WO2024045544A1

WO2024045544A1 - Decoding path determination method and apparatus, and computer device and storage medium

Info

Publication number: WO2024045544A1
Application number: PCT/CN2023/080813
Authority: WO
Inventors: 张川; 周华羿; 黄永明; 尤肖虎
Original assignee: 网络通信与安全紫金山实验室
Priority date: 2022-08-29
Filing date: 2023-03-10
Publication date: 2024-03-07
Also published as: CN115426077A

Abstract

The present application relates to a decoding path determination method and apparatus, and a computer device and a storage medium. The method comprises: according to a polar code generation matrix, constructing a symbol-based equivalent directed graph; acquiring at least two initial symbol synchronization sets according to the equivalent directed graph; optimizing the initial symbol synchronization sets according to freeze symbols in the initial symbol synchronization sets and information symbols in a preset set, so as to obtain optimized symbol synchronization sets; and determining a target decoding path according to the optimized symbol synchronization sets.

Description

Decoding path determination method, device, computer equipment and storage medium

Cross-references to related applications

This application requests the priority of the Chinese patent application submitted to the China Patent Office on August 29, 2022, with the application number 202211039840X, and the application name is "Decoding Path Determination Method, Device, Computer Equipment and Storage Medium", which is hereby included. The entire text is incorporated by reference.

Technical field

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

Background technique

With the development of communication technology, signal detection and channel decoding have become important research directions in baseband signal processing. For example, at the transmitter end, the information bits are channel-coded and then the coded signal is modulated into symbols. At the receiving end, the detector first estimates the symbol from the received signal, converts the estimated symbol into a log-likelihood ratio of bits, and sends it to the decoder as soft information. The decoder finally recovers the information through the decoding algorithm. bits.

The channel polarization method using polar code is very common in the fifth generation mobile communication technology (5th Generation Mobile Communication Technology, 5G). For example, in polar code encoding Multiple-Input Multiple-Output (MIMO) In communication systems, the currently commonly used detection and decoding method is the MIMO fusion detection and decoding algorithm, which is generally implemented using spherical decoding or K-best algorithm. Through bit enumeration, the decoding method with the smallest Euclidean distance from the received information is finally output. code path.

However, the current MIMO fusion detection and decoding algorithm has the problem of low accuracy of the decoding path.

Contents of the invention

Based on this, it is necessary to provide a decoding path determination method, device, computer equipment and storage medium to address the above technical problems.

In a first aspect, this application provides a method for determining a decoding path. The methods include:

Construct an equivalent symbol-based directed graph based on the polar code generation matrix;

According to the equivalent directed graph, obtain at least two initial symbol synchronization sets;

According to the frozen symbols in each initial symbol synchronization set and the information symbols in the preset set, optimize each initial symbol synchronization set to obtain an optimized symbol synchronization set;

The target decoding path is determined based on the optimized symbol synchronization set.

In a second aspect, this application also provides a decoding path determination device, which includes:

Building module, constructing a symbol-based equivalent directed graph based on the polar code generation matrix;

The acquisition module obtains at least two initial symbol synchronization sets according to the equivalent directed graph;

The optimization module optimizes each initial symbol synchronization set according to the frozen symbols in each initial symbol synchronization set and the information symbols in the preset set to obtain an optimized symbol synchronization set;

The determination module determines the target decoding path according to the optimized symbol synchronization set.

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, the decoding path determination method according to any embodiment of the first aspect is implemented.

In a fourth aspect, this application also provides a computer-readable storage medium. A computer-readable storage medium on which is stored A computer program is stored, and when the computer program is executed by the processor, the decoding path determination method of any embodiment of the first aspect is implemented.

The decoding path determination method, device, computer equipment and storage medium provided by the embodiments of the present application construct a symbol-based equivalent directed graph according to the polar code generation matrix, and obtain at least two initial symbol synchronizations based on the equivalent directed graph. Set, according to the frozen symbols in each initial symbol synchronization set and the information symbols in the preset set, optimize each initial symbol synchronization set to obtain an optimized symbol synchronization set, and determine the target decoding path based on the optimized symbol synchronization set . 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 applications disclosed herein, reference may be made to one or more of the accompanying drawings. Additional details or examples describing the figures are not to be construed as limiting the scope of any of the disclosed applications, the presently described embodiments and/or examples, and the best mode currently understood of these applications.

Figure 1 is an application environment diagram of a decoding path determination method in an embodiment;

Figure 2 is a schematic flowchart of a decoding path determination method in one embodiment;

Figure 3 is an equivalent directed graph based on bit synchronization nodes in one embodiment;

Figure 4 is a schematic flowchart of determining an initial symbol synchronization set in another embodiment;

Figure 5 shows a preliminary symbol equivalent directed graph and an improved symbol equivalent directed graph in one embodiment;

Figure 6 is a schematic flowchart of determining an optimized symbol synchronization set in one embodiment;

Figure 7 is a schematic flowchart of determining an optimized symbol synchronization set in one embodiment;

Figure 8 is a schematic flowchart of determining a target decoding path in another embodiment;

Figure 9 is a schematic flowchart of determining a target decoding path in yet another embodiment;

Figure 10 is a schematic flowchart of determining a target decoding path in one embodiment;

Figure 11 is a comparison chart of FER performance tested in the simulation environment;

Figure 12 is a block diagram of a decoding path determination device in one embodiment;

Figure 13 is a block diagram of a decoding path determination device in one embodiment;

Figure 14 is a block diagram of a decoding path determination device in one embodiment;

Figure 15 is a block diagram of a decoding path determination device in one embodiment;

Figure 16 is an internal structure diagram of a computer device in one embodiment;

Figure 17 is an internal structure diagram of a computer device in one embodiment.

Specific embodiments

In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described here can be used to explain the present application, but do not limit the present application.

With the development of communication technology and the increase in user needs, users have higher and higher requirements for communication quality. The MIMO communication system uses multiple antennas at the transmitting end to send signals independently, and at the same time, multiple antennas at the receiving end are used to receive and restore the original information, greatly improving the capacity and reliability of the channel. However, due to the large number of antennas at the receiving end, signal detection and decoding become more difficult.

