Channel noise distribution probability classification sorting and guess decoding complexity limit calculation method and system

Abstract

The invention discloses a channel noise distribution probability classification sorting and guess decoding complexity limit calculation method and system, and the method comprises the steps: S1, obtaining a binary channel noise sample sequence through a receiving end, and carrying out the classification sorting of the channel noise distribution probability and the guess decoding complexity limit calculation; calculating a noise parameter of each sample sequence; s2, calculating distribution probabilities corresponding to channel noise samples according to the calculated noise parameters of each sample sequence, and sorting the distribution probabilities from large to small; and S3, calculating the limit of the guess decoding complexity according to the sorted distribution probability and the preset maximum error correction capability. According to the invention, the decoding complexity is reduced, the consumption of computing resources is optimized, and the decoding process is more efficient. Therefore, more possible error modes are sequenced preferentially, and the success rate of error correction is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, in particular to a channel noise distribution probability classification sorting and guessing decoding complexity limit calculation method and system. BACKGROUND

[0002] In the field of modern communication, the reliability and security of data transmission are of great importance. In the prior art, the universality of the guessing random additive noise decoding (GRAND) algorithm is used to decode linear block codes, which can effectively improve the security of data transmission. Under the condition of hard decision, the improved data transmission architecture is as shown in Figure 1 However, the prior art has obvious limitations: channel noise modeling is mostly dependent on idealized assumptions, which is difficult to accurately reflect the actual complex and variable channel environment, resulting in limited noise distribution probability analysis and decoding performance optimization.

[0003] The patent with publication number CN119995786A discloses a residual error probability estimation method for secure communication under guessing decoding, which uses the GRAND algorithm, generates noise error patterns for the receiver SPDU, performs guessing decoding on the receiver SPDU, calculates the guessed SPDU sequence; calculates the syndrome of CRC encoding, judges the guessing decoding result according to the value of the syndrome, calculates the REP performance after guessing decoding; searches for the minimum CRC signature length under the given maximum error correction capability M, which satisfies the same or lower REP performance as the traditional CRC error detection mechanism. The above patent gives a method for calculating the REP performance under the GRAND algorithm, but does not involve the probability distribution statistics and sorting of channel noise under any channel condition. In step 1 of the above patent, the probability distribution of channel noise needs to be known in advance, and then the corresponding noise error patterns can be generated in descending order of probability distribution. In actual application environment, it is difficult to accurately model the channel noise with an ideal channel model, therefore, in the application of hard decision GRAND algorithm, how to analyze the probability distribution of any channel noise needs to find an effective way.

[0004] Therefore, in view of the above technical problems, the present application provides a channel noise distribution probability classification sorting and guessing decoding complexity limit calculation method and system. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a channel noise distribution probability classification sorting and guessing decoding complexity limit calculation method and system.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] The channel noise distribution probability classification sorting and guessing decoding complexity limit calculation method comprises:

[0008] Step S1. The receiving end obtains a sequence of binary channel noise samples and calculates the noise parameter of each sample sequence;

[0009] Step S2. According to the calculated noise parameter of each sample sequence, the distribution probability corresponding to the channel noise sample is calculated, and the distribution probability is sorted in descending order;

[0010] Step S3. According to the sorted distribution probability and the preset maximum error correction capability, the limit of the guessing decoding complexity is calculated.

[0011] Further, the step S1 is specifically:

[0012] S11. Initialize the noise parameter (m, l m , C α ) to 0, and preset the intermediate variable Z T =0; wherein m represents the number of error bit strings; l m represents the total number of error bits; C α represents the structure of the channel noise sample sequence Z; and a represents different situations of the channel noise sample sequence Z.

[0013] S12. Input the sequence of binary channel noise samples Z, and obtain the first bit and the last bit of the channel noise sample sequence Z.

[0014] S13. Traverse each bit in the channel noise sample sequence in order to obtain the noise parameter (m, l m , C α ) of the channel noise sample sequence Z.

