WO2024036933A1

WO2024036933A1 - Detection and decoding method and apparatus, computer device and readable storage medium

Info

Publication number: WO2024036933A1
Application number: PCT/CN2023/080793
Authority: WO
Inventors: 张川; 周华羿; 郑健; 黄永明; 尤肖虎
Original assignee: 网络通信与安全紫金山实验室
Priority date: 2022-08-16
Filing date: 2023-03-10
Publication date: 2024-02-22
Also published as: CN115396064A; CN115396064B

Abstract

The present application provides a detection and decoding method and apparatus, a computer device and a readable storage medium. The method comprises: determining a symbol vector of each channel on the basis of input bits received by a communication system and a channel matrix corresponding to each channel of the communication system; obtaining multiple synchronization sets corresponding to the input bits according to the symbol vector of each channel; performing decoding search on each synchronization set to obtain a target path; and determining a target detection and decoding result according to the target path and a target detection and decoding function.

Description

Detection and decoding methods, devices, computer equipment and readable storage media

Related applications:

This application claims priority to the Chinese patent application filed on August 16, 2022, with application number 202210983507.8 and titled "Detection and decoding method, device, computer equipment and readable storage medium", the full text of which is hereby incorporated. Reference.

Technical field

The present application relates to the field of wireless communication technology, and in particular to a detection and decoding method, device, computer equipment and readable storage medium.

Background technique

With the rapid development of the information society, mobile communication data has grown exponentially, and the communication quality requirements for mobile communication data have become increasingly higher. In order to improve communication quality, channel capacity is usually increased in communication systems, such as nonlinear (Multiple-Input Multiple-Output, MIMO) systems.

During the operation of the MIMO system, the transmitter of the MIMO system mainly sends signals to the receiver, and the receiver correctly recovers the multi-channel transmit signals corresponding to the transmitter. Among them, the signal transmission and reception process will include two processing processes. , that is, signal detection and decoding processing. However, using related technologies to implement signal detection and decoding processing will result in poor error correction performance of the MIMO system.

Contents of the invention

According to various embodiments of the present application, a detection and decoding method, device, computer equipment and readable storage medium that can improve the error correction performance of a MIMO system are provided.

In the first aspect, this application provides a detection and decoding method, which method includes:

Determine the symbol vector of each channel based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system;

According to the symbol vector of each channel, multiple synchronization sets corresponding to the input bits are obtained; the synchronization sets include sets after combining the input bits;

Perform decoding search on each synchronization set to obtain the target path; the target path includes the path with the smallest sum of corresponding Euclidean distances in detection and decoding;

According to the target path and the target detection decoding function, the target detection decoding result is determined.

In a second aspect, this application also provides a detection and decoding device, which includes:

The symbol vector determination module is configured to determine the symbol vector of each channel based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system;

The synchronization set module is configured to obtain multiple synchronization sets corresponding to the input bits according to the symbol vector of each channel; the synchronization set is a set after combining the input bits;

The decoding search module is configured to perform decoding search on each synchronization set to obtain the target path; the target path includes detecting the path with the smallest Euclidean distance in decoding;

The detection decoding result determination module is configured to determine the target detection decoding result based on the target path and the target detection decoding function.

In a third aspect, this application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the steps of the method of any embodiment of the first aspect.

In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method of any embodiment of the first aspect is implemented. step.

In a fifth aspect, the present application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the method of any embodiment of the first aspect.

The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below. Other features, objects and advantages of the application will become apparent from the description, drawings and claims.

Description of drawings

To better describe and illustrate embodiments and/or examples of those inventions disclosed herein, reference may be made to one or more of the accompanying drawings. The additional details or examples used to describe the drawings should not be construed as limiting the scope of any of the disclosed inventions, the embodiments and/or examples presently described, and the best modes currently understood of these inventions.

Figure 1 is an application environment diagram of the detection and decoding method in one embodiment;

Figure 2 is a schematic flowchart of a detection and decoding method in an embodiment;

Figure 3 is a schematic flow chart of a detection and decoding method in another embodiment;

Figure 4 is a schematic flow chart of a detection and decoding method in another embodiment;

Figure 5 is a schematic diagram of the process of generating a synchronization set in one embodiment;

Figure 6 is a schematic flow chart of a detection and decoding method in another embodiment;

Figure 7 is a structural diagram of a search tree established in one embodiment;

Figure 8 is a structural block diagram of a detection and decoding device in an embodiment.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be optionally described in detail below in conjunction with the drawings and embodiments. It can be understood that the specific embodiments described here are used to explain the present application, but are not used to limit the present application.