Signal detection and channel decoding are important research directions in baseband signal processing. At the transmitting end, the information bits are first channel-coded and modulated into symbols. At the receiving end, detection and decoding are usually viewed as two independent processing modules: the MIMO detector first estimates the symbols from the received signal, and then converts them into log-likelihood ratios of bits and sends them to the decoder as soft information. , the decoding algorithm finally recovers the information bits.

5G adopts the channel polarization of polar codes as an important way to structurally approximate the channel capacity. Polar codes are defined as Standard for eMBB control channel. Facing the application scenarios of next-generation mobile communication systems, baseband signal processing technology faces huge challenges. Joint optimization of MIMO signal detection and channel decoding has demonstrated the possibility of bringing huge gains to the system. For the polar code selected as the standard for the eMBB control channel of 5G, the traditional separate type and joint iterative type can no longer meet the communication requirements of high reliability and low latency. Therefore, a polar code encoding MIMO system is proposed. The synchronization set-assisted breadth-first sphere decoding method is of great significance.

In a polar code-encoded MIMO communication system, the working modes of the MIMO detection and polar code decoding modules can be divided into three types. The first is the separated type, which regards detection and decoding as two independent processing modules, that is, a simple cascade of MIMO detection and polar code decoding modules. It processes information in a serial manner and finally obtains the decoding result. . Since the separate detection module fails to maximize the use of known polar code encoding characteristics, its error correction performance is still far from the Shannon limit, and there is huge room for improvement. In order to improve the separate error correction performance, the researchers proposed a second type of joint iterative detection and decoding. Through technical means, the polar code decoding module outputs soft information and feeds it back to the detection module for repeated iterations, thereby optimizing error correction. performance. However, multiple iterations of the joint iteration type inevitably increase the system delay.

The problems existing in the above two MIMO detection and decoding algorithms are as follows:

The detection and decoding modules always rely on the transfer of soft information. The calculation and storage of floating-point soft information results in high space complexity and a large consumption of hardware resources;

The independent detection module fails to maximize the use of the a priori information of the adopted channel coding, resulting in a certain degree of performance loss;

Multiple iterations of joint iterative detection and decoding produce higher time complexity, causing the system to generate higher delays.

Therefore, there is currently a third type of fusion detection decoding, which is generally implemented using spherical decoding or K-best algorithm. Through bit enumeration, the decoding path with the smallest Euclidean distance from the received information is finally output. The advantage of the MIMO fusion detection and decoding algorithm for polar code encoding is that the decoding part is based on bit enumeration, and the decoding architecture is the same as polar code encoding, which is beneficial to saving storage resources and there is no delay caused by iteration ; Fusion detection decoding can fully consider the prior information of polar coding when enumerating bits, such as the distribution of information bits and frozen bits, which can improve detection performance and reduce the original detection search space.

However, since the current decoding bit enumeration uses a serial bit-by-bit order, the decoding order is based on the total length of the codeword, in order from largest to smallest, and the minimum Euclidean distance of each bit is calculated bit by bit. , the current bit-by-bit enumeration order used in decoding is obviously not optimal, that is, the accuracy of the current decoding path is low, and the detection performance of the system still has huge room for improvement.

Next, the implementation environment involved in the decoding path determination method provided by the embodiment of the present application will be briefly described. The decoding path determination method provided by the embodiment of the present application can be applied in the application environment as shown in Figure 1. As shown in Figure 1, the application environment may include a receiving end 102 and a transmitting end 101. The transmitting end 101 maps the data signal to be sent to multiple antennas through space-time mapping and sends it out. The receiving end 102 performs space-time decoding on the signals received by each antenna to recover the data signal sent by the transmitting end. Signals are transmitted and received through the antennas of the transmitting end 101 and the receiving end 102.

The transmitting end 101 can use multiple transmitting antennas, and the receiving end 102 can use multiple receiving antennas. For example, MIMO technology uses multiple antennas at the transmitter 101 to independently transmit signals, and at the same time, multiple antennas are used at the receiver 102 to receive and restore the original information, thereby achieving higher user rates at a lower cost.

Among them, the receiving end 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, and smart vehicle-mounted devices. wait. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The transmitter 101 can be implemented as an independent transmitter or a transmitter cluster composed of multiple transmitters.

After the above has introduced the application scenarios of the decoding path determination method provided by the embodiment of the present application, the following will first focus on the extreme The coding process of decoding.

Polar code is a channel coding technology with practical linear complexity encoding and decoding capabilities. 5G communications set polar code as the standard for enhanced mobile broadband (Enhanced Mobile Broadband, eMBB) control channel.

Among them, the encoding process of polar code x is x=uG, where u is the bit sequence to be encoded, and G is the generating matrix of the polar code. For example: Multiple Quadrature Amplitude Modulation (M-QAM) is used to modulate x. The modulated symbol S=map{uG}, where map{} represents the modulation function. After the symbol S is transmitted through the channel, it becomes a complex-valued received symbol. each symbol It is mapped from M possible constellation diagrams.

For a MIMO system with Nt transmit antennas and Nr receive antennas, the received information is expressed as in is the complex channel matrix of Nr×Nt, is Gaussian white noise. Use the Maximum Likelihood (ML) method to estimate the symbol with the smallest Euclidean distance:

Through real-valued decomposition, the equivalent channel model is: y=Hs+n, where H is a 2Nr×2Nt real-domain channel matrix. The length of S after real-valuation is 2Nt. Perform orthogonal triangular (QR) decomposition for the channel matrix H: H=QR, where Q represents a 2Nr×2Nt regular orthogonal matrix (unitary matrix), and R is a 2Nr×2Nt upper triangular matrix. Let z=Q ^H y, use ML detection method to estimate the symbol with the minimum Euclidean distance, which can be expressed as: According to s=map{uG}, this notation can be further written as: Since the formula of polar code spherical decoding is This formula has the same structure as the formula obtained using the ML detection method (y replaces z, G replaces R, u replaces s, where G and R are both triangular matrices), so ML detection is the same as polar code spherical decoding, K-best There are certain similarities between detection and list-based ball decoding. Among them, K-best detection and list-based spherical decoding are obtained by ML detection and polar code spherical decoding respectively by retaining K decoding paths with the minimum Euclidean distance.

