLR k,b is the log-likelihood ratio of the b-th bit of the k-th user; m b =1 and m b =0 are the modulation orders corresponding to when the b-th bit is 1 and 0 respectively; Ω k The number of time slots sent for the kth user; F j Passed to V k The m-th order information of the l-th path signal.

[0040] In a second aspect, a single carrier signal detection device based on an expansion factor graph and Gaussian interference is provided, comprising:

[0041] A signal module, used for establishing a signal model according to a received signal;

[0042] A Gaussian model module, configured to model the interference term in the signal model as a Gaussian random variable distribution model, and determine Gaussian information according to the Gaussian random variable distribution model;

[0043] An expansion factor module is used to construct an expansion factor graph based on the maximum multipath delay spread of the actual channel;

[0044] a function node updating module, configured to update the function node according to the Gaussian information and the received signal, calculate and obtain transfer information, and transfer the transfer message between the function node and the variable node via the extended factor graph;

[0045] a variable node updating module, configured to update the variable node according to the transfer information, aggregate all the transfer messages and perform normalization calculation to obtain normalized variable node messages;

[0046] An iteration module is used to return the variable node message to the function node for iterative update until a preset number of times is reached; during the iteration process, the variable node message is mixed with the variable node information of multiple iterations by a damping factor;

[0047] The log-likelihood ratio output module is used to calculate and output the log-likelihood ratio of the bit stream according to the variable node message obtained after the iterative update after the last iteration is completed.

[0048] According to a third aspect, an electronic device is provided, comprising the single-carrier signal detection device based on an expansion factor graph and Gaussian interference as described in the second aspect.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. This invention proposes a signal model to accurately characterize the signal composition by targeting intra-symbol interference, inter-symbol interference, and non-orthogonal superposition interference in the SC-SCMA system. It also expands the dimension of the factor graph through the maximum multipath delay and forms an extended factor graph mapping representation method to accurately characterize the multi-user detection process. By modeling the multipath interference signal as a Gaussian random variable, the detector complexity increases linearly with the number of multipath signals, significantly reducing the computational burden and significantly improving the detection performance.

[0051] 2. By using an expansion factor graph to characterize the relationship between functional nodes (FNs) and variable nodes (VNs) in a frequency-selective channel environment, the present invention can accurately characterize the signal transmission relationship between the transmitter and receiver in a multipath propagation environment, thereby significantly improving detection performance.

[0052] 3. The confidence propagation detection device based on Gaussian approximate interference of the expansion factor graph proposed in the present invention solves the problem that traditional single-carrier frequency domain equalization technology and message passing algorithm technology fail in the SCMA system, and its computational complexity only increases linearly with the number of multipath signals, providing a reliable low-complexity SC-SCMA signal detection solution for ultra-dense access systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0054] Figure 1 This is a flowchart of Example 1 of the present invention;

[0055] Figure 2 This is a schematic diagram of Example 1 of the present invention;

[0056] Figure 3 is the expansion factor graph in embodiment 1 of the present invention;

[0057] Figure 4 This is a module block diagram in Example 2 of the present invention. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0059] Embodiment 1: The present invention provides a single carrier signal detection method based on an expansion factor graph and Gaussian interference, such as Figure 1 As shown, the following steps are included:

[0060] S1. Establish a signal model based on the received signal for system intra-symbol interference, inter-symbol interference and non-orthogonal superposition interference;

[0061] S2. Modeling the interference term in the signal model as a Gaussian random variable distribution model, and determining Gaussian information based on the Gaussian random variable distribution model;

[0062] S3. Constructing a spreading factor graph based on the maximum multipath delay spread of the actual channel;

[0063] S4. Update the function node according to the Gaussian information and the received signal, calculate the transfer information, and transfer the transfer message between the function node and the variable node through the extended factor graph;

[0064] S5. Update the variable node according to the transmitted information, aggregate all transmitted messages and complete normalization calculation to obtain normalized variable node messages;

[0065] S6. Return the variable node message to the function node for iterative update until a preset number of times is reached;

[0066] S7. During the iteration process, the variable node message is mixed with the variable node information in multiple iterations through the damping factor;

[0067] S8. After the last iteration is completed, calculation is performed based on the variable node message obtained after the iterative update, and the log-likelihood ratio of the bit stream is output.

