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

Abstract

This invention discloses a method and system for classifying and ranking channel noise distribution probabilities and calculating the limit of guessing decoding complexity. The method includes: Step S1. The receiving end acquires a binary channel noise sample sequence and calculates the noise parameters for each sample sequence; Step S2. Based on the calculated noise parameters for each sample sequence, the distribution probability corresponding to the channel noise sample is calculated, and the distribution probabilities are sorted in descending order; Step S3. The limit of guessing decoding complexity is calculated based on the sorted distribution probabilities and a preset maximum error correction capability. This invention helps reduce decoding complexity, optimizes computational resource consumption, and makes the decoding process more efficient. It prioritizes more likely error modes, which helps improve the success rate of error correction.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method and system for calculating the probability classification and sorting of channel noise distribution and the limit of guessing decoding complexity. Background Technology

[0002] In modern communications, the reliability and security of data transmission are paramount. Existing technologies utilize the versatility of the guessing random additive noise decoding (GRAND) algorithm to decode linear block codes, effectively improving data transmission security. Under hard-decision conditions, this improved data transmission architecture, as shown... Figure 1 As shown. However, existing technologies have significant limitations: channel noise modeling relies heavily on idealized assumptions, making it difficult to accurately reflect the complex and ever-changing channel environment in reality, which limits noise distribution probability analysis and decoding performance optimization.

[0003] For example, patent CN119995786A discloses a method for estimating the residual error probability (REP) of secure communication under guessing decoding conditions. It utilizes the GRAND algorithm, where the receiver's SPDU generates a noise error pattern, performs guessing decoding on the SPDU, calculates the guessed SPDU sequence, calculates the CRC-encoded corrector, judges the guessing decoding result based on the corrector value, and calculates the REP performance after guessing decoding. It then searches for the minimum CRC signature length under a given maximum error correction capability M, achieving the same or lower REP performance as with traditional CRC error detection mechanisms. While the patent provides a method for calculating REP performance under the GRAND algorithm, it does not address the statistical analysis and ranking of the channel noise probability distribution under arbitrary channel conditions. Step 1 of the patent requires prior knowledge of the channel noise probability distribution before generating the corresponding noise error pattern according to the descending order of probability. In practical applications, channel noise is difficult to accurately model using an ideal channel model. Therefore, in hard-decision GRAND algorithm applications, finding effective methods to analyze the probability distribution of arbitrary channel noise is crucial.

[0004] Therefore, in response to the above-mentioned technical problems, this invention provides a method and system for calculating the probability classification and sorting of channel noise distribution and the limit of guessing decoding complexity. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a method and system for calculating the probability classification, sorting, and guessing decoding complexity limits of channel noise distribution.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] Methods for calculating the complexity limit of channel noise distribution probability classification, ranking, and guessing decoding include:

[0008] Step S1. The receiver acquires a binary channel noise sample sequence and calculates the noise parameters for each sample sequence;

[0009] 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.

[0010] Step S3. Calculate the limit of guessing decoding complexity based on the sorted probability distribution and the preset maximum error correction capability.

[0011] Furthermore, step S1 specifically includes:

[0012] 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 the number of error bit strings; l m 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.

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

[0014] 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 ).

[0015] Furthermore, the step S13 of sequentially traversing each bit in the channel noise sample sequence specifically involves:

[0016] 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;

[0017] 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.

[0018] 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 iIt then continues to process the next bit in the channel noise sample sequence until all bits have been processed.

[0019] Furthermore, step S2 specifically includes:

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

[0021] S22. Calculate the probability distribution of the channel noise samples based on the number of channel noise samples corresponding to the same noise parameters. ;

[0022] 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);

[0023] S24. Based on the probability distribution P n The value of (K) will determine the probability distribution P. n (K) Arranged in descending order;

[0024] S25. The probability distribution P n (K) m =K multiple distribution probability According to the probability distribution The values ​​are arranged in descending order;

[0025] 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.

[0026] Furthermore, in step S22, the probability distribution corresponding to the channel noise sample is calculated. , is represented as:

[0027]

[0028] 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.

[0029] Furthermore, in step S23, the probability distribution P of K erroneous bits appearing in the channel noise sequence is calculated. n (K) is represented as:

[0030]

[0031] 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.

[0032] Furthermore, 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:

[0033]

[0034] 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.