[0015] Further, the step S13 of traversing each bit in the channel noise sample sequence in order is specifically:

[0016] If the current bit Z i =1, update the total number of error bits to l m +1, and continue to process the next bit in the channel noise sample sequence until all bits are processed;

[0017] If the current bit Z i =0 and Z T =0, continue to process the next bit in the channel noise sample sequence until all bits are processed;

[0018] If the current bit Z i =0 and Z T =1, update the number of error bit strings to m+1, and update the intermediate variable Z T to the current bit Z iand continue to process the next bit in the sequence of channel noise samples until all bits are processed.

[0019] Further, the step S2 is specifically:

[0020] S21. Count the number of channel noise samples corresponding to the same noise parameter;

[0021] S22. Calculate the distribution probability of the channel noise samples according to the number of channel noise samples corresponding to the same noise parameter;

[0022] S23. Calculate the distribution probability P (K) of K error bits in the sequence of channel noise samples according to the distribution probability of the channel noise samples; n

[0023] S24. According to the value of the distribution probability P n (K), arrange the distribution probability P n (K) in descending order of value;

[0024] S25. Arrange the multiple distribution probabilities P n (K) in the distribution probability P m (K) according to the value of the distribution probability P (K) in descending order;

[0025] S26. Synthesize the arrangement order of steps S24 and S25 to obtain the arrangement order of the final noise distribution probability , where j = 1, 2, …, represents the arrangement number.

[0026] Further, the distribution probability of the channel noise samples calculated in step S22 is:

[0027]

[0028] wherein, represents the distribution probability of the channel noise samples; represents the total number of channel noise samples corresponding to the same noise parameter; N all represents the total number of channel noise samples; n represents the length of the sequence of channel noise samples Z, and n represents the length of the sequence of channel noise samples.

[0029] Further, the distribution probability P n (K) of K error bits in the sequence of channel noise samples calculated in step S23 is:

[0030] ​​​​

[0031] wherein P n (K) represents the distribution probability of error bits in the channel noise; K represents the number of error bits in the channel noise sample sequence Z, wherein 1≤K≤n.

[0032] Further, the step S5 of calculating the limit of the guess decoding complexity according to the sorted distribution probability and the preset maximum error correction capability comprises: when there are f error bits in the channel noise sample sequence and f≤M, the maximum number of guess decoding operations required is:

[0033]

[0034] wherein, represents the maximum number of guess decoding operations required; f represents the number of error bits, f∈0, 1, …, K; M represents the maximum number of error bits corrected by guess random additive noise decoding GRAND; represents the number of noise error patterns corresponding to the channel noise sample; j represents the arrangement serial number; J represents the maximum value of the serial number j when the noise parameters are (m, l m , C α ) satisfy m≤f and l m ≤f.

[0035] Further, the step S5 of calculating the limit of the guess decoding complexity according to the sorted distribution probability and the preset maximum error correction capability further comprises: when there are f error bits in the channel noise sample sequence and f>M, the maximum number of guess decoding operations is:

[0036]

[0037] wherein N n (M) represents the maximum number of guess decoding operations; represents the distribution probability corresponding to the number of error bits f>M in the channel noise sequence.

[0038] Further, the step S5 of calculating the limit of the guess decoding complexity according to the sorted distribution probability and the preset maximum error correction capability further comprises: when the maximum number of error corrections of guess random additive noise decoding GRAND is M, the average maximum guess decoding complexity is f≤M and f>M, and the maximum number of guess decoding operations is represented as:

[0039]

[0040] wherein N ave (M) represents the weighted average value of the maximum number of guess decoding operations; represents the Jth distribution probability after sorting.

[0041] Correspondingly, a channel noise distribution probability classification sorting and guessing decoding complexity limit calculation system is also provided, comprising:

[0042] A calculation module is configured to receive a binary channel noise sample sequence at an end and calculate noise parameters of each sample sequence;

[0043] A sorting module is configured to calculate distribution probabilities corresponding to the channel noise samples according to the noise parameters of each sample sequence calculated, and sort the distribution probabilities in descending order;

[0044] A second calculation module is configured to calculate a limit of guessing decoding complexity according to the sorted distribution probabilities and a preset maximum error correction capability.