[0068] The present invention defines the overload factor as ρ = K / J, which characterizes the system resource multiplexing capability, where K represents the number of users and J represents the number of time slots. In some implementations, an SCMA codebook configuration scheme of ρ = 150% is generally adopted, the multiplexing degree η of each physical resource element (RE) is 3, and the multiplexing degree λ of each user is 2. It should be noted that the present invention is based on the time-division multiplexing (TDM) system architecture, in which the time slot (TS) is the minimum scheduling unit, so the physical resource unit corresponds to a TS, and each TS transmits N symbols, that is, it contains N symbol positions (SP).

[0069] like Figure 2 As shown in FIG. 1 , the present invention relates to an uplink SC-SCMA system diagram. The basic system unit includes J TSs and K users. For the kth user, the SCMA encoder maps log2M coded bits into a J-dimensional codeword x k (n) = [x 1，k (n), x 2，k (n), ..., x J，k (n)] T , where x j,k (n) represents the symbol of the nth SP of the kth user in the jth TS. Due to the sparsity of SCMA, a small part of x j,k (n) has a non-zero value.

[0070] In the SC-SCMA system, the interference characteristics are more complex, specifically manifested in the coexistence of intra-symbol interference, inter-symbol interference and multi-user interference.

[0071] In step S1, for example, under the condition of a quasi-static multipath fading channel with a memory length of L, the system of the present invention receives a signal vector y(n) = [y1(n), y2(n), K, y J (n)] T It can be modeled as:

[0072]

[0073] x k (nl)=[x 1，k (nl), x 2，k (nl),...,x J，k(nl)] T ; Wherein, y(n) is the received signal vector; is the channel response matrix; is the multipath signal vector of the kth user.

[0074] At the same time, the channel response matrix It can be expressed as:

[0075]

[0076] Among them, h j,k (n, l) represents the channel response of the lth signal of the kth user in the nth SP of the jth TS, and the channel responses of different users are independent.

[0077] If the target signal is the p-th path signal of the d-th user, based on the central limit theorem, the interference signal received at the n-th symbol position SP in the j-th time slot TS can be modeled as approximately obeying the Gaussian random variable distribution. Therefore, in step S1, a signal model is established according to the received signal, and the received signal y j (n) can be expressed as:

[0078]

[0079] Among them, y j (n) is the received signal vector at the nth symbol position in the jth time slot; h j,d (n,p) is the channel response of the p-th signal of the d-th user at the n-th symbol position in the j-th time slot; x j,d (n-L+p) is the symbol at the n-L+pth symbol position of the dth user in the jth time slot; x j,k (n-L+1) is the symbol at the n-L+1th symbol position of the dth user in the jth time slot; Ω j is the set of users carried by the jth time slot; h j,k (n, l) represents the channel response of the lth signal of the kth user at the nth symbol position in the jth time slot; w j (n) is the nth noise in the jth time slot; Z j,d (n,p) represents the interference term and obeys

[0080]

[0081] The basic principle for updating the functional nodes is to calculate the marginal conditional probability of each user based on the received signal.

[0082] In step S2, the Gaussian information includes the interference term mean and the interference term variance; the interference term mean is obtained by inputting the channel response estimate and the symbol mean into the Gaussian random variable distribution model, and the calculation formula is:

[0083]

[0084] Among them, μ j,d (n,p) is the expected interference term of the p-th path signal of the d-th user at the n-th symbol position in the j-th time slot; x j,k (n-L+l) is the symbol at the n-L+lth symbol position of the kth user in the jth time slot; E[x j,k (n-L+l)] is the symbol x j,k The mean of (n-L+l); Ω represents the channel response estimate of the lth signal of the kth user at the nth symbol position in the jth time slot; j is the set of users carried by the j-th time slot.

