Combination degree distribution design and optimization method for online fountain codes

Abstract

The invention discloses a combination degree distribution design and optimization method for an online fountain code, and the method comprises the steps: designing combination degree distribution based on four basic degree distributions, namely ideal arc distribution, robust arc distribution, ripple reduction degree distribution and scale-free LT code; and optimizing the coefficient of the combination degree distribution by using a marine predator algorithm under a specific decoding success rate or an equivalent symbol error rate and by taking the minimum decoding overhead as a constraint. According to the optimization method, the proportionality coefficient of the members is optimized by increasing the number of the member degree distribution in the combination degree distribution and taking the minimum decoding overhead under the specific symbol error rate as a target function, so that the performance of the fountain code is improved. Simulation results show that the combination degree distribution designed and optimized by the method provided by the invention can significantly improve the performance of the fountain code.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle communication, and in particular to, but not limited to, a combination degree distribution design and optimization method of an online fountain code. Background Art

[0002] With the development of 5G / 6G communication technology, the demand for UAV communication is increasing. Usually, UAVs involve multiple scenarios when performing flight missions. The environment of the flight mission is affected by factors such as geographical location, weather, flight altitude, speed, and external interference, resulting in significant randomness in the type and characteristics of UAV communication channels. Therefore, UAV communication systems need to adapt to multiple channels with different characteristics during the mission. Since traditional channel coding usually has a fixed bit rate and adopts a packet-by-packet feedback mechanism, it is difficult to achieve good performance under such conditions.

[0003] Fountain codes have the characteristics of adaptive code rate and rarely require feedback or retransmission. Fountain codes can be applied to the transport layer or application layer to improve the performance of drone communications. Therefore, they can be used as an effective supplement to channel coding to enhance the reliability and stability of data communications. In addition, in 6G multipath transmission scenarios, fountain codes can ensure stable end-to-end transmission performance and have broad application potential in 6G ultra-reliable low-latency communication (URLLC) scenarios and secure communication scenarios.

[0004] Although the fountain code with single degree distribution can adapt to the changing channel environment to a certain extent, it cannot fully utilize the advantage of the rate-free characteristic of the fountain code. Therefore, it is necessary to study the application of fountain codes with combined degree distribution in UAV communication scenarios. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a combined degree distribution design and optimization method for online fountain codes, which at least solves the problem that single degree distribution fountain codes have optimization bottlenecks in adapting to changing channel conditions.

[0006] The technical solutions of the embodiments of the present invention are as follows:

[0007] An embodiment of the present invention provides a combined degree distribution design and optimization method for an online fountain code, the method comprising:

[0008] Based on four basic degree distributions, namely ideal arc distribution, robust arc distribution, ripple-reduced degree distribution and scale-free LT code, a combined degree distribution is designed. The coefficients of the combined degree distribution are optimized using the ocean predator algorithm under a specific decoding success rate or equivalent symbol error rate and with the minimum decoding overhead as a constraint.

[0009] Further, the combination degree distribution includes a three-member combination degree distribution and a four-member combination degree distribution;

[0010] The combined degree distribution model of the three-member combined degree distribution is as follows:

[0011]

[0012] Where Ω1(x) represents the probability model of the three-member combination degree distribution, a1, a2, a3 are three proportional coefficients, μ(d) represents the robust arc distribution, θ(d) represents the ripple reduction degree distribution, and λ(d) represents the scale-free LT code;

[0013] The combined degree distribution model of the four-member combined degree distribution is as follows:

[0014]

[0015] Where Ω2(x) represents the probability model of the four-member combination degree distribution, a1, a2, a3, a4 represent four proportional coefficients, and ρ(d) represents the ideal arc distribution.

