Beam hopping satellite clustering method, device, equipment, medium and program product

Abstract

The invention provides a beam hopping satellite clustering method, device and equipment, a medium and a program product, and relates to the technical field of wireless communication. The method comprises the steps that one or more candidate solutions are obtained, and each candidate solution corresponds to one clustering scheme; based on a preset optimization function, the fitness value of each candidate solution is subjected to iterative calculation, preset parameters are updated in the iteration process, the preset optimization function is used for minimizing the difference of the user demand quantity among the clusters, and the preset parameters are used for optimizing the clustering scheme corresponding to each candidate solution; and when a preset number of iterations is reached, determining a target candidate solution, and taking a clustering scheme corresponding to the target candidate solution as a target clustering scheme. According to the invention, dynamic resource requirements can be met, and the overall throughput of the system is improved.

Description

Background Art

[0002] Hopping beam clustering is a technique used in satellite communication. It partitions and manages the beams within the satellite coverage area. Different beams are grouped into a cluster, and resource management is carried out within the cluster, aiming to optimize the resource allocation and spectrum efficiency of the multi-beam satellite system. Among them, the hopping beam technique allows the satellite to "hop" its transmission beam between different geographical areas it covers, enabling dynamic adjustment of the beam pointing and bandwidth allocation to adapt to the changing demands of different areas, thereby improving the overall system performance.

[0003] In the related art, the hopping beam clustering method mainly adopts the uniform clustering method, that is, each cluster group has the same number of beams, and the resources are evenly divided accordingly. Once the clustering is completed, the resource allocation relationship between clusters does not change over time, that is, the related art is a static hopping beam clustering method. Obviously, this method is not suitable for the current dynamically changing scenario requirements and requires a more intelligent and efficient dynamic clustering method.

[0004] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The present disclosure provides a hopping beam satellite clustering method, device, equipment, medium, and program product, which at least overcome to a certain extent the problem in the related art that the fixed resource division cannot meet the requirements of dynamically changing scenarios.

[0006] Other features and advantages of the present disclosure will become apparent through the following detailed description, or be learned in part through the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a hopping beam satellite clustering method is provided, including: obtaining one or more candidate solutions, where each candidate solution corresponds to a clustering scheme; iteratively calculating the fitness values of each candidate solution based on a preset optimization function, and updating the preset parameters during the iteration, where the preset optimization function is used to minimize the difference in user demand between each cluster group, and the preset parameters are used to optimize the clustering scheme corresponding to each candidate solution; when the preset number of iterations is reached, determining the target candidate solution, and taking the clustering scheme corresponding to the target candidate solution as the target clustering scheme.

[0008] In some embodiments, based on a preset optimization function, the fitness values of each candidate solution are iteratively calculated, and the preset parameters are updated during the iteration, including: calculating the fitness values of each candidate solution based on the preset optimization function, and obtaining the current target candidate solution; determining whether the current iteration number reaches the preset iteration number; if the current iteration number does not reach the preset iteration number, updating the preset parameters according to the current target candidate solution; performing the next iteration update on the clustering scheme corresponding to each candidate solution according to the updated preset parameters; calculating the fitness values of each candidate solution in the next iteration process according to the updated candidate solutions.

[0009] In some embodiments, calculating the fitness values of each candidate solution based on a preset optimization function, and obtaining the current target candidate solution, includes: calculating the fitness values of each candidate solution based on the preset optimization function; sorting the fitness values of each candidate solution in descending order, and using the candidate solution corresponding to the minimum fitness value as the current target candidate solution.

[0010] In some embodiments, performing the next iteration update on the clustering scheme corresponding to each candidate solution according to the updated preset parameters, includes: performing the next iteration update on the clustering scheme corresponding to each candidate solution by combining the whale optimization algorithm according to the updated preset parameters.

[0011] In some embodiments, the preset parameters at least include: a shrinking encircling coefficient and an execution probability.