[0085] The mean interference term is obtained by inputting the channel response estimate, symbol variance, and noise power into the Gaussian random variable distribution model. The calculation formula is:

[0086]

[0087] in, is the variance of the interference term of the p-th path signal of the d-th user at the n-th symbol position in the j-th time slot; Var[x j，k (n-L+l)] is the symbol x j,k The variance of (n-L+l); N0 is the noise power.

[0088] in:

[0089]

[0090] Var[x j,k (n-L+l)]=E[|x j,k (n-L+l)| 2 ]-E 2 [x j,k (n-L+l)]

[0091] For frequency-selective fading channels, the original factor graph fails to accurately and completely represent the relationship between functional nodes (FNs) and variable nodes (VNs) in frequency-selective channel environments, resulting in degraded detection performance. Therefore, the original factor graph is not suitable for frequency-selective fading channels and needs to be expanded.

[0092] In step S3, the maximum multipath delay is extended to a product of the first multiple and the number of symbol periods, and the dimension of the original factor graph is extended according to the first multiple.

[0093] In some embodiments, in a frequency selective channel, the user signal may experience a multipath delay spread channel. This channel characteristic will directly affect the distribution characteristics of the connection sequence and the number of edges in the factor graph. Specifically, the expansion dimension of the factor graph depends on the maximum delay spread of the actual channel. For example, if the symbol period is defined as T s And the maximum multipath delay spread is L×T s , the dimension of the factor graph needs to be expanded by L times, where L is the first multiple.

[0094] The present invention constructs an expansion factor graph structure suitable for frequency selective channels, such as Figure 3 It can be clearly observed through this structure that the factor graph is expanded in the dimension of the number of multipath signals, and the connection rules between functional nodes and variable nodes are redefined. This improved scheme can accurately characterize the signal transmission relationship between the transmitter and the receiver in a multipath propagation environment. Based on this innovative structure, the present invention converts multipath signals into useful information rather than interference signals for processing, thereby achieving a significant improvement in detection performance.

[0095] In step S4, using μ j,d (n,p) and σ 2 j,d (n,p) can obtain the information transmission information of the p-th path signal transmitted from the j-th function node FN to the d-th variable node VN The calculation formula is:

[0096]

[0097] in, F j Passed to V d The m-th order information of the p-th path signal; V d is the dth variable node; F j is the jth functional node; is x j,d The m-th order symbol of (n-L+p).

[0098] In step S5, the main processing of the variable node VN update is to aggregate the messages transmitted by all functional nodes FNs and complete the normalization calculation. Specifically, the variable node message of the p-th path signal transmitted from the d-th variable node VN to the j-th functional node FN is calculated as follows:

[0099]

[0100] in, V k Pass to F z The m-th order information of the p-th path signal; L is the number of multipath signals; M is the modulation order; F j Passed to V k The m-th order information of the l-th path signal; F j Passed to V k The m'th order information of the lth path signal.

[0101] Message damping is crucial in improving the convergence speed of the iterative message passing algorithm. In the tth iteration, the damping factor α is used to mix the current message with the message in the t-1th iteration. Therefore, in this mechanism, the calculation formula for the variable node message mixing in step S7 is:

[0102]

[0103] Where t is the number of iterations; α is the damping factor, α∈[0,1]; V after the tth iteration k Pass to F z Mixed variable node message.

[0104] In step S8, after completing multiple updates of the function nodes FN and the variable nodes VN, the logarithmic likelihood ratio (LLR) of the b-th bit of the k-th user is calculated as follows:

[0105]

[0106] Among them, LLR k,b is the log-likelihood ratio of the b-th bit of the k-th user; m b =1 and m b =0 are the modulation orders corresponding to when the b-th bit is 1 and 0 respectively; Ω k The number of time slots sent for the kth user; F j Passed to V k The m-th order information of the l-th path signal.

