CN115378524A

CN115378524A - Optimized expected propagation detection method and signal detection device

Info

Publication number: CN115378524A
Application number: CN202210952192.0A
Authority: CN
Inventors: 张川; 刘雅婷; 申怡飞; 黄永明; 尤肖虎
Original assignee: Network Communication and Security Zijinshan Laboratory
Current assignee: Network Communication and Security Zijinshan Laboratory
Priority date: 2022-08-09
Filing date: 2022-08-09
Publication date: 2022-11-22
Also published as: WO2024031979A1

Abstract

The present application relates to an optimized expected propagation detection method and a signal detection apparatus. Firstly, obtaining coding information and modulation information of a bit sequence before coding, a channel matrix between a sending end and a receiving end and a received signal, constructing a linear programming model, determining prior probability and initial parameters of a sending symbol according to the linear programming model, executing a first iterative convergence operation according to the prior probability and the initial parameters of the sending symbol to obtain a target log-likelihood ratio of the corresponding bit sequence before coding when the preset first iterative convergence operation is finished, and determining the bit sequence before coding according to the target log-likelihood ratio. The method obtains more reasonable and effective initial parameters and the prior information of the sending symbols for signal detection, so that compared with the traditional signal detection method, the signal detection method in the application can obtain an accurate bit sequence before coding only by fewer iteration times.

Description

Optimized expected propagation detection method and signal detection device

Technical Field

The present application relates to the field of wireless communication technologies, and in particular, to an optimized expected propagation detection method and an optimized expected propagation detection device.

Background

Multiple-input multiple-output (MIMO) technology has high spectrum efficiency and energy efficiency, and has become a key technology of modern wireless communication systems, and therefore, detection of MIMO is particularly important.

Taking MIMO detection by the expected propagation detection method as an example, in the expected propagation detection method, the receiver usually exchanges external information with the detector and the decoder during the iterative process of detection and decoding, so that the detector and the decoder can obtain accurate soft information, so that the receiver determines the transmission sequence before encoding.

However, the expected propagation detection methods in the prior art require multiple iterations to achieve the desired performance.

Disclosure of Invention

In view of the foregoing, it is desirable to provide an optimized expected propagation detection method and an optimized expected propagation detection apparatus, which can achieve better detection performance with fewer iterations.

In a first aspect, the present application provides a signal detection method, including:

acquiring coding information and modulation information of a bit sequence before coding, a channel matrix between a sending end and a receiving signal, and constructing a linear programming model, wherein the coding information comprises a code length and a code rate;

determining prior probability and initial parameters of a transmitted symbol according to a linear programming model; the sending symbol is generated by the coded bit sequence according to the modulation information;

executing a first iterative convergence operation according to the prior probability of the sending symbol and the initial parameter, and determining the posterior log-likelihood ratio of the posterior probability of the coded front bit sequence during each first iterative convergence operation;

and determining the bit sequence before encoding according to a target log-likelihood ratio corresponding to the bit sequence before encoding, wherein the target log-likelihood ratio is a posterior log-likelihood ratio corresponding to the first iterative convergence operation when the first iterative convergence operation is finished.

In one embodiment, performing a first iterative convergence operation according to the a priori probabilities of the transmitted symbols and the initial parameters, and determining a posterior log-likelihood ratio of the a posteriori probabilities of the coded previous bit sequence at each first iterative convergence operation, includes:

determining a cavity log-likelihood ratio of the cavity probability of the coded bit sequence during each first iterative convergence operation according to the prior probability, the initial parameters and a preset mapping relation between the bit sequence and the symbol;

and determining the posterior log-likelihood ratio corresponding to each first iterative convergence operation according to the cavity log-likelihood ratio in each first iterative convergence operation.

In one embodiment, acquiring coding information, modulation information, a channel matrix between a transmitting end and a receiving end, and a received signal of a bit sequence before coding, and constructing a linear programming model, includes:

acquiring a frozen bit set of a sending symbol, wherein the coding information comprises the frozen bit set;

determining a change relation between a bit sequence before coding and a bit sequence after coding according to the code length, the code rate and the frozen bit set;

determining a target function according to the mapping relation, the modulation information, the channel matrix and the received signal;

and determining a linear programming model according to the change relation and the objective function.

In one embodiment, determining the objective function according to the mapping relation, the channel matrix and the received signal comprises:

determining a transmission symbol by using the coded bit sequence based on the mapping relation and the modulation information;

determining a 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.

In one embodiment, determining the prior probability and the initial parameter of the transmitted symbol according to a linear programming model includes:

determining prior probability of a transmitted symbol according to a linear programming model;

determining the mean and variance of the prior probability of the transmission symbol according to the prior probability of the transmission symbol;

determining a first initial parameter according to the mean and variance of the prior probability of the transmitted symbol;

determining a second initial parameter according to the variance of the prior probability of the transmitted symbol;

the initial parameters include a first initial parameter and a second initial parameter.

In one embodiment, determining the prior probabilities of the transmitted symbols according to a linear programming model comprises:

acquiring the minimum value of a target function in a linear programming model, and determining a coded bit sequence corresponding to the minimum value of the target function as a target coded bit sequence;

determining the prior probability of the target coded bit sequence according to the coded value of the target coded bit sequence;

and determining the prior probability of the sending symbol according to the prior probability and the mapping relation of the bit sequence after the target coding.

In one embodiment, determining a cavity log-likelihood ratio of a cavity probability of an encoded bit sequence at each convergence operation of a first iteration according to a prior probability, an initial parameter, and a preset mapping relationship between the bit sequence and a symbol includes:

executing a second iteration convergence operation according to the prior probability and the initial parameters until a preset second iteration convergence ending condition is met, and obtaining a mean value and a variance of the cavity probability of the sending symbol;

and determining the cavity log-likelihood ratio of the cavity probability of the coded bit sequence in each first iteration convergence operation according to the mean value and the variance of the cavity probability of the transmission symbol corresponding to the second iteration convergence operation when the second iteration convergence ending condition is met and the mapping relation.

In one embodiment, the second iterative convergence operation comprises:

determining the variance and mean of the cavity probability of the sending symbol according to the initial parameters, the system noise variance and the channel matrix;

determining the variance and the mean of the discrete posterior probability of the sending symbol according to the variance and the mean of the cavity probability of the sending symbol and the prior probability of the sending symbol;

and updating the initial parameters according to the variance and the mean of the discrete posterior probability of the sending symbol, and taking the updated initial parameters as the initial parameters in the next iteration.

In one embodiment, determining the variance and mean of the discrete a posteriori probabilities of the transmitted symbols based on the variance and mean of the cavity probabilities of the transmitted symbols and the prior probabilities of the transmitted symbols comprises:

determining the cavity probability of the sending symbol according to the variance and the mean of the cavity probability of the sending symbol;

determining the discrete posterior probability of the sending symbol according to the cavity probability of the sending symbol and the prior probability of the sending symbol;

and determining the variance and the mean of the discrete posterior probability of the sending symbol according to the discrete posterior probability of the sending symbol.

In one embodiment, updating the initial parameter according to the variance and the mean of the discrete a posteriori probabilities of the transmission symbols, and using the updated initial parameter as the initial parameter in the next iteration includes:

determining a first candidate initial parameter according to the variance of the cavity probability and the variance of the discrete posterior probability of the transmission symbol;

determining a second candidate initial parameter according to the variance and the mean of the cavity probability of the sending symbol and the variance and the mean of the discrete posterior probability;

updating the first initial parameter according to the first candidate initial parameter to obtain the first initial parameter in the next iteration;

and updating the second initial parameter according to the second candidate initial parameter to obtain the second initial parameter in the next iteration.

In one embodiment, determining the a posteriori log likelihood ratio corresponding to each first iterative convergence operation according to the cavity log likelihood ratio in each first iterative convergence operation includes:

and decoding the cavity log-likelihood ratio in each first iteration convergence operation to obtain the posterior log-likelihood ratio corresponding to each first iteration convergence operation.

In one embodiment, the method further comprises:

determining the log-likelihood ratio of the prior probability of the coded bit sequence according to the candidate log-likelihood ratio of each cavity log-likelihood ratio and the posterior probability of the coded bit sequence, wherein the candidate log-likelihood ratio is obtained by decoding the cavity log-likelihood ratio in each first iterative convergence operation;

and determining the new prior probability of the sending symbol according to the log-likelihood ratio and the mapping relation of the prior probability of the coded bit sequence, and taking the new prior probability as the prior probability of the sending symbol in the next iteration.

In one embodiment, determining the pre-coding bit sequence according to the target log-likelihood ratio corresponding to the pre-coding bit sequence includes:

and carrying out hard decision processing on the target log-likelihood ratio corresponding to the bit sequence before coding to obtain the bit sequence before coding.