[0012] In some embodiments, performing the next iteration update on the clustering scheme corresponding to each candidate solution by combining the whale optimization algorithm according to the updated preset parameters, includes: determining whether the execution probability is less than a preset probability threshold; if the execution probability is less than the preset probability threshold, determining whether the shrinking encircling coefficient is greater than a preset shrinking threshold; if the shrinking encircling coefficient is greater than the preset shrinking threshold, performing the next iteration update on the clustering scheme corresponding to each candidate solution by using a random search method; if the shrinking encircling coefficient is less than or equal to the preset shrinking threshold, performing the next iteration update on the clustering scheme corresponding to each candidate solution by using a shrinking encircling method.

[0013] In some embodiments, the method further includes: if the execution probability is greater than the preset probability threshold, performing the next iteration update on the clustering scheme corresponding to each candidate solution by using a spiral update method.

[0014] According to another aspect of the present disclosure, there is also provided a hopping beam satellite clustering device, including: a candidate solution acquisition module configured to acquire one or more candidate solutions, where each candidate solution corresponds to a clustering scheme; an evaluation metric update module configured to iteratively calculate the fitness values of the respective candidate solutions based on a preset optimization function and update preset parameters during the iteration, where the preset optimization function is used to minimize the difference in user demand amounts between each cluster group, and the preset parameters are used to optimize the clustering schemes corresponding to the respective candidate solutions; and a clustering scheme determination module configured to determine a target candidate solution when a preset number of iterations is reached and use the clustering scheme corresponding to the target candidate solution as the target clustering scheme.

[0015] According to another aspect of the present disclosure, there is also provided an electronic device, which includes: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to execute the hopping beam satellite clustering method according to any one of the above by executing the executable instructions.

[0016] According to another aspect of the present disclosure, there is also provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the hopping beam satellite clustering method according to any one of the above.

[0017] According to another aspect of the present disclosure, there is also provided a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the hopping beam satellite clustering method according to any one of the above.

[0018] In the hopping beam satellite clustering method, device, equipment, medium, and program product provided in the embodiments of the present disclosure, after obtaining an initial number of candidate solutions, the fitness values of the respective candidate solutions are iteratively calculated based on a preset optimization function, and preset parameters are updated during the iteration. When the preset number of iterations is reached, the iteration is stopped and a target candidate solution is determined, and the clustering scheme corresponding to this candidate solution is used as the final clustering scheme. The embodiments of the present disclosure can meet dynamic resource requirements and improve the overall throughput of the system.

[0019] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure and used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0021] Figure 1 Show the flowchart of a hopping beam satellite clustering method in an embodiment of the present disclosure;

[0022] Figure 2 Show another hopping beam satellite clustering method in an embodiment of the present disclosure;

[0023] Figure 3 Show a schematic diagram of non-uniform clustering of hopping beam satellites in an embodiment of the present disclosure;

[0024] Figure 4 Show a schematic diagram of a hopping beam satellite clustering device in an embodiment of the present disclosure;

[0025] Figure 5 Show a structural block diagram of an electronic device in an embodiment of the present disclosure. Detailed implementation manners

[0026] Now, example embodiments will be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art. The features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments.

[0027] In addition, the drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0028] Next, with reference to the accompanying drawings, the detailed implementation manners of the embodiments of the present disclosure will be described in detail.

[0029] Figure 1 Show the flowchart of a hopping beam satellite clustering method in an embodiment of the present disclosure, as Figure 1 shown, the method includes the following steps:

[0030] S102. Obtain one or more candidate solutions, where each candidate solution corresponds to a clustering scheme.

[0031] In one embodiment of the present disclosure, a certain number of candidate solutions can be initialized first. Each candidate solution corresponds to a possible clustering scheme (in the scenario where the concept of the whale optimization algorithm is introduced, the candidate solution can refer to the number of whales, and the clustering scheme can refer to the whale positions). Among them, each clustering scheme defines how to allocate beams to different cluster groups.