The detection and decoding method provided by this application can be applied to the computer equipment shown in Figure 1. The computer device can be implemented as an independent server or a server cluster composed of multiple servers. Of course, it can also be implemented as, but not limited to, various personal computers, laptops, smart phones, tablets, and portable wearable devices.

The embodiment of the present application provides a detection and decoding method. Figure 2 is a schematic flow chart of the detection and decoding method. This method can realize the joint detection and decoding process, reduce the complexity of the spherical decoding algorithm, has a simple processing process, can improve the detection and decoding speed, and can also reduce the detection and decoding delay of the MIMO system when targeting low code rates. On the premise that the processing process is simple, the accuracy of the detection and decoding results can also be improved, thereby improving the error correction performance of the MIMO system. The following embodiment takes this method applied to the computer equipment in Figure 1 as an example to explain the detection and decoding method. The detection and decoding method may include the following steps:

S100. Determine the symbol vector of each channel based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system.

The above communication system may be a wired communication system or a wireless communication system. However, in the embodiment of the present application, the communication system is a wireless communication system as an example for description. In one embodiment, the communication system is a MIMO system.

In one embodiment, the signal received by the receiving end of the communication system, that is, the input bits, can be expressed as u=[u ₁ , u ₂ ,...,u _N ], N represents the total number of input bits, and each channel of the communication system corresponds to channel matrix N _t represents the number of transmitting antennas of the MIMO system, and N _r represents the number of receiving antennas of the MIMO system. Represents a complex domain. In the embodiment of the present application, the MIMO system is an uplink flat fading MIMO system as an example for explanation.

For example, to determine the symbol vector s of each channel based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system, an algorithm model may be pre-trained to combine the input bits received by the communication system and the The channel matrix corresponding to each channel is input into the algorithm model, and the symbol vector of each channel is output through the algorithm model.

What needs to be explained here is that the channel of the MIMO system can be divided into two parts: reliable channel and unreliable channel. For the polar code of the channel N represents the total number of input bits, of which K information bits are placed in the K most reliable channels for transmission, and NK frozen bits (set to a fixed value, usually 0) are placed in the remaining NK unreliable channels. Medium transmission, that is, the input bits include information bits and frozen bits. Among them, the set of information bits transmitted by the reliable channel is expressed as The frozen bit set transmitted by the unreliable channel is expressed as

In one embodiment, as shown in Figure 3, the step of determining the symbol vector of each channel based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system in S100 may include:

S110. Perform orthogonal triangular decomposition on the channel matrix corresponding to each channel to obtain a triangular matrix.

It should be noted that the channel matrix corresponding to each channel can be The upper triangular matrix R _i obtained by performing orthogonal triangular decomposition (QR decomposition) is a triangular matrix, and i represents the i-th channel.

S120. Modulate the input bits according to the triangular matrix and the polarization matrix to obtain the symbol vector of each channel.

Based on the obtained triangular matrix _Ri , the input bit u can be modulated according to the triangular matrix R _i and the polarization matrix G _N to obtain the symbol vector s of each channel, that is, s=MAP{x}=MAP{uB _N G _N }.

Among them, x represents the codeword (obtained through polar code construction), B _N represents the bit flip permutation, F is nth Kronecker product (i.e. Kronecker product), n is equal to log ₂ N, and s is equal to (s ₁ ,..., s _i ).

In the actual processing process, the symbol complex vector transmitted by the MIMO system Obtained from the polar code through M-order quadrature amplitude modulation, the MIMO system receives the symbol complex vector It can be expressed as in, is the channel matrix. In this embodiment, H is an independent and identically distributed Gaussian channel matrix, Represents an i.i.d. cyclically symmetric Gaussian with variance σ ² White Noise. After real-valued decomposition of the symbolic complex vector, the equivalent model y=Hs+n is obtained, where y and n are both real vectors of length 2N r, and H represents a real matrix of 2N _t × _{2N r} _.

Among them, the equivalent modulation process can be expressed as s _i =map(x _m(i-1)+1 ,...,x _mi ), and one real symbol corresponds to bits, so the vector output form of the map(·) function can be expressed as
MAP(x)=[map(x ₁ ,…,x _m ),…,map(x _N-m+1 ,…,x _N )] ^T (1).

S200. According to the symbol vector of each channel, obtain multiple synchronization sets corresponding to the input bits. The synchronization set includes a set of combined input bits.

Among them, the above synchronization set can be expressed as j is less than N. For example, the method of obtaining multiple synchronization sets corresponding to the input bits may be to search for symbol vectors equal to the symbol vectors of each channel in the mapping relationship, and determine the synchronization sets corresponding to the found symbol vectors as multiple synchronization sets corresponding to the input bits. Sync collection. Among them, the mapping relationship includes different symbol vectors, different synchronization sets, and the corresponding relationship between the two.