In a second aspect, the present application also provides a signal detection apparatus, comprising:

the system comprises an acquisition module, a coding module and a decoding module, wherein the acquisition module is used for acquiring coding information and modulation information of a bit sequence before coding, a channel matrix between a sending end and a receiving end and a received signal and constructing a linear programming model, and the coding information comprises a code length and a code rate;

the parameter determining module is used for determining the prior probability and the initial parameter of the sending symbol according to the linear programming model; the sending symbol is generated by the coded bit sequence according to the modulation information;

the convergence module is used for executing first iterative convergence operation according to the prior probability of the sending symbol and the initial parameter and determining the posterior log-likelihood ratio of the posterior probability of the coded bit sequence during each first iterative convergence operation;

and the pre-coding bit sequence determining module is used for determining the pre-coding bit sequence according to a target log-likelihood ratio corresponding to the pre-coding bit sequence, wherein the target log-likelihood ratio is a posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation.

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 the processor implements the steps of any one of the methods provided in the embodiments of the first aspect when executing the computer program.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of any one of the methods provided in the foregoing embodiments of the first aspect.

In a fifth aspect, the present application provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of any one of the methods provided in the embodiments of the first aspect.

The embodiment of the application provides an optimized expected propagation detection method and a signal detection device, which includes the steps of firstly obtaining coding information and modulation information of a bit sequence before coding, a channel matrix between a sending end and a receiving end and a received signal, constructing a linear programming model, determining prior probability and initial parameters of a sending symbol according to the linear programming model, executing first iterative convergence operation according to the prior probability and the initial parameters of the sending symbol, determining a posterior log-likelihood ratio of the posterior probability of the bit sequence before coding during each first iterative convergence operation, and determining the bit sequence before coding according to a target log-likelihood ratio corresponding to the bit sequence before coding, wherein the target log-likelihood ratio is the corresponding posterior log-likelihood ratio at the end of the first iterative convergence operation. According to the method, a linear programming problem is constructed through the code length, the code rate, the modulation information, the channel matrix and the information of a received signal corresponding to the coding mode of a bit sequence before coding, the initialization information of a signal detection method is searched by the linear programming problem, and the prior information of a sending symbol and more reasonable and effective initial parameters are obtained for signal detection by fully considering the structural structure and the characteristics of the coding mode, so that under the same condition, compared with the traditional detection method, the signal detection method in the application performs the first iteration convergence operation by using fewer iteration times, the detection result the same as that of the traditional method can be obtained, the detection efficiency of the signal detection method in the application is higher, namely, compared with the traditional signal detection method, the accurate bit sequence before coding can be obtained only by using fewer iteration times.

Drawings

FIG. 1 is a diagram of an exemplary signal detection method;

FIG. 2 is a schematic flow chart of a signal detection method according to an embodiment;

FIG. 3 is a schematic diagram of a signal detection method according to an embodiment;

FIG. 4 is a schematic flow chart of a signal detection method according to another embodiment;

FIG. 5 is a schematic flow chart of a signal detection method according to another embodiment;

FIG. 6 is a polar code factor graph in one embodiment;

FIG. 7 is a schematic flow chart of a signal detection method according to another embodiment;

FIG. 8 is a schematic flow chart of a signal detection method according to another embodiment;

FIG. 9 is a schematic flow chart of a signal detection method according to another embodiment;

FIG. 10 is a schematic flow chart diagram of a signal detection method in another embodiment;

FIG. 11 is a schematic flow chart of a signal detection method according to another embodiment;

FIG. 12 is a schematic flow chart of a signal detection method according to another embodiment;

FIG. 13 is a schematic diagram of a signal detection system in another embodiment;

FIG. 14 is a schematic flow chart diagram of a signal detection method in another embodiment;

FIG. 15 is a schematic flow chart of a signal detection method according to another embodiment;

FIG. 16 is a block diagram showing the construction of a signal detection device according to an embodiment;

FIG. 17 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.

The signal detection method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the sender 102 communicates with the receiver 104. The data storage system may store data that needs to be processed by the receiver 104. The data storage system may be integrated on the receiving end 104, or may be placed on the cloud or other network server.

The transmitting end may be one end of a transmitting antenna, and the receiving end may be one end of a receiving antenna.

The embodiment of the application provides an optimized expected propagation detection method and an optimized expected propagation detection device, which can improve the response speed of functions in a workflow.

The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, of the embodiments of the present application.

In an embodiment, taking the application environment in fig. 1 as an example, a signal detection method is provided, and this embodiment relates to determining a prior probability and an initial parameter of a transmission symbol according to a constructed linear programming model, and performing a first iterative convergence operation using the prior probability and the initial parameter of the transmission symbol to determine a posterior log-likelihood ratio of the posterior probability of a bit sequence before coding, so as to determine a specific process of the bit sequence before coding according to a target log-likelihood ratio corresponding to the bit sequence before coding, as shown in fig. 2, this embodiment includes the following steps:

s201, obtaining coding information, modulation information, a channel matrix between a sending end and a receiving signal of a bit sequence before coding, and constructing a linear programming model, wherein the coding information comprises a code length and a code rate.

In the MIMO detection problem, the conventional linear Minimum Mean Square Error (MMSE) detection algorithm enjoys low complexity, but the detection performance is not ideal enough, and Expected Propagation (EP) detection is used as a detection algorithm based on the bayesian approximate posterior probability, and after several iterations, the performance far exceeding the MMSE detection can be achieved, and the EP detection can naturally output the posterior probability of a detected symbol, so that an Iterative Detection and Decoding (IDD) system can be conveniently formed with a subsequent decoding module.

At present, in an IDD receiver based on EP detection, generally, during an iterative process of detection and decoding, an EP detector and a decoder exchange extrinsic information (extrinsic information) with each other, and the detector and the decoder can obtain more accurate soft information, thereby accelerating a convergence rate and improving system performance. However, all the IDD receivers based on EP detection pay attention to the iterative strategy and ignore the characteristic of coding, and since the polar code is already used as the forward error correction code of the downlink control link, the embodiment of the present application is a MIMO system based on the polar code coding.

Taking the EP detection-based IDD receiver as a Dual EP (DEP) IDD receiver as an example, the receiver can apply the EP algorithm twice when applying the feedback extrinsic information of the detector and the decoder, and feed back the discrete output of the decoder to the detector to initialize the inner loop of the next iteration through the outer loop better approximating the discrete output of the decoder. However, the currently proposed EP detection algorithm-based IDD receivers use the coding characteristics that are not fully considered, and even ignore the coding type, so that in the first iteration, since the sequence of detection and decoding is followed, the EP detector has no feedback information from the decoder and can only perform the most original EP detection, so that EP-based IDD receivers such as Dual EP (DEP) do not consider the coding characteristics during detection, and multiple iterations are required to achieve the ideal performance.

Therefore, the embodiment of the present application finds an optimal solution for initialization of an EP by using a Linear Programming (LP) method in combination with a coding feature.

Firstly, acquiring coding information and modulation information of a bit sequence before coding, a channel matrix between a sending end and a receiving end and a received signal, and constructing a linear programming problem according to the coding information and the modulation information of the bit sequence before coding, the channel matrix between the sending end and the receiving end and the received signal; specifically, the coding information includes a code length and a code rate, a polarization code factor graph corresponding to a coding mode of the bit sequence before coding can be determined according to the code length and the code rate, and then a linear programming model is constructed according to the polarization code factor graph, the modulation information, the channel matrix and the received signal.

The linear programming model comprises an objective function and a constraint condition, so that the linear programming model can be determined by using a network model, a polarization code factor graph, modulation information, a channel matrix and a received signal are input into the network model, and the objective function and the constraint condition of the linear programming model are obtained through analysis of the network model.

It should be noted that, in the present application, signal detection is performed by taking polarization code coding as an example, therefore, the coding information includes a code length and a code rate, a polarization code factor graph can be determined according to the code length and the code rate, and the polarization code factor graph is a coding mode of a polarization code.

S202, determining the prior probability and the initial parameters of the transmitted symbols according to the linear programming model.

Wherein, the sending symbol is generated by the coded bit sequence according to the modulation information.

And determining the prior probability and the initial parameters of the transmitted symbols based on the obtained linear programming model, namely an objective function and constraint conditions in the linear programming model, wherein the objective function of the linear programming problem can be maximum or minimum, and the maximum or minimum of the objective function is determined according to the actual situation.

Taking the minimum value of the objective function as an example, solving the linear programming model according to the objective function and the constraint condition, and determining the prior probability and the initial parameter of the transmission symbol corresponding to the minimum value of the objective function and meeting the constraint condition as the prior probability and the initial parameter of the transmission symbol.

It should be noted that the linear programming model is constructed based on the polarization code factor graph, the channel matrix and the received signal, so that in the solving 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 during the first iteration, and the initial parameter is determined by the initial prior probability, so that the initial parameter can be determined according to the prior probability of the transmitted symbol.

S203, executing a first iterative convergence operation according to the prior probability of the sending symbol and the initial parameter, and determining a posterior log-likelihood ratio of the posterior probability of the coded front bit sequence in each first iterative convergence operation.

In the MIMO detection process, according to the initial parameter in the first iteration and the prior probability of the sending symbol, the first iteration convergence operation is carried out according to the detector and the decoder, and in the process of the first iteration convergence operation, the posterior log likelihood ratio of the posterior probability of the bit sequence before encoding in each iteration process can be obtained.