[0032] S104. Based on a preset optimization function, iteratively calculate the fitness values of each candidate solution, and update the preset parameters during the iteration process. The preset optimization function is used to minimize the difference in user demand among each cluster group, and the preset parameters are used to optimize the clustering scheme corresponding to each candidate solution.

[0033] In one embodiment of the present disclosure, an optimization function is designed in advance. The optimization function aims to make the user demand between cluster groups as equal as possible. The preset parameters are used to update and optimize the clustering scheme corresponding to each candidate solution in each round of iteration. The optimal solution among each candidate solution can be determined according to the optimization function, and then the preset parameters are updated based on the optimal solution, and further update the clustering scheme corresponding to each candidate solution.

[0034] S108. When the preset number of iterations is reached, determine the target candidate solution, and use the clustering scheme corresponding to the target candidate solution as the target clustering scheme.

[0035] In one embodiment of the present disclosure, the maximum number of iterations can also be preset during the initialization process. When the current number of iterations reaches the maximum number of iterations, use the currently obtained optimal candidate solution as the final candidate solution, that is, the clustering scheme corresponding to this candidate solution is the final clustering scheme.

[0036] As can be seen from the above, after obtaining the initial number of candidate solutions, based on the preset optimization function, iteratively calculate the fitness values of each candidate solution, and update the preset parameters during the iteration process. When the preset number of iterations is reached, stop the iteration and determine the target candidate solution, and use the clustering scheme corresponding to this candidate solution as the final clustering scheme. The present disclosure can meet the dynamic resource requirements and improve the overall throughput of the system.

[0037] In one embodiment of the present disclosure, the above S104 includes: calculating the fitness value of each candidate solution based on the preset optimization function, and obtaining the current target candidate solution; determining whether the current number of iterations reaches the preset number of iterations; if the current number of iterations does not reach the preset number of iterations, update the preset parameters according to the current target candidate solution; perform the next round of iterative update on the clustering scheme corresponding to each candidate solution according to the updated preset parameters; calculate the fitness values of each candidate solution in the next round of iteration according to the updated candidate solutions.

[0038] In one embodiment of the present disclosure, it is assumed that the high-orbit satellite provides n c beams (i.e., the number of beams providing services) cover n cells on the ground, where N c Indicates the number of clusters, and the cell is represented by C set ={c j |j=1,2,…,n}, the user demand of each cell is expressed as R set ={R j |j=1,2,…,N},R tot represents the total user demand, B tot Indicates the total bandwidth.

[0039] In actual situations, the number of beams is often the same as the number of cells. For example, if there are 49 cells on the ground, there are 49 beams corresponding to the cells one by one. The cells can be divided into 7 clusters, each containing 7 cells. At this time, the satellite provides service transmission services to one cell in the cluster in each time slot. Therefore, there are 7 beams in each time slot to cover the ground cells and provide services.

[0040] It should be noted that the above quantity settings are only for illustrative purposes and can be adjusted according to actual needs. The embodiments of the present disclosure do not make specific limitations on this.

[0041] In one embodiment of the present disclosure, the preset optimization function can be expressed by the following formula:

[0042]

[0043] Among them, x i,j Indicates whether beam j is assigned to cluster i, x i,j =1 means beam j is assigned to cluster i, x i,j = 0 means beam j is not assigned to cluster i, R j represents the user demand in cell j, Represents the average user demand of each cluster, N m Indicates the number of clusters where user demand is greater than the average user demand, Indicates N m The total user demand of a cluster.

[0044] It should be noted that, since the embodiments of the present disclosure are based on a one-to-one correspondence between beams and cells, the parameters used to represent beams and the parameters used to represent cells can be interoperable.

[0045] In one embodiment of the present disclosure, the above constraint condition ① means that x i,jThe value of is 0 or 1. The above constraint ② represents the value range of the number of cluster groups. The above constraint ③ means that beam j can only be assigned to one cluster group. The above constraint ④ represents the value range of the number of beams in cluster group i.