[0045] Compared with the prior art, the present application reduces the number of guesses by analyzing channel noise distribution probabilities and sorting noise error modes, and is beneficial to optimizing the consumption of calculation resources, quantifying the relationship between decoding complexity and maximum error correction capability, and optimizing system performance and resource allocation. It does not depend on an ideal channel model and can be applied to various actual communication scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a data transmission architecture diagram based on a hard decision guessing random additive noise decoding algorithm provided by the background technology;

[0047] Figure 2 is a flowchart of a channel noise distribution probability classification sorting and guessing decoding complexity limit calculation method provided by embodiment one;

[0048] Figure 3 is a flowchart of computer calculation of noise parameters (m, l m , C α ) provided by embodiment one;

[0049] Figure 4 is a calculation and sorting flowchart of distribution probabilities provided by embodiment one. DETAILED DESCRIPTION

[0050] The embodiments of the present application are described below through specific, concrete examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure in the specification. The present application can also be implemented or applied through other different specific embodiments, and the details in the specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0051] The application aims at the defects of the prior art, and provides a channel noise distribution probability classification sorting and guessing decoding complexity limit calculation method and system.

[0052] Embodiment one

[0053] The embodiment provides a channel noise distribution probability classification sorting and guessing decoding complexity limit calculation method, as shown in the following formula (1), which comprises the following steps. Figure 2

[0054] Step S1, a receiving end acquires a binary channel noise sample sequence, and calculates noise parameters of each sample sequence.

[0055] Step S2, according to the noise parameters of each sample sequence calculated, a distribution probability corresponding to the channel noise sample is calculated, and the distribution probability is sorted in descending order.

[0056] Step S3, according to the sorted distribution probability and a preset maximum error correction capability, a limit of guessing decoding complexity is calculated.

[0057] In step S1, the receiving end acquires a binary channel noise sample sequence, and calculates noise parameters of each sample sequence.

[0058] On the basis of the prior art, a large number of binary channel noise sample sequences Z can be obtained through actual tests. It is assumed that a sample sequence Z contains m error bit strings, and the total number of error bits is l m . C α is used to represent the structure of Z, wherein the value of a is 1, 2 and 3 respectively. Any sample sequence Z can be represented by noise parameters (m, l m , C α ), and there are noise error modes E corresponding to the noise parameters. It is assumed that the cases when a is 1, 2 and 3 are as follows.

[0059] Case 1: If the sample sequence Z is expressed in the form of “0Z2…Z i …Z (n-1) 0”, the first bit (1) and the last bit (n) in the sample sequence Z are both 0, a=1, the noise parameters of the sample sequence Z are represented as (m, l m , C1), according to theoretical derivation, the sample sequence Z contains m+1 “0” bit strings, and the total number of “0” bit strings is n-l m . There are noise error modes E satisfying the noise parameters (m, l m , C1), and the calculation method is as follows formula (1):

[0060] (1) ​

[0061] Theoretical derivation: for example, there is a sequence Z = 0110011100 with n = 10 bits, m = 2, l m = 5; it is known that there are m + 1 = 3 "0" bit strings, and the total number of "0" bits is n - l m = 10 - 5 = 5.

[0062] Case 2: If the sample sequence Z is expressed in the form of "0Z2...Z i ...Z (n-1) 1" or 1Z2...Z i ...Z (n-1) 0, and the first bit (1) in the sample sequence Z is 0, the last bit (n) is 1, or the first bit (1) is 1 and the last bit (n) is 0, and α = 2, then the noise parameter of the sample sequence Z is (m, l m , C2). According to the theoretical derivation, the sample sequence Z contains m "0" bit strings, and the total number of "0" bit strings is n - l m . There are m noise error modes E satisfying the noise parameter (m, l , C2), and the calculation method is as follows formula (2):

[0063] (2)

[0064] Theoretical derivation: for example, there is a sequence Z = 0110000111 with n = 10 bits, m = 2, l m = 5; it is known that there are m = 2 "0" bit strings, and the total number of "0" bits is n - l m = 10 - 5 = 5.