As shown in fig. 3, fig. 3 is a schematic structural diagram of MIMO, where a transmitter transmits a bit sequence before encoding, performs a polarization coding operation on the bit sequence before encoding to obtain a bit sequence after encoding, then performs a mapping operation on the bit sequence after encoding to determine a sending symbol, an antenna in a sending end transmits the sending symbol, a receiving antenna in a receiving end receives a signal, the received signal is a received signal, and the received signal is detected and decoded by a detector and a decoder to determine the bit sequence before encoding corresponding to the received signal.

The first iterative convergence operation is iterative convergence performed in the detector and the decoder, and the detector and the decoder perform the first iterative convergence operation according to the obtained prior probability of the transmission symbol and the initial parameter, so as to obtain a posterior log-likelihood ratio of the posterior probability of the bit sequence before coding in each first iterative convergence operation.

The posterior log-likelihood ratio refers to the log-likelihood ratio of the posterior probability of the bit sequence before coding obtained by the decoder in each iteration process.

It should be noted that the first iteration convergence operation includes a detection process in the detector and a decoding process in the decoder, and a detection algorithm used in the detector and a decoding algorithm in the decoder are not limited in the embodiment of the present application.

And S204, determining the bit sequence before encoding according to a target log-likelihood ratio corresponding to the bit sequence before encoding, wherein the target log-likelihood ratio is a posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation.

Obtaining a posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation based on the obtained posterior log-likelihood ratio of the posterior probability of the coded previous bit sequence during each first iterative convergence operation, and determining the posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation as a target log-likelihood ratio, wherein the first iterative convergence operation corresponds to a first iterative convergence end condition, and the end of the first iterative convergence operation is that the first iterative convergence operation satisfies the first iterative convergence end condition, so that the posterior log-likelihood ratio satisfying the first iterative convergence end condition is taken as the target log-likelihood ratio; for example, if the first iteration convergence end condition is iteration T times, the posterior log-likelihood ratio obtained by the T-th iteration is determined as the target log-likelihood ratio corresponding to the bit sequence before encoding.

And then determining the bit sequence before coding according to the target log-likelihood ratio corresponding to the bit sequence before coding. Log-likelihood ratios (LLRs) are commonly used in communications for soft decoding, e.g., a log-likelihood ratio of a bit may be expressed as a ratio of a probability of 0 to a probability of 1 taken as a natural logarithm; it can also be expressed that the ratio of the probability of a bit being 1 to the probability of a bit being 0 is taken from the natural logarithm.

Taking the ratio of the probability that the target log-likelihood ratio is 0 to the probability that the bit is 1 as a natural logarithm, the magnitudes of the probabilities that the bit is 0 and the bit is 1 can be determined according to the first target log-natural ratio, and the value of the bit corresponding to the larger probability is determined as the initial bit, so that the bit sequence before encoding is determined according to the log-likelihood ratio of any bit of the initial bit.

The signal detection method comprises the steps of firstly obtaining coding information, modulation information, a channel matrix between a sending end and a receiving end and a received signal of a bit sequence before coding, constructing a linear programming model, determining prior probability and initial parameters of a sending symbol according to the linear programming model, executing first iterative convergence operation according to the prior probability and the initial parameters of the sending symbol, determining posterior log likelihood ratio of posterior probability of the bit sequence before coding during each first iterative convergence operation, and determining the bit sequence before coding according to a target log likelihood ratio corresponding to the bit sequence before coding, wherein the target log likelihood ratio is the corresponding posterior log likelihood ratio when the first iterative convergence operation is finished. According to the method, a linear programming problem is constructed through the code length, the code rate, the modulation information, the channel matrix and the information of a received signal corresponding to the coding mode of a bit sequence before coding, the initialization information of the signal detection method is searched by the linear programming problem, and the prior information of a more reasonable and effective initial parameter and a sending symbol is obtained for signal detection by fully considering the construction structure and the characteristics of the coding mode, so that under the same condition, compared with the traditional detection method, the signal detection method in the application performs the first iteration convergence operation by using fewer iteration times, the detection result which is the same as that of the traditional method can be obtained.

In one embodiment, as shown in fig. 4, performing a first iterative convergence operation according to the prior probability of the transmitted symbol and the initial parameter, and determining a posterior log-likelihood ratio of the posterior probability of the coded previous bit sequence at each first iterative convergence operation, includes the following steps:

s401, determining a cavity log-likelihood ratio of the cavity probability of the coded bit sequence in each first iterative convergence operation according to the prior probability, the initial parameters and the preset mapping relation between the bit sequence and the symbol.

The mapping relationship between the bit sequence and the symbol is a corresponding relationship between the bit sequence and the signal, for example, the bit sequence 00 may be mapped to the symbol a, the bit sequence 01, and may be mapped to the symbol b, so that the transmission symbol may be obtained according to the encoded bit sequence and the mapping relationship between the bit sequence and the symbol.

The preset iteration times can be iterated in the first iterative convergence operation, so that in each iteration process, the cavity log-likelihood ratio of the cavity probability of the coded bit sequence in each iteration in the first iterative convergence operation can be determined according to the prior probability of the transmitted symbol in each iteration process, the initial parameter and the preset mapping relation between the bit sequence and the symbol.

Please refer to fig. 3, the prior probability and the initial parameter of the transmitted symbol obtained in the above embodiment and the mapping relationship between the preset bit sequence and the symbol can be input into the detector, the detector analyzes the prior probability and the initial parameter of the transmitted symbol and the mapping relationship between the preset bit sequence and the symbol, and performs a first iterative convergence operation in each iteration to obtain a cavity log-likelihood ratio of the cavity probability of the coded bit sequence in each iterative convergence operation.

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

It should be noted that the cavity log-likelihood ratio is a log-likelihood ratio of the cavity probability of the coded bit sequence, which is also passed between the detector and the decoder, and both the output of the detector and the input of the decoder are the log-likelihood ratio, that is, extrinsic information.

S402, determining the posterior log-likelihood ratio corresponding to each first iterative convergence operation according to the cavity log-likelihood ratio in each first iterative convergence operation.

Referring to fig. 3, based on the cavity log-likelihood ratios obtained by the detector during each first iterative convergence operation, the cavity log-likelihood ratios obtained by the detector during each first iterative convergence operation are sent to a decoder, and the decoder determines the a posteriori log-likelihood ratios corresponding to each first iterative convergence operation according to the cavity log-likelihood ratios obtained by the detector during each first iterative convergence operation.

Specifically, based on a decoding algorithm preset in the decoder, the cavity log-likelihood ratio during each first iterative convergence operation is decoded, and the posterior log-likelihood ratio corresponding to each first iterative convergence operation is determined.

The signal detection method determines the cavity log-likelihood ratio of the cavity probability of the coded bit sequence during each first iterative convergence operation according to the prior probability, the initial parameter and the preset mapping relation between the bit sequence and the symbol, and determines 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. The method carries out first iterative convergence operation based on the prior probability and initial parameters of the sending symbols which are obtained for the first time to obtain the corresponding posterior log-likelihood ratio during each first iterative convergence operation, and obtains more reasonable and effective initial parameters and prior information of the sending symbols for signal detection, so that compared with the traditional signal detection method, the signal detection method in the application can obtain an accurate bit sequence before coding only by fewer iteration times.

Based on the foregoing embodiment, the prior probability and the initial parameter of the transmission symbol are obtained through the linear programming model, and the process of constructing the linear programming model is described below through an embodiment, in an embodiment, as shown in fig. 5, encoding information, modulation information, a channel matrix between a transmitting end and a receiving end, and a received signal of a bit sequence before encoding are obtained, and the linear programming model is constructed, including the following steps:

s501, a freezing bit set of the sending symbol is obtained, wherein the coding information comprises the freezing bit set.

The polar code is a forward error correction coding method used for signal transmission, and is constructed by selecting K good bits from N bits for transmitting information, and the other bits are frozen bits, which are generally assumed to be all zeros.

For example, if the a-bit sequence is 8 bits and the frozen bit set of the a-bit sequence is {1,2,3}, then the frozen bits of the a-bit sequence are all 0's.

Alternatively, the set of frozen bits may be determined by the code length and the code rate, or may be preset.

S502, determining the change relation between the bit sequence before coding and the bit sequence after coding according to the code length, the code rate and the frozen bit set.

The coded bit sequence is a sequence of the bit sequence before coding which is coded by coding information.

First, a polarization code factor graph is determined according to the code length, and the polarization code factor graph is a coding structure mode of the polarization code, for example, a structure mode of the polarization code factor graph with the code length of 8 is shown in fig. 6 by taking the code length as an example.

The polar code factor graph shown in FIG. 6 is a polar code cyclic structure, where N is a code length of 8, and a sparse graph representation with (1 + log N) N constant nodes can be constructed, where the auxiliary constant nodes have N log N. In the figure, the round nodes 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 solution of MMSE adopted by EP in the first iteration, so that the first iteration considers the coding characteristic and accelerates the convergence speed.