After the above process, MIMO system detection and decoding are integrated into one process, and then a symbol-based equivalent directed graph is constructed according to the polar code generation matrix; based on the equivalent directed graph, at least two initial symbol synchronization sets are obtained ; According to the frozen symbols in each initial symbol synchronization set and the information symbols in the preset set, optimize each initial symbol synchronization set to obtain an optimized symbol synchronization set; finally, the receiving end can be based on the above optimized symbol synchronization set Determine the target decoding path.

In one embodiment, as shown in Figure 2, a decoding path determination method is provided. This method is explained by taking the method applied to the receiving end in Figure 1 as an example, and includes the following steps:

S201. Construct a symbol-based equivalent directed graph according to the polar code generation matrix.

Among them, the polar code generation matrix can be the polar code generation matrix G in the aforementioned scenario introduction, and the equivalent directed graph is a directed graph composed of symbols as nodes and connection relationships between symbols as edges.

In this embodiment of the present application, the polar code generation matrix can be analyzed based on preset mapping rules to construct an equivalent directed graph. For example: the symbol-based equivalent directed graph can be constructed as follows: Constructing the generation matrix from polar codes Mapping rules to equivalent directed graphs; according to the mapping rules, an equivalent directed graph based on bits is mapped; treating at least two bits as a symbol, an equivalent directed graph of the symbol is obtained.

Among them, the mapping rules from the polar code generation matrix to the equivalent directed graph are as follows: N bits correspond to N nodes in the equivalent directed graph of the symbol; if the i-th row in the polar code generation matrix G The element g _(i,j) of column j = 1 and satisfies i>j,i∈A, then in the equivalent directed graph, node i has a directed edge pointing to node j.

Optionally, define frozen bits as frozen bits and define other types of bits as information bits. Directed edges can lead from information bits to frozen bits. Figure 3 shows the equivalent directed graph mapped by the polar code generation matrix for a code length of 8, information bits of 3, and information bit set {6,7,8}. For example, the directed edge can only be sent by information bits 6, 7 and 8 and point to frozen bits 1, 2, 3, 4 and 5, and satisfies the element g of the i-th row and j-th column in the polar code generation matrix G ₍ When _i,j) =1, there will be a directed edge between the information bit and the frozen bit. For example, in Figure 3, there is information bit 6 pointing to frozen bit 1, which means that the element g _(6,1) in row 6 and column 1 of the polar code generation matrix G = 1; there is information bit 8 pointing to frozen bit 4, Then it means that the element g _(8,4) = 1 in the 8th row and 4th column of the polar code generation matrix G.

Optionally, for M-QAM modulation, set M=2 ^m . After real-valued decomposition, one symbol corresponds to m/2 bits. For example, for 16-QAM modulation, one symbol corresponds to 2 bits, that is, the two adjacent bits are regarded as a whole, the starting point and end point of the edge are corrected into symbols, duplicate edges are deleted, and edges that generate self-loops are deleted, that is, Equivalent directed graphs based on symbols can be obtained.

S202. Obtain at least two initial symbol synchronization sets according to the equivalent directed graph.

The initial symbol synchronization set may include information symbols and frozen symbols. A symbol containing all frozen bits is defined as a frozen symbol, and other types of symbols are defined as information symbols.

In this embodiment, the equivalent directed graph can be disassembled, the edges between the symbols in the equivalent directed graph can be removed, and symbols with a certain relationship can be placed in the same set to generate an initial symbol synchronization set. . For example, for the information symbol A in the equivalent directed graph, you can delete the edge starting from the information symbol A, and then check the frozen symbol B connected to the information symbol A. At the in-degree of the frozen symbol connected to the information symbol A, Under the condition that is 0, put the information symbol A and the information symbol B in the same set. You can follow the above method to traverse all symbols in the equivalent directed graph, classify all symbols into each set, and finally obtain multiple initial symbol synchronization sets.

For example, starting from the last information bit, remove the edges passing through it, and check all frozen symbols. Under the condition that the in-degree of the frozen bit is 0, the information bit and the frozen bit will be placed in the same set. The set is called the initial bit synchronization set, and the Euclidean distance of the bits in the same bit synchronization set will be calculated simultaneously. All information bits are traversed in sequence to obtain all initial bit synchronization sets of the equivalent directed graph corresponding to the polar code generation matrix.

S203. Optimize each initial symbol synchronization set according to the frozen symbols in each initial symbol synchronization set and the information symbols in the preset set to obtain an optimized symbol synchronization set.

The preset set may be any of the initial symbol synchronization sets. For example, the at least two initial symbol synchronization sets may include initial symbol synchronization set 1, initial symbol synchronization set 2, and initial symbol synchronization set 3. Under the condition that the initial symbol synchronization set 1 is optimized, the preset set may be the initial symbol synchronization set 2 or the initial symbol synchronization set 3.

In this embodiment, each initial symbol synchronization set can be optimized according to the dependency relationship between the frozen symbols in the initial symbol synchronization set and the information symbols in the preset set. For example, according to the set In the enumeration order, the initial symbol synchronization set is before the preset set. Under the condition that the information symbols in the preset set will affect the value of the frozen symbols in the initial symbol synchronization set, the frozen symbols in the initial symbol synchronization set cannot Appears in the current initial symbol synchronization set. The frozen symbol can be placed in the preset set or in the current initial symbol synchronization set. The initial symbol sync set follows the enumeration order of the number sync set. Each initial symbol synchronization set can be optimized in the above manner, so that the position of the symbols in each symbol synchronization set is relatively accurate, thereby obtaining an optimized symbol synchronization set.

S204. Determine the target decoding path according to the optimized symbol synchronization set.