[0016] Furthermore, the method of using the marine predator algorithm to optimize the coefficient of the combination degree distribution under a specific decoding success rate or an equivalent symbol error rate and with the minimum decoding overhead as a constraint includes: initializing a search proxy population and setting a maximum number of iterations; wherein the position of each prey in the search proxy population represents a set of combination degree distribution coefficients; for each prey in the search proxy population, calculating the symbol error rate of the fountain code during LT decoding; if the symbol error rate of a certain prey is less than a set value, calculating the fitness of the prey; if the symbol error rate of a certain prey is greater than or equal to the set value, applying a penalty value to the prey; selecting the prey with the smallest fitness as the top predator and obtaining the corresponding position; if the current number of iterations is less than the maximum number of iterations, updating the positions of all prey in the search proxy population, and recalculating the symbol error rate of each prey for iteration; the optimization is completed when the current number of iterations is greater than or equal to the maximum number of iterations, and outputting the parameters of the target prey with the smallest current fitness; wherein the parameters of the target prey represent the optimized combination degree distribution coefficient.

[0017] Furthermore, in the step of calculating the fitness of the prey, the fitness is specifically calculated by the following formula:

[0018]

[0019] In the formula, n is the number of samples required to calculate the fitness, T r represents the number of coded symbols received when the rth sample is successfully decoded;

[0020] In the step of calculating the symbol error rate of all prey in the LT decoding process, it is specifically calculated by the following formula:

[0021]

[0022] In the formula, p LT is the symbol error rate of the prey during LT decoding, k is the code length, l is the decoding step variable, and l max is the total number of decoding steps, is the probability that decoding terminates when l=1.

[0023] Furthermore, the method also includes: using Monte Carlo simulation to evaluate the performance of the combined degree distribution, including performance improvement evaluation obtained by increasing the number of members and performance evaluation of coefficient optimization.

[0024] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0025] In the embodiment of the present invention, the performance of the fountain code is improved by increasing the number of member degree distributions in the combined degree distribution and optimizing the proportional coefficients of the members with the minimum decoding overhead under a specific symbol error rate as the objective function. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0027] Figure 1 A schematic diagram of a flow chart of a combined degree distribution design and optimization method for online fountain codes provided in an embodiment of the present invention;

[0028] Figure 2 Schematic diagram of ripple evolution of five degree distributions provided in an embodiment of the present invention;

[0029] Figure 3 A schematic diagram of the relationship between decoding success rate and redundancy provided in an embodiment of the present invention;

[0030] Figure 4 A schematic diagram of the probability distribution of the number of coded packets required for successful decoding of different code lengths provided by an embodiment of the present invention;

[0031] Figure 5 A schematic diagram showing a comparison of the optimization effects of the MPA algorithm under different code length conditions provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0033] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0034] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described here can be implemented in an order other than that illustrated or described here.

[0035] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the embodiments of the present invention belong. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0036] Figure 1 A schematic diagram of a flow chart of a combined degree distribution design and optimization method of an online fountain code provided by an embodiment of the present invention, such as Figure 1 As shown, the method comprises at least the following steps:

[0037] Step S110, designing a combined degree distribution based on four basic degree distributions, namely, ideal arc distribution, robust arc distribution, ripple reduction degree distribution and scale-free LT code.

[0038] Here, the ideal soliton distribution (ISD) is the degree distribution with the lowest expected overhead. The present invention adopts the ideal soliton distribution to reduce the risk of decoding process failure due to the reduction of ripple to zero. The probability expression of the ideal soliton distribution is as follows:

[0039]

[0040] Where ρ(d) is the probability of the ideal arc distribution, k is the number of source data packets, i.e., the code length, and d is the encoding packet value.

[0041] Although the ideal arc distribution has the lowest expected overhead in theory, the performance of the ideal arc distribution is not as expected in the actual communication system environment. Robust Soliton Distribution (RSD) can improve the performance of the ideal arc distribution. Therefore, the present invention also adopts the robust arc distribution to make up for the shortcomings of the ideal arc distribution. The probability expression of the robust arc distribution is as follows:

[0042]

[0043] Where μ(d) is the probability of robust arc distribution, let is the expected ripple size of the decoding process, then the robustness factor τ(d) can be defined as:

[0044]

[0045] Where k is the number of source packets, i.e., the code length, c is a non-zero positive constant, δ is the allowed probability of decoding failure, and R represents the expected ripple size of the decoding process.