To transition the polar code encoding constraint from the Galois field to the real number field, the binary constant nodes in the graph may be used at [0, 1%]Inner real nodes are substituted. As can be seen from fig. 6, the check nodes only involve two or three constant nodes. For any node containing three constants { a ₁ ,a ₂ ,a ₃ Check node j of { fraction (j) }, locally optimal polyhedron P _j Can be determined by the following linear inequality:

similarly, for each node containing two constants { a ₁ ,a ₂ Check nodes j, P of _j The definition is as follows:

0≤a ₁ ＝a ₂ ≤1 (2)

in addition, according to the polarization code factor graph, the code rate and the frozen bit set, the position and the value of the frozen bit are determined, the frozen bit is taken as 0 for example, the cutting plane tau is defined as the plane with all the frozen bit bits as 0, therefore, the polyhedron P is the cutting plane tau and all the local optimal polyhedrons P _j Intersection of (a):

where N is the code length, and N = logN.

With continued reference to FIG. 6, any of the circular nodes in FIG. 6 have varying relationships with the aforementioned connecting nodes, e.g., a _2，0 Node and a _3，0 And a _3,4 Has a variation relationship:

such as node a _2,7 And a _3,7 Has a variation relationship: a is a _2,7 ＝a _3,7 ，

And the like, the change relations of all the nodes in the graph can be obtained.

In FIG. 6, the bit sequence before encoding is { u } ₀ ,u ₁ ,u ₂ ,u ₃ ,u ₄ ,u ₅ ,u ₆ ,u ₇ Obtaining a coding sequence { x ] through a coding construction mode in the figure 6 ₀ ,x ₁ ,x ₂ ,x ₃ ,x ₄ ,x ₅ ,x ₆ ,x ₇ Therefore, according to the change relationship of each node, the change relationship between the bit sequence before coding and the bit sequence after coding can be determined.

Optionally, the variation relationship between the pre-coding bit sequence and the post-coding bit sequence further includes a variation relationship between nodes, an inequality relationship between nodes in formulas (1) to (3), and a value of [0,1] for each node.

And S503, determining an objective function according to the mapping relation, the modulation information, the channel matrix and the received signal.

Based on the mapping relation between the bit sequence and the symbol, the sending symbol can be determined according to the coded bit sequence, and then an objective function in a linear programming model is constructed according to the coded bit sequence, the channel matrix and the received signal

In one embodiment, as shown in fig. 7, determining the objective function according to the mapping relation, the channel matrix and the received signal includes the following steps:

and S701, determining a transmission symbol by using the coded bit sequence based on the mapping relation and the modulation information.

Firstly, the corresponding relation between the coded bit sequence and the sending symbol is determined according to the modulation information, and then the sending symbol is determined based on the mapping relation between the bit sequence and the sending symbol.

M(x)＝s (4)

Wherein, M (·) is the mapping relation between the bit sequence and the symbol, x is the coded bit sequence, and s is the transmission symbol.

S702, determining a predicted received signal according to the transmission symbol and the channel matrix.

And determining a predicted received signal based on the transmitted symbol and the channel matrix, wherein if the transmitted symbol is s and the channel matrix is H, the predicted received signal is Hs.

S703, 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.

Where H is a channel matrix, y is a received signal, hs is a predicted received signal, e is an error between the predicted received signal and the received signal, and N is _r In order to receive the number of antennas,

m is the length of the coded bit sequence, Q is the modulation order, and the value less than or equal to is the inequality relation of elements in the matrix.

And S704, determining a linear programming model according to the change relation and the objective function.

Based on the obtained change relation and the objective function, a linear programming model is determined together, and the linear programming problem can be solved according to the change relation and the objective function.

The signal detection method comprises the steps of obtaining a frozen bit set of a sending symbol, wherein the coding information further comprises the frozen bit set, determining a change relation between a bit sequence before coding and a bit sequence after coding according to a code length, a code rate and the frozen bit set, determining a target function according to a mapping relation, a channel matrix and a received signal, and determining a linear programming model according to the change relation and the target function, wherein the bit sequence after coding is a sequence of an initial sequence which is coded through the coding information. According to the method, a linear programming problem is established by considering a coding construction mode and a frozen bit set, more reasonable and effective initial parameters and prior information of a transmitted symbol can be obtained for signal detection, and compared with a traditional signal detection method, the signal detection method in the application can obtain an accurate bit sequence before coding only by fewer iteration times.

In one embodiment, as shown in fig. 8, the determining the prior probability and the initial parameter of the transmitted symbol according to the linear programming model includes the following steps:

and S801, determining the prior probability of the transmitted symbol according to the linear programming model.

In one embodiment, as shown in fig. 9, determining the prior probability of the transmitted symbol according to a linear programming model includes the following steps:

s901, obtaining the minimum value of the target function in the linear programming model, and determining the coded bit sequence corresponding to the minimum value of the target function as the target coded bit sequence.

And solving the linear programming model to obtain the minimum value of the target function in the linear programming model, and then determining the coded bit sequence corresponding to the minimum value of the target function as the target coded bit sequence.

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

S902, determining the prior probability of the target coded bit sequence according to the coded value of the target coded bit sequence.

The target coded bit sequence is determined according to a linear programming model, and the code value of each bit in the target coded bit sequence is between [0,1], so that the prior probability of the target coded bit sequence is determined according to the code value of each bit in the target coded bit sequence.

For example, if the coded value of a bit is 0.99, the probability that the bit is 1 is determined to be 99%, and the probability that the bit is 0 is determined to be 1%; for another example, if the code value of a bit is 0.4, the probability that the bit is 1 is determined to be 40%, and the probability that the bit is 0 is determined to be 60%. Thus, the probability distribution of each bit can be determined from the encoded value of each bit. And obtaining the prior probability of the coded bit sequence based on the probability distribution of each bit in the coded bit sequence.

Alternatively, the way of calculating the encoded bit sequence may be the same as the way of calculating the total probability formula, for example, the total probability formula transforms the probability solution problem for a complex event a into the summation problem of the probabilities of simple events occurring under different conditions, if the events B1, B2, B3 \8230, bn constitute a complete event group, i.e. they are mutually incompatible two by two, and the sum is the complete set; and P (Bi) is greater than 0, then for any event a there is P (a) = P (a | B1) × P (B1) + P (a | B2) × P (B2) +. + P (a | Bn) × P (Bn).

And S903, determining the prior probability of the sending symbol according to the prior probability and the mapping relation of the bit sequence after the target coding.

And determining the prior probability of the sending symbol according to the prior probability of the bit sequence after target coding and the mapping relation between the bit sequence and the symbol.

For example, the probability and the mapping relation of each bit in the target coded bit sequence determine the probability of each symbol in the transmitted symbols; for example, if the bit 00 can be mapped to a, and the probability of 0 for the first bit is 40%, and the probability of 0 for the second bit is 60%, the probability of a in the corresponding transmission symbol is 24%.

TABLE 1

TABLE 2

For example, as shown in table 1 and table 2, table 1 shows the probability of the first bit and the second bit and the combined probability distribution, and table 2 shows the mapping relationship between the bit sequence and the symbol, the probability distribution corresponding to each symbol in the signal can be determined correspondingly, for example, the probability corresponding to symbol a is 32%, and the probability corresponding to symbol b is 48%.

Similarly, based on the above manner, the prior probability of the transmitted symbol can be determined according to the prior probability and the mapping relation of the target coded bit sequence.

It should be noted that the mapping relationship between the bit sequence and the symbol in the embodiment of the present application may be specifically set according to actual situations, and is not limited in this application.

S802, according to the prior probability of the sending symbol, the mean value and the variance of the prior probability of the sending symbol are determined.

If the prior probability distribution of the transmitted symbol xi is as follows:

ξ	x ₁	x ₂	...	x _n
					P	p ₁	p ₂	...	p _n

the mean of the prior probabilities of the transmitted symbols may be calculated according to equation (5) and the variance of the prior probabilities of the transmitted symbols may be calculated according to equation (6).

Eξ＝x ₁ p ₁ +x ₂ p ₂ +...+x _n p _n (5)

Dξ＝(x ₁ -Eξ) ² ·p ₁ +(x ₂ -Eξ) ² ·p ₂ +...+(x _n -Eξ) ² ·p _n (6)

Therefore, from the prior probabilities of the transmission symbols, the mean and variance of the prior probabilities of the transmission symbols can be calculated using equations (5) and (6).

S803, determining a first initial parameter according to the mean and variance of the prior probability of the transmission symbol.

As shown in equation (7), the first initial parameter may be calculated using equation (7).

Wherein, λ is a first initial parameter, emaxi is a mean value of prior probability of the transmission symbol, and D ξ is a variance of the prior probability of the transmission symbol.

S804, according to the variance of the prior probability of the sending symbol, a second initial parameter is determined.

Wherein the initial parameters comprise a first initial parameter and a second initial parameter.

As shown in equation (8), the second initial parameter may be calculated using equation (8).

Where γ is the second initial parameter and Dξ is the variance of the prior probability of the transmitted symbol.

The signal detection method comprises the steps of determining the prior probability of a sending symbol according to a linear programming model, determining the mean value and the variance of the prior probability of the sending symbol according to the prior probability of the sending symbol, determining a first initial parameter according to the mean value and the variance of the prior probability of the sending symbol, and determining a second initial parameter according to the variance of the prior probability of the sending symbol, wherein the initial parameters comprise the first initial parameter and the second initial parameter. In the method, prior information of the sending symbol is calculated before first iteration detection, and the initial parameter is determined according to the prior information, so that coding information is considered during the first iteration detection, the detection efficiency is improved, and the iteration times are reduced.