In this embodiment, the Euclidean distance of each symbol in each optimized symbol synchronization set can be enumerated according to the set enumeration order and the enumeration order of each symbol in each optimized symbol synchronization set, so that according to each The Euclidean distance of each symbol in the optimized symbol synchronization set determines the target decoding path. For example, when the optimized symbol synchronization set 1 and the optimized symbol synchronization set 2 are included, the Euclidean distance of each symbol in the optimized symbol synchronization set 1 can be calculated first, and then the optimized symbol synchronization set 2 can be calculated The Euclidean distance of each symbol in , the target decoding path is determined based on all the Euclidean distances calculated above. Alternatively, K1 minimum Euclidean distances can be selected from the Euclidean distances of each symbol in the optimized symbol synchronization set 1, and K2 minimum Euclidean distances can be selected from the Euclidean distances of each symbol in the optimized symbol synchronization set 2. The smallest Euclidean distance determines the target decoding path based on the K1 smallest Euclidean distances and the K2 smallest Euclidean distances.

The decoding path determination method provided by the embodiment of the present application constructs a symbol-based equivalent directed graph according to the polar code generation matrix, obtains at least two initial symbol synchronization sets based on the equivalent directed graph, and obtains at least two initial symbol synchronization sets based on each initial symbol synchronization set. The frozen symbols in and the information symbols in the preset set are optimized for each initial symbol synchronization set to obtain an optimized symbol synchronization set, and the target decoding path is determined based on the optimized symbol synchronization set. In the embodiment of this application, symbols are classified into sets based on equivalent directed graphs, and the decoding path is determined based on the sets, so that the enumeration order of symbols is more optimized, and the path evaluation at each decoding level becomes more accurate. , and the initial symbol synchronization set is optimized so that the corresponding enumeration order in the optimized symbol synchronization set is further optimized, so that the final target decoding path is more accurate.

Based on the embodiment shown in Figure 2, after deleting the edges in the equivalent directed graph, the initial symbol synchronization set can be determined according to the in-degree of the frozen symbol. Step S202 "According to the equivalent directed graph, obtain "At least two initial symbol synchronization sets" may include the following steps: according to the enumeration order of each symbol in the equivalent directed graph, after deleting the target edge of each information symbol, determine the initial symbol synchronization set according to the in-degree of the frozen symbol; The target edge is the edge starting from the semaphore symbol.

Among them, the in-degree of the frozen symbol refers to the number of directed edges entering the frozen symbol.

In this embodiment, each symbol in the equivalent directed graph has a certain enumeration order. Based on the enumeration order, the edge starting from each information symbol can be deleted, and then the frozen symbols connected to the information symbol can be viewed. Under the condition that the in-degree of the frozen symbol is 0, put the information symbol and the frozen symbol in an initial symbol synchronization set, and traverse each information symbol and frozen symbol in the enumeration order with reference to the above method, Finally, each initial symbol synchronization set is obtained.

Optionally, as shown in Figure 4, the above process of obtaining the initial symbol synchronization set may include the following steps:

S401. Perform a classification operation; wherein the classification operation includes: according to the enumeration order of each symbol in the equivalent directed graph, after deleting the target edge of the current information symbol, obtaining the deletion of the target edge connected to the current information symbol. The in-degree of the target freeze symbol, under the condition that the in-degree of the target freeze signal is zero, then the current information symbol and the target freeze symbol are classified into the same set.

S402. According to the enumeration sequence, determine the next information symbol adjacent to the current information symbol as the new current information symbol, and return to perform the classification operation until all symbols are classified into the set, and each initial symbol synchronization is obtained. gather.

Please refer to the upper part of Figure 5 , which shows a preliminary symbolic equivalent directed graph provided by the embodiment of the present application. As shown in the upper part of Figure 5, for 16-QAM modulation, one symbol corresponds to 2 bits, that is, the two adjacent bits are regarded as a whole, that is, the two adjacent bits are regarded as a whole according to the bit-by-bit sequence of bit enumeration. A bit is regarded as a symbol. For example, bit 1 and bit 2 can be regarded as the symbol b1, and bit 3 and bit 4 can be regarded as the symbol b2. It should be noted that bit 8 and bit 1 cannot be regarded as one symbol because they do not conform to the bit Bit-by-bit sequence of the enumeration.

Optionally, starting from the last information symbol b4, the frozen symbols connected to b4 are b1 and b2, delete the edge starting from symbol b4, and then check the in-degree of frozen symbols b1 and b2, where the in-degree of frozen symbol b2 is zero, then the information symbol b4 and the frozen symbol b2 are placed in the same initial synchronization set. Next, check other information symbols according to the above method to obtain the initial synchronization set. As shown in the upper part of Figure 5, the last information symbol b4 and the frozen symbol b2 are in the same initial synchronization set T1'={b4, b2}. The information symbol b3 and the frozen symbol b1 are in the same initial synchronization set T2'={b3, b1}.

Optionally, the enumeration order of the initial symbol synchronization sets is determined according to each initial symbol synchronization set. For example, as shown in the upper part of Figure 5, the enumeration order of the initial symbol synchronization set is T1’>>T2’.

Each initial symbol synchronization set provided by the embodiment of this application is based on the enumeration order of each symbol in the equivalent directed graph. After deleting the target edge of each information symbol, the initial symbol synchronization set is determined based on the in-degree of the frozen symbol. In the embodiment of this application, the initial symbol synchronization set is determined based on the in-degree of the frozen symbol, and the serial bit-by-bit enumeration sequence is converted into the initial symbol synchronization set. Since the Euclidean distances of all symbols in the same initial symbol synchronization set can be simultaneously There is no need to calculate the Euclidean distance of each symbol in the initial synchronization set one by one according to the enumeration, which makes the enumeration order of symbols more optimized and further speeds up the decoding efficiency.

In one embodiment, in order to further improve the accuracy of the decoding path, the symbols in the initial symbol synchronization set can also be optimized. As shown in Figure 6, step S203 can include the following steps:

S601. Obtain the first sequence number of the frozen symbol in the current symbol synchronization set and the second sequence number of the information symbol in the preset set; the current symbol synchronization set is any one of at least two initial symbol synchronization sets.