[0046] The Damped Ripple Soliton Distribution (DRSD) is designed for the ripple size during the decoding process, and its performance is better than the robust arc distribution and the ideal arc distribution. Using the ripple-reduced degree distribution in the combined degree distribution can reduce the redundancy required for successful decoding by maintaining an appropriate ripple size during the decoding process. The expression of the ripple-reduced degree distribution is as follows:

[0047]

[0048] Among them, θ(d) is the probability of ripple reduction distribution, n is the normalization factor, Q is the ripple size with degree 1, and d represents the coding packet value.

[0049] Scale-free LT code is a degree distribution with scale-free characteristics designed based on the idea of ​​scale-free network. The scale-free property of scale-free LT code can be exploited in the combined degree distribution to achieve lower coding complexity and mitigate the adverse effects associated with random packet loss. Its expression is as follows:

[0050]

[0051] Among them, λ(d) is the probability of scale-free LT code, P1 represents the proportion of coded symbols with degree 1, A is the normalization coefficient, and γ is the characteristic coefficient.

[0052] In the embodiment of the present invention, the number of member degree distributions is expanded. The combined degree distribution model consisting of n member degree distributions is defined as follows:

[0053]

[0054] where a1, a2, a3…a n is the proportionality coefficient, λ1(d),λ2(d),λ3(d)…λ n (d) is the membership degree distribution used in the combined degree distribution.

[0055] It should be noted that the performance of fountain codes depends largely on the design of their degree distribution. The degree distribution determines the probability of generating coded symbols, which in turn affects the performance of encoding and decoding. A good degree distribution design can reduce the number of coded symbols required while ensuring a low error rate, thereby reducing the decoding overhead.

[0056] The embodiment of the present invention can design a new combined degree distribution by selecting appropriate degree distribution and quantity for combination. This combination can take advantage of the advantages of various degree distributions to achieve better performance under different channel conditions. Different degree distributions may have different effects on the coverage of the code, decoding complexity and decoding overhead.

[0057] Step S120, using the Ocean Predator algorithm to optimize the coefficients of the combined degree distribution under a specific decoding success rate or an equivalent symbol error rate and with a minimum decoding overhead as a constraint.

[0058] Here, the member degree distributions of the combined degree distribution indirectly affect the performance of the combined degree distribution by affecting the structure of the overall degree distribution. As the number of member degree distributions increases, the optimization of the combination coefficient becomes challenging. The present invention proposes a method to optimize the coefficient of the combined degree distribution under a specific decoding success rate with the minimum decoding overhead as a constraint using the marine predator algorithm (MPA). MPA has strong robustness and excellent convergence performance.

[0059] During the optimization process, an objective function needs to be determined to evaluate the performance of fountain codes under different scaling factors. This objective function is related to the symbol error rate (SER) and the decoding overhead. By minimizing this objective function, the optimal scaling factor can be found. In order to find the optimal scaling factor, various optimization algorithms can be applied, such as differential evolution algorithm, sequential quadratic programming (SQP), etc. These algorithms can find the optimal solution in a complex search space.

[0060] In the embodiment of the present invention, the performance of the fountain code is improved by increasing the number of member degree distributions in the combined degree distribution and optimizing the proportional coefficients of the members with the minimum decoding overhead under a specific symbol error rate as the objective function.

[0061] In some embodiments, the combination degree distribution includes a three-member combination degree distribution and a four-member combination degree distribution; wherein the combination degree distribution model of the three-member combination degree distribution is as follows:

[0062]

[0063] Where Ω1(x) represents the probability model of the three-member combination degree distribution, a1, a2, a3 are three proportional coefficients, μ(d) represents the probability distribution of the robust arc distribution, θ(d) represents the probability distribution of the ripple reduction degree distribution, and λ(d) represents the probability distribution of the scale-free LT code;

[0064] The combined degree distribution model of the four-member combined degree distribution is as follows:

[0065]

[0066] Where Ω2(x) represents the probability model of the four-member combination degree distribution, a1, a2, a3, a4 represent four proportional coefficients, and ρ(d) represents the probability distribution of the ideal arc distribution.