In one embodiment, as shown in fig. 10, determining a cavity log-likelihood ratio of a cavity probability of an encoded bit sequence at each first iterative convergence operation according to a prior probability, an initial parameter, and a preset mapping relationship between the bit sequence and a symbol includes the following steps:

and S1001, executing second iterative convergence operation according to the prior probability and the initial parameters until a preset second iterative convergence ending condition is met, and obtaining the mean value and the variance of the cavity probability of the sending symbol.

And performing second iterative convergence operation by using an EP detection algorithm according to the prior probability and the initial parameters until a preset second iterative convergence ending condition is met, wherein the preset second iterative convergence ending condition is T times, and then taking the mean value and the variance of the cavity probability of the transmission symbol obtained by the Tth second iterative convergence operation as the mean value and the variance of the cavity probability of the transmission symbol finally obtained.

Specifically, the second iterative convergence ending condition of the EP detection algorithm is set to T times, then the prior probability and the initial parameter are used as the inputs of the EP detection algorithm, the second iterative convergence operation is executed through the EP detection algorithm, and finally the mean value and the variance of the cavity probability of the transmission symbol are output. Wherein the second iterative convergence operation refers to an iterative convergence operation in the detector.

And S1002, determining a cavity log-likelihood ratio of the cavity probability of the coded bit sequence during each first iterative convergence operation according to the mean value and the variance of the cavity probability of the transmission symbol corresponding to the second iterative convergence operation when a second iterative convergence ending condition is met and a mapping relation.

As shown in equation (9), the cavity log-likelihood ratio of the encoded bit sequence can be calculated according to equation (9).

Wherein L is _E (x _n ) Cavity log-likelihood ratio, x, representing the cavity probability of an encoded bit sequence _n For the coded bit sequence, Ω _n,ζ Is defined by x _n The constellation point modulated by the M bits, i.e. the symbol corresponding to the mapping relationship between the bit sequence and the symbol, and x _n ζ =0 or 1,

is the average of the cavity probabilities of the transmitted symbols,

is the variance of the cavity probability of the transmitted symbol.

According to the signal detection method, second iteration convergence operation is executed according to the prior probability and the initial parameters until a preset second iteration convergence end condition is met, the mean value and the variance of the cavity probability of the transmitted symbol are obtained, and the cavity log-likelihood ratio of the cavity probability of the coded bit sequence in each first iteration convergence operation is determined according to the mean value and the variance of the cavity probability of the transmitted symbol corresponding to the second iteration convergence operation when the second iteration convergence end condition is met and the mapping relation. Compared with the traditional detection method, the method can obtain the same detection result as the traditional method by executing the first iteration convergence operation with fewer iterations.

In one embodiment, as shown in fig. 11, the second iterative convergence operation includes the following steps:

and S1101, determining the variance and the mean of the cavity probability of the sending symbol according to the initial parameters, the system noise variance and the channel matrix.

In one embodiment, the determining the variance and the mean of the cavity probability of the transmission symbol according to the initial parameter, the system noise variance, and the channel matrix between the transmitting end and the receiving end may be performed by first determining a covariance matrix and a mean of an initial posterior probability of the transmission symbol according to a first initial parameter, a second initial parameter, a system noise variance, and a channel matrix, then traversing diagonal elements in the covariance matrix of the initial posterior probability of the transmission symbol, determining the variance of the cavity probability of the transmission symbol according to each diagonal element and the first initial parameter, traversing each element in the mean of the initial posterior probability of the transmission symbol, and determining the mean of the cavity probability of the transmission symbol according to each element, each diagonal element, the variance of the cavity probability of the transmission symbol, the first initial parameter, and the second initial parameter.

At the transmitting end, the bit sequence u = [ u ] ₁ ,u ₂ ,...,u _N ] ^T Performing a polar code encoding criterion results in codewords x = uG, where N is the code length and G is the order of N (N = log N)

Kronecker product of (iv). The codeword x is mapped into a complex constellation, finally a complex constellation having N _t A transmitting antenna and N _r MIMO system transmission of the individual receiving antennas.

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

y＝Hs+w (10)

wherein H is 2N _r ×2N _t Channel matrix, s is the transmitted symbol and s _i ∈A，

Is a constellation diagram of M order modulation, w is a system noise variance of

White gaussian noise.

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

where p(s) is the prior probability of a transmitted symbol, p(s) will be initialized to an equiprobable distribution, i.e. equal probability distribution, without any feedback

Wherein,

is an indicator function when s _i The value of epsilon A is 1 or 0;

means that the received signal y follows a Gaussian distribution, the mean value is Hs, and the covariance matrix is

The EP detection algorithm is implemented by constructing a fully Gaussian distributed q ^[l] (s) to approximate p (s | y), the approximated a posteriori probability may be factored in each iteration as:

wherein,

and is provided with

Is a fixed item of the form of,

is an approximation term.

According to the Bayes rule of the linear Gaussian system, q ^[l] (s) covariance matrix of (A posteriori probability of transmitted symbol s)

Sum mean value

Can be represented by the following formulae (13) and (14).

Wherein,

the covariance matrix and mean of the initial a posteriori probabilities of the transmitted symbols,

system noise variance, H denotes the channel matrix, λ ^[l] Which is indicative of a first initial parameter,

mean value of initial a posteriori probabilities of transmitted symbols, y denotes received signal, y ^[l] Representing the second initial parameter.

In the above description, the mean value of the initial a posteriori probabilities of the transmission symbols is in the form of a vector, and is a joint a posteriori probability distribution of each antenna, and since the transmission symbols are independent from each other between the antennas, the component method corresponding to their joint a posteriori is effective.

Thus, q ^[l] (s _i ) The edge probability of (c) can be expressed as:

wherein,

is that

The item (i) of (1),

is that

The ith diagonal element of (1). Thus, define q ^[l] Probability of cavitation of(s), i.e. probability of cavitation:

therefore, the variance and mean of the cavity probability of the transmission symbol may be calculated according to equation (15) and equation (16), respectively.

Wherein,

represents the variance of the cavity probability of the transmitted symbol,

to represent

The ith diagonal element of (a) is,

which is indicative of a first of the initial parameters,

represents the mean of the cavity probabilities of the transmitted symbols,

represent

The item (i) of (1),

representing the second initial parameter.

S1102, determining the variance and mean of the discrete posterior probability of the sending symbol according to the variance and mean of the cavity probability of the sending symbol and the prior probability of the sending symbol.

In one embodiment, the variance and the mean of the discrete a posteriori probabilities of the transmission symbols are determined according to the variance and the mean of the cavity probabilities of the transmission symbols and the prior probabilities of the transmission symbols, and the variance and the mean of the discrete a posteriori probabilities of the transmission symbols may be determined according to the variance and the mean of the cavity probabilities of the transmission symbols, the discrete a posteriori probabilities of the transmission symbols are determined according to the cavity probabilities of the transmission symbols and the prior probabilities of the transmission symbols, and the variance and the mean of the discrete a posteriori probabilities of the transmission symbols are determined according to the discrete a posteriori probabilities of the transmission symbols.

Firstly, determining the cavity probability distribution of a sending symbol according to the variance and the mean of the cavity probability of the sending symbol, and then determining the posterior probability of the sending symbol according to the cavity probability distribution of the sending symbol and the prior probability of the sending symbol, wherein the posterior probability of the sending symbol is the discrete posterior probability because the cavity probability is the cavity probability of each antenna; and determining the variance and the mean of the discrete posterior probability of the posterior probability according to the discrete posterior probability of the posterior probability.

In particular, according to

A discrete a posteriori probability distribution of the transmitted symbols is calculated, wherein,

for transmitting discrete posterior probability distribution of symbols, p _D (s _i ) To be the a priori probabilities of the transmitted symbols,

is the cavity probability of the transmitted symbol. Thus, the discrete a posteriori probability of the transmitted symbol is determined and then based onDiscrete a posteriori probabilities of transmitted symbols, calculating the mean of the discrete a posteriori probabilities

Sum variance

It should be noted that the way of calculating the mean and the variance is the same as that in the above embodiment, and no further description is given here.

Optionally, an allowable minimum variance may be set to

Wherein epsilon is a preset parameter.

And S1103, updating the initial parameter according to the variance and the mean of the discrete posterior probability of the sending symbol, and taking the updated initial parameter as the initial parameter in the next iteration.

Updating the initial parameters according to the variance and the mean value of the discrete posterior probability of the sending symbol, and then taking the updated initial parameters as the initial parameters in the next iteration.

In one embodiment, the initial parameter is updated according to the variance and the mean of the discrete a posteriori probability of the transmission symbol, and the updated initial parameter is used as the initial parameter at the next iteration by determining a first candidate initial parameter according to the variance of the cavity probability of the transmission symbol and the variance of the discrete a posteriori probability, and determining a second candidate initial parameter according to the variance and the mean of the cavity probability of the transmission symbol and the variance and the mean of the discrete a posteriori probability; and updating the first initial parameter according to the first candidate initial parameter to obtain a first initial parameter during the next iteration, and updating the second initial parameter according to the second candidate initial parameter to obtain a second initial parameter during the next iteration.