[0065] Case 3: If the sample sequence Z is expressed in the form of "1Z2...Z i ...Z (n-1) 1", and the first bit (1) and the last bit (n) in the sample sequence Z are both 1, and α = 3, then the noise parameter of the sample sequence Z is (m, l m , C3). According to the theoretical derivation, the sample sequence Z contains m - 1 "0" bit strings, and the total number of "0" bit strings is n - l m . There are m noise error modes E satisfying the noise parameter (m, l , C3), and the calculation method is as follows formula (3):

[0066] (3)

[0067] Theoretical derivation: for example, there is a sequence Z = 1100000111 with n = 10 bits, m = 2, l m=5; it is known that there are m-1=1 0-bit strings, and the total number of 0 strings is n-1 m =10-5=5.

[0068] From the above, for the classification of an input arbitrary n-bit long sample sequence Z, first, its noise parameters (m, l m , C α ) are calculated Figure 3 The flow chart of computer calculation is shown as follows.

[0069] S11. Initialize the noise parameters (m, l m , C α ) to all 0, and preset the intermediate variable Z T =0;

[0070] S12. Input the binary channel noise sample sequence Z, and obtain the first bit (1) and the last bit (n) of the channel noise sample sequence Z, and set the value of parameter a according to the different cases set in the foregoing;

[0071] S13. Traverse each bit in the channel noise sample sequence in order, specifically:

[0072] Input the current bit Z i in order, judge the type of the current bit Z i , is it 0 or 1;

[0073] If Z i =1, update the total number of error bits l m =l m +1, continue to process the next bit in the channel noise sample sequence until all bits are processed;

[0074] If Z i =0, judge the type of the intermediate variable Z T ; if Z T =0, continue to process the next bit in the channel noise sample sequence until all bits are processed; if Z T =1, update the number of error bit strings m=m+1, and update the intermediate variable Z T to Z i , and continue to process the next bit in the channel noise sample sequence until all bits are processed.

[0075] Finally, the noise parameters (m, l m , C α ) of the channel noise sample sequence Z are obtained.

[0076] For example, there is a sequence Z=0000011001 of n=10 bits, it is known that m=2, lm =3, the first bit z1=0, the 10th bit z 10 =1, C α =C2, the noise parameter is (2, 3, C2).

[0077] In step S2, according to the noise parameter of each sample sequence calculated, the distribution probability corresponding to the channel noise sample is calculated, and the distribution probability is sorted in descending order.

[0078] As Figure 4 shown, step S2 specifically includes:

[0079] S21. Statistics of the number of channel noise samples corresponding to the same noise parameter;

[0080] S22. According to the number of channel noise samples corresponding to the same noise parameter calculated in step S21, the distribution probability corresponding to the channel noise sample is calculated .

[0081] Using the prior art channel detection, a large number of channel noise sample sequences Z can be obtained, and all sample sequences Z are processed in step S1 to obtain the corresponding noise parameters (m, l m , C α ). The channel noise samples with the same noise parameters are screened and the sample statistics are performed, the total number of samples is recorded as , according to the following formula (4), the distribution probability of the channel noise sample with the noise parameter (m, l m , C α ) is calculated , which is represented as:

[0082] (4)

[0083] Wherein, represents the distribution probability corresponding to the channel noise sample; represents the total number of channel noise samples corresponding to the same noise parameter; N all represents the total number of channel noise samples; n represents the length of the channel noise sample sequence Z.

[0084] S23. According to the distribution probability of the channel noise sample, the distribution probability P n (K) of K error bits in the channel noise sequence is calculated.

[0085] Suppose the number of error bits in the channel noise sample sequence Z is K, where 1≤K≤n. Then the distribution probability P n (K) of K error bits in the channel noise sequence with length n in the channel is calculated as shown in formula (5):

[0086] (5)

[0087] wherein P n (K) represents the distribution probability of error bits in channel noise; K represents the number of error bits in the sequence of channel noise samples Z.

[0088] S24. According to the value of the distribution probability P n (K), the distribution probability P n (K) is arranged in descending order of value.

[0089] S25. The plurality of distribution probabilities P n (K) with l m =K are arranged in descending order of value of the distribution probability P .