[0067] Here, in order to study the performance of the combined degree distribution, the embodiment of the present invention designs two combined degree distributions. The first is a three-member combined degree distribution (TMCD), which uses a robust arc distribution, a ripple reduction degree distribution and a scale-free LT code, and is defined as formula (7); the other is a four-member combined degree distribution (FMCD), which uses an ideal arc distribution, a robust arc distribution, a ripple reduction degree distribution and a scale-free LT code, and is defined as formula (8).

[0068] In some embodiments, the above step S120 "optimizing the coefficients of the combined degree distribution by using the ocean predator algorithm under a specific decoding success rate or an equivalent symbol error rate and with a minimum decoding overhead as a constraint" can be implemented by the following steps:

[0069] S121, initializing a search agent population and setting a maximum number of iterations; wherein the position of each prey in the search agent population represents a set of combined degree distribution coefficients;

[0070] S122, for each prey in the search agent population, calculating a symbol error rate of the fountain code during LT decoding;

[0071] S123, if the symbol error rate of a certain prey is less than a set value, calculating the fitness of the prey;

[0072] S124, if the symbol error rate of a certain prey is greater than or equal to the set value, impose a penalty value on the prey;

[0073] S125, select the prey with the smallest fitness as the top predator and obtain the corresponding position;

[0074] S126, if the current number of iterations is less than the maximum number of iterations, updating the positions of all prey in the search agent population, and recalculating the symbol error rate of each prey to iterate;

[0075] S127, when the current number of iterations is greater than or equal to the maximum number of iterations, the optimization is completed, and the parameters of the target prey with the minimum current fitness are output; wherein the parameters of the target prey represent the optimized combination degree distribution coefficient.

[0076] Here, the goal of MPA optimization is to minimize the decoding overhead under a specific decoding success rate (or equivalent symbol error rate). The optimization problem can be expressed as min(f(Ω)) ,in f(Ω) is a function for calculating decoding overhead, and its expression is shown in formula (9). The specific process of the coefficient optimization method of combined degree distribution is shown in Table 1:

[0077] Table 1 Coefficient optimization method of combination degree distribution

[0078]

[0079] In this way, the design and optimization of the combined degree distribution can significantly improve the performance of fountain codes under various channel conditions, especially in environments with high error rates or high delays. This optimization method provides an effective strategy for the application of fountain codes in practical communication systems.

[0080] In some embodiments, the step of calculating the fitness of the prey is specifically calculated by the following formula:

[0081]

[0082] In the formula, n is the number of samples required to calculate the fitness, T r represents the number of coded symbols received when the rth sample is successfully decoded;

[0083] In the step of calculating the symbol error rate of all prey in the LT decoding process, it is specifically calculated by the following formula:

[0084]

[0085] In the formula, p LT is the symbol error rate of the prey during LT decoding, k is the code length, l is the decoding step variable, and l max is the total number of decoding steps, is the probability that decoding terminates when l=1.

[0086] Here, when optimizing the coefficients, it is necessary to calculate the symbol error rate of the LT decoding process. A SER calculation method called GERS is proposed in the related art, which uses a classical probability model, but its calculation complexity is relatively high. There is also a SER performance analysis scheme for LT codes, called DDEA, and the calculation method is shown in formula (10), and its complexity is lower than GERS. Therefore, the present invention uses DDEA to calculate the SER of LT encoding and decoding.

[0087] In some embodiments, the method further comprises: using Monte Carlo simulation to evaluate the performance of the combined degree distribution, including performance improvement evaluation obtained by increasing the number of members and performance evaluation of coefficient optimization.

[0088] Here, through simulation and experiments, the performance of optimized fountain codes under different channel conditions can be evaluated. Performance indicators may include bit error rate, decoding overhead, encoding and decoding time, etc.

[0089] In the BP decoding process, there should be a sufficient number of degree 1 packets in the early stage to promote the decoding process. However, when the initial ripple size is too large, it means that the number of low-degree encoded packets is large and it is difficult to cover all source packets. If the ripple size of the decoding process is too small, the decoding process may fail. Therefore, during the decoding process, the ripple size should be reduced at an appropriate speed and maintained above a certain level.

