An Optimized Expectation Propagation Detection Method and a Signal Detection Device

Abstract

The present application relates to an optimized expectation propagation detection method and a signal detection device. First, obtain the coding information, modulation information, channel matrix between the transmitting end and the receiving end, and received signal of the pre-coding bit sequence, construct a linear programming model, determine the prior probability of the transmitted symbol and the initial parameters according to the linear programming model, perform a first iterative convergence operation according to the prior probability of the transmitted symbol and the initial parameters, obtain the target log-likelihood ratio of the pre-coding bit sequence corresponding to the end of the preset first iterative convergence operation, and determine the pre-coding bit sequence according to the target log-likelihood ratio. In this method, more reasonable and effective initial parameters and prior information of the transmitted symbol are obtained for signal detection, so that compared with the traditional signal detection method in the present application, an accurate pre-coding bit sequence can be obtained with fewer iterative times.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technologies, and particularly to an optimized expectation propagation detection method and a signal detection device. Background Art

[0002] Multiple-input multiple-output (MIMO) technology has high spectral efficiency and energy efficiency and has become a key technology in modern wireless communication systems. Therefore, the detection of MIMO is particularly important.

[0003] Taking the expectation propagation detection method for MIMO detection as an example, in the expectation propagation detection method, a receiver usually exchanges extrinsic information between a detector and a decoder during the iterative process of detection and decoding, so that the detector and the decoder can obtain accurate soft information, enabling the receiver to determine the transmission sequence before encoding.

[0004] However, the expectation propagation detection method in the prior art requires multiple iterations to achieve ideal performance. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide an optimized expectation propagation detection method and a signal detection device, which can achieve better detection performance with fewer iterations.

[0006] In a first aspect, the present application provides a signal detection method, which includes:

[0007] Obtain the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the bit sequence before encoding, and construct a linear programming model, where the coding information includes the code length and the code rate;

[0008] Determine the prior probability and initial parameters of the transmitted symbol according to the linear programming model; the transmitted symbol is generated from the bit sequence after encoding according to the modulation information;

[0009] Perform a first iterative convergence operation according to the prior probability and initial parameters of the transmitted symbol, and determine the posterior log-likelihood ratio of the posterior probability of the bit sequence before encoding at each first iterative convergence operation;

[0010] Determine the bit sequence before encoding according to the target log-likelihood ratio corresponding to the bit sequence before encoding, where the target log-likelihood ratio is the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation.

[0011] In one embodiment, performing a first iterative convergence operation according to the prior probability and initial parameters of the transmitted symbol, and determining the posterior log-likelihood ratio of the posterior probability of the bit sequence before encoding at each first iterative convergence operation includes:

[0012] Determine the cavity log-likelihood ratio of the encoded bit sequence during each first iterative convergence operation according to the prior probability, initial parameters, and the mapping relationship between the preset bit sequence and symbols.

[0013] Determine the posterior log-likelihood ratio corresponding to each first iterative convergence operation according to the cavity log-likelihood ratio during each first iterative convergence operation.

[0014] In one embodiment, obtain the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the bit sequence before coding, and construct a linear programming model, including:

[0015] Obtain the set of frozen bits of the transmitted symbol, where the coding information includes the set of frozen bits.

[0016] Determine the variation relationship between the bit sequence before coding and the bit sequence after coding according to the code length, code rate, and set of frozen bits.

[0017] Determine the objective function according to the mapping relationship, modulation information, channel matrix, and received signal.

[0018] Determine the linear programming model according to the variation relationship and the objective function.

[0019] In one embodiment, determine the objective function according to the mapping relationship, channel matrix, and received signal, including:

[0020] Based on the mapping relationship and modulation information, determine the transmitted symbol using the encoded bit sequence.

[0021] Determine the predicted received signal according to the transmitted symbol and the channel matrix.

[0022] Construct the objective function according to the predicted received signal and the received signal; the objective function is used to characterize the error between the predicted received signal and the received signal.

[0023] In one embodiment, determine the prior probability and initial parameters of the transmitted symbol according to the linear programming model, including:

[0024] Determine the prior probability of the transmitted symbol according to the linear programming model.

[0025] Determine the mean and variance of the prior probability of the transmitted symbol according to the prior probability of the transmitted symbol.

[0026] Determine the first initial parameter according to the mean and variance of the prior probability of the transmitted symbol.

[0027] Determine the second initial parameter according to the variance of the prior probability of the transmitted symbol.

[0028] The initial parameters include a first initial parameter and a second initial parameter.

[0029] In one embodiment, according to the linear programming model, determining the prior probability of the transmitted symbol includes:

[0030] Obtaining the minimum value of the objective function in the linear programming model, and determining the target encoded bit sequence as the encoded bit sequence corresponding to the minimum value of the objective function;

[0031] Determining the prior probability of the target encoded bit sequence according to the encoded value of the target encoded bit sequence;

[0032] Determining the prior probability of the transmitted symbol according to the prior probability of the target encoded bit sequence and the mapping relationship.

[0033] In one embodiment, according to the prior probability, the initial parameters, and the preset mapping relationship between the bit sequence and the symbol, determining the cavity log-likelihood ratio of the cavity probability of the encoded bit sequence during each first iterative convergence operation includes:

[0034] Performing a second iterative convergence operation according to the prior probability and the initial parameters until the preset second iterative convergence end condition is satisfied, to obtain the mean and variance of the cavity probability of the transmitted symbol;

[0035] Determining the cavity log-likelihood ratio of the cavity probability of the encoded bit sequence during each first iterative convergence operation according to the mean and variance of the cavity probability of the transmitted symbol corresponding to the second iterative convergence operation when the second iterative convergence end condition is satisfied, and the mapping relationship.

[0036] In one embodiment, the second iterative convergence operation includes:

[0037] Determining the variance and mean of the cavity probability of the transmitted symbol according to the initial parameters, the system noise variance, and the channel matrix;

[0038] Determining the variance and mean of the discrete posterior probability of the transmitted symbol according to the variance and mean of the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol;

[0039] Updating the initial parameters according to the variance and mean of the discrete posterior probability of the transmitted symbol, and using the updated initial parameters as the initial parameters for the next iteration.

[0040] In one embodiment, determining the variance and mean of the discrete posterior probability of the transmitted symbol according to the variance and mean of the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol includes:

[0041] Determining the cavity probability of the transmitted symbol according to the variance and mean of the cavity probability of the transmitted symbol;

[0042] Determine the discrete posterior probability of the transmitted symbol according to the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol;

[0043] Determine the variance and mean of the discrete posterior probability of the transmitted symbol according to the discrete posterior probability of the transmitted symbol.

[0044] In one embodiment, update the initial parameters according to the variance and mean of the discrete posterior probability of the transmitted symbol, and use the updated initial parameters as the initial parameters for the next iteration, including:

[0045] Determine the first candidate initial parameter according to the variance of the cavity probability of the transmitted symbol and the variance of the discrete posterior probability;

[0046] Determine the second candidate initial parameter according to the variance and mean of the cavity probability of the transmitted symbol, and the variance and mean of the discrete posterior probability;

[0047] Update the first initial parameter according to the first candidate initial parameter to obtain the first initial parameter for the next iteration;

[0048] Update the second initial parameter according to the second candidate initial parameter to obtain the second initial parameter for the next iteration.

[0049] In one embodiment, determine the posterior log-likelihood ratio corresponding to each first iteration convergence operation according to the cavity log-likelihood ratio during each first iteration convergence operation, including:

[0050] Perform decoding operation on the cavity log-likelihood ratio during each first iteration convergence operation to obtain the posterior log-likelihood ratio corresponding to each first iteration convergence operation.

[0051] In one embodiment, the method further includes:

[0052] Determine the log-likelihood ratio of the prior probability of the encoded bit sequence according to the cavity log-likelihood ratio and the candidate log-likelihood ratio of the posterior probability of the encoded bit sequence, where the candidate log-likelihood ratio is obtained by performing decoding operation on the cavity log-likelihood ratio during each first iteration convergence operation;

[0053] Determine the new prior probability of the transmitted symbol according to the log-likelihood ratio of the prior probability of the encoded bit sequence and the mapping relationship, and use the new prior probability as the prior probability of the transmitted symbol for the next iteration.

[0054] In one embodiment, determine the pre-encoded bit sequence according to the target log-likelihood ratio corresponding to the pre-encoded bit sequence, including:

[0055] Perform a hard decision on the target log-likelihood ratio corresponding to the pre-coding bit sequence to obtain the pre-coding bit sequence.

[0056] In a second aspect, the present application also provides a signal detection device, which includes:

[0057] An acquisition module, configured to acquire the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coding bit sequence, and construct a linear programming model, where the coding information includes the code length and the code rate;

[0058] A parameter determination module, configured to determine the prior probability and initial parameters of the transmitted symbol according to the linear programming model; the transmitted symbol is generated from the post-coding bit sequence according to the modulation information;

[0059] A convergence module, configured to perform a first iterative convergence operation according to the prior probability and initial parameters of the transmitted symbol, and determine the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence during each first iterative convergence operation;

[0060] A pre-coding bit sequence determination module, configured to determine the pre-coding bit sequence according to the target log-likelihood ratio corresponding to the pre-coding bit sequence, where the target log-likelihood ratio is the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation.

[0061] In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of any method provided in the first aspect embodiment are implemented.

[0062] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any method provided in the first aspect embodiment are implemented.

[0063] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of any method provided in the first aspect embodiment are implemented.