[0090] S26. The arrangement order of the final noise distribution probability is obtained by synthesizing the arrangement orders of steps S24 and S25. wherein j=1,2, …, represents the arrangement number, and the corresponding noise error mode E has .

[0091] Suppose there is a channel that transmits data 100 times, and each transmission is 10 bits. Statistics show that there are 30 times of transmission with 1 error bit, 20 times with 2 error bits, 10 times with 3 error bits, and the remaining 40 times without error.

[0092] Then, first, arrange according to the number of error bits K from small to large, because the fewer errors, the higher the distribution probability P n (K) of error bits, that is, first process the case without error, then the case with 1 error bit, then the case with 2 error bits, and finally the case with 3 error bits, so that the result of arranging the distribution probability Pn(K) in descending order is obtained.

[0093] Then, under the same number of error bits K, arrange according to the distribution probability P from large to small. For example, in the case of K=1, if there are two different noise structures that lead to this error, one structure appears more frequently and the other structure appears less frequently, then the distribution probability P of the structure that appears more frequently is larger and will be arranged first.

[0094] Finally, integrate the sorting results of the previous two steps to form a final processing order. In this way, different noise conditions can be processed in this order, and conditions with high occurrence probability and few error bits are processed first, thereby improving processing efficiency.

[0095] ​In step S3, the limit of the guess decoding complexity is calculated according to the sorted distribution probability and the preset maximum error correction capability.

[0096] When the guess decoding is used, it is assumed that the maximum M errors can be corrected. According to the above discussion, when f≤M, the f distribution probabilities P n (f) are calculated by formula (5). If the noise parameter of the channel noise sequence Z is (m, l m , C α ), the corresponding distribution probability is , and the noise error mode E has .

[0097] In the GRAND decoding, the noise error mode E is sorted according to the distribution probability , and the guess decoding is performed. If there are f error bits (f≤M) in the channel noise sequence, the corresponding distribution probability is , J is the sequence number corresponding to the noise parameter (m, l m , C α ), and the maximum number of guess decoding operations is , which is calculated as shown in formula (6).

[0098] (6)

[0099] wherein N represents the maximum number of guess decoding operations; f represents the number of error bits, f∈0, 1, …, K; M represents the maximum number of error bits that can be corrected by the guess random additive noise decoding GRAND; represents the number of noise error modes corresponding to the channel noise sample; j represents the sequence number; J represents the maximum value of the sequence number j when the noise parameter (m, l m , C α ) satisfies m≤f and l m ≤f.

[0100] For the predetermined maximum error correction capability M, if the number of error bits f in the channel noise sequence is greater than M, the error bits cannot be corrected correctly, and the maximum number of guess decoding is N n (M), and the distribution probability of this case is . The calculation is shown in formula (7).

[0101] (7)

[0102] wherein N n (M) represents the maximum number of guess decoding operations; represents the distribution probability corresponding to the number of error bits f>M in the channel noise sequence.

[0103] Based on the above analysis, when the maximum error correction capability is M, the average maximum guess decoding complexity N ave (M) is the weighted average of the maximum decoding times in the two cases of f≤M and f>M, which is shown in equation (8).

[0104] (8)

[0105] where N ave (M) represents the weighted average of the maximum guess decoding times; represents the Jth distribution probability after sorting.

[0106] The channel noise distribution probability sorting and decoding complexity limit calculation are suitable for any channel case.

[0107] The present embodiment is beneficial to reduce decoding complexity, optimize the consumption of computing resources, and make the decoding process more efficient. It makes it possible to prioritize error patterns, which is beneficial to improve the success rate of error correction.

[0108] Correspondingly, a channel noise distribution probability classification sorting and guess decoding complexity limit calculation system is also provided, which includes:

[0109] A computing module is configured to receive a binary channel noise sample sequence at the receiving end and calculate the noise parameter of each sample sequence;

[0110] A sorting module is configured to calculate the distribution probability corresponding to the channel noise sample according to the noise parameter of each sample sequence calculated, and sort the distribution probability in descending order;

[0111] A second computing module is configured to calculate the limit of the guess decoding complexity according to the sorted distribution probability and the preset maximum error correction capability.