S300: Perform decoding search on each synchronization set to obtain a target path; the target path includes the path with the smallest sum of corresponding Euclidean distances in the detection and decoding.

Based on the synchronization set obtained in the above steps, a tree structure can be established first, and then a search algorithm is used to traverse each tree node in the tree structure along multiple preset paths until the end node is traversed. Determine the target path in the path. The tree structure includes multiple tree nodes, and each tree node may include information in each synchronization set.

For example, when a preset path in the tree structure is A-B-C, after traversing the tail node C, the sum of the corresponding Euclidean distances in the detection and decoding can be equal to the distance between tree node A and tree node B. Euclidean distance plus the Euclidean distance between tree node B and tree node C.

S400. Determine the target detection decoding result according to the target path and the target detection decoding function.

Among them, the target proportion result of the sum of all Euclidean distances corresponding to the target path can be taken, and then the target proportion result is substituted into the target detection decoding function to obtain the target detection decoding result. Optionally, the above target proportion can be 80%, 90% or 95%, etc. Correspondingly, the target proportion result can be equal to the product of the target proportion and the sum of all Euclidean distances corresponding to the target path. In addition, the sum of the Euclidean distances corresponding to the target path can also be compared with the corresponding preset search threshold, and then the target detection decoding result can be obtained based on the comparison result and the target detection decoding function. Optionally, the above target detection decoding function may represent a corresponding optimization function during detection decoding processing.

The detection and decoding method provided by the embodiment of the present application can determine the symbol vector of each channel based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system, and obtain multiple corresponding input bits based on the symbol vector of each channel. Synchronization set, perform decoding search on each synchronization set to obtain the target path, and determine the target detection decoding result based on the target path and target detection decoding function; this method can perform decoding search based on multiple synchronization sets obtained, and There is no need to directly perform decoding searches on input bits, thereby reducing the number of decoding searches, reducing the complexity of the detection and decoding process, improving the accuracy of detection and decoding results, and optionally improving the error correction performance of the MIMO system. ; At the same time, this method can also improve the speed of detection and decoding, obtain the detection and decoding results in time, and reduce the delay in obtaining the detection and decoding results.

The following describes the process of obtaining multiple synchronization sets corresponding to the input bits based on the symbol vectors of each channel in S200. In an embodiment, as shown in Figure 4, the above steps in S200 may include:

S210. Determine the Euclidean distance increment corresponding to each symbol vector based on the equivalent reception vector and symbol vector of each channel.

It can be understood that the equivalent reception vector z and symbol vector s of each channel can be processed to obtain the Euclidean distance increment e _i corresponding to each symbol vector.

Before the above-mentioned step S210 is executed, the above-mentioned method may further include: determining the equivalent reception vector of each channel according to the symbol vector, target noise vector and triangular matrix of each channel.

The above target noise vector may include a custom uniformly distributed noise vector, an exponentially distributed noise vector, a gamma noise vector, etc. In this embodiment, the target noise vector is an additive Gaussian white noise vector as an example for explanation.

In the embodiment of the present application, the MIMO system has a joint detection and decoding model, and the joint detection and decoding model is used to implement the detection and decoding process. In one embodiment, when there are equivalent channels N _c =N/(M _c N _t ) shared in the joint detection and decoding model, N _c can be equal to 1,...,i, and the values of each channel are equal to The effective reception vector z can be equal to

In formula (2), is the symbol vector of the N _c channel, is the equivalent receiving vector of the N _c channel, is the additive Gaussian white noise vector of the N _c channel, is the triangular matrix of the N _c channel.

Before performing the step of determining the target detection decoding result based on the target path and the target detection decoding function, the above method may also obtain the target detection decoding function based on the equivalent reception vector and symbol vector of each channel.

In order to improve the error correction performance of the MIMO system while reducing the complexity of the detection and decoding algorithm, the maximum likelihood detection algorithm can be used to implement the detection and decoding process. Therefore, in practical applications, the maximum likelihood detection problem of the MIMO system It can be transformed into a joint maximum likelihood (ML) detection and decoding problem, that is, the target detection decoding function:

In formula (3), the set χ = {0, 1}, u' is the vector of the output vector UB _N after the bit inversion operation.

Since both G _N and R are triangular matrices, the total Euclidean distance in maximum likelihood joint detection decoding can be equal to the sum of the Euclidean distance increments e _i of each symbol vector s _i , that is

Among them, the above [G _N ] _j represents the j-th column of _GN .

S220. Obtain multiple synchronization sets according to the Euclidean distance increment corresponding to each symbol vector.