[0064] An optimized expectation propagation detection method and a signal detection device provided by an embodiment of the present application first obtain the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coding bit sequence, construct a linear programming model, determine the prior probability of the transmitted symbol and the initial parameters according to the linear programming model, perform a first iterative convergence operation according to the prior probability of the transmitted symbol and the initial parameters, and determine the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence during each first iterative convergence operation. According to the target log-likelihood ratio corresponding to the pre-coding bit sequence, the pre-coding bit sequence is determined, where the target log-likelihood ratio is the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation. In this method, a linear programming problem is constructed through the code length, code rate, modulation information, channel matrix, and information of the received signal corresponding to the coding method of the pre-coding bit sequence, and the initialization information of the signal detection method is found using the linear programming problem. By fully considering the construction structure and characteristics of the coding method, more reasonable and effective initial parameters and prior information of the transmitted symbol are obtained for signal detection. Under the same conditions, compared with the traditional detection method, the signal detection method in the present application can obtain the same detection result with fewer iterations in the first iterative convergence operation. The detection efficiency of the signal detection method in the present application is higher. That is, compared with the traditional signal detection method, the signal detection method in the present application can obtain the accurate pre-coding bit sequence with fewer iterations. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is an application environment diagram of the signal detection method in an embodiment;

[0066] Figure 2 It is a flowchart of the signal detection method in an embodiment;

[0067] Figure 3 It is a structural diagram of the signal detection method in an embodiment;

[0068] Figure 4 It is a flowchart of the signal detection method in another embodiment;

[0069] Figure 5 It is a flowchart of the signal detection method in another embodiment;

[0070] Figure 6 It is a factor graph of a polar code in an embodiment;

[0071] Figure 7 It is a flowchart of the signal detection method in another embodiment;

[0072] Figure 8 It is a flowchart of the signal detection method in another embodiment;

[0073] Figure 9 is a schematic flowchart of a signal detection method in another embodiment;

[0074] Figure 10 is a schematic flowchart of a signal detection method in another embodiment;

[0075] Figure 11 is a schematic flowchart of a signal detection method in another embodiment;

[0076] Figure 12 is a schematic flowchart of a signal detection method in another embodiment;

[0077] Figure 13 is a schematic structural diagram of a signal detection system in another embodiment;

[0078] Figure 14 is a schematic flowchart of a signal detection method in another embodiment;

[0079] Figure 15 is a schematic flowchart of a signal detection method in another embodiment;

[0080] Figure 16 is a schematic block diagram of a signal detection device in one embodiment;

[0081] Figure 17 is an internal structural diagram of a computer device in one embodiment. Detailed implementation manners

[0082] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0083] The signal detection method provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the sending end 102 communicates with the receiving end 104. The data storage system can store the data that the receiving end 104 needs to process. The data storage system can be integrated on the receiving end 104, or can be placed in the cloud or other network servers.

[0084] Among them, the sending end can be one end of a transmitting antenna, and the receiving end can be one end of a receiving antenna.

[0085] The embodiments of the present application provide an optimized expected propagation detection method and a signal detection device, which can improve the response speed of functions in the workflow.

[0086] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments.

[0087] In one embodiment, a signal detection method is provided for application in Figure 1 Taking the application environment in as an example, this embodiment involves determining the prior probability and initial parameters of the transmitted symbol according to the constructed linear programming model, and performing a first iterative convergence operation using the prior probability and initial parameters of the transmitted symbol to determine the posterior log-likelihood ratio of the bit sequence before encoding, so as to determine the specific process of the bit sequence before encoding according to the target log-likelihood ratio corresponding to the bit sequence before encoding, as Figure 2 shown, this embodiment includes the following steps:

[0088] S201, obtain the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the bit sequence before encoding, and construct a linear programming model, where the coding information includes the code length and the code rate.

[0089] In the MIMO detection problem, the traditional linear minimum mean square error (MMSE) detection algorithm has a relatively low complexity, but the detection performance is not ideal. The expectation propagation (EP) detection, as a detection algorithm based on the Bayesian approximate posterior probability, can achieve a performance far exceeding that of the MMSE detection after several iterations. Moreover, the EP detection can also naturally output the posterior probability of the detected symbol, so that it can be easily combined with the subsequent decoding module to form an iterative detection and decoding (IDD) system.

[0090] Currently, the IDD receiver based on EP detection usually exchanges extrinsic information between the EP detector and the decoder during the iterative process of detection and decoding, so that the detector and the decoder can obtain more accurate soft information, thereby accelerating the convergence speed and improving the system performance. However, the IDD receivers based on EP detection all focus on the iterative strategy and ignore the coding characteristics. Since polar codes have been used as the forward error correction codes for the downlink control link, the embodiments of the present application are based on the MIMO system encoded by polar codes.

[0091] Taking the IDD receiver based on EP detection as a double EP (DEP) IDD receiver as an example, when this receiver works, it can apply the EP algorithm twice when applying the feedback extrinsic information of the application detector and decoder, and better approximate the discrete output of the decoder through the outer loop, and feedback it to the detector to initialize the inner loop of the next iteration. However, the currently proposed IDD receivers based on the EP detection algorithm do not fully consider the coding characteristics, and some even ignore the coding type. This makes it impossible for the EP detector to use the feedback information from the decoder in the first iteration and can only perform the most primitive EP detection due to following the order of detection first and then decoding. As a result, EP-based IDD receivers such as double EP (DEP) do not consider the coding characteristics during detection and need multiple iterations to achieve ideal performance.

[0092] Therefore, in the embodiments of this application, by combining the coding characteristics, the method of linear programming (LP) is used to find the optimal solution for the initialization of EP.

[0093] First, obtain the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coded bit sequence, and construct a linear programming problem according to the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coded bit sequence; specifically, the coding information includes the code length and code rate, and the polarization code factor graph corresponding to the coding method of the pre-coded bit sequence can be determined according to the code length and code rate, and then a linear programming model is constructed according to the polarization code factor graph, modulation information, channel matrix, and received signal.

[0094] The linear programming model includes an objective function and constraint conditions. Therefore, the linear programming model can be determined by using the method of a network model. Input the polarization code factor graph, modulation information, channel matrix, and received signal into the network model, and through the analysis of the network model, obtain the objective function and constraint conditions of the linear programming model.

[0095] It should be noted that in this application, signal detection is performed taking polarization code coding as an example. Therefore, the coding information includes the code length and code rate, and the polarization code factor graph can be determined according to the code length and code rate. The polarization code factor graph is the coding method of the polarization code. However, the embodiments of this application do not limit the coding method, and different code lengths and code rates result in different polarization code factor graphs, and the embodiments of this application do not limit this.

[0096] S202, determine the prior probability and initial parameters of the transmitted symbol according to the linear programming model.

[0097] Among them, the transmitted symbol is generated according to the modulation information from the coded bit sequence.

[0098] Based on the obtained linear programming model above, that is, the objective function and constraint conditions in the linear programming model, determine the prior probability of the transmitted symbol and the initial parameters. The objective function of the linear programming problem can be to maximize or minimize. According to the actual situation, determine the maximum or minimum value of the objective function.

[0099] Taking the minimization of the objective function as an example, solve the linear programming model according to the objective function and constraint conditions, and determine the prior probability of the transmitted symbol and the initial parameters corresponding to the minimum value of the objective function that satisfy the constraint conditions as the prior probability of the transmitted symbol and the initial parameters.

[0100] It should be noted that the linear programming model is constructed based on the polar code factor graph, the channel matrix, and the received signal. Therefore, during the solution process of the linear programming model, the prior probability of the transmitted symbol can be determined. The initial parameter is the initial parameter of the detector in the first iteration, and the initial parameter is determined based on the initial prior probability. Therefore, the initial parameter can be determined according to the prior probability of the transmitted symbol.

[0101] S203, perform the first iterative convergence operation according to the prior probability of the transmitted symbol and the initial parameters, and determine the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence during each first iterative convergence operation.

[0102] During the MIMO detection process, according to the initial parameters and the prior probability of the transmitted symbol in the first iteration, perform the first iterative convergence operation according to the detector and the decoder. During the first iterative convergence operation, the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence in each iteration process can be obtained.

[0103] As Figure 3 shown, Figure 3 is a schematic diagram of the MIMO structure. Among them, the transmitter transmits the pre-coding bit sequence, performs polar code encoding operation on the pre-coding bit sequence to obtain the post-coding bit sequence, then performs mapping operation on the post-coding bit sequence to determine the transmitted symbol, and the antenna in the transmitting end transmits the transmitted symbol. The receiving antenna at the receiving end receives the signal, the received signal is the received signal, and the received signal is detected and decoded through the detector and the decoder to determine the pre-coding bit sequence corresponding to the received signal.

[0104] The first iterative convergence operation is the iterative convergence in the detector and the decoder. The detector and the decoder perform the first iterative convergence operation according to the prior probability of the transmitted symbol and the initial parameters obtained above, and obtain the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence during each first iterative convergence operation.

[0105] Among them, the posterior log-likelihood ratio refers to the log-likelihood ratio of the posterior probability of the pre-coding bit sequence obtained by the decoder during each iteration process.

[0106] It should be noted that the first iterative convergence operation includes the detection process in the detector and the decoding process in the decoder, and the detection algorithm used in the detector and the decoding algorithm in the decoder are not limited in the embodiments of the present application.

[0107] S204. Determine the pre-encoding bit sequence according to the target log-likelihood ratio corresponding to the pre-encoding bit sequence, where the target log-likelihood ratio is the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation.