[0046] In an embodiment of the present disclosure, some beams form a separate cluster due to their high user demand, indicating that these beams are not suitable for merging with other beams into larger cluster groups, but need to form their own clusters independently to ensure effective resource allocation and service quality, which is the above-mentioned N m the cluster groups included in.

[0047] For example, if the total user demand is 1000 units and the number of cluster groups is 5, the theoretical average user demand is 200 units. If, at this time, there are:

[0048] Beam A: The user demand is 220 units;

[0049] Beam B: The user demand is 210 units;

[0050] Beam C: The user demand is 180 units;

[0051] Beam D: The user demand is 150 units;

[0052] Beam E: The user demand is 120 units;

[0053] Beam F: The user demand is 190 units.

[0054] At this time, it can be known that the above-mentioned Beam A and Beam B exceed the average user demand and need to be separately divided into a cluster group. For example, Cluster Group 1: includes Beam A (demand 200 units); Cluster Group 2: includes Beam B (demand 210 units).

[0055] The remaining Beams C, D, E, and F are recombined into the remaining cluster groups so that the service demands between the new cluster groups are as close as possible. For example, Cluster Group 3: includes Beam C (demand 180 units); Cluster Group 4: includes Beam D + Beam E (demand 270 units); Cluster Group 5: includes Beam F (demand 190 units).

[0056] It should be noted that the numerical values and corresponding clustering schemes in the above examples only serve as examples and are only the schemes for a certain iteration or initialization. In actual situations, it may be the same as the above clustering scheme or adjusted as needed. The embodiments of the present disclosure do not make specific limitations in this regard.

[0057] In one embodiment of the present disclosure, based on a preset optimization function, the fitness values of each candidate solution are calculated, and the current target candidate solution is obtained, including: calculating the fitness values of each candidate solution based on the preset optimization function; sorting the fitness values of each candidate solution in descending order, and taking the candidate solution corresponding to the minimum fitness value as the current target candidate solution.

[0058] In one embodiment of the present disclosure, by finding the optimal candidate solution that makes the function value (i.e., the fitness value) the minimum, that is, finding a clustering scheme that can minimize the difference in user demand between each cluster group, so as to more effectively utilize resources and improve the overall performance of the system.

[0059] In one embodiment of the present disclosure, according to the updated preset parameters, the clustering schemes corresponding to each candidate solution are iteratively updated in the next round, including: according to the updated preset parameters, combined with the whale optimization algorithm, the clustering schemes corresponding to each candidate solution are iteratively updated in the next round.

[0060] In one embodiment of the present disclosure, the preset parameters at least include: the shrinking encircling coefficient and the execution probability. In addition, the preset parameters may also include: the linearly decreasing weight coefficient, the random number scaling factor, and the random number for controlling the change range of the spiral shape control parameter, etc. It should be noted that the specific updated parameters can be determined according to the position update formula adopted in the actual situation. The above parameter types only play an exemplary role, and the embodiments of the present disclosure do not make specific limitations on this.

[0061] In one embodiment of the present disclosure, the whale optimization algorithm is a meta-heuristic optimization algorithm that searches for the optimal solution in the search space. It mainly explores the search space by simulating the following three behaviors: encircling the prey, bubble-net hunting, and random search. Among them, bubble-net hunting can include: shrinking encircling and spiral updating two steps.

[0062] In one embodiment of the present disclosure, the process of using encircling the prey for position update can be represented by the following formula:

[0063]

[0064] where X(t + 1) represents the position of the whale at the (t + 1)-th iteration, X * (t) represents the optimal position of the whale at the t-th iteration, A represents the shrinking encircling coefficient, D represents the distance between the whale and the prey, a represents the linearly decreasing weight coefficient, rand represents a random number between 0 and 1, and C represents the random number scaling factor.