The first candidate initial parameter and the second candidate initial parameter are updated by using moment matching as a criterion, and are specifically updated by using a formula (17) and a formula (18).

Wherein,

a first candidate initial parameter is represented which,

represents the variance of the discrete a posteriori probability of the transmitted symbol,

represents the variance of the cavity probability of the transmitted symbol,

a second candidate initial parameter is represented that,

represents the mean of the discrete a posteriori probabilities of the transmitted symbols,

representing the mean of the cavity probabilities of the transmitted symbols.

After the moment matching operation is performed, the robustness is improved in the damping process with the running parameter beta, the first candidate initial parameter is used for updating the first initial parameter, the second candidate initial parameter is used for updating the second initial parameter, and the calculation is specifically performed according to a formula (19) and a formula (20).

Wherein,

represents the first initial parameter at the next iteration, beta represents a preset parameter,

a first candidate initial parameter is represented which,

which is indicative of a first initial parameter,

representing a second initial parameter at the next iteration,

a second candidate initial parameter is represented that,

representing a second initial parameter and l representing the number of iterations.

When it occurs

Then the result of the last iteration is retained, i.e.:

when for all i =1,2 _t Parameter of

And

all have been updated, then

And

it can be updated again with equations (13) and (14) up to the maximum number of iterations L.

The signal detection method determines the variance and the mean of the cavity probability of the sending symbol according to the initial parameters, the system noise variance and the channel matrix, determines the variance and the mean of the discrete posterior probability of the sending symbol according to the variance and the mean of the cavity probability of the sending symbol and the prior probability of the sending symbol, updates the initial parameters according to the variance and the mean of the discrete posterior probability of the sending symbol, and takes the updated initial parameters as the initial parameters in the next iteration. According to the method, the initial parameters are updated, so that the accurate detection result can be obtained by executing the first iterative convergence operation with less iteration times in signal detection.

In one embodiment, determining the a posteriori log likelihood ratio corresponding to each first iterative convergence operation according to the cavity log likelihood ratio for each first iterative convergence operation includes: and decoding the cavity log-likelihood ratio during each first iterative convergence operation to obtain a posterior log-likelihood ratio corresponding to each first iterative convergence operation.

When the decoder executes the first iterative convergence operation each time, decoding operation is carried out on the received cavity log-likelihood ratio, and a posterior log-likelihood ratio corresponding to the first iterative convergence operation each time can be obtained, wherein the posterior log-likelihood ratio is the posterior log-likelihood ratio of the bit sequence before coding obtained by decoding the cavity log-likelihood ratio of the coding sequence.

The decoding operation is determined by a decoding algorithm in the decoder, in the embodiment of the present application, the decoding algorithm is a decoding algorithm of soft input and soft output, in the embodiment of the present application, no limitation is made on the type of the decoding algorithm, for example, the decoding operation may be decoding by using a maximum a posteriori probability decoding algorithm.

In the above embodiment, decoding the cavity log-likelihood ratio to obtain the a posteriori log-likelihood ratio of the bit sequence before encoding, and in the process of decoding the cavity log-likelihood ratio, further includes some other steps, which is described below by an embodiment, in an embodiment, as shown in fig. 12, the embodiment includes the following steps:

and S1201, determining the log-likelihood ratio of the prior probability of the coded bit sequence according to the candidate log-likelihood ratios of the cavities and the posterior probability of the coded bit sequence.

And the candidate log-likelihood ratio is obtained by decoding the cavity log-likelihood ratio in each first iteration convergence operation.

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 in the process of decoding the cavity log-likelihood ratio by the decoder.

The log-likelihood ratio of the prior probabilities of the encoded bit sequence can be calculated according to equation (23).

Wherein L is _D (x _n ) Log-likelihood ratio, L (x), representing the prior probability of the coded bit sequence _n ) Candidate log-likelihood ratios, L, representing the posterior probabilities of coded bit sequences _E (x _n ) A cavity log-likelihood ratio representing the cavity probability of the encoded bit sequence.

S1202, determining a new prior probability of the sending symbol according to the log-likelihood ratio and the mapping relation of the prior probability of the coded bit sequence, and taking the new prior probability as the prior probability of the sending symbol in the next iteration.

Mapping the log-likelihood ratio of the prior probabilities of the coded bit sequence to new prior probabilities of the transmitted symbols:

wherein p is _D (s _i ) New a priori probability for transmitting symbols, psi (omega) _m ) Corresponding constellation point omega _m B is a normalization factor for ensuring that the sum of the probabilities of the constellation points of each symbol in the transmitted symbol is 1, p _D (x _n ) Is the prior probability of the coded bit sequence.

The prior probability of the coded bit sequence can be determined according to the log-likelihood ratio of the prior probability of the coded bit sequence, and then the new prior probability of the sending symbol is determined according to the prior probability of the coded bit sequence and the mapping relation between the bit sequence and the symbol.

The signal detection method determines the log-likelihood ratio of the prior probability of the coded bit sequence according to the candidate log-likelihood ratios of the cavity log-likelihood ratios and the posterior probability of the coded bit sequence, wherein the candidate log-likelihood ratios are obtained by decoding the cavity log-likelihood ratios in each first iteration convergence operation, the new prior probability of a transmitted symbol is determined according to the log-likelihood ratios and the mapping relation of the prior probabilities of the coded bit sequence, and the new prior probability is used as the prior probability of the transmitted symbol in the next iteration. The method can make the detection performance superior to that of the traditional method by using less iteration times to execute the first iteration convergence operation in signal detection, namely obtaining more accurate bit sequence before coding.

In one embodiment, determining the pre-coding bit sequence according to the target log-likelihood ratio corresponding to the pre-coding bit sequence includes: and carrying out hard decision processing on the target log-likelihood ratio corresponding to the bit sequence before coding to obtain the bit sequence before coding.

Hard decisions are simply made by setting a threshold to determine the output, typically a 1 greater than 0 and a 0 less than 0 in terms of binary.

In one embodiment, if the target log-likelihood ratio corresponding to the bit sequence before encoding is a logarithm of a ratio of a probability that a bit is 0 to a probability that the bit is 1, for the target log-likelihood ratio of any bit in the bit sequence before encoding, if the target log-likelihood ratio of the bit is greater than 0, it indicates that the probability that the bit is 0 is greater than the probability that the bit is 1, and it is determined that the bit is 0; if the target log likelihood ratio of the bit is less than 0, it indicates that the probability of the bit being 1 is greater than the probability of the bit being 0, and the bit can be determined to be 1. Namely:

wherein x is _u For encoding the pre-bit sequence, L (x) _u ) Is the target log-likelihood ratio corresponding to the bit sequence before coding.

In one embodiment, if the target log-likelihood ratio corresponding to the pre-coding bit sequence is a logarithm of a ratio of a probability that a bit is 1 to a probability that a bit is 0, for the target log-likelihood ratio of any bit in the pre-coding bit sequence, if the target log-likelihood ratio of the bit is greater than 0, it indicates that the probability that the bit is 1 is greater than the probability that the bit is 0, and it may be determined that the bit is 1; if the target log likelihood ratio of the bit is less than 0, it indicates that the probability of the bit being 0 is greater than the probability of the bit being 1, and the bit can be determined to be 0.

In an embodiment, this embodiment focuses on a polar code coded MIMO system, and proposes an EP detection algorithm (EPLP) based on Linear Programming (LP), and connects the EP detection algorithm (EPLP) with a polar code decoder to form an IDD-EPLP system, where the IDD receiver fully considers a special structure of a polar code and a constraint condition with a frozen bit, and establishes an LP optimization problem, so as to provide a more reasonable and effective initial parameter for EP detection.

Compared with the most advanced DEP detection algorithm at present, the embodiment can achieve the detection performance superior to DEP by using fewer EP iterations, and certain advantages are kept in decoding performance after the IDD system is accessed. Especially under the scene that the number of transmitting and receiving antennas is close, the IDD receiver has more obvious performance advantages.

The reason why the performance of the IDD-EP is improved compared with the System (SDD) of original sequential detection decoding is that the prior probability of the EP detector in the IDD-EP is mapped by the decoding information feedback, and the SDD-EP has uniform distribution as the initial prior probability. However, in the first IDD iteration of IDD-EP, since no decoding feedback is available to the un-decoded EP detector, the a priori information can still only be initialized to a uniform distribution, so the initialization of EP is degenerated to minimum mean square error detection (MMSE), i.e., MMSE

Wherein E is _s Is the average energy of the transmitted symbols.

This embodiment differs from the original EP-based IDD system (IDD-EP) described above in the procedure of initialization in the first EP iteration. Specifically, the commonalities and differences between IDD-EP and IDD-EPLP can be seen in FIG. 13.