[0112] Embodiment Two

[0113] The channel noise distribution probability classification sorting and guess decoding complexity limit calculation method provided in the present embodiment is different from that of Embodiment One in that:

[0114] To facilitate the reproducible implementation of the present method to prove its correctness, the binomial distribution of equation (9) is taken as an example for illustration.

[0115] (9)

[0116] where P e is the average error probability on the binomial distribution channel. Let n be 64 bits, and P e =10 -3 and P e =10 -2Two cases, according to formula (9) computer simulation, get 10 9 Channel noise sequence Z sample, then according to the process of steps S1-S4, P e =10 -3 Noise parameters (m, l m , C α ) of the first 19 arrangements as shown in table 1; P e =10 -2 Noise parameters (m, l m , C α ) of the first 24 arrangements as shown in table 2.

[0117] Table 1 binomial distribution P e =10 -3 The first 19 channel noise sequence distribution probability arrangement (n=64 bit)

[0118] Table 2 binomial distribution P e =10 -2 The first 24 channel noise sequence distribution probability arrangement (n=64 bit)

[0119] Based on table 1 and 2, according to formula (6), formula (7) and formula (8) in example one, the decoding complexity limit is calculated as shown in table 3. The maximum error correction capability M is 1, 2, 3 and 4 respectively.

[0120] Table 3 decoding complexity limit under different conditions (n=64 bit)

[0121] The beneficial effects of this embodiment mainly reflect in the following aspects:

[0122] 1. It is beneficial to improve the reliability of communication system: through more accurate noise modeling and classification, it is beneficial to reduce the occurrence of errors and ensure the reliability of data transmission.

[0123] 2. The upper limit of decoding complexity is given: more accurate sorting is conducive to reducing the decoding complexity, reasonable calculation of resource consumption, so that the decoding process is more controllable.

[0124] 3. Improve decoding accuracy: by guessing the more likely noise mode first, it is beneficial to improve the success rate of error correction.

[0125] It has wide applicability: it is suitable for any type of channel condition, and has good universality.

[0126] Note that the above merely describes preferred embodiments of the present application and the principles of the technology applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, modifications and substitutions can be made without departing from the scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A method for calculating the limit of channel noise distribution probability classification and sorting and guessing decoding complexity, characterized in that, include: Step S1. The receiver acquires a binary channel noise sample sequence and calculates the noise parameters for each sample sequence; Step S2. Calculate the probability distribution of the channel noise sample based on the noise parameters of each sample sequence, and sort the probability distributions in descending order. Step S3. Calculate the limit of guessing decoding complexity based on the sorted probability distribution and the preset maximum error correction capability.

2. The method for calculating the channel noise distribution probability classification, sorting, and guessing decoding complexity limit according to claim 1, is characterized in that, Step S1 specifically involves: S11. Initialize noise parameters (m, l) m C α The value is set to 0, and the intermediate variable Z is preset. T =0; where m represents Number of error bit strings; l m This represents the total number of error bits; C α The structure of the channel noise sample sequence Z is represented by α; α represents different cases of the channel noise sample sequence Z. S12. Input the binary channel noise sample sequence Z, and obtain the first and last bits of the channel noise sample sequence Z; S13. Iterate through each bit in the channel noise sample sequence in sequence to obtain the noise parameters (m, l) of the channel noise sample sequence Z. m C α ).

3. The method for calculating the channel noise distribution probability classification, sorting, and guessing decoding complexity limit according to claim 2, is characterized in that, The step S13, which involves sequentially traversing each bit in the channel noise sample sequence, specifically involves: If the current bit Z i =1, then the total number of error bits updated is l m +1, and continue processing the next bit in the channel noise sample sequence until all bits have been processed; If the current bit Z i =0 and Z T If =0, then continue processing the next bit in the channel noise sample sequence until all bits have been processed. If the current bit Z i =0 and Z T If the value is 1, then the number of erroneous bit strings is updated to m+1, and the intermediate variable Z is also updated. T For the current bit Z i It then continues to process the next bit in the channel noise sample sequence until all bits have been processed.