[0065] In an embodiment of the present disclosure, on the premise that the execution probability of the shrinking encirclement is equal to the spiral update probability, when the execution probability p < 0.5, the shrinking encirclement is executed, and when p ≥ 0.5, the spiral update is executed. The process of updating the position by using the bubble net predation can be expressed by the following formula:

[0066]

[0067] where D * represents the absolute value of the distance between the whale and the prey, b represents the spiral shape control parameter, and l represents a random number uniformly distributed in the range [-1, 1], which is used to control the amplitude of the spiral shape change. By means of the spiral update, the behavior of the whale approaching the prey along the spiral path is simulated to improve the current clustering scheme; by means of the shrinking encirclement, the whale is moved in a better direction.

[0068] In an embodiment of the present disclosure, the process of updating the position by using random search can be expressed by the following formula:

[0069] X(t + 1) = X rand (t) - A × |C × X rand (t) - X(t)| (4)

[0070] where X rand (t) represents the position of the whale individual randomly selected at the t-th iteration. By means of random search, an attempt is made to explore new areas to avoid falling into local optima.

[0071] In an embodiment of the present disclosure, according to the updated preset parameters, combined with the whale optimization algorithm, the clustering schemes corresponding to each candidate solution are iteratively updated in the next round, including: determining whether the execution probability is less than the preset probability threshold; if the execution probability is less than the preset probability threshold, determining whether the shrinking encirclement coefficient is greater than the preset shrinking threshold; if the shrinking encirclement coefficient is greater than the preset shrinking threshold, a random search method is used to iteratively update the clustering schemes corresponding to each candidate solution in the next round; if the shrinking encirclement coefficient is less than or equal to the preset shrinking threshold, a shrinking encirclement method is used to iteratively update the clustering schemes corresponding to each candidate solution in the next round.

[0072] In an embodiment of the present disclosure, the method further includes: if the execution probability is greater than the preset probability threshold, a spiral update method is used to iteratively update the clustering schemes corresponding to each candidate solution in the next round.

[0073] In an embodiment of the present disclosure, on the premise that the execution of shrinking encirclement is equal to the spiral update probability, the preset probability threshold can be set to 0.5; to avoid the search of whale individuals falling into local optimum in some cases, the preset shrinking threshold can be defined as 1. When the probability p < 0.5 and the absolute value of the shrinking encirclement coefficient |A| > 1, the position of the whale individual is updated by means of random search; when the probability p < 0.5 and the absolute value of the shrinking encirclement coefficient |A| ≤ 1, the position of the whale individual is updated by means of shrinking encirclement; when the probability p ≥ 0.5, the position of the whale individual is updated by means of spiral update.

[0074] It should be noted that the values of the above preset probability threshold and preset shrinking threshold only play an exemplary role and can be adjusted according to the actual situation, and the embodiments of the present disclosure do not make specific limitations thereon.

[0075] In an embodiment of the present disclosure, after the above S106, the hopping beam satellites can be non-uniformly clustered according to the optimal solution obtained by the whale optimization algorithm, and the resource allocation of the hopping beam satellites is completed to meet the service requirements of the ground cells.

[0076] In an embodiment of the present disclosure, since the current static uniform clustering method for hopping beams cannot meet the requirements of dynamic scenarios, therefore, the embodiment of the present disclosure adopts a non-uniform clustering method for hopping beams. Among them, the principle of cluster group division in non-uniform clustering is that the service demand amounts between clusters are close, and it is not required that the number of beams in each cluster group is the same, which can adapt to the requirements of dynamic scenarios. The embodiment of the present disclosure obtains the optimal solution of non-uniform clustering of hopping beams through the whale optimization algorithm, makes the demand amounts between cluster groups close, meets the dynamic resource requirements, and improves the overall throughput of the system.

[0077] Figure 2 Another method for clustering hopping beam satellites in the embodiment of the present disclosure is shown as Figure 2 shown, and this method includes the following steps:

[0078] S201, initialize the number of whales, the positions of whales, and the maximum number of iterations.

[0079] In an embodiment of the present disclosure, the number of whales can be understood as the number of candidate solutions, the positions of whales can be understood as the specific clustering schemes corresponding to the candidate solutions, and the maximum number of iterations can be understood as the preset maximum number of iterations. When the number of iterations reaches this maximum number of iterations, the iteration is stopped, and the current optimal candidate solution is used as the final candidate solution, and the clustering scheme corresponding to this final candidate solution is used as the final clustering scheme.