[0108] Based on the posterior log-likelihood ratios of the posterior probabilities of the pre-encoding bit sequences at each first iterative convergence operation obtained above, obtain the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation, and determine the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation as the target log-likelihood ratio. Among them, the first iterative convergence operation corresponds to the first iterative convergence end condition, and the end of the first iterative convergence operation means that the first iterative convergence operation satisfies the first iterative convergence end condition. Therefore, the posterior log-likelihood ratio when the first iterative convergence end condition is satisfied is used as the target log-likelihood ratio. For example, if the first iterative convergence end condition is to iterate T times, then the posterior log-likelihood ratio obtained in the T-th iteration is determined as the target log-likelihood ratio corresponding to the pre-encoding bit sequence.

[0109] Then, determine the pre-encoding bit sequence according to the target log-likelihood ratio corresponding to the pre-encoding bit sequence. The log-likelihood ratio (LLR) is often used in soft decoding in communication. For example, the log-likelihood ratio of a bit can be expressed as the natural logarithm of the ratio of the probability that the bit is 0 to the probability that the bit is 1; it can also be expressed as the natural logarithm of the ratio of the probability that the bit is 1 to the probability that the bit is 0.

[0110] If the target log-likelihood ratio is the natural logarithm of the ratio of the probability that the bit is 0 to the probability that the bit is 1, then the probabilities of the bit being 0 and the bit being 1 can be determined according to the first target natural logarithm ratio, and the value of the bit corresponding to the larger probability is determined as the initial bit. Therefore, for the log-likelihood ratio of any bit of the initial bit, the pre-encoding bit sequence is determined.

[0111] The above signal detection method first obtains the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coding bit sequence, constructs a linear programming model, determines the prior probability of the transmitted symbol and the initial parameters according to the linear programming model, performs a first iterative convergence operation according to the prior probability of the transmitted symbol and the initial parameters, and determines the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence at each first iterative convergence operation. According to the target log-likelihood ratio corresponding to the pre-coding bit sequence, the pre-coding bit sequence is determined, where the target log-likelihood ratio is the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation. In this method, a linear programming problem is constructed through the code length, code rate, modulation information, channel matrix, and received signal information corresponding to the coding method of the pre-coding bit sequence, and the initialization information of the signal detection method is found using the linear programming problem. By fully considering the structural characteristics of the coding method, more reasonable and effective initial parameters and prior information of the transmitted symbol are obtained for signal detection. Under the same conditions, compared with the traditional detection method, the signal detection method in this application can perform the first iterative convergence operation with fewer iteration times and obtain the same detection result as the traditional method. The detection efficiency of the signal detection method in this application is higher. That is, compared with the traditional signal detection method, the signal detection method in this application can obtain an accurate pre-coding bit sequence with fewer iteration times.

[0112] In one embodiment, as Figure 4 shown, performing the first iterative convergence operation according to the prior probability of the transmitted symbol and the initial parameters, and determining the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence at each first iterative convergence operation includes the following steps:

[0113] S401, determine the cavity log-likelihood ratio of the cavity probability of the post-coding bit sequence at each first iterative convergence operation according to the prior probability, the initial parameters, and the preset mapping relationship between the bit sequence and the symbol.

[0114] The mapping relationship between the bit sequence and the symbol is the correspondence between the bit sequence and the signal. For example, the bit sequence 00 can be mapped to the symbol a, and the bit sequence 01 can be mapped to b. Therefore, according to the post-coding bit sequence and the mapping relationship between the bit sequence and the symbol, the transmitted symbol can be obtained.

[0115] The first iterative convergence operation can be iterated a preset number of times. Therefore, in each iteration process, the cavity log-likelihood ratio of the cavity probability of the post-coding bit sequence at each iteration in the first iterative convergence operation can be determined according to the prior probability of the transmitted symbol, the initial parameters, and the preset mapping relationship between the bit sequence and the symbol in each iteration process.

[0116] Please continue to refer to Figure 3 The prior probability of the transmitted symbol, the initial parameters, and the mapping relationship between the preset bit sequence and the symbol obtained in the above embodiments can be input into the detector. The detector analyzes the prior probability of the transmitted symbol, the initial parameters, and the mapping relationship between the preset bit sequence and the symbol, and performs the first iterative convergence operation in each iteration to obtain the cavity log-likelihood ratio of the cavity probability of the encoded bit sequence during each first iterative convergence operation.

[0117] Specifically, based on the prior probability, the initial parameters, and the mapping relationship between the preset bit sequence and the symbol, the cavity log-likelihood ratio of the cavity probability of the encoded bit sequence during each first iterative convergence operation is determined according to the EP detection algorithm.

[0118] It should be noted that the cavity log-likelihood ratio is the log-likelihood ratio of the cavity probability of the encoded bit sequence, and the log-likelihood ratio is also transmitted between the detector and the decoder. The output of the detector and the input of the decoder are both this log-likelihood ratio, that is, the extrinsic information.

[0119] S402. According to the cavity log-likelihood ratio during each first iterative convergence operation, determine the corresponding posterior log-likelihood ratio during each first iterative convergence operation.

[0120] Please continue to refer to Figure 3 Based on the cavity log-likelihood ratio during each first iterative convergence operation obtained by the above detector, then transmit the cavity log-likelihood ratio during each first iterative convergence operation to the decoder. The decoder determines the corresponding posterior log-likelihood ratio during each first iterative convergence operation according to the cavity log-likelihood ratio during each first iterative convergence operation.

[0121] Specifically, based on the preset decoding algorithm in the decoder, the cavity log-likelihood ratio during each first iterative convergence operation is decoded to determine the corresponding posterior log-likelihood ratio during each first iterative convergence operation.

[0122] In the above signal detection method, according to the prior probability, the initial parameters, and the mapping relationship between the preset bit sequence and the symbol, the cavity log-likelihood ratio of the cavity probability of the encoded bit sequence during each first iterative convergence operation is determined, and according to the cavity log-likelihood ratio during each first iterative convergence operation, the corresponding posterior log-likelihood ratio during each first iterative convergence operation is determined. This method performs the first iterative convergence operation based on the initially obtained prior probability of the transmitted symbol and the initial parameters to obtain the corresponding posterior log-likelihood ratio during each first iterative convergence operation. This method provides more reasonable and effective initial parameters and prior information of the transmitted symbol for signal detection, so that compared with the traditional signal detection method in this application, the accurate pre-encoded bit sequence can be obtained with fewer iteration times.

[0123] Based on the prior probabilities and initial parameters of the transmitted symbols obtained through the linear programming model in the above embodiments, the process of constructing the linear programming model will be described below through an embodiment. In one embodiment, as Figure 5 shown, obtain the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coding bit sequence, and construct a linear programming model, including the following steps:

[0124] S501, obtain the set of frozen bits of the transmitted symbol, where the coding information includes the set of frozen bits.

[0125] Polar codes are a forward error correction coding method used for signal transmission. The construction of polar codes is to select K good bits from N bits to transmit information, and the other bits are frozen bits, which are generally assumed to be all zeros.

[0126] For example, if the A bit sequence is 8 bits and the set of frozen bits of the A sequence is {1, 2, 3}, then all the frozen bits of the A bit sequence are 0.

[0127] Optionally, the set of frozen bits can be determined by the code length and code rate, or the set of frozen bits can be preset.

[0128] S502, determine the change relationship between the pre-coding bit sequence and the post-coding bit sequence according to the code length, code rate, and set of frozen bits.

[0129] Among them, the post-coding bit sequence is the sequence obtained by encoding the pre-coding bit sequence through the coding information.

[0130] First, determine the polar code factor graph according to the code length. The polar code factor graph is the coding construction method of polar codes. For example, taking the code length of 8 as an example, the construction method of the polar code factor graph with a code length of 8 is as Figure 6 shown.

[0131] Figure 6 The polar code factor graph shown is a polar code cyclic structure. If N is the code length of 8, a sparse graph representation with (1 + log N)N constant nodes can be constructed, where there are N log N auxiliary constant nodes. The circular nodes in the figure represent constant nodes, and the square nodes represent check nodes. Based on this graph representation, a polyhedron P can be defined to generate linear coding constraints and construct the corresponding LP optimization problem. The solution of the LP problem can replace the MMSE solution used in the first iteration of EP, so that the coding characteristics are considered in the first iteration and the convergence speed is accelerated.

[0132] In order to transform the polar code coding constraints from the Galois field to the real number field, the binary constant nodes in the figure can be replaced by real number nodes within [0, 1]. From Figure 6It can be seen that the check node only involves two or three constant nodes. For any check node j containing three constant nodes {a1, a2, a3}, the local optimal polyhedron P j can be determined by the following linear inequalities:

[0133]

[0134] Similarly, for each check node j containing two constant nodes {a1, a2}, P j is defined as follows:

[0135] 0 ≤ a1 = a2 ≤ 1 (2)

[0136] In addition, according to the polar code factor graph, code rate, and frozen bit set, the positions and values of the frozen bits are determined. Taking the frozen bit as 0 as an example, the cutting plane τ is defined as the plane where all frozen bits are 0. Therefore, the polyhedron P is the intersection of the cutting plane τ and all local optimal polyhedra P j :

[0137]

[0138] where N is the code length and n = log N.

[0139] Continuing to refer to Figure 6 , Figure 6 any circular node in has a varying relationship with the aforementioned connection nodes. For example, a 2，0 node has a varying relationship with a 3，0 and a 3,4 : For example, node a 2,7 has a varying relationship with a 3,7 : a 2,7 = a 3,7 , etc. Similarly, the varying relationships of all nodes in the figure can be obtained.

[0140] Figure 6 In, the pre - encoding bit sequence is {u0, u1, u2, u3, u4, u5, u6, u7}. Through the encoding construction method in Figure 6 , the encoded sequence {x0, x1, x2, x3, x4, x5, x6, x7} is obtained. Therefore, according to the varying relationships of the above - mentioned nodes, the varying relationship between the pre - encoding bit sequence and the encoded bit sequence can be determined.