In an embodiment, as shown in fig. 14, fig. 14 is a flowchart of a signal detection method in this embodiment of the present application, first, according to a combination of a code length, a code rate, and a frozen bit, an LP optimization problem is constructed by an MIMO channel matrix H and a received signal, a priori probability of a transmitted symbol is solved, an initial parameter of an EPLP algorithm is calculated according to the priori probability, then the EPLP algorithm, i.e., a moment matching and damping process (MMD) is performed for L times, then a cavity probability of the transmitted symbol is obtained, the cavity probability of the transmitted symbol is mapped into a cavity LLR of an encoded bit sequence, a polarization code decoding is performed on the cavity LLR, then an LLR solution of the encoded bit sequence is obtained, the LLR solution of the encoded bit sequence is mapped into a priori probability of the transmitted symbol, and the obtained priori probability of the transmitted symbol is decodedPerforming the next iteration until outputting the bit sequence x before encoding _u Log likelihood ratio L (x) of a posteriori probability of (c) _u ) Then to L (x) _u ) And carrying out hard decision processing to obtain a bit sequence before coding.

In one embodiment, as shown in fig. 15, this embodiment includes the steps of:

s1501, constructing a linear programming model according to the coding length, the frozen bit set, the transmission channel matrix and the received signal;

firstly, acquiring a change relation between a bit sequence before coding and a sequence after decoding according to a coding length; determining a coding limitation condition according to a variation relation between a bit sequence before coding and a sequence after decoding; and determining a linear programming model according to the variation relation between the bit sequence before coding and the coding sequence and the coding limiting condition.

S1502, solving the linear programming model, determining a coded bit sequence, and determining a first initial parameter and a second initial parameter according to the coded bit sequence;

calculating probability distribution of a coding sequence according to the coded bit sequence, and determining the probability of the coding sequence as the prior probability of a sending symbol; and calculating a variance and a mean of the prior probabilities of the transmission symbols according to the prior probabilities of the transmission symbols, and determining a first initial parameter lambda (mean divided by variance) and a second initial parameter gamma (variance-divided) according to the variance and the mean.

S1503, operating the MMD for L times according to the prior probability of the sending symbol, the first initial parameter and the second initial parameter, and outputting a log-likelihood ratio of the cavity probability of the coded bit sequence;

calculating a covariance matrix of a posterior probability of a transmitted symbol according to the first initial parameter, the system noise variance and the channel matrix; calculating the mean vector of the posterior probability of the sending symbol according to the parameter gamma, the covariance matrix of the sending symbol, the channel matrix, the system noise variance and the receiving signal y; any one of any diagonal element and mean vector for the covariance matrix; determining the variance of the cavity probability of the transmitted symbol according to any diagonal element of the covariance matrix and any item of the mean vector and according to any diagonal element of the covariance matrix and lambda; determining the mean value of the cavity probability of the sending symbol according to any item of the posterior probability of the sending symbol, any diagonal element of the covariance matrix, the variance of the cavity probability of the sending symbol and the parameter gamma; determining the cavity probability of the sending symbol according to the mean and the variance of the cavity probability of the sending symbol, and determining the mean and the variance of the posterior probability of the sending symbol according to the cavity probability of the sending symbol and the prior probability of the sending symbol; based on a moment matching criterion, obtaining a candidate first initial parameter according to the variance of the cavity probability of the sending symbol and the variance of the posterior probability; based on a moment matching criterion, obtaining candidate second initial parameters according to the mean value and the variance of the cavity probability of the sending symbol and the mean value and the variance of the posterior probability; determining a new first initial parameter according to the first initial parameter and the candidate first initial parameter; updating a 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, not updating the first initial parameter and the second initial parameter; continuing the next iteration according to the updated first initial parameter and the updated second initial parameter until the iteration is finished; outputting the mean vector and the variance of the cavity probability of the sending symbol as the sending symbol; and obtaining the cavity log-likelihood ratio of the posterior probability of the coded bit sequence according to the mean vector and the variance of the cavity probability of the sending symbol obtained after iteration and the mapping relation between the bits and the symbols.

S1504, after the detector obtains the log-likelihood ratio of the cavity probability, the log-likelihood ratio of the cavity probability is sent to a decoder, the decoder determines the candidate log-likelihood ratio of the posterior probability of the coded bit sequence according to the log-likelihood ratio of the cavity probability, and the prior log-likelihood ratio of the prior probability distribution of the coded bit sequence is calculated according to the log-likelihood ratio of the cavity probability and the candidate log-likelihood ratio.

S1505, mapping the prior log-likelihood ratio to obtain the prior probability of the transmitted symbol.

And S1506, when the iteration times are reached, finally outputting a log-likelihood ratio Lu of the posterior probability of the bit sequence before coding, and carrying out hard decision on Lu to obtain the bit sequence before coding.

The specific limitations of the signal detection method provided in this embodiment may refer to the step limitations of each embodiment in the signal detection method, which are not described herein again.

It should be understood that, although the respective steps in the flowcharts attached in the above-described embodiments are sequentially shown as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the figures attached to the above-mentioned embodiments may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.

In one embodiment, as shown in fig. 16, the present application further provides a signal detection apparatus 1600, where the apparatus 1600 includes: an obtaining module 1601, a parameter determining module 1602, a converging module 1603, and a pre-coding bit sequence determining module 1604, wherein:

an obtaining module 1601, configured to obtain coding information of a bit sequence before coding, modulation information, a channel matrix between a sending end and a receiving end, and a received signal, and construct a linear programming model, where the coding information includes a code length and a code rate;

a parameter determining module 1602, configured to determine a prior probability and an initial parameter of a transmission symbol according to a linear programming model; the sending symbol is generated by a coded bit sequence according to modulation information;

a convergence module 1603 configured to perform a first iterative convergence operation according to the prior probability of the transmitted symbol and the initial parameter, and determine a posterior log-likelihood ratio of a posterior probability of a coded previous bit sequence at each first iterative convergence operation;

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

In one embodiment, convergence module 1603 includes:

the first determining unit is used for determining a cavity log-likelihood ratio of the cavity probability of the coded bit sequence during each first iterative convergence operation according to the prior probability, the initial parameters and the mapping relation between the preset bit sequence and the symbol;

and the second determining unit is used for determining the posterior log-likelihood ratio corresponding to each first iterative convergence operation according to the cavity log-likelihood ratio in each first iterative convergence operation.

In one embodiment, the obtaining module 1601 includes:

an obtaining unit, configured to obtain a frozen bit set of a transmission symbol, where the coding information includes the frozen bit set;

a third determining unit, configured to determine a change relationship between the bit sequence before encoding and the bit sequence after encoding according to the code length, the code rate, and the frozen bit set;

a fourth determining unit, configured to determine an objective function according to the mapping relationship, the modulation information, the channel matrix, and the received signal;

and the fifth determining unit is used for determining the linear programming model according to the change relation and the objective function.

In one embodiment, the fourth determination unit includes:

a first determining subunit, configured to determine, based on the mapping relationship, a transmission symbol by using the coded bit sequence;

a second determining subunit, configured to determine a predicted received signal according to the transmission symbol and the channel matrix;

a construction subunit, configured to construct 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.

In one embodiment, the parameter determination module 1602 includes:

a sixth determining unit, configured to determine a prior probability of the transmission symbol according to the linear programming model;

a seventh determining unit, configured to determine a mean and a variance of the prior probability of the transmission symbol according to the prior probability of the transmission symbol;

an eighth determining unit, configured to determine the first initial parameter according to a mean and a variance of the prior probability of the transmission symbol;

a ninth determining unit, configured to determine a second initial parameter according to a variance of the prior probability of the transmission symbol;

In one embodiment, the sixth determination unit includes:

the third determining subunit is used for obtaining the minimum value of the target function in the linear programming model and determining the coded bit sequence corresponding to the minimum value of the target function as a target coded bit sequence;

the fourth determining subunit is configured to determine, according to the code value of the target coded bit sequence, a prior probability of the target coded bit sequence;

and the fifth determining subunit is used for determining the prior probability of the sending symbol according to the prior probability and the mapping relation of the bit sequence after the target coding.

In one embodiment, the first determination unit includes:

the obtaining subunit is used for executing a second iterative convergence operation according to the prior probability and the initial parameter until a preset second iterative convergence finishing condition is met, and obtaining a mean value and a variance of the cavity probability of the sending symbol;

and a sixth determining subunit, configured to determine, according to the mean and the variance of the cavity probabilities of the transmission symbols corresponding to the second iterative convergence operation when the second iterative convergence termination condition is satisfied, and the mapping relationship, a cavity log-likelihood ratio of the cavity probabilities of the coded bit sequence in each first iterative convergence operation.

In one embodiment, obtaining the sub-unit comprises:

a seventh determining subunit, configured to determine a variance and a mean of a cavity probability of the transmission symbol according to the initial parameter, the system noise variance, and the channel matrix;

an eighth determining subunit, configured to determine a variance and a mean of the discrete posterior probability of the transmission symbol according to the variance and the mean of the cavity probability of the transmission symbol and the prior probability of the transmission symbol;

and the updating subunit is used for updating the initial parameter according to the variance and the mean of the discrete posterior probability of the sending symbol, and taking the updated initial parameter as the initial parameter in the next iteration.