Based on the above formula (4), it can be seen that when the bits are enumerated in order from u _N to u ₁ , each time a symbol i is determined (m consecutive bits are determined), its corresponding Euclidean distance e _i can be determined come out. Therefore, in this embodiment, the maximum likelihood joint detection decoding problem can be solved by using depth-first search spherical decoding. It should be noted here that the depth-first search spherical decoding algorithm only needs to satisfy the polar code coding rules. By enumerating bits in the space, compared with the spherical decoding algorithm detected by the MIMO system, the search space is greatly reduced, thereby reducing the complexity of the detection and decoding algorithm. Among them, the frozen bits in the input bits do not participate in the enumeration process during the detection and decoding process.

In practical applications, in order to use depth-first search spherical decoding to solve the maximum likelihood joint detection and decoding problem, multiple synchronization sets need to be obtained first.

In one embodiment, the step of obtaining multiple synchronization sets based on the Euclidean distance increment corresponding to each symbol vector in S220 may include: obtaining the determination time of the Euclidean distance increment corresponding to each symbol vector; Euclidean distance increments at the same time are determined as corresponding multiple synchronization sets.

After obtaining the determination time of the Euclidean distance increment corresponding to each symbol vector, the Euclidean distance increment with the same determination time can be obtained, and the Euclidean distance increment with the same determination time can be determined as the same synchronization set. Among them, in the case of multiple different determined times, multiple synchronization sets can be obtained That is, the number of synchronization sets can be equal to the number of different determination times.

As an example, take a polar code Coding, 16th-order quadrature amplitude modulation, and 2×2 MIMO system are taken as an example. In the MIMO system with polar code coding, the optimal search order of symbol vectors _i not only depends on G _N but also on R. In this paper In the embodiment, the Euclidean distance increment e _i is expressed as

In this embodiment, if or Then the corresponding s _i is the information symbol. If it is assumed that the number of information symbols is Ls. Among them, the number of information symbols can be equal to 1/2 of the total number of input bits. in order to achieve A preprocessing needs to be done first, that is, Ls corresponding bits (u _2i-1 , u _2i ) corresponding to Ls information symbols are enumerated. As shown in Figure 5 _The generation _process _of The influence of _i on the codeword x _i (or symbol vector s _i ) determines whether the input bit has a corresponding output arrow, that is, whether the input bit is an information bit or a frozen bit, where the information bit has a corresponding output arrow. , the frozen bit has no corresponding output arrow, and the two codewords form an information symbol s _i .

Among them, in Figure 5, each arrow pointing from u _i represents that [G _N ] _i,j is non-zero, that is, x _j changes as u _i changes. In Figure 5, the Euclidean distance increment _{e i} _is obtained based on the information symbol si, which describes the influence of the modulation symbol on the uncertainty of the Euclidean distance increment e _i based on R. Each arrow pointing from s _i to e _i represents [ R] _{j, i} are non-zero, and the synchronization sets obtained in this example are

The detection and decoding method provided by the embodiment of the present application can transform the MIMO system maximum likelihood detection problem into a joint ML detection and decoding problem, and can use a depth-first search spherical decoding algorithm to solve the joint ML detection and decoding problem, and can search the space is greatly reduced, reducing the complexity of the detection and decoding algorithm; in addition, this method uses a joint detection and decoding model, takes channel coding into the MIMO system, integrates detection and decoding, and fully encodes information when performing detection operations. , which can improve the error correction performance of the MIMO system and reduce the complexity of detection. In addition, the joint detection and decoding processing does not rely on the transmission of soft information, avoiding the waste of hardware resources caused by the storage of soft information and avoiding the generation of soft information. The resulting information loss and joint detection and decoding processing can also avoid the high complexity caused by the iterative process in iterative detection and decoding.

The process of decoding and searching each synchronization set to obtain the target path in S300 will be described below. In an embodiment, as shown in Figure 6, the above steps in S300 may include:

S310. Based on the multi-tree search strategy, determine the preset search threshold for decoding search.

Since the existing spherical decoding algorithm usually does not set the initial sphere radius, it can be considered that the initial sphere radius is infinite, which will cause a lot of unnecessary search processing, ultimately leading to slow convergence of the decoding search algorithm and relatively high algorithm complexity. . Therefore, the embodiment of the present application proposes a polar code spherical decoding algorithm assisted by a multi-tree search strategy to solve this problem. The multi-tree search strategy is a depth-first search strategy as an example for explanation.

In practical applications, the depth-first search strategy defines in is the Euclidean distance D ₁ when the path in the search tree is the real u'. Since (1/σ ² )||n|| ² ~ χ ² (2N _t ), given a constant ε (0<ε<<1), a can be determined, making formula (5) true.