In this embodiment, each symbol in the current symbol synchronization set has a sequence number. For example, the current symbol synchronization set is the initial synchronization set T1'={b4, b2} in Figure 5 above, where b2 is a frozen symbol, Then the first sequence number of frozen symbol b2 is 2. The preset set can be other sets besides the current symbol synchronization set. For example, in this application, the preset set can be the initial synchronization set T2'={b3, b1} in Figure 5, and the information symbol is b3, then The second sequence number of this information symbol is 3. The embodiments of this application are only used as examples for illustration, and are not intended to limit this solution.

S602: Optimize the current symbol synchronization set according to the upper triangular matrix corresponding to the first sequence number, the second sequence number and the channel matrix, and obtain an optimized symbol synchronization set.

In this embodiment, the upper triangular matrix can be the matrix R introduced in the foreground. The first serial number and the second serial number can be regarded as the index of the elements in the matrix R, so that the value of the element is located from the matrix R according to the first serial number and the second serial number, and the current symbol synchronization set is optimized according to the value of the element. Obtain the optimized symbol synchronization set.

Optionally, as shown in Figure 7, step S602 may include the following steps:

S701. Using the first serial number as the row and the second serial number as the column, determine the value of the target element from the upper triangular matrix.

In this embodiment, the current symbol synchronization set is continued to be the initial synchronization set T1'={b4, b2} in Figure 5, and the default set is the initial synchronization set T2'={b3, b1} in Figure 5. For example, the frozen symbol in T1'={b4,b2} is b2, and the information symbol in T2'={b3,b1} is b3, then the first serial number is 2 and the second symbol is 3, then 2 is the row , with 3 as the column, find the value of element r(2,3) in the upper triangular matrix R as the value of the target element.

S702. Under the condition that the second sequence number is greater than the first sequence number and the value of the target element is not zero, delete the frozen symbol from the current symbol synchronization set, and add the frozen symbol to the target symbol synchronization set; the target symbol synchronization set The third sequence number of the information symbol in is less than the first sequence number, and the value of the element in the upper triangular matrix corresponding to the first sequence number and the third sequence number is not zero; or, the target symbol synchronization set is a preset set.

Please refer to the lower part of FIG. 5 , which shows an improved signed equivalent directed graph provided by the embodiment of the present application. As shown in the lower part of Figure 5, for the frozen symbol b2 in the first initial symbol synchronization set T1'={b4,b2}, find a preset set k, in which the information symbol bi∈k, if i>2, And if the element r(2,i)≠0 in row 2 and column i of the R matrix is satisfied, then b2 must not be before bi. That is to say, b2 is either in the same set as bi, or b2 is after bi. In a set, since the target symbol synchronization set includes two T1' and T2', b2 must be in the T2' set. Continue to use the current symbol synchronization set as the initial synchronization set T1'={b4, b2} in Figure 5 above, the default set Taking the initial synchronization set T2'={b3,b1} in Figure 5 as an example, the frozen symbol in T1'={b4,b2} is b2, and the information symbol in T2'={b3,b1} is b3, then The first sequence number is 2, the second symbol is 3, the second sequence number 3 is greater than the first sequence number 2, and the element r(2,3) in the second row and third column of the upper triangular matrix R is not zero, synchronize the current symbol The frozen symbol b2 in the set T1'={b4,b2} is deleted from the symbol synchronization set, and the frozen symbol b2 is added to the target symbol synchronization set. Since the target symbol synchronization set includes two T1' and T2', therefore b2 must be in the T2' set.

Optionally, as shown in Figure 5, the improved symbol synchronization set T1={b4} and T2={b3, b2, b1} are obtained, and then the improved symbol synchronization set is determined based on the improved symbol synchronization set. Enumeration order. For example, as shown in the lower part of Figure 5, the enumeration order of the initial symbol synchronization set is T1>>T2.

Each initial symbol synchronization set provided in this embodiment is based on the first sequence number of the frozen symbol in the current symbol synchronization set and the second sequence number of the information symbol in the preset set; according to the first sequence number, the second sequence number and the channel matrix corresponding The upper triangular matrix optimizes the current symbol synchronization set to obtain the optimized symbol synchronization set. In the embodiment of the present application, based on the frozen symbols in each initial symbol synchronization set and the information symbols in the preset set, each initial symbol synchronization set is optimized to obtain an optimized symbol synchronization set, so that each symbol can be classified in the correct The symbol synchronization set avoids the influence of the Euclidean distance calculation due to symbol position errors, making the enumeration order of symbols in each symbol synchronization set more accurate, and the final target decoding path is also more accurate.

In one embodiment, after the optimized symbol synchronization set is obtained, the target decoding path can be determined based on the Euclidean distance of the symbols in the optimized symbol synchronization set. As shown in Figure 8, step S204 may include the following steps:

S801. According to the enumeration order of each optimized symbol synchronization set, obtain the Euclidean distance of each information symbol in each optimized symbol synchronization set.

In this embodiment, the Euclidean distance of each information symbol can be calculated according to the formula It is calculated that in this formula, u is the bit sequence corresponding to the two bits in each symbol.

S802. Determine the target decoding path according to the Euclidean distance of each information symbol in each optimized symbol synchronization set.

Among them, since each information symbol may have multiple Euclidean distances, the Euclidean distance of the optimized symbol synchronization set is related to the Euclidean distance of each information symbol, and the Euclidean distance of the frozen symbol is related to the Euclidean distance of the optimized symbol synchronization set. No effect.

For example, the Euclidean distance of each information symbol in each optimized symbol synchronization set can be calculated first, and then the Euclidean distance of each information symbol in each optimized symbol synchronization set can be combined with each other to finally determine Target decoding path. Alternatively, the Euclidean distances of the information symbols in each optimized symbol synchronization set can be calculated sequentially according to the enumeration order of the symbol synchronization sets, and N optimized Euclidean distances are determined for each symbol. The target decoding path is determined by the N optimized Euclidean distances of each information symbol in the symbol synchronization set. Alternatively, you can also calculate the Euclidean distance of the information symbols in each optimized symbol synchronization set in sequence according to the enumeration order of the symbol synchronization sets. For each optimized symbol synchronization set, according to the Euclidean distance of the symbols in it, The Euclidean distance of each optimized symbol synchronization set is determined, and the decoding path is determined based on the Euclidean distance of each optimized symbol synchronization set.