In one embodiment, the eighth determining subunit includes:

the cavity probability determining subunit is used for determining the cavity probability of the sending symbol according to the variance and the mean of the cavity probability of the sending symbol;

a discrete posterior probability determining subunit, configured to determine a discrete posterior probability of the transmission symbol according to the cavity probability of the transmission symbol and the prior probability of the transmission symbol;

and the discrete probability distribution determining subunit is used for determining the variance and the mean of the discrete posterior probability of the sending symbol according to the discrete posterior probability of the sending symbol.

In one embodiment, the update subunit includes:

a first candidate parameter determining subunit, configured to determine a first candidate initial parameter according to a variance of a cavity probability of a transmission symbol and a variance of a discrete posterior probability;

a second candidate parameter determination subunit, configured to determine a second candidate initial parameter according to the variance and the mean of the cavity probability of the transmission symbol and the variance and the mean of the discrete posterior probability;

the first parameter determining subunit is used for updating the first initial parameter according to the first candidate initial parameter to obtain a first initial parameter during the next iteration;

and the second parameter determining subunit is used for updating the second initial parameter according to the second candidate initial parameter to obtain the second initial parameter during the next iteration.

In one embodiment, the second determination unit includes:

and the decoding subunit is used for performing decoding operation on the cavity log-likelihood ratio during each first iterative convergence operation to obtain a posterior log-likelihood ratio corresponding to each first iterative convergence operation.

In one embodiment, the second determining unit further includes:

the tenth determining subunit is configured to determine a log-likelihood ratio of the prior probability of the coded bit sequence according to each cavity log-likelihood ratio and a candidate log-likelihood ratio of the posterior probability of the coded bit sequence, where the candidate log-likelihood ratio is obtained by performing a decoding operation on the cavity log-likelihood ratio in each first iterative convergence operation;

and the mapping subunit is used for determining a new prior probability of the sending symbol according to the log-likelihood ratio and the mapping relation of the prior probability of the coded bit sequence, and taking the new prior probability as the prior probability of the sending symbol in the next iteration.

In one embodiment, the pre-coding bit sequence determination module 1604 comprises:

and the decision unit is used for carrying out hard decision processing on the target log-likelihood ratio corresponding to the bit sequence before coding to obtain the bit sequence before coding.

For specific limitations of the signal detection apparatus, reference may be made to the above limitations of the steps in the signal detection method, which are not described herein again. The modules in the signal detection device can be wholly or partially implemented by software, hardware and a combination thereof. The modules may be embedded in hardware or independent of the target device, or may be stored in software in a memory of the target device, so that the target device invokes and executes operations corresponding to the modules.

In one embodiment, a computer device is provided, as shown in fig. 17, which includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of signal detection. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those skilled in the art that the configuration shown in fig. 17 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.

The implementation principle and technical effect of each step implemented by the processor in this embodiment are similar to the principle of the signal detection method described above, and are not described herein again.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

In the embodiments, the implementation principle and technical effect of each step implemented when the computer program is executed by the processor are similar to those of the signal detection method described above, and are not described herein again.

In an embodiment, a computer program product is provided, comprising a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

In the embodiment, the implementation principle and the technical effect of each step implemented when the computer program is executed by the processor are similar to the principle of the signal detection method described above, and are not described herein again.

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.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method of signal detection, the method comprising:

acquiring coding information, modulation information, a channel matrix between a sending end and a receiving signal of a bit sequence before coding, and constructing a linear programming model, wherein the coding information comprises a code length and a code rate;

determining prior probability and initial parameters of the transmitted symbols according to the linear programming model; the sending symbol is generated by a coded bit sequence according to the modulation information;

executing a first iterative convergence operation according to the prior probability of the sending symbol and the initial parameter, and determining the posterior log-likelihood ratio of the posterior probability of the bit sequence before coding during each first iterative convergence operation;

and determining the bit sequence before encoding according to a target log-likelihood ratio corresponding to the bit sequence before encoding, wherein the target log-likelihood ratio is a posterior log-likelihood ratio corresponding to the end of the first iterative convergence operation.

2. The method of claim 1, wherein the performing a first iterative convergence operation according to the prior probabilities of the transmitted symbols and an initial parameter and determining a posterior log-likelihood ratio of the posterior probabilities of the pre-coding bit sequence at each first iterative convergence operation comprises:

3. The method of claim 1, wherein the obtaining of the coding information, the modulation information, the channel matrix between the transmitting end and the receiving end, and the received signal of the pre-coding bit sequence to construct a linear programming model comprises:

obtaining a set of frozen bits of the transmitted symbol, wherein the coding information comprises the set of frozen bits;

determining a change relation between the bit sequence before coding and the bit sequence after coding according to the code length, the code rate and the freezing bit set;

determining an objective function according to the mapping relation, the modulation information, the channel matrix and the received signal;

and determining the linear programming model according to the change relation and the objective function.

4. The method of claim 3, wherein determining an objective function according to the mapping relationship, the modulation information, the channel matrix, and the received signal comprises:

determining the transmission symbol by using the coded bit sequence based on the mapping relation and the modulation information;

5. The method of claim 2, wherein determining the prior probability and initial parameters of the transmitted symbol according to the linear programming model comprises:

determining the prior probability of the transmitted symbol according to the linear programming model;

determining the first initial parameter according to the mean and variance of the prior probability of the sending symbol;

determining the second initial parameter according to the variance of the prior probability of the sending symbol;

6. The method of claim 5, wherein determining the prior probability of transmitting the symbol according to the linear programming model comprises:

acquiring the minimum value of a target function in the linear programming model, and determining a coded bit sequence corresponding to the minimum value of the target function as a target coded bit sequence;

and determining the prior probability of the sending symbol according to the prior probability of the target coded bit sequence and the mapping relation.

7. The method of claim 5, wherein determining the cavity log-likelihood ratio of the cavity probability of the coded bit sequence at each convergence operation of the first iteration according to the prior probability, the initial parameter and a preset mapping relationship between the bit sequence and the symbol comprises:

executing a second iterative convergence operation according to the prior probability and the initial parameter until a preset second iterative convergence finishing condition is met to obtain a mean value and a variance of the cavity probability of the sending symbol;

and determining the cavity log-likelihood ratio of the cavity probability of the coded bit sequence in each first iteration convergence operation according to the mean value and the variance of the cavity probability of the transmitted symbol corresponding to the second iteration convergence operation when the second iteration convergence ending condition is met and the mapping relation.

8. The method of claim 7, wherein the second iterative convergence operation comprises:

determining the variance and mean of the discrete posterior probability of the sending symbol according to the variance and mean of the cavity probability of the sending symbol and the prior probability of the sending symbol;

and updating the initial parameters according to the variance and the mean of the discrete posterior probability of the sending symbols, and taking the updated initial parameters as the initial parameters in the next iteration.

9. The method of claim 8, wherein determining the variance and the mean of the discrete a posteriori probabilities of the transmitted symbols based on the variance and the mean of the cavity probabilities of the transmitted symbols and the prior probabilities of the transmitted symbols comprises:

10. The method of claim 8, wherein the updating the initial parameter according to the variance and the mean of the discrete a posteriori probabilities of the transmission symbols, and using the updated initial parameter as the initial parameter in the next iteration comprises:

determining a first candidate initial parameter according to the variance of the cavity probability of the sending symbol and the variance of the discrete posterior probability;

updating the first initial parameter according to the first candidate initial parameter to obtain a first initial parameter during the next iteration;

and updating the second initial parameter according to the second candidate initial parameter to obtain the second initial parameter during the next iteration.

11. The method of claim 2, wherein determining the a posteriori log likelihood ratio for each first iterative convergence operation based on the cavity log likelihood ratio for each first iterative convergence operation comprises:

and decoding the cavity log-likelihood ratio in each first iterative convergence operation to obtain the posterior log-likelihood ratio corresponding to each first iterative convergence operation.

12. The method of claim 11, further comprising:

determining a log-likelihood ratio of the prior probability of the coded bit sequence according to the candidate log-likelihood ratios of the cavity log-likelihood ratios and the posterior probability of the coded bit sequence, wherein the candidate log-likelihood ratios are obtained by decoding the cavity log-likelihood ratios in each first iteration convergence operation;

and determining a new prior probability of the sending symbol according to the log-likelihood ratio of the prior probability of the coded bit sequence and the mapping relation, and taking the new prior probability as the prior probability of the sending symbol in the next iteration.

13. The method according to claim 1 or 2, wherein the determining the pre-coding bit sequence according to the target log-likelihood ratio corresponding to the pre-coding bit sequence comprises:

14. A signal detection apparatus, the apparatus comprising:

the system comprises an acquisition module, a linear programming model and a control module, wherein the acquisition module is used for acquiring coding information, modulation information, a channel matrix between a sending end and a receiving signal of a bit sequence before coding and constructing the linear programming model, and the coding information comprises code length and code rate;

the parameter determining module is used for determining the prior probability and the initial parameter of the sending symbol according to the linear programming model; the sending symbol is generated by a coded bit sequence according to modulation information;

a convergence module, configured to perform a first iterative convergence operation according to the prior probability of the transmission symbol and the initial parameter, and determine a posterior log-likelihood ratio of the posterior probability of the pre-coding bit sequence at each first iterative convergence operation;

15. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 13 when executing the computer program.

16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 13.