[0107] In Example 2, in order to solve the problem of failure of traditional single carrier frequency domain equalization SC-FDE technology and message passing algorithm MPA, the present invention proposes a single carrier signal detection device based on expansion factor graph and Gaussian interference, such as Figure 4As shown, it includes: a signal module, a Gaussian model module, an expansion factor module, a function node update module, a variable node update module, an iteration module, and a log-likelihood ratio output module, wherein the signal module is used to establish a signal model according to the received signal for system intra-symbol interference, inter-symbol interference, and non-orthogonal superposition interference; the Gaussian model module is used to model the interference term in the signal model as a Gaussian random variable distribution model, and obtain Gaussian information according to the Gaussian random variable distribution model; the expansion factor module is used to construct an expansion factor graph according to the maximum multipath delay spread of the actual channel; the function node update module is used to update the function node according to the Gaussian information and the received signal, and calculate the transmission information. The transmission information is transmitted between the functional node and the variable node through the extended factor graph; the variable node update module is used to update the variable node according to the transmission information, aggregate all the transmission messages and complete the normalization calculation to obtain the normalized variable node message; the iteration module is used to return the variable node message to the functional node for iterative update until the preset number of times is reached; during the iteration process, the variable node message is mixed with the variable node information in multiple iterations through the damping factor; the log-likelihood ratio output module is used to calculate according to the variable node message obtained after the iterative update after the last iteration is completed, and output the log-likelihood ratio of the bit stream.

[0108] The function node update module includes: a function node update unit, which is used to update the function node according to Gaussian information and the received signal, and calculate the transmission information; an expansion factor unit, which is used to transmit the transmission message between the function node and the variable node according to the expansion factor graph.

[0109] In a third embodiment, the present invention provides an electronic device, which includes the single-carrier signal detection device based on an expansion factor graph and Gaussian interference as described in the second embodiment.

[0110] Working principle: By targeting the intra-symbol interference, inter-symbol interference and non-orthogonal superposition interference of the SC-SCMA system, a signal model is proposed to accurately characterize the signal composition. The dimension of the factor graph is expanded through the maximum multipath delay to form an extended factor graph mapping representation method, which accurately characterizes the multi-user detection process. By modeling the multipath interference signal as a Gaussian random variable, the detector complexity increases linearly with the number of multipath signals, significantly reducing the computational burden and significantly improving the detection performance.

[0111] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0115] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A single carrier signal detection method based on an expansion factor graph and Gaussian interference, characterized in that: The steps include: S1. Establish a signal model based on the received signal; S2. Modeling the interference term in the signal model as a Gaussian random variable distribution model, and determining Gaussian information according to the Gaussian random variable distribution model; S3. Constructing a spreading factor graph based on the maximum multipath delay spread of the actual channel; S4. Update the function node according to the Gaussian information and the received signal, calculate and obtain transfer information, and transfer the transfer message between the function node and the variable node through the extended factor graph; S5. Update the variable node according to the transferred information, aggregate all the transferred messages and complete normalization calculation to obtain a normalized variable node message; S6. Return the variable node message to the function node for iterative update until a preset number of times is reached; S7, during the iteration process, the variable node message is mixed with the variable node information in multiple iterations by using a damping factor; S8. After the last iteration is completed, calculation is performed based on the variable node message obtained after the iterative update, and the log-likelihood ratio of the bit stream is output.

2. The signal detection method based on Gaussian approximate interference and parallel architecture according to claim 1, characterized in that: The calculation formula of the signal model in step S1 is: Among them, y j (n) is the received signal vector at the nth symbol position in the jth time slot; h j,d (n,p) is the channel response of the p-th signal of the d-th user at the n-th symbol position in the j-th time slot; x j,d (n-L+p) is the symbol at the n-L+pth symbol position of the dth user in the jth time slot; x j,k (n-L+1) is the symbol at the n-L+1th symbol position of the dth user in the jth time slot; Ω j is the set of users carried by the jth time slot; h j,k (n, l) represents the channel response of the lth signal of the kth user at the nth symbol position in the jth time slot; w j (n) is the nth noise in the jth time slot.