Optionally, as shown in Figure 9, step S802 may include the following steps:

S901. According to the Euclidean distance of each information symbol in each optimized symbol synchronization set, determine a preset number of candidate decoding paths for each optimized symbol synchronization set.

In this embodiment, the preset number of candidate decoding paths may be all decoding paths corresponding to the Euclidean distance of each information symbol in each optimized symbol synchronization set, or may be each information symbol in each optimized symbol synchronization set. The K bars with the smallest distance among the Euclidean distances of the symbols.

S902. Determine the target decoding path according to the candidate decoding paths corresponding to each optimized symbol synchronization set.

Specifically, for example, assuming that there are improved symbol synchronization sets T1 and T2, first the improved symbol synchronization set T1 Calculate its Euclidean distance, retain the K1 best paths (that is, the paths with the smallest Euclidean distance), then calculate the Euclidean distance for the set T2, retain the K2 best paths, and accumulate the sum of the K1 best paths of the set T1 Set the K2 best paths of T2, a total of K1×K2 paths, and then retain the K3 paths with the smallest Euclidean distance from the K1×K2 paths as the target decoding path.

The target decoding path provided by this embodiment obtains the Euclidean distance of each information symbol in each optimized symbol synchronization set according to the enumeration order of each optimized symbol synchronization set; according to each information in each optimized symbol synchronization set The Euclidean distance of symbols determines the target decoding path. In the embodiment of the present application, due to the further optimization of the symbols in the symbol synchronization set, the enumeration order of the symbols is more accurate, the path evaluation at each decoding level becomes more accurate, and the final target decoding path is also More precise.

Figure 10 is a flow chart of a decoding path determination method provided by an embodiment of the present application. As shown in Figure 9, the method may include the following steps:

S1001. Construct a mapping rule from the polar code generation matrix to the equivalent directed graph.

Among them, the mapping rules include: N bits correspond to N nodes in the directed graph;

If the element g _(i,j) of the i-th row and j-th column of the polar code generation matrix = 1 and satisfies i>j,i∈A, then in the equivalent directed graph, node i has a directed edge Point to node j.

S1002. Construct an equivalent directed graph based on bit nodes based on the mapping rules and polar code generation matrix.

S1003. Equivalent directed graph based on bits. Map continuous bits into symbols, correct the starting point and end point of the edges into symbols, delete duplicate edges, and delete edges that generate self-loops. Then you can obtain equivalent directed graphs based on symbols. to the graph.

S1004. According to the enumeration order of each symbol in the equivalent directed graph, after deleting the target edge of the current information symbol, obtain the in-degree of the target frozen symbol connected to the current information symbol after deleting the target edge. In the target frozen signal Under the condition that the in-degree is zero, the current information symbol and the target frozen symbol are classified into the same set;

S1005. According to the enumeration sequence, determine the next information symbol adjacent to the current information symbol as the new current information symbol, and return to execute the deletion of the target edge of the new current information symbol, and obtain the deleted target edge and the new The in-degree of the target frozen symbol connected to the current information symbol, under the condition that the in-degree of the target frozen signal is zero, the step of classifying the new current information symbol and the target frozen symbol into the same set until all symbols From the sets that are all classified, each initial symbol synchronization set is obtained.

S1006. Obtain the first sequence number of the frozen symbol in the current symbol synchronization set and the second sequence number of the information symbol in the preset set; the current symbol synchronization set is any one of at least two initial symbol synchronization sets.

S1007. Using the first serial number as the row and the second serial number as the column, determine the value of the target element from the upper triangular matrix.

S1008. Under the condition that the second sequence number is greater than the first sequence number and the value of the target element is not zero, delete the frozen symbol from the current symbol synchronization set and add the frozen symbol to the target symbol synchronization set; the target symbol synchronization The third sequence number of the information symbol in the set is smaller than the first sequence number, and the value of the element in the upper triangular matrix corresponding to the first sequence number and the third sequence number is not zero; or the target symbol synchronization set is a preset set.

S1009. According to the enumeration order of each optimized symbol synchronization set, obtain the Euclidean distance of each information symbol in each optimized symbol synchronization set.

S1010. Based on the Euclidean distance of each information symbol in each optimized symbol synchronization set, determine a preset number of candidate decoding paths for each optimized symbol synchronization set;

S1011. Determine the target decoding path according to the candidate decoding paths corresponding to each optimized symbol synchronization set.

The target decoding path provided by this embodiment obtains the Euclidean distance of each information symbol in each optimized symbol synchronization set according to the enumeration order of each optimized symbol synchronization set; according to each information in each optimized symbol synchronization set The Euclidean distance of symbols determines the target decoding path. Take the modulation of 4 symbols as an example. Among them, symbol 4 and symbol 3 are information symbols, containing multiple possibilities. Symbol 2 and symbol 1 are frozen symbols with fixed values. Then, the existing Fusion detection decoding requires four enumeration levels. In the embodiment of this application, each edge from node i to node j in the equivalent directed graph in the symbolic sense represents the Euclidean value of symbol i for symbol j. The calculation of distance has an impact. The generated synchronization sets in the symbolic sense are T1 and T2, which only require 2 enumeration levels. That is, at the same enumeration level, the Euclidean distance of more symbols is considered, which improves path evaluation. accuracy.

Optionally, as shown in Figure 11, in a simulation environment with N=128 and information bits of 11, the proposed width-first spherical decoding method is compared with the existing width-first spherical decoding method. When the parameter K= In the case of 4, the signal-to-noise ratio (SNR) required to achieve a frame error rate (Frame Error Rate, FER) of 10-3 is only 9dB, which is improved compared to the existing width-first spherical decoding method. up to 7.7dB.