4. The method for calculating the channel noise distribution probability classification, sorting, and guessing decoding complexity limit according to claim 1, characterized in that, Step S2 specifically involves: S21. Count the number of channel noise samples corresponding to the same noise parameters; S22. Calculate the probability distribution of the channel noise samples based on the number of channel noise samples corresponding to the same noise parameters. ; S23. Based on the probability distribution corresponding to the channel noise samples Calculate the probability distribution P of K erroneous bits in a channel noise sequence. n (K); S24. Based on the probability distribution P n The value of (K) will determine the probability distribution P. n (K) Arranged in descending order; S25. The probability distribution P n (K) m =K multiple distribution probability According to the probability distribution The values ​​are arranged in descending order; S26. Combining the order of steps S24 and S25, we obtain the final order of noise distribution probabilities. , where j=1,2,…. represents the sequence number.

5. The method for calculating the channel noise distribution probability classification, sorting, and guessing decoding complexity limit according to claim 4, is characterized in that, In step S22, the probability distribution corresponding to the channel noise sample is calculated. , represented as: ； in, This represents the probability distribution corresponding to a channel noise sample; N represents the total number of channel noise samples corresponding to the same noise parameters; all The total number of channel noise samples is represented by ; n represents the length of the channel noise sample sequence Z, where n is the length of the channel noise sample sequence.

6. The method for calculating the limit of channel noise distribution probability classification, sorting, and guessing decoding complexity according to claim 5, is characterized in that, In step S23, the probability distribution P of K error bits appearing in the channel noise sequence is calculated. n (K) is represented as: ； Among them, P n (K) represents the probability distribution of erroneous bits in the channel noise; K represents the number of erroneous bits in the channel noise sample sequence Z, where 1≤K≤n.

7. The method for calculating the limit of channel noise distribution probability classification, sorting, and guessing decoding complexity according to claim 1, is characterized in that, In step S5, calculating the limit of guessing decoding complexity based on the sorted probability distribution and the preset maximum error correction capability includes: when there are f error bits in the channel noise sample sequence, and f≤M, the maximum number of guessing decoding operations required is: ； in, The maximum number of guessing decoding operations required is indicated by f; f represents the number of error bits, f∈0,1,….,K; M represents the maximum number of error bits that can be corrected by the guessing random additive noise decoding GRAND. The number of noise error modes corresponding to the channel noise sample is represented by j; the permutation number is j; and the noise parameter is (m, l). m C α (satisfying m≤f and l) m The maximum value of the corresponding index j when ≤f.

8. The method for calculating the channel noise distribution probability classification, sorting, and guessing decoding complexity limit according to claim 7, is characterized in that, The step S5, which calculates the limit of guessing decoding complexity based on the sorted probability distribution and the preset maximum error correction capability, further includes: when there are f error bits in the channel noise sample sequence, and f > M, the maximum number of guessing decoding operations is: ； Where, N n (M) represents the maximum number of guess-decoding operations; This represents the probability distribution corresponding to the number of error bits f > M in the channel noise sequence.

9. The method for calculating the limit of channel noise distribution probability classification, sorting, and guessing decoding complexity according to claim 8, is characterized in that, Step S5, which calculates the limit of guessing decoding complexity based on the sorted probability distribution and the preset maximum error correction capability, further includes: when the maximum number of corrected errors in guessing random additive noise decoding GRAND is M, the average maximum guessing decoding complexity has two cases: f≤M and f>M. The maximum number of guesses is then expressed as: ； Where, N ave (M) represents the weighted average of the maximum number of guesses to decode; This represents the probability of the J-th distribution after sorting.

10. A system for calculating the limit of channel noise distribution probability classification, sorting, and guessing decoding complexity, based on the method for calculating the limit of channel noise distribution probability classification, sorting, and guessing decoding complexity as described in any one of claims 1-9, characterized in that... The system includes: The calculation module is used by the receiver to acquire binary channel noise sample sequences and calculate the noise parameters for each sample sequence; The sorting module is used to calculate the probability distribution of the channel noise sample based on the noise parameters of each sample sequence, and sort the probability distribution in descending order. The second calculation module is used to calculate the limit of guessing decoding complexity based on the sorted probability distribution and the preset maximum error correction capability.

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