[0141] Optionally, the varying relationship between the pre - encoding bit sequence and the encoded bit sequence also includes the varying relationships between the nodes, the inequality relationships of the nodes in formulas (1) - (3), and the range of each node in [0, 1].

[0142] S503. Determine the objective function according to the mapping relationship, modulation information, channel matrix, and received signal.

[0143] Based on the mapping relationship between the bit sequence and the symbol, the transmitted symbol can be determined according to the encoded bit sequence, and then the objective function in the linear programming model can be constructed based on the encoded bit sequence, channel matrix, and received signal.

[0144] In one embodiment, as Figure 7 shown, determining the objective function according to the mapping relationship, channel matrix, and received signal includes the following steps:

[0145] S701. Determine the transmitted symbol using the encoded bit sequence based on the mapping relationship and modulation information.

[0146] First, determine the correspondence between the encoded bit sequence and the transmitted symbol according to the modulation information, and then determine the transmitted symbol based on the mapping relationship between the bit sequence and the transmitted symbol.

[0147] M(x) = s (4)

[0148] where M(·) is the mapping relationship between the bit sequence and the symbol, x is the encoded bit sequence, and s is the transmitted symbol.

[0149] S702. Determine the predicted received signal according to the transmitted symbol and the channel matrix.

[0150] Based on the transmitted symbol and the channel matrix, determine the predicted received signal. Where, if the transmitted symbol is s and the channel matrix is H, the predicted received signal is Hs.

[0151] S703. Construct the objective function according to the predicted received signal and the received signal; the objective function is used to characterize the error between the predicted received signal and the received signal.

[0152]

[0153] where H is the channel matrix, y is the received signal, Hs is the predicted received signal, e is the error between the predicted received signal and the received signal, N r is the number of receiving antennas, M is the length of the encoded bit sequence, Q is the modulation order, and ≤ refers to the inequality relationship of the elements in the matrix.

[0154] S704. Determine the linear programming model according to the variation relationship and the objective function.

[0155] Based on the variation relationship and the objective function obtained above, the linear programming model is jointly determined. According to the variation relationship and the objective function, the linear programming problem can be solved.

[0156] The above signal detection method obtains the frozen bit set of the transmitted symbols. Among them, the coding information also includes the frozen bit set. According to the code length, code rate, and frozen bit set, the change relationship between the pre-coded bit sequence and the post-coded bit sequence is determined. The post-coded bit sequence is the sequence obtained by encoding the initial sequence with the coding information. According to the mapping relationship, channel matrix, and received signal, the objective function is determined. According to the change relationship and the objective function, the linear programming model is determined. By considering the coding construction method and the frozen bit set, this method constructs a linear programming problem, which can obtain more reasonable and effective initial parameters and prior information of the transmitted symbols for signal detection. Compared with the traditional signal detection method, the signal detection method in this application can obtain the accurate pre-coded bit sequence with fewer iterations.

[0157] In one embodiment, as Figure 8 shown, according to the linear programming model, determining the prior probability and initial parameters of the transmitted symbols includes the following steps:

[0158] S801, According to the linear programming model, determine the prior probability of the transmitted symbols.

[0159] In one embodiment, as Figure 9 shown, according to the linear programming model, determining the prior probability of the transmitted symbols includes the following steps:

[0160] S901, Obtain the minimum value of the objective function in the linear programming model, and determine the post-coded bit sequence corresponding to the minimum value of the objective function as the target post-coded bit sequence.

[0161] Solve the linear programming model to obtain the minimum value of the objective function in the linear programming model, and then determine the post-coded bit sequence corresponding to the minimum value of the objective function as the target post-coded bit sequence.

[0162] Specifically, in the process of solving the linear programming model, in each solution, the post-coded bit sequence can be obtained, and the post-coded bit sequence corresponding to the minimum value of the objective function is used as the target post-coded bit sequence.

[0163] S902, According to the coding values of the target post-coded bit sequence, determine the prior probability of the target post-coded bit sequence.

[0164] The target post-coded bit sequence is determined according to the linear programming model. The coding values of each bit in the target post-coded bit sequence are between [0, 1]. Therefore, according to the coding values of each bit in the target post-coded bit sequence, the prior probability of the target post-coded bit sequence is determined.

[0165] For example, if the encoded value of a bit is 0.99, then the probability that the bit is 1 is determined to be 99%, and the probability that it is 0 is 1%; for another example, if the encoded value of a certain bit is 0.4, then the probability that the bit is 1 is determined to be 40%, and the probability that it is 0 is 60%. Therefore, the probability distribution of each bit can be determined according to the encoded value of each bit. Based on the probability distribution of each bit in the encoded bit sequence, the prior probability of the encoded bit sequence is obtained.

[0166] Optionally, the method for calculating the encoded bit sequence can be the same as the calculation method of the total probability formula. For example, the total probability formula transforms the problem of solving the probability of a complex event A into the problem of summing the probabilities of simple events occurring under different circumstances. If events B1, B2, B3... Bn form a complete set of events, that is, they are pairwise mutually exclusive and their sum is the entire set; and P(Bi) is greater than 0, then for any event A, P(A) = P(A|B1)*P(B1) + P(A|B2)*P(B2) +... + P(A|Bn)*P(Bn).

[0167] S903. Determine the prior probability of the transmitted symbol according to the prior probability of the target encoded bit sequence and the mapping relationship.

[0168] Determine the prior probability of the transmitted symbol according to the prior probability of the target encoded bit sequence and the mapping relationship between the bit sequence and the symbol.

[0169] For example, determine the probability of each symbol in the transmitted symbol according to the probability of each bit in the target encoded bit sequence and the mapping relationship; for example, if bit 00 can be mapped to a, the probability that the first bit is 0 is 40%, and the probability that the second bit is 0 is 60%, then the probability of a in the corresponding transmitted symbol is 24%.

[0170] Table 1

[0171]

[0172] Table 2

[0173]

[0174] For example, as shown in Table 1 and Table 2 above, Table 1 shows the probabilities of the first bit and the second bit, as well as the combined probability distribution, and Table 2 shows the mapping relationship between the bit sequence and the symbol. Then, the probability distribution corresponding to each symbol in the signal can be determined accordingly. For example, the probability corresponding to symbol a is 32%, and the probability corresponding to b is 48%, etc.

[0175] Similarly, based on the above method, the prior probability of the transmitted symbol can be determined according to the prior probability of the target encoded bit sequence and the mapping relationship.

[0176] It should be noted that the mapping relationship between the bit sequence and the symbol in the embodiments of the present application can be specifically set according to the actual situation, and no limitation is made in the present application.

[0177] S802. Determine the mean and variance of the prior probability of the transmitted symbol according to the prior probability of the transmitted symbol.

[0178] If the prior probability distribution of the transmitted symbol ξ is as follows:

[0179] ξ <![CDATA[x1]]> <![CDATA[x2]]> ... <![CDATA[x n > P <![CDATA[p1]]> <![CDATA[p2]]> ... <![CDATA[p n >

[0180] Then, the mean of the prior probability of the transmitted symbol can be calculated according to formula (5), and the variance of the prior probability of the transmitted symbol can be calculated according to formula (6).

[0181] Eξ = x1p1 + x2p2 +... + x n p n (5)

[0182] Dξ = (x1 - Eξ) 2 ·p1 + (x2 - Eξ) 2 ·p2 +... + (x n - Eξ) 2 ·p n (6)

[0183] Therefore, according to the prior probability of the transmitted symbol, using formula (5) and formula (6), the mean and variance of the prior probability of the transmitted symbol can be calculated.

[0184] S803. Determine the first initial parameter according to the mean and variance of the prior probability of the transmitted symbol.

[0185] As shown in formula (7), the first initial parameter can be calculated using formula (7).

[0186]

[0187] Where λ is the first initial parameter, Eξ is the mean of the prior probability of the transmitted symbol, and Dξ is the variance of the prior probability of the transmitted symbol.

[0188] S804. Determine the second initial parameter according to the variance of the prior probability of the transmitted symbol.

[0189] Among them, the initial parameters include the first initial parameter and the second initial parameter.

[0190] As shown in formula (8), the second initial parameter can be calculated using formula (8).

[0191]

[0192] Among them, γ is the second initial parameter, and Dξ is the variance of the prior probability of the transmitted symbol.

[0193] For the above signal detection method, according to the linear programming model, the prior probability of the transmitted symbol is determined. Based on the prior probability of the transmitted symbol, the mean and variance of the prior probability of the transmitted symbol are determined. Based on the mean and variance of the prior probability of the transmitted symbol, the first initial parameter is determined. Based on the variance of the prior probability of the transmitted symbol, the second initial parameter is determined. The initial parameters include the first initial parameter and the second initial parameter. In this method, before the first iterative detection, the prior information of the transmitted symbol is calculated, and the initial parameters are determined according to the prior information, ensuring that the coding information is considered in the first iterative detection, improving the detection efficiency, and reducing the number of iterations.

[0194] In one embodiment, as Figure 10 shown, according to the prior probability, the initial parameters, and the preset mapping relationship between the bit sequence and the symbol, the cavity logarithmic likelihood ratio of the coded bit sequence at each first iterative convergence operation is determined, including the following steps:

[0195] S1001, according to the prior probability and the initial parameters, perform the second iterative convergence operation until the preset second iterative convergence end condition is satisfied, and obtain the mean and variance of the cavity probability of the transmitted symbol.

[0196] According to the prior probability and the initial parameters, and using the EP detection algorithm to perform the second iterative convergence operation until the preset second iterative convergence end condition is satisfied. The preset second iterative convergence end condition is T times. Then, the mean and variance of the cavity probability of the transmitted symbol obtained from the T-th second iterative convergence operation are used as the final mean and variance of the cavity probability of the transmitted symbol.