It can be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the 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 the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

Based on the same inventive concept, embodiments of the present application also provide a detection decoding device that implements the above-mentioned decoding path determination 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 decoding path determination method mentioned above. The limitations will not be repeated here.

In one embodiment, as shown in Figure 12, it is a structural block diagram of a decoding path determination device in an embodiment. A decoding path determination device includes: a construction module 201, an acquisition module 202, an optimization module 203 and a determination module 204, wherein:

Building module 201, constructs a symbol-based equivalent directed graph according to the polar code generation matrix;

The acquisition module 202 acquires at least two initial symbol synchronization sets according to the equivalent directed graph;

The optimization module 203 optimizes each initial symbol synchronization set according to the frozen symbols in each initial symbol synchronization set and the information symbols in the preset set to obtain an optimized symbol synchronization set;

The determination module 204 determines the target decoding path according to the optimized symbol synchronization set.

Optionally, based on the embodiment shown in Figure 12, the acquisition module 202 specifically deletes the target edge of each information symbol according to the enumeration order of each symbol in the equivalent directed graph, and then deletes the target edge of each information symbol according to the input of the frozen symbol. The degree determines the initial symbol synchronization set; the target edge is the edge starting from the signal symbol.

In one embodiment, as shown in Figure 13, the acquisition module 202 includes:

The classification unit 2021 performs a classification operation; wherein the classification operation includes: according to the enumeration order of each symbol in the equivalent directed graph, after deleting the target edge of the current information symbol, obtaining the deletion of the target edge and the current The in-degree of the target freeze symbol connected by the information symbol, under the condition that the in-degree of the target freeze signal is zero, classifies the current information symbol and the target freeze symbol into the same set.

The first determination unit 2022 determines the next information symbol adjacent to the current information symbol as the new current information symbol according to the enumeration sequence, and returns to perform the classification operation until all symbols are classified into the set, obtaining Each initial symbol synchronization set.

In one embodiment, as shown in Figure 14, the optimization module 203 includes:

The first obtaining unit 2031 obtains the first sequence number of the frozen symbol in the current symbol synchronization set and the second sequence number of the information symbol in the preset set; the current symbol synchronization set is any one of at least two initial symbol synchronization sets;

The optimization unit 2032 optimizes the current symbol based on the first sequence number, the second sequence number and the upper triangular matrix corresponding to the channel matrix. The number synchronization set is optimized to obtain the optimized symbol synchronization set.

In one embodiment, the optimization unit 2032 specifically uses the first serial number as the row and the second serial number as the column to determine the value of the target element from the upper triangular matrix; when the second serial number is greater than the first serial number, and the value of the target element If it is not zero, the frozen symbol is deleted from the current symbol synchronization set and the frozen symbol is added to the target symbol synchronization set; the third sequence number of the information symbol in the target symbol synchronization set is less than the first sequence number, and the first The value of the element in the upper triangular matrix corresponding to the serial number and the third serial number is not zero; or, the target symbol synchronization set is a preset set.

In one embodiment, as shown in Figure 15, the determining module 204 includes:

The second acquisition unit 2041 obtains the Euclidean distance of each information symbol in each optimized symbol synchronization set according to the enumeration order of each optimized symbol synchronization set; according to the Euclidean distance of each information symbol in each optimized symbol synchronization set , determine the target decoding path.

The second determination unit 2042 determines the target decoding path based on the Euclidean distance of each information symbol in each optimized symbol synchronization set.

In one embodiment, the optimization unit 2042 determines a preset number of candidate decoding paths for each optimized symbol synchronization set based on the Euclidean distance of each information symbol in each optimized symbol synchronization set; according to each optimized symbol synchronization set, The candidate decoding path corresponding to the symbol synchronization set determines the target decoding path.

The implementation principles and beneficial effects of the decoding path determination device provided by the above embodiments can be referred to the corresponding embodiments of the decoding path determination method, and will not be described again here.

For specific limitations on the decoding path determination device, please refer to the above limitations on the decoding path determination method, which will not be described again here. Each module in the above-mentioned decoding path determination device may be implemented in whole or in part by software, hardware, or 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.

Each module in the above-mentioned decoding path determination device may be implemented in whole or in part by software, hardware, or 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 16. The computer device includes a processor, memory, and network interfaces connected through a system bus. Wherein, the processor of the computer device provides 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 stores decoding paths, polar codes, sets, and other data. The network interface of the computer device communicates with the external terminal through a network connection. The computer program implements a decoding path determination method when executed by a processor.

In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in Figure 17. The computer device includes a processor, memory, communication interface, display screen and input device connected through a system bus. Wherein, the processor of the computer device provides 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 and computer programs. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media. The communication interface of the computer device communicates with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, mobile cellular network, NFC (Near Field Communication) or other technologies. The computer program implements a decoding path determination method when executed by a processor. The display screen of the computer device may be a liquid crystal display or an electronic ink display. The input device of the computer device may be a touch layer covered on the display screen, or may be a button, trackball or touch pad provided on the computer device shell. , it can also be an external keyboard, trackpad or mouse, etc.

Those skilled in the art can understand that the structures shown in Figures 16 and 17 may be block diagrams of partial structures related to the solution of the present application, and do not constitute a limitation on the computer equipment to which the solution of the present application is applied. Specifically, count A computer device may include more or fewer components than shown in the figures, or some combinations of components, or have a different arrangement of components.

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:

In one embodiment, the processor also implements the following steps when executing the computer program:

According to the enumeration order of each symbol in the equivalent directed graph, after deleting the target edge of each information symbol, the initial symbol synchronization set is determined according to the in-degree of the frozen symbol; the target edge is the edge starting from the signal symbol.

Perform a classification operation; the classification operation includes: according to the enumeration order of each symbol in the equivalent directed graph, after deleting the target edge of the current information symbol, obtaining the target freeze connected to the current information symbol after deleting the target edge. The in-degree of the symbol, under the condition that the in-degree of the target freezing signal is zero, the current information symbol and the target freezing symbol are classified into the same set;

According to the enumeration sequence, the next information symbol adjacent to the current information symbol is determined as the new current information symbol, and the classification operation is returned until all symbols are classified into sets, and each initial symbol synchronization set is obtained.