Before starting to perform the deep search, the preset search threshold of the decoding search can be determined to be aσ ² , where aσ ² can be inversely solved through formula (5). The smallest D ₁ among all paths in the search tree is equal to In the case of The search threshold is used to perform a new round of depth search, and the above steps are repeated until the path with the smallest sum of Euclidean distances is found.

S320. Establish a search tree based on each synchronization set.

Based on all synchronization sets obtained in the above steps, the synchronization set with the earliest determined time can be filtered out first, and the input bits corresponding to the Euclidean distance increment in the synchronized set with the earliest determined time can be determined as the top-level tree in the search tree nodes, and then determine the other layer tree nodes in the search tree through the input bits corresponding to the Euclidean distance increments in the remaining synchronization sets according to the order of determination time, and then determine the other layer tree nodes in the search tree according to the top-level tree node and other layer trees. Nodes build a search tree. Optionally, the level of the search tree can be equal to the total number of synchronization sets, and each level corresponds to a synchronization set. It should be noted here that the top to the bottom of the search tree respectively correspond to the synchronization set with the earliest determined time to the determined time. Latest sync set. Continuing to refer to the example in Figure 5, the search tree is a tree structure from level Ls/2 to level 1, where Ls/2 is the total number of synchronization sets, and level 1 is the lowest layer of the search tree.

Another example, in synchronized collections including e ₄ , When e ₃ , e ₂ , and e ₁ are included, the synchronized set The input bits corresponding to the Euclidean distance increment e ₄ in are u ₇ and u ₈ , and the synchronization set The input bits corresponding to the Euclidean distance increments e ₃ , e ₂ , and e ₁ are u ₅ and u ₆ , u ₄ and u ₃ , u ₂ and u ₁ respectively, that is, the corresponding search tree is established. The search tree It includes two levels of tree nodes, the topmost tree nodes are u ₇ and u ₈ , and the bottommost tree nodes are u ₅ and u ₆ , u ₄ and u ₃ , u ₂ and u ₁ , but in this embodiment, each tree The nodes only include the information bits from the input bits, not the frozen bits, and are the same for every tree node in the same layer.

During the depth-first search process, there is no need to enumerate frozen bits, so that the input bit enumeration order can be optimized by using the synchronization set-assisted polar code spherical decoding algorithm.

Among them, the top level of the search tree includes two tree nodes. Based on these two tree nodes, tree nodes in other levels of the search tree are constructed sequentially. The number of branches of each tree node can be equal to 2 ⁿ , n represents the The number of information bits in a tree node, and each tree node corresponds to the same tree node at the next level.

S330. According to the preset order in the search tree, according to the input bits corresponding to each level in the search tree, determine the adjacent Euclidean distance between any two adjacent input bits in any two adjacent levels in the search tree.

The preset order in the search tree can be from the top to the bottom of the search tree, or from the bottom to the top of the search tree. Of course, it can also be customized according to the search tree. fixed order. However, in the embodiment of the present application, the preset order is taken as an example from the topmost to the bottommost order of the search tree.

In one embodiment, a tree node in each level of the search tree is traversed in sequence according to a preset order in the search tree, and during the traversal process, the adjacent nodes traversed in any two adjacent levels in the search tree are calculated. The adjacent Euclidean distance between two input bits. Optionally, after one or more rounds of traversal, the target path finally obtained includes a tree node in each level of the search tree.

S340. Select a path that satisfies the target search condition from the search tree and determine it as the target path; the target search condition includes adjacent Euclidean values between any two adjacent input bits in any two adjacent levels in the search tree. Input bits whose distances are all smaller than the preset search threshold value include multiple input bits in the path.

Among them, the adjacent Euclidean distance between any two adjacent input bits in any two adjacent levels in the search tree can be compared with the preset search threshold, and the adjacent Euclidean distance is smaller than the preset search threshold. The lowest tree node (i.e. input bit) is retained, and the lowest tree node whose adjacent Euclidean distance is smaller than the preset search threshold is filtered out, and all the retained tree nodes are combined to form the target path.

For example, take the polar code The application of coding, 16th-order quadrature amplitude modulation, and 2×2 MIMO system is used to illustrate the above search process. If there are 4 synchronization sets: Figure 7 shows the search tree established based on these four synchronization sets. The access to tree nodes and their corresponding path metrics are shown in the figure. The adjacent Euclidean distance correspondence between two adjacent tree nodes is also shown in the figure. is displayed (marked value); when the adjacent Euclidean distance is greater than or equal to the preset search threshold, the lowest tree node with the adjacent Euclidean distance will be filtered out, that is, the lowest tree node will no longer be Branch tree nodes are generated downward, and the adjacent Euclidean distances marked at the filtered tree nodes in the figure are marked in gray font. In the case where the search proceeds to the end of a path, that is, the lowest-level tree node, the adjacent Euclidean distance is updated to the lowest-level tree node between the lowest-level tree node and the adjacent upper-level tree node.