[0197] Specifically, first set the second iterative convergence end condition of the EP detection algorithm to T times. Then, use the prior probability and the initial parameters as the input of the EP detection algorithm, and perform the second iterative convergence operation through the EP detection algorithm, and finally output the mean and variance of the cavity probability of the transmitted symbol. Among them, the second iterative convergence operation refers to the iterative convergence operation in the detector.

[0198] S1002, according to the mean and variance of the cavity probability of the transmitted symbol corresponding to the second iterative convergence operation when the second iterative convergence end condition is satisfied, and the mapping relationship, determine the cavity logarithmic likelihood ratio of the coded bit sequence at each first iterative convergence operation.

[0199] As shown in formula (9), the cavity logarithmic likelihood ratio of the coded bit sequence can be calculated according to formula (9).

[0200]

[0201] Among them, L E (x n ) represents the cavity log-likelihood ratio of the cavity probability of the encoded bit sequence, where x n is the encoded bit sequence, and Ω n,ζ refers to the constellation point modulated by the M bits where x n is located, that is, the symbol corresponding to the mapping relationship between the bit sequence and the symbol, and x n = ζ (ζ = 0 or 1), is the mean of the cavity probability of the transmitted symbol, is the variance of the cavity probability of the transmitted symbol.

[0202] The above signal detection method performs a second iterative convergence operation according to the prior probability and initial parameters until the preset second iterative convergence end condition is satisfied, obtaining the mean and variance of the cavity probability of the transmitted symbol. According to the mean and variance of the cavity probability of the transmitted symbol corresponding to the second iterative convergence operation when the second iterative convergence end condition is satisfied, and the mapping relationship, the cavity log-likelihood ratio of the cavity probability of the encoded bit sequence at each first iterative convergence operation is determined. This method can obtain the same detection result as the traditional method with fewer iterative times for the first iterative convergence operation compared to the traditional detection method.

[0203] In one embodiment, as Figure 11 shown, the second iterative convergence operation includes the following steps:

[0204] S1101, determine the variance and mean of the cavity probability of the transmitted symbol according to the initial parameters, system noise variance, and channel matrix.

[0205] In one embodiment, the method for determining the variance and mean of the cavity probability of the transmitted symbol according to the initial parameters, system noise variance, and channel matrix between the transmitter and the receiver can be as follows: First, determine the covariance matrix and mean of the initial posterior probability of the transmitted symbol according to the first initial parameter, second initial parameter, system noise variance, and channel matrix. Then, traverse the diagonal elements in the covariance matrix of the initial posterior probability of the transmitted symbol, and determine the variance of the cavity probability of the transmitted symbol according to each diagonal element and the first initial parameter. Traverse each element in the mean of the initial posterior probability of the transmitted symbol, and determine the mean of the cavity probability of the transmitted symbol according to each element, each diagonal element, the variance of the cavity probability of the transmitted symbol, the first initial parameter, and the second initial parameter.

[0206] At the transmitter, for the bit sequence u = [u1, u2,..., u N T executing the polarization code encoding criterion to obtain the codeword x = uG, where N is the code length and G is an n (n = log N)-order​ Kronecker product. The codeword x is mapped into a complex constellation and finally transmitted by a MIMO system with N t transmitting antennas and N r receiving antennas.

[0207] Since in actual signal processing, the real - number form is more common than the complex - number form, the system model in the real - number domain is directly shown here:

[0208] y = Hs+w (10)

[0209] where H is a 2N r ×2N t channel matrix, s is the transmitted symbol and s i ∈A, is the constellation of M - order modulation, w is the Gaussian white noise with system noise variance .

[0210] According to the above model, the posterior probability of the transmitted symbol can be expressed as:

[0211]

[0212] where p(s) is the prior probability of the transmitted symbol. In the case of no feedback, p(s) will be initialized to an equiprobable distribution, that is where is the indicator function, which takes the value of 1 or 0 when s i ∈A; means that the received signal y follows a Gaussian distribution with mean Hs and covariance matrix

[0213] The EP detection algorithm approximates p(s|y) by constructing a fully Gaussian distribution q [l] (s), then the approximate posterior probability can be factorized in each iteration as:

[0214]

[0215] where and is the fixed term, is the approximate term.

[0216] According to the Bayesian rule of the linear Gaussian system, the covariance matrix [l] of q (s) (the posterior probability of the transmitted symbol s) and the mean can be expressed by the following equations (13) and (14).

[0217] ​

[0218]

[0219] Among them, the covariance matrix and mean of the initial posterior probability of the transmitted symbol, the system noise variance, H represents the channel matrix, λ [l] represents the first initial parameter, the mean of the initial posterior probability of the transmitted symbol, y represents the received signal, γ [l] represents the second initial parameter.

[0220] It should be noted that the mean of the initial posterior probability of the transmitted symbol above is in the form of a vector, which is the joint posterior probability distribution of each antenna. Since the transmitted symbols between each antenna are independent of each other, the corresponding component method of their joint posterior is effective.

[0221] Therefore, q [l] (s i )'s marginal probability can be expressed as: Among them, is the i-th term of is the i-th diagonal element of [l] . Therefore, define the cavity probability of q

[0222] Therefore, the variance and mean of the cavity probability of the transmitted symbol can be calculated according to formula (15) and formula (16) respectively.

[0223]

[0224]

[0225] Among them, represents the variance of the cavity probability of the transmitted symbol, represents the i-th diagonal element of represents the first initial parameter, represents the mean of the cavity probability of the transmitted symbol, represents the i-th term of represents the second initial parameter.

[0226] S1102, determine the variance and mean of the discrete posterior probability of the transmitted symbol according to the variance, mean of the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol.

[0227] In one embodiment, the method for determining the variance and mean of the discrete posterior probability of a transmitted symbol based on the variance and mean of the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol may be as follows: First, determine the cavity probability of the transmitted symbol based on the variance and mean of the cavity probability of the transmitted symbol, then determine the discrete posterior probability of the transmitted symbol based on the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol, and finally determine the variance and mean of the discrete posterior probability of the transmitted symbol based on the discrete posterior probability of the transmitted symbol.

[0228] First, determine the cavity probability distribution of the transmitted symbol according to the variance and mean of the cavity probability of the transmitted symbol, and then determine the posterior probability of the transmitted symbol according to the cavity probability distribution of the transmitted symbol and the prior probability of the transmitted symbol. Since the cavity probability is the cavity probability of each antenna, the posterior probability of this transmitted symbol is the discrete posterior probability; determine the variance and mean of the discrete posterior probability of the posterior probability according to the discrete posterior probability.

[0229] Specifically, according to calculate the discrete posterior probability distribution of the transmitted symbol, where is the discrete posterior probability distribution of the transmitted symbol, and p D (s i ) is the prior probability of the transmitted symbol, is the cavity probability of the transmitted symbol. Therefore, determine the discrete posterior probability of the transmitted symbol, and then calculate the mean and variance

[0230] It should be noted that the methods for calculating the mean and variance are the same as those in the above embodiments and will not be elaborated here.

[0231] Optionally, the minimum allowable variance can be set to where ε is a preset parameter.

[0232] S1103, update the initial parameters according to the variance and mean of the discrete posterior probability of the transmitted symbol, and use the updated initial parameters as the initial parameters for the next iteration.

[0233] Update the initial parameters according to the variance and mean of the discrete posterior probability of the transmitted symbol, and then the updated initial parameters can be used as the initial parameters for the next iteration.

[0234] In one embodiment, the initial parameters are updated according to the variance and mean of the discrete posterior probability of the transmitted symbol. The way to use the updated initial parameters as the initial parameters for the next iteration can be as follows: determine the first candidate initial parameter according to the variance of the cavity probability and the variance of the discrete posterior probability of the transmitted symbol, and determine the second candidate initial parameter according to the variance and mean of the cavity probability of the transmitted symbol and the variance and mean of the discrete posterior probability; update the first initial parameter according to the first candidate initial parameter to obtain the first initial parameter for the next iteration, and update the second initial parameter according to the second candidate initial parameter to obtain the second initial parameter for the next iteration.

[0235] The first candidate initial parameter and the second candidate initial parameter are updated based on the moment matching criterion, specifically updated according to formulas (17) and (18).

[0236]

[0237]

[0238] Among them, represents the first candidate initial parameter, represents the variance of the discrete posterior probability of the transmitted symbol, represents the variance of the cavity probability of the transmitted symbol, represents the second candidate initial parameter, represents the mean of the discrete posterior probability of the transmitted symbol, represents the mean of the cavity probability of the transmitted symbol.

[0239] After performing the moment matching operation, a damping process with parameter β is run to improve robustness, and the first initial parameter is updated using the first candidate initial parameter, and the second initial parameter is updated using the second candidate initial parameter, specifically calculated according to formulas (19) and (20).

[0240]

[0241]

[0242] Among them, represents the first initial parameter for the next iteration, β represents the preset parameter, represents the first candidate initial parameter, represents the first initial parameter, represents the second initial parameter for the next iteration, represents the second candidate initial parameter, represents the second initial parameter, and l represents the number of iterations.

[0243] When In the case of, the result of the previous iteration is retained, i.e.:

[0244]

[0245]

[0246] When for all i = 1, 2,..., 2N t , the parameter and have been updated, then and can be updated again using formulas (13) and (14) until the maximum number of iterations L.