[0090] Ripple evolution is used to evaluate the performance of different types of degree distributions. Figure 2 The evolution of ripple size for various degree distributions is shown when k = 1000 and redundancy α is 0.05. Figure 2 As shown in FIG. 1 , the TMCD and FMCD distributions proposed in the embodiment of the present invention achieve a continuous reduction in the ripple size during the decoding process and keep the fluctuation within a relatively small range. This reduces the probability of decoding failure due to a lack of data packets with a degree of 1 during the decoding process. Figure 2 In the above, the ripple size performance of DRS is better than that of TMCD and FMCD proposed in the present invention. However, the decoding success rate performance of DRS is not better than that of the proposed combined degree distribution. This is because DRS mainly focuses on the design of the expected ripple size during decoding, but ignores the optimization of the degree distribution itself.

[0091] First, the performance improvement obtained by increasing the number of members is evaluated as follows:

[0092] Assuming that the simulation is performed under ideal channel conditions, the degree distributions listed in Table 2 are used for comparison. These degree distributions represent single degree distributions and combined degree distributions of two members, three members, and four members, respectively. The parameter settings of DRSD, SF-LT, and RSF-LT (a combination of RSD and SF-LT) are based on the empirical conclusions of existing studies. The degree distributions TMCD and FMCD proposed in the present invention are given by formulas (7) and (8), respectively.

[0093] Table 2 Detailed parameters of degree distribution

[0094]

[0095]

[0096] The simulations are performed with code lengths of k=500, k=1000, and k=2000 to evaluate the performance of combined degree distributions with different numbers of members.

[0097] Figure 3 The relationship between the decoding success rate and the decoding redundancy overhead is shown. As the number of member degree distributions increases, the decoding success rate of LT codes under low redundancy conditions improves. Specifically, compared with the two-member RSF-LT, the three-member degree distribution TMCD has a 20% improvement in decoding success rate when k=1000 and redundancy is 0.1. In addition, under the same redundancy conditions, the four-member degree distribution FMCD has a 12% improvement in decoding success rate when k=1000 compared to TMCD, and an 18% improvement when k=2000 compared to TMCD. Therefore, increasing the number of member degree distributions of the combined degree distribution can significantly increase the probability of successful decoding.

[0098] The comparison of the average decoding overhead and average degree value required for successful decoding under different degree distributions is shown in Table 3. It can be seen that as the number of member degree distributions increases, the decoding overhead required for successful decoding gradually decreases, but the average degree value increases slightly.

[0099] Table 3 Average overhead required to decode all input symbols and average degree value of each coded symbol

[0100]

[0101] Figure 4 The distribution of the number of coded packets required for successful decoding under four degree distributions is shown. As the number of member degree distributions increases, the number of coded packets required for successful decoding decreases and the distribution becomes more concentrated. This shows that combining degree distributions can enhance the performance of LT codes and reduce the overall decoding overhead.

[0102] Then, the performance evaluation process for coefficient optimization is as follows:

[0103] The above experiments show that increasing the number of member degree distributions is an effective way to improve the performance of combined degree distribution. In order to verify the effectiveness of the algorithm for increasing the number of member degree distribution coefficients through MPA optimization, the TMCD performance before and after MPA optimization is compared. The comparison results are shown in Figure 5 As shown, the optimized proportional coefficient is shown in Table 4:

[0104] Table 4 Optimized proportional coefficients under different code lengths

[0105] RSD DRSD SF-LT k＝500 0.5637 0.2495 0.1868 k＝1000 0.5215 0.1969 0.2815 k＝2000 0.4255 0.4793 0.0952

[0106] like Figure 5 As shown in the figure, when k=2000, after MPA optimization, the performance of the combined degree distribution is improved compared with the unoptimized version when the redundancy is 0.05. When the redundancy is 0.1, the probability of successful decoding is increased by 18% after MPA optimization. When the redundancy is 0.15, the probability of successful decoding of the TMCD after MPA optimization exceeds 99%, while the TMCD without MPA optimization requires 0.35 redundancy to achieve a similar performance level. At this time, the TMCD after MPA optimization can reduce the decoding overhead by about 20%.