In this example, assuming ε = 0.05, the preset search threshold is 42.6. Since any adjacent Euclidean distance on the maximum likelihood path (marked with a bold dotted line) is smaller than the preset search threshold, the first round The search process will end after the traversal, and there is no need to perform a second round of traversal to implement the tree search operation. Among them, in the actual processing process, taking Figure 7 as an example, both branches between the preset search thresholds need to be traversed, which can be traversed synchronously or asynchronously, and each tree node is traversed first during the traversal process. The left branch tree node of It is the bold dotted path in Figure 7.

Optionally, the step of determining the target detection decoding result based on the target path and the target detection decoding function in S400 above may include: substituting the sum of all adjacent Euclidean distances corresponding to the target path into the target detection decoding function, Get the target detection decoding results.

In the embodiment of the present application, the sum of adjacent Euclidean distances between all adjacent tree nodes in the target path can be substituted into the target detection decoding function to obtain the target detection decoding result.

The detection and decoding method provided by the embodiment of the present application can transform the MIMO system maximum likelihood detection problem into a joint ML detection and decoding problem, and can use a depth-first search spherical decoding algorithm to solve the joint ML detection and decoding problem, and can search the space It is greatly reduced and reduces the complexity of the detection and decoding algorithm.

In order to facilitate the understanding of those skilled in the art, the detection and decoding method provided by this application is introduced by taking the execution subject as a computer device as an example. The method includes:

(1) Based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system, determine the symbol vector of each channel.

(2) Determine the equivalent receiving vector of each channel based on the symbol vector, target noise vector and triangular matrix of each channel.

(3) Based on the equivalent reception vector and symbol vector of each channel, determine the Euclidean distance increment corresponding to each symbol vector.

(4) Obtain the determination time of the Euclidean distance increment corresponding to each symbol vector.

(5) Determine the Euclidean distance increments with the same determination time as corresponding multiple synchronization sets. The synchronization set includes a set of combined input bits.

(6) Based on the multi-tree search strategy, determine the preset search threshold for decoding search.

(7) Establish a search tree based on each synchronization set.

(8) According to the preset order in the search tree, according to the input bits corresponding to each level in the search tree, determine the adjacent Euclidean distance between any two adjacent input bits in any two adjacent levels in the search tree .

(9) Select a path that satisfies the target search conditions from the search tree and determine it as the target path; the target search conditions include the adjacent Euclidean values between any two adjacent input bits in any two adjacent levels in the search tree. The paths whose Euclidean distances are all less than the preset search threshold value include multiple input bits; the target path includes the path with the smallest sum of corresponding Euclidean distances in detection and decoding.

(10) Obtain the target detection decoding function based on the equivalent reception vector and symbol vector of each channel.

(11) Substitute the sum of all adjacent Euclidean distances corresponding to the target path into the target detection decoding function to obtain the target detection decoding result.

For details of the execution processes in (1) to (11) above, please refer to the description of the above embodiments. The implementation principles and technical effects are similar and will not be described again here.

It can be understood that although the steps in the flow charts of Figures 2-4 and 6 are shown in sequence as indicated by arrows, these steps are not necessarily executed in the order indicated by arrows. Unless explicitly stated in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in Figures 2-4 and 6 may include multiple steps or stages. These steps or stages are not necessarily executed at the same time, but may be executed at different times. These steps or The execution order of the stages is not necessarily sequential, but may be performed in turn or alternately with other steps or steps in other steps or at least part of the stages.

Based on the same inventive concept, embodiments of the present application also provide a detection and decoding device for implementing the above-mentioned detection and decoding method. The implementation solution provided by this device to solve the problem is similar to the implementation solution recorded in the above method. Therefore, for the specific limitations in one or more detection and decoding device embodiments provided below, please refer to the above description of the detection and decoding method. Limitations will not be repeated here.

In one embodiment, as shown in Figure 8, a detection decoding device is provided, including: a symbol vector determination module 11, a synchronization set module 12, a decoding search module 13 and a detection decoding result determination module 14, wherein:

The symbol vector determination module 11 is configured to determine the symbol vector of each channel based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system;

The synchronization set module 12 is configured to obtain multiple synchronization sets corresponding to the input bits according to the symbol vector of each channel; wherein the synchronization set includes a set after combining the input bits;

The decoding search module 13 is configured to perform a decoding search on each synchronization set to obtain a target path; the target path includes detecting the path with the smallest Euclidean distance in decoding;

The detection decoding result determination module 14 is configured to determine the target detection decoding result according to the target path and the target detection decoding function.