3. The single carrier signal detection method based on expansion factor graph and Gaussian interference according to claim 1, characterized in that In step S2, the Gaussian information includes the interference term mean and the interference term variance; The interference term mean is obtained by inputting the channel response estimation value and the symbol mean into the Gaussian random variable distribution model, and the calculation formula is: Among them, μ j,d (n,p) is the expected interference term of the p-th path signal of the d-th user at the n-th symbol position in the j-th time slot; x j,k (n-L+l) is the symbol at the n-L+lth symbol position of the kth user in the jth time slot; E[x j,k (n-L+l)] is the symbol x j,k The mean of (n-L+l); Ω represents the channel response estimate of the lth signal of the kth user at the nth symbol position in the jth time slot; j is the set of users carried by the j-th time slot. The interference term variance is obtained by inputting the channel response estimation value, symbol variance and noise power into the Gaussian random variable distribution model, and the calculation formula is: in, is the variance of the interference term of the p-th path signal of the d-th user at the n-th symbol position in the j-th time slot; Var[x j，k (n-L+l)] is the symbol x j,k The variance of (n-L+l); N0 is the noise power.

4. The single carrier signal detection method based on expansion factor graph and Gaussian interference according to claim 1, characterized in that In step S3, the maximum multipath delay is extended to a product of the first multiple and the number of symbol periods, and the dimension of the original factor graph is extended according to the first multiple.

5. The single carrier signal detection method based on expansion factor graph and Gaussian interference according to claim 1, characterized in that: In step S4, the transfer information calculation formula is: in, F j Passed to V d The m-th order information of the p-th path signal; V d is the dth variable node; F j is the jth functional node; is x j,d The m-th order symbol of (n-L+p).

6. The single carrier signal detection method based on expansion factor graph and Gaussian interference according to claim 1, characterized in that: In step S5, the calculation formula of the variable node message is: in, V k Pass to F z The m-th order information of the p-th path signal; L is the number of multipath signals; M is the modulation order; F j Passed to V k The m-th order information of the l-th path signal; F j Passed to V k The m'th order information of the lth path signal.

7. The single carrier signal detection method based on expansion factor graph and Gaussian interference according to claim 1, characterized in that: In step S7, the calculation formula of the variable node message mix is: Where t is the number of iterations; α is the damping factor, α∈[0,1]; V after the tth iteration k Pass to F z Mixed variable node message.

8. The single carrier signal detection method based on expansion factor graph and Gaussian interference according to claim 1, characterized in that: In step S8, the log-likelihood ratio of the bit stream is calculated as follows: Among them, LLR k,b is the log-likelihood ratio of the b-th bit of the k-th user; m b =1 and m b =0 are the modulation orders corresponding to when the b-th bit is 1 and 0 respectively; Ω k The number of time slots sent for the kth user; F j Passed to V k The m-th order information of the l-th path signal.

9. A single carrier signal detection device based on an expansion factor graph and Gaussian interference, characterized in that: include: A signal module, used for establishing a signal model according to a received signal; A Gaussian model module, configured to model the interference term in the signal model as a Gaussian random variable distribution model, and determine Gaussian information according to the Gaussian random variable distribution model; An expansion factor module is used to construct an expansion factor graph based on the maximum multipath delay spread of the actual channel; a function node updating module, configured to update the function node according to the Gaussian information and the received signal, calculate and obtain transfer information, and transfer the transfer message between the function node and the variable node via the extended factor graph; a variable node updating module, configured to update the variable node according to the transfer information, aggregate all the transfer messages and perform normalization calculation to obtain normalized variable node messages; An iteration module, configured to return the variable node message to the function node for iterative update until a preset number of times is reached; During the iteration process, the variable node message is mixed with the variable node information in multiple iterations by a damping factor. The log-likelihood ratio output module is used to calculate and output the log-likelihood ratio of the bit stream according to the variable node message obtained after the iterative update after the last iteration is completed.

10. An electronic device, characterized in that: The electronic device comprises the single carrier signal detection device based on expansion factor graph and Gaussian interference as claimed in claim 8.

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