[0107] Table 5 Comparison of degree distribution effects before and after MPA optimization under different code length conditions

[0108]

[0109] Table 5 shows the characteristics of the MPA-optimized TMCD. Compared with the unoptimized version, the MPA-optimized TMCD reduces the decoding overhead by 11.3% to 23.6%. The optimized TMCD has a slight increase in the average degree value, which reduces the probability of not covering all source packets during the encoding process.

[0110] The present invention studies the method of designing combined degree distributions from various complementary single degree distributions and evaluates their impact on the performance of fountain codes. In order to determine the optimal coefficients of the combined degree distribution, the marine predator algorithm (MPA) is used to optimize these coefficients with the goal of minimum decoding overhead under a specific symbol error rate. Simulation results show that increasing the number of member degree distributions of the combined degree distribution and optimizing their coefficients using MPA can improve the decoding success rate by 10%-20%. In addition, simulation results show that combined degree distributions with more members show more stable performance and significantly higher overall decoding success rate.

[0111] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0112] It should be noted that, in the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0113] In the several embodiments provided by the present invention, it should be understood that the disclosed methods can be implemented in other ways. The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments. The features disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0114] The above is only an embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A combined degree distribution design and optimization method for online fountain codes, characterized in that: include: Based on four basic degree distributions, namely, ideal arc distribution, robust arc distribution, ripple-reduced degree distribution and scale-free LT code, a combined degree distribution is designed. The coefficients of the combined degree distribution are optimized using the ocean predator algorithm under a specific decoding success rate or equivalent symbol error rate and with a minimum decoding overhead as a constraint.

2. The method according to claim 1, characterized in that The combination degree distribution includes a three-member combination degree distribution and a four-member combination degree distribution; The combined degree distribution model of the three-member combined degree distribution is as follows: Where Ω1(x) represents the probability model of the three-member combination degree distribution, a1, a2, a3 are three proportional coefficients, μ(d) represents the probability distribution of the robust arc distribution, θ(d) represents the probability distribution of the ripple reduction degree distribution, and λ(d) represents the probability distribution of the scale-free LT code; The combined degree distribution model of the four-member combined degree distribution is as follows: Where Ω2(x) represents the probability model of the four-member combination degree distribution, a1, a2, a3, a4 represent four proportional coefficients, and ρ(d) represents the probability distribution of the ideal arc distribution.

3. The method according to claim 1, characterized in that The method of optimizing the coefficient of the combined degree distribution by using the ocean predator algorithm under a specific decoding success rate or an equivalent symbol error rate and with a minimum decoding overhead as a constraint includes: Initialize the search agent population and set the maximum number of iterations; wherein the position of each prey in the search agent population represents a set of combined degree distribution coefficients; For each prey in the search agent population, calculating a symbol error rate of the fountain code during LT decoding; If the symbol error rate of a certain prey is less than the set value, the fitness of the prey is calculated; If the symbol error rate of a certain prey is greater than or equal to the set value, a penalty value is imposed on the prey; Select the prey with the smallest fitness as the top predator and obtain the corresponding position; If the current number of iterations is less than the maximum number of iterations, the positions of all prey in the search agent population are updated, and the symbol error rate of each prey is recalculated to iterate; The optimization is completed when the current number of iterations is greater than or equal to the maximum number of iterations, and the parameters of the target prey with the minimum current fitness are output; wherein the parameters of the target prey represent the optimized combination degree distribution coefficient.

4. The method according to claim 3, characterized in that The step of calculating the fitness of the prey is specifically calculated by the following formula: In the formula, n is the number of samples required to calculate the fitness, T r represents the number of coded symbols received when the rth sample is successfully decoded; In the step of calculating the symbol error rate of all prey in the LT decoding process, it is specifically calculated by the following formula: In the formula, p LT is the symbol error rate of the prey during LT decoding, k is the code length, l is the decoding step variable, and l max is the total number of decoding steps, is the probability that decoding terminates when l=1.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Monte Carlo simulation is used to evaluate the performance of the combined degree distribution, including the evaluation of the performance improvement obtained by increasing the number of members and the performance evaluation of coefficient optimization.

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