[0035] Furthermore, step S5, which calculates the limit of guessing decoding complexity based on the sorted probability distribution and the preset maximum error correction capability, also includes: when there are f error bits in the channel noise sample sequence, and f > M, the maximum number of guessing decoding operations is:

[0036]

[0037] 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.

[0038] Furthermore, step S5, which calculates the limit of guessing decoding complexity based on the sorted probability distribution and the preset maximum error correction capability, also 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:

[0039]

[0040] 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.

[0041] Correspondingly, a system for calculating the probability classification and ranking of channel noise distribution and the limit of guessing decoding complexity is also provided, including:

[0042] The calculation module is used by the receiver to acquire binary channel noise sample sequences and calculate the noise parameters for each sample sequence;

[0043] 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.

[0044] 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.

[0045] Compared with existing technologies, this invention reduces the number of guesses by analyzing the probability distribution of channel noise and sorting noise error patterns; it also helps to optimize computational resource consumption, quantify the relationship between decoding complexity and maximum error correction capability, and optimize system performance and resource allocation; thus, it does not depend on an ideal channel model and can be applied to a variety of practical communication scenarios. Attached Figure Description

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

[0047] Figure 2 This is a flowchart of the channel noise distribution probability classification and sorting and guessing decoding complexity limit calculation method provided in Example 1;

[0048] Figure 3 The computer-calculated noise parameters (m, l) provided in Example 1 are... m C α )flow chart;

[0049] Figure 4 The probability distribution provided in Example 1 The calculation and sorting flowchart. Detailed Implementation

[0050] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0051] The purpose of this invention is to address the shortcomings of existing technologies by providing a method and system for calculating the probability classification, sorting, and guessing decoding complexity limits of channel noise distribution.

[0052] Example 1

[0053] This embodiment provides a method for classifying and ranking the probability distribution of channel noise and calculating the limit of guessing decoding complexity, such as... Figure 2 As shown, it includes:

[0054] Step S1. The receiver acquires a binary channel noise sample sequence and calculates the noise parameters for each sample sequence;

[0055] 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.

[0056] Step S3. Calculate the limit of guessing decoding complexity based on the sorted probability distribution and the preset maximum error correction capability.

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

[0058] Based on existing technology, a large number of binary channel noise sample sequences Z can be obtained through actual testing. Suppose that a certain sample sequence Z contains m error bit strings, and the total number of error bits is l. m And use C α Let Z represent the structure, where α takes values ​​of 1, 2, and 3. Any sample sequence Z can be represented using noise parameters (m, l). m C α ) indicates that there are corresponding There are three noise error modes E, assuming α takes the values ​​1, 2, and 3 respectively:

[0059] Scenario 1: If the sample sequence Z is represented as "0Z2…Z i …Z (n-1) In the form of "0", the first bit (1) and the last bit (n) in the sample sequence Z are both 0, and α=1. Then the noise parameter of the sample sequence Z is expressed as (m, l m According to theoretical derivation, the sample sequence Z contains m+1 strings of "0" bits, and the total number of "0" bit strings is nl. m One. Satisfying noise parameters (m, l) m The noise error mode E of C1) has a total of The number is calculated using the following formula (1):

[0060] (1)

[0061] Theoretical derivation example: Given a sequence of length n = 10 bits Z = 0110011100, m = 2, l m =5; therefore, there are m+1=3 strings of 0 bits, and the total number of bits in the strings of 0 is n-1. m =10-5=5.

[0062] Scenario 2: If the sample sequence Z is represented as "0Z2…Z i …Z (n-1) 1" or 1Z2...Z i …Z (n-1) In the form of 0, the first bit (1) of the sample sequence Z is 0 and the last bit (n) is 1, or the first bit (1) is 1 and the last bit (n) is 0. In this case, α=2, and the noise parameter of the sample sequence Z is expressed as (m, l m (C2) According to theoretical derivation, the sample sequence Z contains m strings of "0" bits, and the total number of "0" bit strings is nl. m One. Satisfying noise parameters (m, l) m The noise error mode E of C2) has a total of The number is calculated using the following formula (2):

[0063] (2)

[0064] Theoretical derivation example: Given a sequence of length n = 10 bits Z = 0110000111, m = 2, l m =5; therefore, there are m = 2 strings of 0 bits, and the total number of bits in the 0 strings is n - l. m =10-5=5.