Obtain the first sequence number of the frozen symbol in the current symbol synchronization set and the second sequence number of the information symbol in the preset set; the current symbol synchronization set is any one of at least two initial symbol synchronization sets;

According to the upper triangular matrix corresponding to the first sequence number, the second sequence number and the channel matrix, the current symbol synchronization set is optimized to obtain an optimized symbol synchronization set.

With the first serial number as the row and the second serial number as the column, determine the value of the target element from the upper triangular matrix;

Under the condition that the second sequence number is greater than the first sequence number and the value of the target element is not zero, the frozen symbol is deleted from the current symbol synchronization set and the frozen symbol is added to the target symbol synchronization set; in the target symbol synchronization set The third sequence number of the information symbol is smaller than the first sequence number, and the value of the element in the upper triangular matrix corresponding to the first sequence number and the third sequence number is not zero; or the target symbol synchronization set is a preset set.

According to the enumeration order of each optimized symbol synchronization set, obtain the Euclidean distance of each information symbol in each optimized symbol synchronization set;

The target decoding path is determined based on the Euclidean distance of each information symbol in each optimized symbol synchronization set.

According to the Euclidean distance of each information symbol in each optimized symbol synchronization set, a preset number of candidate decoding paths are determined for each optimized symbol synchronization set;

The target decoding path is determined based on the candidate decoding paths corresponding to each optimized symbol synchronization set.

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, the computer program, when executed by the processor, also 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 implemented by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage 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. By way of illustration but not limitation, RAM can be in various 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, data processing logic devices based on quantum computing, etc. are not limited to these.

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 embodiments only express several implementation modes of the present application, and their descriptions are relatively specific and detailed, but they 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 decoding path determination method, wherein the method includes:

Construct an equivalent symbol-based directed graph based on the polar code generation matrix;

According to the equivalent directed graph, obtain at least two initial symbol synchronization sets;

According to the frozen symbols in each of the initial symbol synchronization sets and the information symbols in the preset set, optimize each of the initial symbol synchronization sets to obtain an optimized symbol synchronization set;

The target decoding path is determined according to the optimized symbol synchronization set.
The method according to claim 1, wherein said obtaining at least two initial symbol synchronization sets according to the equivalent directed graph includes:

According to the enumeration order of each symbol in the equivalent directed graph, after deleting the target edge of each information symbol, the initial symbol synchronization set is determined according to the in-degree of the frozen symbol; the target edge is based on the signal The edge from which the symbol originates.
The method according to claim 2, wherein, after deleting the target edge of each information symbol according to the enumeration order of each symbol in the equivalent directed graph, the initial determination is based on the in-degree of the frozen symbol. Symbol synchronization collection, including:

Perform a classification operation; wherein the classification operation includes: according to the enumeration order of each symbol in the equivalent directed graph, after deleting the target edge of the current information symbol, obtaining the deletion of the target edge and the current The in-degree of the target frozen symbol connected by the information symbol, under the condition that the in-degree of the target frozen signal is zero, the current information symbol and the target frozen symbol are classified into the same set;

According to the enumeration sequence, the next information symbol adjacent to the current information symbol is determined as the new current information symbol, and the classification operation is returned until all symbols are classified into the set, and we obtain Each of the initial symbol synchronization sets.
The method according to any one of claims 1 to 3, wherein each of the initial symbol synchronization sets is optimized according to the frozen symbols in each of the initial symbol synchronization sets and the information symbols in the preset set, Obtain the optimized symbol synchronization set, including:

Obtain the first sequence number of the frozen symbol in the current symbol synchronization set and the second sequence number of the information symbol in the preset set; the current symbol synchronization set is any one of the at least two initial symbol synchronization sets;

According to the upper triangular matrix corresponding to the first sequence number, the second sequence number and the channel matrix, the current symbol synchronization set is optimized to obtain an optimized symbol synchronization set.
The method according to claim 4, wherein each of the initial symbol synchronization sets is optimized according to the upper triangular matrix corresponding to the first sequence number, the second sequence number and the channel matrix, to obtain an optimized symbol Synchronized collections, including:

Using the first serial number as a row and the second serial number as a column, determine the value of the target element from the upper triangular matrix; when the second serial number is greater than the first serial number and the target element If the value is not zero, the frozen symbol is deleted from the current symbol synchronization set, and the frozen symbol is added to the target symbol synchronization set; the third information symbol in the target symbol synchronization set The sequence number is smaller than the first sequence number, and the value of the element in the upper triangular matrix corresponding to the first sequence number and the third sequence number is not zero; or the target symbol synchronization set is the preset set.
The method according to any one of claims 1-3, wherein determining the target decoding path according to the optimized symbol synchronization set includes:

Obtain the Euclidean distance of each information symbol in each of the optimized symbol synchronization sets according to the enumeration order of each of the optimized symbol synchronization sets;

The target decoding path is determined according to the Euclidean distance of each information symbol in each of the optimized symbol synchronization sets.
The method according to claim 6, wherein determining the target decoding path based on the Euclidean distance of each information symbol in each of the optimized symbol synchronization sets includes:

According to the Euclidean distance of each information symbol in each of the optimized symbol synchronization sets, a preset number of candidate decoding paths are determined for each of the optimized symbol synchronization sets;

The target decoding path is determined according to the candidate decoding paths corresponding to each of the optimized symbol synchronization sets.
A decoding path determination device, wherein the device includes:

A building module to construct a symbol-based equivalent directed graph according to the polar code generation matrix;

The acquisition module acquires at least two initial symbol synchronization sets according to the equivalent directed graph;

An optimization module that optimizes each initial symbol synchronization set according to the frozen symbols in each initial symbol synchronization set and the information symbols in the preset set to obtain an optimized symbol synchronization set;

A determining module determines a target decoding path according to the optimized symbol synchronization set.
A computer device includes a memory and a processor, the memory stores a computer program, wherein the steps of the method according to any one of claims 1 to 7 are implemented 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 7 are implemented when the computer program is executed by a processor.