[0247] The above signal detection method determines the variance and mean of the cavity probability of the transmitted symbol according to the initial parameters, system noise variance, and channel matrix, determines the variance and mean of the discrete posterior probability of the transmitted symbol according to the variance, mean of the cavity probability of the transmitted symbol, and the prior probability of the transmitted symbol, updates the initial parameters according to the variance and mean of the discrete posterior probability of the transmitted symbol, and uses the updated initial parameters as the initial parameters for the next iteration. By updating the initial parameters, this method can further enable the signal detection to obtain accurate detection results by performing the first iteration convergence operation with fewer iterations.

[0248] In one embodiment, according to the cavity log-likelihood ratio at each first iteration convergence operation, determining the corresponding posterior log-likelihood ratio at each first iteration convergence operation includes: performing a decoding operation on the cavity log-likelihood ratio at each first iteration convergence operation to obtain the corresponding posterior log-likelihood ratio at each first iteration convergence operation.

[0249] When the decoder performs the first iteration convergence operation each time, a decoding operation is performed on the received cavity log-likelihood ratio, and the corresponding posterior log-likelihood ratio for each first iteration convergence operation can be obtained. The posterior log-likelihood ratio is the posterior log-likelihood ratio of the pre-coding bit sequence obtained by decoding the cavity log-likelihood ratio of the coded sequence.

[0250] Among them, the decoding operation is determined by the decoding algorithm in the decoder. In the embodiment of the present application, the decoding algorithm is a soft-input and soft-output decoding algorithm. In the embodiment of the present application, no limitation is imposed on the type of the decoding algorithm. For example, the decoding operation can be performed using the maximum a posteriori probability decoding algorithm.

[0251] In the above embodiment, the cavity log-likelihood ratio is decoded to obtain the posterior log-likelihood ratio of the pre-coding bit sequence. During the process of decoding the cavity log-likelihood ratio, there are also some other steps, which will be described below through an example. In one embodiment, such asFigure 12 As shown in Figure 12 , this embodiment includes the following steps:

[0252] S1201. Determine the log-likelihood ratio of the prior probability of the coded bit sequence according to the cavity log-likelihood ratio of each cavity and the candidate log-likelihood ratio of the posterior probability of the coded bit sequence.

[0253] Among them, the candidate log-likelihood ratio is obtained by decoding the cavity log-likelihood ratio during each first iterative convergence operation.

[0254] The candidate log-likelihood ratio of the posterior probability of the coded bit sequence is the log-likelihood ratio of the posterior probability of the coded bit obtained during the process of the decoder decoding the cavity log-likelihood ratio.

[0255] Then, the log-likelihood ratio of the prior probability of the coded bit sequence can be calculated according to formula (23).

[0256]

[0257] Among them, L D (x n ) represents the log-likelihood ratio of the prior probability of the coded bit sequence, L(x n ) represents the candidate log-likelihood ratio of the posterior probability of the coded bit sequence, and L E (x n ) represents the cavity log-likelihood ratio of the cavity probability of the coded bit sequence.

[0258] S1202. Determine the new prior probability of the transmitted symbol according to the log-likelihood ratio of the prior probability of the coded bit sequence and the mapping relationship, and use the new prior probability as the prior probability of the transmitted symbol in the next iteration.

[0259] Map the log-likelihood ratio of the prior probability of the coded bit sequence to the new prior probability of the transmitted symbol:

[0260]

[0261] Among them, p D (s i ) is the new prior probability of the transmitted symbol, ψ(Ω m ) corresponds to the jth bit of the constellation point Ω m , b is a normalization factor to ensure that the sum of the probabilities of each constellation point in the transmitted symbol is 1, and p D (x n ) is the prior probability of the coded bit sequence.

[0262] According to the log-likelihood ratio of the prior probability of the encoded bit sequence, the prior probability of the encoded bit sequence can be determined. Then, based on the prior probability of the encoded bit sequence and the mapping relationship between the bit sequence and the symbol, the new prior probability of the transmitted symbol can be determined.

[0263] For the above signal detection method, according to the cavity log-likelihood ratio of each cavity and the candidate log-likelihood ratio of the posterior probability of the encoded bit sequence, the log-likelihood ratio of the prior probability of the encoded bit sequence is determined, where the candidate log-likelihood ratio is obtained by performing a decoding operation on the cavity log-likelihood ratio during each first iteration convergence operation. According to the log-likelihood ratio of the prior probability of the encoded bit sequence and the mapping relationship, the new prior probability of the transmitted symbol is determined, and the new prior probability is used as the prior probability of the transmitted symbol in the next iteration. This method can enable the signal detection to obtain better detection performance than the traditional method by performing the first iteration convergence operation with fewer iteration times, that is, obtaining a more accurate pre-encoded bit sequence.

[0264] In one embodiment, determining the pre-encoded bit sequence according to the target log-likelihood ratio corresponding to the pre-encoded bit sequence includes: performing a hard decision process on the target log-likelihood ratio corresponding to the pre-encoded bit sequence to obtain the pre-encoded bit sequence.

[0265] Hard decision is simply to judge the output by setting a threshold. In terms of binary, generally, values greater than 0 are judged as 1, and values less than 0 are judged as 0.

[0266] In one embodiment, if the target log-likelihood ratio corresponding to the pre-encoded bit sequence is the logarithm of the ratio of the probability that the bit is 0 to the probability that the bit is 1, then for the target log-likelihood ratio of any bit in the pre-encoded bit sequence, if the target log-likelihood ratio of this bit is greater than 0, it means that the probability that the bit is 0 is greater than the probability that the bit is 1, and then it can be determined that this bit is 0; if the target log-likelihood ratio of this bit is less than 0, it means that the probability that the bit is 1 is greater than the probability that the bit is 0, and then it can be determined that this bit is 1. That is:

[0267]

[0268] where x u is the pre-encoded bit sequence, and L(x u ) is the target log-likelihood ratio corresponding to the pre-encoded bit sequence.

[0269] In one embodiment, if the target log-likelihood ratio corresponding to the pre-encoded bit sequence is the logarithm of the ratio of the probability that the bit is 1 to the probability that the bit is 0, then for the target log-likelihood ratio of any bit in the pre-encoded bit sequence, if the target log-likelihood ratio of this bit is greater than 0, it means that the probability that the bit is 1 is greater than the probability that the bit is 0, and then it can be determined that this bit is 1; if the target log-likelihood ratio of this bit is less than 0, it means that the probability that the bit is 0 is greater than the probability that the bit is 1, and then it can be determined that this bit is 0.

[0270]

[0271] where x u is the pre-encoded bit sequence, and L(x u ) is the target log-likelihood ratio corresponding to the pre-encoded bit sequence.

[0272] In one embodiment, this embodiment focuses on a MIMO system with polar code encoding, proposes an EP detection algorithm (EPLP) based on linear programming (LP), and connects it to a polar code decoder to form an IDD-EPLP system. This IDD receiver fully considers the special structural construction of polar codes and the restricted conditions with frozen bits, establishes an LP optimization problem, and provides more reasonable and effective initial parameters for EP detection.

[0273] Compared with the currently most advanced DEP detection algorithm, this embodiment can achieve detection performance superior to DEP with fewer EP iteration times, and also maintains a certain advantage in decoding performance after being connected to the IDD system. Especially in the scenario where the number of transmitting and receiving antennas is close, this IDD receiver has more obvious performance advantages.

[0274] The reason for the performance improvement of IDD-EP compared with the original sequential detection and decoding system (SDD) is that the prior probability in the EP detector of IDD-EP is mapped from the decoded information feedback, while in SDD-EP, the initial prior probability is uniformly distributed. However, in the first IDD iteration of IDD-EP, since there is no decoded feedback available for the EP detector yet, the prior information can only be initialized to a uniform distribution. Therefore, the initialization of EP degenerates into minimum mean square error detection (MMSE), that is where E s is the average energy of the transmitted symbol.

[0275] Compared with the above-mentioned original EP-based IDD system (IDD-EP), the difference in this embodiment lies in the initialization process in the first EP iteration. Specifically, the commonalities and differences between IDD-EP and IDD-EPLP can be seen in Figure 13 .

[0276] In one embodiment, as Figure 14 shown, Figure 14 is a flowchart of the signal detection method in the embodiment of the present application. First, a LP optimization problem is constructed and solved based on the code length, code rate, frozen bits, MIMO channel matrix H, and received signal to obtain the prior probability of the transmitted symbol, and the initial parameters of the EPLP algorithm are calculated according to the prior probability. Then, the EPLP algorithm, that is, the moment matching and damping process (MMD), is executed L times. Then, the cavity probability of the transmitted symbol is obtained, and the cavity probability of the transmitted symbol is mapped into the cavity LLR of the encoded bit sequence. The cavity LLR is decoded by a polar code, and then the LLR solution of the encoded bit sequence is obtained. The LLR solution of the encoded bit sequence is mapped into the prior probability of the transmitted symbol, and the obtained prior probability of the transmitted symbol is used for the next iteration until the log-likelihood ratio of the posterior probability of the bit sequence x u before encoding, L(x u ), is output. Then, hard decision processing is performed on L(x u ), and the bit sequence before encoding is obtained.

[0277] In one embodiment, as Figure 15 shown, this embodiment includes the following steps:

[0278] S1501, constructing a linear programming model according to the coding length, frozen bit set, transmission channel matrix, and received signal;

[0279] First, according to the coding length, obtain the change relationship between the bit sequence before encoding and the decoded sequence; according to the change relationship between the bit sequence before encoding and the decoded sequence, determine the coding constraint conditions; according to the change relationship between the bit sequence before encoding and the encoded sequence and the coding constraint conditions, determine the linear programming model.