[0065] Scenario 3: If the sample sequence Z is represented as "1Z2…Z i …Z (n-1) In the form of "1", 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 expressed as (m, l m According to theoretical derivation, the sample sequence Z contains m-1 strings of "0" bits, and the total number of "0" bit strings is nl. m One. Satisfying noise parameters (m, l) m The noise error mode E of C3) has a total of The number of items is calculated using the following formula (3):

[0066] (3)

[0067] Theoretical derivation example: Given a sequence of length n = 10 bits Z = 1100000111, m = 2, l m=5; therefore, there are m-1=1 strings of 0 bits, and the total number of bits in the strings of 0 is n-1. m =10-5=5.

[0068] As described above, for the classification of any n-bit long input sample sequence Z, the noise parameters (m, l) must first be calculated. m C α ), the flowchart calculated by computer, such as Figure 3 As shown.

[0069] S11. Initialize noise parameters (m, l) m C α The value is set to all zeros, and the intermediate variable Z is preset. 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. Set the value of parameter α according to the different situations set above.

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

[0072] Enter the current bit Z in sequence. i Determine the current bit Z i The type, whether it is 0 or 1;

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

[0074] If Z i =0, then determine the intermediate variable Z T The type; if Z T If Z = 0, then continue processing the next bit in the channel noise sample sequence until all bits have been processed; if Z T If the value is 1, then update the number of erroneous bit strings m = m + 1, and update the intermediate variable Z. T For Z i It then continues to process the next bit in the channel noise sample sequence until all bits have been processed.

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

[0076] For example, given a sequence Z = 0000011001 with a length of n = 10 bits, we know that m = 2, lm =3, the first bit z1=0, the 10th bit z 10 =1, C α =C2, noise parameters are (2, 3, C2).

[0077] In step S2, the probability distribution of the channel noise sample is calculated based on the noise parameters of each sample sequence, and the probability distribution is sorted in descending order.

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

[0079] S21. Count the number of channel noise samples corresponding to the same noise parameters;

[0080] S22. Calculate the probability distribution of the channel noise samples based on the number of channel noise samples corresponding to the same noise parameters. ;

[0081] Existing channel detection techniques can obtain a large number of channel noise sample sequences Z. The processing in step S1 is then applied to all sample sequences Z to obtain the corresponding noise parameters (m, l). m C α Channel noise samples with identical noise parameters are selected and statistically analyzed. The total number of samples is denoted as . According to the following formula (4), the noise parameters are calculated as (m, l) m C α The probability distribution corresponding to the channel noise samples. , is represented as:

[0082] (4)

[0083] 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 represents the total number of channel noise samples; n represents the length of the channel noise sample sequence Z.

[0084] 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);

[0085] Let K be the number of erroneous bits in a channel noise sample sequence Z, where 1 ≤ K ≤ n. Then, what is the probability distribution P of a channel noise sequence of length n containing K erroneous bits? n (K) is calculated as shown in equation (5):

[0086] (5)

[0087] 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.

[0088] S24. Based on the probability distribution P n The value of (K) will determine the probability distribution P. n (K) Arranged in descending order;

[0089] S25. The probability distribution P n (K) m =K multiple distribution probability According to the probability distribution The values ​​are arranged in descending order;

[0090] S26. Combining the order of steps S24 and S25, we obtain the final order of noise distribution probabilities. Where j=1,2,…, represents the permutation number, and the corresponding noise error modes E are: indivual.

[0091] Suppose there is a channel that transmits data 100 times, each time transmitting 10 bits. Statistical analysis shows that 30 transmissions have 1 error bit, 20 transmissions have 2 error bits, 10 transmissions have 3 error bits, and the remaining 40 transmissions have no errors.

[0092] Therefore, we first sort them according to the number of error bits K in ascending order, because the fewer the errors, the lower the probability P of the error bits. n The higher the value of Pn(K), the better. This means that the case without errors is processed first, followed by the case with 1 error bit, then the case with 2 error bits, and finally the case with 3 error bits. This results in the distribution probability Pn(K) being arranged in descending order.

[0093] Then, under the same number of error bits K, according to the probability distribution Sort from largest to smallest. For example, with K=1, if there are two different noise structures causing this error, one structure occurring more frequently and the other less frequently, then what is the probability distribution of the more frequently occurring structure? The bigger it is, the higher it will be in the rankings.