The detection and decoding device provided in this embodiment can execute the above method embodiments. Its implementation principles and technical effects are similar and will not be described again here.

In one embodiment, the synchronization set module 12 includes: a Euclidean distance increment determination unit and a synchronization set acquisition unit, where:

The Euclidean distance increment determination unit is configured to determine the Euclidean distance increment corresponding to each symbol vector based on the equivalent reception vector and symbol vector of each channel;

The synchronization set acquisition unit is configured to obtain multiple synchronization sets based on the Euclidean distance increment corresponding to each symbol vector.

In one embodiment, the detection decoding device further includes: an equivalent reception vector determination module, wherein:

The equivalent reception vector determination module is configured to determine the equivalent reception vector of each channel based on the symbol vector, target noise vector and triangular matrix of each channel.

In one of the embodiments, the synchronization collection acquisition unit is set to:

Obtain the determination time of the Euclidean distance increment corresponding to each symbol vector;

The Euclidean distance increments with the same determination time are respectively determined as corresponding multiple synchronization sets.

In one embodiment, the decoding search module 13 includes: a threshold determination unit, a search tree acquisition unit, a Euclidean distance calculation unit and a target path determination unit, wherein:

A threshold determination unit configured to determine a preset search threshold for decoding search according to the multi-tree search strategy;

The search tree acquisition unit is configured to establish a search tree based on each synchronization set;

The Euclidean distance calculation unit is configured to determine the distance between any two adjacent input bits in any two adjacent levels in the search tree according to the input bits corresponding to each level in the search tree in accordance with the preset order in the search tree. adjacent Euclidean distance;

The target path determination unit is configured to select a path that satisfies the target search conditions from the search tree and determine it as the target path; target The search conditions include a path in which the adjacent Euclidean distance between any two adjacent input bits in any two adjacent levels in the search tree is less than a preset search threshold, and the path includes multiple input bits.

In one of the embodiments, the detection decoding result determination module 14 is configured as:

The sum of all adjacent Euclidean distances corresponding to the target path is substituted into the target detection decoding function to obtain the target detection decoding result.

In one embodiment, the detection and decoding device further includes: a function acquisition module, wherein:

The function acquisition module is configured to acquire the target detection decoding function based on the equivalent reception vector and symbol vector of each channel.

In one embodiment, the symbol vector determination module 11 includes: a decomposition unit and a modulation processing unit, where:

The decomposition unit is configured to perform orthogonal triangular decomposition on the channel matrix corresponding to each channel to obtain a triangular matrix;

The modulation processing unit is configured to modulate the input bits according to the triangular matrix and the polarization matrix to obtain the symbol vector of each channel.

Each module in the above detection and decoding device can be implemented in whole or in part by software, hardware and combinations thereof. Each of the above modules may be embedded in or independent of the processor of the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in Figure 1 . The computer device includes a processor, memory, and network interfaces connected through a system bus. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage medium stores operating systems, computer programs and databases. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media. The computer device's database is used to store input bits received by the communication system. The network interface of the computer device is used to communicate with external terminals through a network connection. The computer program implements a detection decoding method when executed by the processor.

Those skilled in the art can understand that the structure shown in Figure 1 can be a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. The specific computer equipment can be May include more or fewer parts than shown, or combine certain parts, or have a different arrangement of parts.

In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements the following steps:

According to the symbol vector of each channel, multiple synchronization sets corresponding to the input bits are obtained;

In one embodiment, a computer-readable storage medium is provided with a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

In one embodiment, a computer program product is provided, comprising a computer program that when executed by a processor implements the following steps:

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage. In the medium, when executed, the computer program may include the processes of the above method embodiments. Any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random Access Memory (MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in many forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include blockchain-based distributed databases, etc., but are not limited thereto. The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to this.

The technical features of the above embodiments can be combined in any way. To simplify the description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, all possible combinations can be used. It is considered to be within the scope of this manual.