[0280] S1502, solving the linear programming model to determine the encoded bit sequence, and determining the first initial parameter and the second initial parameter according to the encoded bit sequence;

[0281] Calculate the probability distribution of the encoded sequence according to the encoded bit sequence, determine the probability of the encoded sequence as the prior probability of the transmitted symbol; and calculate the variance and mean of the prior probability of the transmitted symbol according to the prior probability of the transmitted symbol, and determine the first initial parameter λ (mean divided by variance) and the second initial parameter γ (one over variance) according to the variance and mean.

[0282] S1503. Run the MMD L times according to the prior probability of the transmitted symbol, the first initial parameter, and the second initial parameter, and output the log-likelihood ratio of the cavity probability of the encoded bit sequence.

[0283] Calculate the covariance matrix of the posterior probability of the transmitted symbol according to the first initial parameter, the system noise variance, and the channel matrix; calculate the mean vector of the posterior probability of the transmitted symbol according to the parameter γ, the covariance matrix of the transmitted symbol, the channel matrix, the system noise variance, and the received signal y; for any diagonal element of the covariance matrix and any item of the mean vector; determine the variance of the cavity probability of the transmitted symbol according to any diagonal element of the covariance matrix, any item of the mean vector, any diagonal element of the covariance matrix, and λ; determine the mean of the cavity probability of the transmitted symbol according to any item of the posterior probability of the transmitted symbol, any diagonal element of the covariance matrix, the variance of the cavity probability of the transmitted symbol, and the parameter γ; determine the cavity probability of the transmitted symbol according to the mean and variance of the cavity probability of the transmitted symbol, and determine the mean and variance of the posterior probability of the transmitted symbol according to the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol; based on the moment matching criterion, obtain the candidate first initial parameter according to the variance of the cavity probability of the transmitted symbol and the variance of the posterior probability; based on the moment matching criterion, obtain the candidate second initial parameter according to the mean and variance of the cavity probability of the transmitted symbol, and the mean and variance of the posterior probability; determine the new first initial parameter according to the first initial parameter and the candidate first initial parameter; update the new second initial parameter according to the second initial parameter and the candidate second initial parameter; if the new first initial parameter is less than 0, do not update the first initial parameter and the second initial parameter; continue the next iteration according to the updated first initial parameter and the second initial parameter until the iteration is completed; output the mean vector and variance of the cavity probability of the transmitted symbol as the transmitted symbol; according to the mean vector and variance of the cavity probability of the transmitted symbol obtained after the above iteration is completed, and the mapping relationship between the bits and the symbols, obtain the cavity log-likelihood ratio of the posterior probability of the encoded bit sequence.

[0284] S1504. After the detector obtains the log-likelihood ratio of the cavity probability, send the log-likelihood ratio of the cavity probability to the decoder. The decoder determines the candidate log-likelihood ratio of the posterior probability of the encoded bit sequence according to the log-likelihood ratio of the cavity probability, and calculates the prior log-likelihood ratio of the prior probability distribution of the encoded bit sequence according to the log-likelihood ratio of the cavity probability and the candidate log-likelihood ratio.

[0285] S1505. Map the prior log-likelihood ratio to obtain the prior probability of the transmitted symbol.

[0286] S1506. When the number of iterations is reached, finally output the log-likelihood ratio Lu of the posterior probability of the pre-coding bit sequence, and perform a hard decision on Lu to obtain the pre-coding bit sequence.

[0287] For the specific limitations of the signal detection method provided in this embodiment, reference can be made to the step limitations of each embodiment in the signal detection method described above, which will not be elaborated here.

[0288] It should be understood that although the steps in the flowcharts attached in the above embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the figures attached in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0289] In one embodiment, as Figure 16 shown, the embodiment of the present application also provides a signal detection device 1600. The device 1600 includes: an acquisition module 1601, a parameter determination module 1602, a convergence module 1603, and a pre-coding bit sequence determination module 1604, where:

[0290] The acquisition module 1601 is configured to acquire the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coding bit sequence, and construct a linear programming model, where the coding information includes the code length and the code rate;

[0291] The parameter determination module 1602 is configured to determine the prior probability and initial parameters of the transmitted symbol according to the linear programming model; the transmitted symbol is generated according to the modulation information from the post-coding bit sequence;

[0292] The convergence module 1603 is configured to perform a first iterative convergence operation according to the prior probability and initial parameters of the transmitted symbol, and determine the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence during each first iterative convergence operation;

[0293] The pre-coding bit sequence determination module 1604 is configured to determine the pre-coding bit sequence according to the target log-likelihood ratio corresponding to the pre-coding bit sequence, where the target log-likelihood ratio is the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation.

[0294] In one embodiment, the convergence module 1603 includes:

[0295] A first determination unit, configured to determine the cavity log-likelihood ratio of the coded bit sequence during each first iterative convergence operation according to the prior probability, the initial parameter, and the preset mapping relationship between the bit sequence and the symbol.

[0296] A second determination unit, configured to determine the posterior log-likelihood ratio corresponding to each first iterative convergence operation according to the cavity log-likelihood ratio during each first iterative convergence operation.

[0297] In one embodiment, the obtaining module 1601 includes:

[0298] An obtaining unit, configured to obtain the set of frozen bits of the transmitted symbol, where the coding information includes the set of frozen bits.

[0299] A third determination unit, configured to determine the variation relationship between the pre-coded bit sequence and the post-coded bit sequence according to the code length, the code rate, and the set of frozen bits.

[0300] A fourth determination unit, configured to determine the objective function according to the mapping relationship, the modulation information, the channel matrix, and the received signal.

[0301] A fifth determination unit, configured to determine the linear programming model according to the variation relationship and the objective function.

[0302] In one embodiment, the fourth determination unit includes:

[0303] A first determination subunit, configured to determine the transmitted symbol by using the post-coded bit sequence based on the mapping relationship.

[0304] A second determination subunit, configured to determine the predicted received signal according to the transmitted symbol and the channel matrix.

[0305] A construction subunit, configured to construct the objective function according to the predicted received signal and the received signal; the objective function is used to characterize the error between the predicted received signal and the received signal.

[0306] In one embodiment, the parameter determination module 1602 includes:

[0307] A sixth determination unit, configured to determine the prior probability of the transmitted symbol according to the linear programming model.

[0308] A seventh determination unit, configured to determine the mean and variance of the prior probability of the transmitted symbol according to the prior probability of the transmitted symbol.

[0309] An eighth determination unit, configured to determine the first initial parameter according to the mean and variance of the prior probability of the transmitted symbol.

[0310] A ninth determination unit, configured to determine the second initial parameter according to the variance of the prior probability of the transmitted symbol.

[0311] The initial parameters include a first initial parameter and a second initial parameter.

[0312] In one embodiment, the sixth determination unit includes:

[0313] A third determination subunit, configured to obtain the minimum value of the objective function in the linear programming model, and determine the encoded bit sequence corresponding to the minimum value of the objective function as the target encoded bit sequence;

[0314] A fourth determination subunit, configured to determine the prior probability of the target encoded bit sequence according to the encoding value of the target encoded bit sequence;

[0315] A fifth determination subunit, configured to determine the prior probability of the transmitted symbol according to the prior probability of the target encoded bit sequence and the mapping relationship.

[0316] In one embodiment, the first determination unit includes:

[0317] An obtaining subunit, configured to perform a second iterative convergence operation according to the prior probability and the initial parameters until a preset second iterative convergence end condition is satisfied, and obtain the mean and variance of the cavity probability of the transmitted symbol;

[0318] A sixth determination subunit, configured to determine the cavity log-likelihood ratio of the encoded bit sequence at each first iterative convergence operation according to the mean and variance of the cavity probability of the transmitted symbol corresponding to the second iterative convergence operation when the second iterative convergence end condition is satisfied, and the mapping relationship.

[0319] In one embodiment, the obtaining subunit includes:

[0320] A seventh determination subunit, configured to determine the variance and mean of the cavity probability of the transmitted symbol according to the initial parameters, the system noise variance, and the channel matrix;

[0321] An eighth determination subunit, configured to determine the variance and mean of the discrete posterior probability of the transmitted symbol according to the variance, mean of the cavity probability of the transmitted symbol, and the prior probability of the transmitted symbol;

[0322] An updating subunit, configured to update the initial parameters according to the variance and mean of the discrete posterior probability of the transmitted symbol, and use the updated initial parameters as the initial parameters for the next iteration.

[0323] In one embodiment, the eighth determination subunit includes:

[0324] A cavity probability determination subunit, configured to determine the cavity probability of the transmitted symbol according to the variance and mean of the cavity probability of the transmitted symbol;

[0325] A discrete posterior probability determination subunit, configured to determine the discrete posterior probability of a transmitted symbol according to the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol;

[0326] A discrete probability distribution determination subunit, configured to determine the variance and mean of the discrete posterior probability of the transmitted symbol according to the discrete posterior probability of the transmitted symbol.

[0327] In one embodiment, the update subunit includes:

[0328] A first candidate parameter determination subunit, configured to determine a first candidate initial parameter according to the variance of the cavity probability of the transmitted symbol and the variance of the discrete posterior probability;

[0329] A second candidate parameter determination subunit, configured to determine a second candidate initial parameter according to the variance and mean of the cavity probability of the transmitted symbol and the variance and mean of the discrete posterior probability;

[0330] A first parameter determination subunit, configured to update the first initial parameter according to the first candidate initial parameter to obtain the first initial parameter for the next iteration;

[0331] A second parameter determination subunit, configured to update the second initial parameter according to the second candidate initial parameter to obtain the second initial parameter for the next iteration.

[0332] In one embodiment, the second determination unit includes:

[0333] A decoding subunit, configured to perform a decoding operation on the cavity log-likelihood ratio during each first iteration convergence operation to obtain the posterior log-likelihood ratio corresponding to each first iteration convergence operation.