[0094] Finally, the sorting results from the first two steps are combined to form a final processing order. This allows different noise conditions to be processed sequentially, prioritizing those with high probability of occurrence and low error bit count, thereby improving processing efficiency.

[0095] In step S3, the limit of the guessing decoding complexity is calculated based on the sorted probability distribution and the preset maximum error correction capability.

[0096] When using guessing decoding, assume that at most M errors can be corrected. Based on the above discussion, when f ≤ M, the probability distribution P of f is... n (f) Calculated by formula (5). If the noise parameters of the channel noise sequence Z are (m, l) m C α Then its corresponding probability distribution is Noise error mode E has indivual.

[0097] Considering that in GRAND decoding, the noise error mode E is distributed according to probability... The order is used for guess decoding. If there are f erroneous bits (f≤M) in the channel noise sequence, the corresponding probability distribution is... J is the noise parameter (m, l) m C α The sequence number corresponding to the given number () requires at most [number] [times]. The next guessing decoding operation is calculated as shown in equation (6).

[0098] (6)

[0099] 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.

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

[0101] (7)

[0102] 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.

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

[0104] (8)

[0105] 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.

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

[0107] This embodiment of the invention helps reduce decoding complexity, optimize computational resource consumption, and make the decoding process more efficient. It also prioritizes more likely error patterns, which helps improve the success rate of error correction.

[0108] Correspondingly, a system for calculating the probability classification and ranking of channel noise distribution and the limit of guessing decoding complexity is also provided, including:

[0109] The calculation module is used by the receiver to acquire binary channel noise sample sequences and calculate the noise parameters for each sample sequence;

[0110] 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.

[0111] 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.

[0112] Example 2

[0113] The channel noise distribution probability classification, sorting, and guessing decoding complexity limit calculation method provided in this embodiment differs from that in Embodiment 1 in that:

[0114] To facilitate the reproducible implementation of this method and to prove its correctness, the binomial distribution of equation (9) will be used as an example for illustration.

[0115] (9)

[0116] Among them, P e Let n be the average error probability on a binomial distribution channel. Assume n is 64 bits, and in P... e =10 -3 and P e =10 -2In both cases, computer simulations are performed according to equation (9) to obtain 10. 9 Samples of a channel noise sequence Z, then according to the process of steps S1-S4, P e =10 -3 Time noise parameters (m, l) m C α The first 19 permutations of ) are shown in Table 1; P e =10 -2 Time noise parameters (m, l) m C α The first 24 permutations of ) are shown in Table 2.

[0117] Table 1 Binomial Distribution P e =10 -3 The probability distribution of the first 19 channel noise sequences (n=64 bits)

[0118] Table 2 Binomial Distribution P e =10 -2 The probability distribution of the first 24 channel noise sequences (n=64 bits) is arranged.

[0119] Based on Tables 1 and 2, and according to formulas (6), (7), and (8) in Example 1, the decoding complexity limits are calculated as shown in Table 3. The maximum error correction capabilities M are 1, 2, 3, and 4, respectively.

[0120] Table 3. Decoding complexity limits under different conditions (n=64 bits)

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

[0122] 1. Improves the reliability of communication systems: More accurate noise modeling and classification helps reduce errors and ensures reliable data transmission.

[0123] 2. An upper limit for decoding complexity is given: more accurate sorting helps to reduce decoding complexity, rationally calculate resource consumption, and make the decoding process more controllable.

[0124] 3. Improve decoding accuracy: Prioritizing the guessing of more likely noise patterns helps improve the success rate of error correction.

[0125] It has wide applicability: it is suitable for any type of channel conditions and has good versatility.

[0126] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended 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; Step S1 specifically involves: S11. Initialize the noise parameters (m, l m , C α ) to 0, and pre-set the intermediate variable Z T = 0; wherein 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 ); 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.

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 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.

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, 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, and m represents the length of the channel noise sample sequence.

4. The method for calculating the channel noise distribution probability classification, sorting, and guessing decoding complexity limit according to claim 3, 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.

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 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.

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, 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.

7. The method for calculating the limit of channel noise distribution probability classification, sorting, and guessing decoding complexity according to claim 6, 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 is divided into 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.

8. 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 1-7, 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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