The above-described embodiments only express several implementation modes of the present application, and their descriptions are relatively specific and detailed, but should not be construed as limiting the patent scope of the present application. It can be pointed out that for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all fall within the protection scope of the present application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

A detection and decoding method, wherein the method includes:

Determine the symbol vector of each channel based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system;

According to the symbol vector of each channel, obtain multiple synchronization sets corresponding to the input bits; the synchronization sets include a set after combining the input bits;

Perform decoding search on each synchronization set to obtain a target path; the target path includes the path with the smallest sum of corresponding Euclidean distances in detection and decoding;

According to the target path and the target detection decoding function, the target detection decoding result is determined.
The method according to claim 1, wherein obtaining multiple synchronization sets corresponding to the input bits according to the symbol vector of each channel includes:

Determine the Euclidean distance increment corresponding to each of the symbol vectors according to the equivalent reception vector of each of the channels and the symbol vector;

According to the Euclidean distance increment corresponding to each of the symbol vectors, a plurality of synchronization sets are obtained.
The method according to claim 2, wherein before determining the Euclidean distance increment corresponding to each of the symbol vectors based on the equivalent reception vector of each of the channels and the symbol vector, the method further includes:

According to the symbol vector, target noise vector and triangular matrix of each channel, the equivalent reception vector of each channel is determined.
The method according to claim 2, wherein the plurality of synchronization sets are obtained according to the Euclidean distance increment corresponding to each symbol vector, including:

Obtain the determination time of the Euclidean distance increment corresponding to each of the symbol vectors;

The Euclidean distance increments with the same determination time are respectively determined as corresponding multiple synchronization sets.
The method according to claim 1, wherein obtaining multiple synchronization sets corresponding to the input bits according to the symbol vector of each channel includes:

Search for a symbol vector equal to the symbol vector of each channel in the mapping relationship, and determine the synchronization set corresponding to the found symbol vector as multiple synchronization sets corresponding to the input bits; the mapping relationship includes Correspondence between different symbol vectors and corresponding synchronization sets.
The method according to any one of claims 1-5, wherein the decoding and searching of each synchronization set to obtain the target path includes:

Based on the multi-tree search strategy, determine the preset search threshold for decoding search;

Establish a search tree based on each of the synchronization sets;

According to the preset order in the search tree, based on the input bits corresponding to each level in the search tree, determine the adjacent input bits between any two adjacent levels in the search tree. Euclidean distance;

Select a path that satisfies the target search condition from the search tree and determine it as the target path; the target search condition includes the distance between any two adjacent input bits in any two adjacent levels in the search tree. Paths whose adjacent Euclidean distances are all smaller than the preset search threshold include multiple input bits.
The method according to claim 6, wherein establishing a search tree based on each of the synchronization sets includes:

Filter out the synchronization set with the earliest determined time from all synchronization sets;

Determine the input bit corresponding to the Euclidean distance increment in the synchronization set with the earliest determination time as the top-most tree node in the search tree;

According to the order of time determined by each synchronization set, the input bits corresponding to the Euclidean distance increment in the remaining synchronization sets are determined as other layer tree nodes in the search tree;

The search tree is constructed based on the topmost tree node and the other layer tree nodes.
The method according to any one of claims 1-5, wherein determining the target detection decoding result according to the target path and the target detection decoding function includes:

The sum of all adjacent Euclidean distances corresponding to the target path is substituted into the target detection decoding function to obtain the target detection decoding result.
The method according to any one of claims 1-5, wherein determining the target detection decoding result according to the target path and the target detection decoding function includes:

Obtain the sum of all Euclidean distances corresponding to the target path, and substitute the target proportion result of the sum of all Euclidean distances into the target detection decoding function to obtain the target detection decoding result.
The method according to claim 9, wherein before determining the target detection decoding result according to the target path and the target detection decoding function, the method further includes:

The target detection decoding function is obtained according to the equivalent reception vector of each channel and the symbol vector.
The method according to any one of claims 1 to 5, wherein determining the symbol vector of each channel based on the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system includes: :

Perform orthogonal triangular decomposition on the channel matrix corresponding to each of the channels to obtain a triangular matrix;

According to the triangular matrix and the polarization matrix, the input bits are modulated to obtain the symbol vector of each channel.
A detection and decoding device, wherein the device includes:

A symbol vector determination module, configured to determine the symbol vector of each channel according to the input bits received by the communication system and the channel matrix corresponding to each channel of the communication system;

A synchronization set module, configured to obtain multiple synchronization sets corresponding to the input bits according to the symbol vector of each channel; the synchronization set includes a set after combining the input bits;

The decoding search module is configured to perform decoding search on each synchronization set to obtain a target path; the target path includes detecting the path with the smallest Euclidean distance in decoding;

The detection decoding result determination module is configured to determine the target detection decoding result according to the target path and the target detection decoding function.
A computer device includes a memory and a processor, the memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 11 when the processor executes the computer program.
A computer-readable storage medium having a computer program stored thereon, wherein the steps of the method according to any one of claims 1 to 11 are implemented when the computer program is executed by a processor.
A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1-11.