[0334] In one embodiment, the second determination unit further includes:

[0335] A tenth determination subunit, configured to determine the log-likelihood ratio of the prior probability of the encoded bit sequence according to each cavity log-likelihood ratio and the candidate log-likelihood ratio of the posterior probability of the encoded bit sequence, where the candidate log-likelihood ratio is obtained by performing a decoding operation on the cavity log-likelihood ratio during each first iteration convergence operation;

[0336] A mapping subunit, configured to determine a new prior probability of the transmitted symbol according to the log-likelihood ratio of the prior probability of the encoded bit sequence and the mapping relationship, and use the new prior probability as the prior probability of the transmitted symbol for the next iteration.

[0337] In one embodiment, the pre-encoding bit sequence determination module 1604 includes:

[0338] A decision unit, configured to perform a hard decision process on the target log-likelihood ratio corresponding to the pre-encoding bit sequence to obtain the pre-encoding bit sequence.

[0339] For the specific limitations of the signal detection device, reference can be made to the limitations of each step in the signal detection method described above, which will not be elaborated here. Each module in the above signal detection device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the target device in the form of hardware or be independent of the target device, or can be stored in the memory of the target device in the form of software, so that the target device can call and execute the operations corresponding to each of the above modules.

[0340] In one embodiment, a computer device is provided, as Figure 17 shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements a signal detection method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0341] Those skilled in the art can understand that Figure 17 the structure shown in

[0342] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0343] For each step implemented by the processor in this embodiment, the implementation principle and technical effect are similar to those of the above signal detection method, which will not be elaborated here.

[0344] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0345] In this embodiment, the steps implemented when the computer program is executed by the processor have similar implementation principles and technical effects to those of the above signal detection method, which will not be elaborated here.

[0346] In one embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps in the above method embodiments.

[0347] In this embodiment, the steps implemented when the computer program is executed by the processor have similar implementation principles and technical effects to those of the above signal detection method, which will not be elaborated here.

[0348] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties.

[0349] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0350] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0351] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A signal detection method, characterized in that, The method includes: Obtaining the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coding bit sequence, and constructing a linear programming model, where the coding information includes the code length and the code rate; Determining the prior probability and initial parameters of the transmitted symbol according to the linear programming model; the transmitted symbol is generated according to the modulation information from the post-coding bit sequence; the initial parameters include a first initial parameter and a second initial parameter; the first initial parameter is determined according to the mean and variance of the prior probability of the transmitted symbol; the second initial parameter is determined according to the variance of the prior probability of the transmitted symbol; Determining the cavity log-likelihood ratio of the cavity probability of the post-coding bit sequence during each first iterative convergence operation according to the prior probability of the transmitted symbol, the initial parameters, and the preset mapping relationship between the bit sequence and the symbol; determining the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence during each first iterative convergence operation according to the cavity log-likelihood ratio during each first iterative convergence operation; Determining the pre-coding bit sequence according to the target log-likelihood ratio corresponding to the pre-coding bit sequence, where the target log-likelihood ratio is the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation.

2. The method according to claim 1, characterized in that The obtaining the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coding bit sequence, and constructing a linear programming model includes: Obtaining the set of frozen bits of the transmitted symbol, where the coding information includes the set of frozen bits; Determining the change relationship between the pre-coding bit sequence and the post-coding bit sequence according to the code length, the code rate, and the set of frozen bits; Determining the objective function according to the mapping relationship, the modulation information, the channel matrix, and the received signal; Determining the linear programming model according to the change relationship and the objective function.

3. The method according to claim 2, characterized in that, The determining the objective function according to the mapping relationship, the modulation information, the channel matrix, and the received signal includes: Based on the mapping relationship and the modulation information, determining the transmitted symbol using the post-coding bit sequence; Determining the predicted received signal according to the transmitted symbol and the channel matrix; Constructing an objective function according to the predicted received signal and the received signal; the objective function is used to characterize the error between the predicted received signal and the received signal.

4. The method according to claim 1, characterized in that, The objective function and constraint conditions of the linear programming model; the obtaining the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coding bit sequence, and constructing a linear programming model includes: Determining the polarization code factor graph corresponding to the coding method of the pre-coding bit sequence according to the code length and the code rate; Inputting the polarization code factor graph, the modulation information, the channel matrix, and the received signal into a network model, and through the analysis of the network model, obtaining the objective function and the constraint conditions of the linear programming model.

5. The method according to any one of claims 1 to 4, characterized in that The determining the prior probability of the transmitted symbol according to the linear programming model includes: Obtain the minimum value of the objective function in the linear programming model, and determine the encoded bit sequence corresponding to the minimum value of the objective function as the target encoded bit sequence; Determine the prior probability of the target encoded bit sequence according to the encoded value of the target encoded bit sequence; Determine the prior probability of the transmitted symbol according to the prior probability of the target encoded bit sequence and the mapping relationship.

6. The method according to any one of claims 1-4, characterized in that The determining the cavity log-likelihood ratio of the cavity probability of the encoded bit sequence during each first iterative convergence operation according to the prior probability of the transmitted symbol, the initial parameter, and the preset mapping relationship between the bit sequence and the symbol includes: Perform a second iterative convergence operation according to the prior probability and the initial parameter until the preset second iterative convergence end condition is satisfied, and obtain the mean and variance of the cavity probability of the transmitted symbol; Determine the cavity log-likelihood ratio of the cavity probability of the encoded bit sequence during each first iterative convergence operation according to the mean and variance of the cavity probability of the transmitted symbol corresponding to the second iterative convergence operation when the second iterative convergence end condition is satisfied, and the mapping relationship.

7. The method according to claim 6, wherein The second iterative convergence operation includes: Determine the variance and mean of the cavity probability of the transmitted symbol according to the initial parameter, the system noise variance, and the channel matrix; Determine the variance and mean of the discrete posterior probability of the transmitted symbol according to the variance and mean of the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol; Update the initial parameter according to the variance and mean of the discrete posterior probability of the transmitted symbol, and use the updated initial parameter as the initial parameter for the next iteration.

8. The method according to claim 7, wherein The determining the variance and mean of the discrete posterior probability of the transmitted symbol according to the variance and mean of the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol includes: Determine the cavity probability of the transmitted symbol according to the variance and mean of the cavity probability of the transmitted symbol; Determine the discrete posterior probability of the transmitted symbol according to the cavity probability of the transmitted symbol and the prior probability of the transmitted symbol; Determine the variance and mean of the discrete posterior probability of the transmitted symbol according to the discrete posterior probability of the transmitted symbol.

9. The method according to claim 7, characterized in that, The updating the initial parameter according to the variance and mean of the discrete posterior probability of the transmitted symbol and using the updated initial parameter as the initial parameter for the next iteration includes: Determine the first candidate initial parameter according to the variance of the cavity probability of the transmitted symbol and the variance of the discrete posterior probability; Determine the second candidate initial parameter according to the variance and mean of the cavity probability of the transmitted symbol and the variance and mean of the discrete posterior probability; Update the first initial parameter according to the first candidate initial parameter to obtain the first initial parameter for the next iteration; Update the second initial parameter according to the second candidate initial parameter to obtain the second initial parameter for the next iteration.

10. The method according to any one of claims 1-4, characterized in that Determining the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence at each first iterative convergence operation according to the cavity log-likelihood ratio at each first iterative convergence operation includes: Performing a decoding operation on the cavity log-likelihood ratio at each first iterative convergence operation to obtain the corresponding posterior log-likelihood ratio at each first iterative convergence operation.

11. The method according to claim 10, wherein The method further includes: Determining the log-likelihood ratio of the prior probability of the post-coding bit sequence according to each of the cavity log-likelihood ratios and the candidate log-likelihood ratio of the posterior probability of the post-coding bit sequence, where the candidate log-likelihood ratio is obtained by performing a decoding operation on the cavity log-likelihood ratio at each first iterative convergence operation; Determining the new prior probability of the transmitted symbol according to the log-likelihood ratio of the prior probability of the post-coding bit sequence and the mapping relationship, and using the new prior probability as the prior probability of the transmitted symbol in the next iteration.

12. The method according to any one of claims 1-4, characterized in that Determining the pre-coding bit sequence according to the target log-likelihood ratio corresponding to the pre-coding bit sequence includes: Performing a hard decision process on the target log-likelihood ratio corresponding to the pre-coding bit sequence to obtain the pre-coding bit sequence.

13. A signal detection device, characterized in that, The apparatus includes: An acquisition module, configured to acquire the coding information, modulation information, channel matrix between the transmitter and the receiver, and received signal of the pre-coding bit sequence, and construct a linear programming model, where the coding information includes the code length and the code rate; A parameter determination module, configured to determine the prior probability of the transmitted symbol and the initial parameters according to the linear programming model; the transmitted symbol is generated from the post-coding bit sequence according to the modulation information; the initial parameters include a first initial parameter and a second initial parameter; the first initial parameter is determined according to the mean and variance of the prior probability of the transmitted symbol; the second initial parameter is determined according to the variance of the prior probability of the transmitted symbol; A convergence module, configured to determine the cavity log-likelihood ratio of the cavity probability of the post-coding bit sequence at each first iterative convergence operation according to the prior probability of the transmitted symbol, the initial parameters, and a preset mapping relationship between the bit sequence and the symbol; determining the posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence at each first iterative convergence operation according to the cavity log-likelihood ratio at each first iterative convergence operation; A pre-coding bit sequence determination module, configured to determine the pre-coding bit sequence according to the target log-likelihood ratio corresponding to the pre-coding bit sequence, where the target log-likelihood ratio is the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation.

14. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 12 are implemented.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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