[0080] S202, calculate the fitness value of each whale individual according to the objective function, and record the optimal whale individual and the position where the optimal whale individual is located.

[0081] In one embodiment of the present disclosure, the objective function is the content shown in the above formula (1), and the value of the objective function is the fitness value. The whale individual corresponding to the minimum value of the objective function can be taken as the optimal whale individual, and the position where the optimal whale individual is located can be taken as the optimal whale position.

[0082] S203. Determine whether the current iteration number is the maximum iteration number. If so, execute S204; if not, execute S205.

[0083] S204. Take the current optimal candidate solution as the final candidate solution, and output the clustering scheme corresponding to the final candidate solution as the final clustering scheme.

[0084] S205. Update the formula parameters.

[0085] In one embodiment of the present disclosure, according to the optimal candidate solution obtained after this iteration, the parameters in the above formulas (2), (3), and (4) can be updated accordingly, which may include but are not limited to: the shrinking encircling coefficient A, the execution probability p, the linearly decreasing weight coefficient a, the random number scaling factor C, and the random number l for controlling the change range of the spiral shape control parameter, etc.

[0086] S206. Determine whether the execution probability is less than the preset probability threshold. If so, execute S207; if not, execute S208.

[0087] S207. Determine whether the absolute value of the shrinking encircling coefficient is greater than the preset shrinking threshold. If so, execute S2071; if not, execute S2072.

[0088] S2071. Update the positions of each whale individual by using a random search method.

[0089] S2072. Update the positions of each whale individual by using a shrinking encircling method.

[0090] S208. Update the positions of each whale individual by using a spiral update method.

[0091] As can be seen from the above, after obtaining the initial number of candidate solutions, based on the preset optimization function, the fitness values of each candidate solution are iteratively calculated, and the preset parameters are updated during the iteration. When the preset iteration number is reached, the iteration is stopped and the target candidate solution is determined, and the clustering scheme corresponding to the candidate solution is used as the final clustering scheme. The present disclosure can meet the dynamic resource requirements and improve the overall throughput of the system.

[0092] In one embodiment of the present disclosure, after the above S208, the hopping beam satellites can be non-uniformly clustered according to the optimal solution obtained by the whale optimization algorithm to complete the resource allocation of the hopping beam satellites and meet the service requirements of the ground cells.

[0093] In one embodiment of the present disclosure, an optimization problem is established with the goal of minimizing the difference in user demand between clusters. The whale optimization algorithm is introduced to solve the optimization problem. Based on the optimal solution obtained by the whale optimization algorithm, non-uniform clustering of the hopping beam satellite is completed, and a non-uniform clustering scheme for the hopping beam satellite is obtained, making the demand between clusters close, enabling the hopping beam satellite to meet dynamic service request changes, and improving the overall system throughput.

[0094] Based on this, Figure 3 FIG. shows a schematic diagram of non-uniform clustering of a hopping beam satellite in an embodiment of the present disclosure, as Figure 3 shown. Each circle represents a beam, numbered from 1 to 49, indicating that there are a total of 49 beams in the system; the part enclosed by the dashed line represents different clusters, each cluster contains multiple beams, and the number of beams between different clusters may not be equal, reflecting the characteristics of non-uniform clustering.

[0095] From Figure 3 it can be seen that the number of beams contained in each cluster is not the same. For example, some clusters may contain 5 beams (such as the beams numbered 1 to 5), and some clusters may contain 7 beams (such as the beams numbered 43 to 49). Since the non-uniform clustering method is a dynamic adjustment based on the actual service demand, the area with a large demand may be divided into a cluster with more beams to provide sufficient resource support; while the area with a small demand may be divided into a cluster with fewer beams to avoid resource waste.

[0096] In one embodiment of the present disclosure, through non-uniform clustering, the system can dynamically adjust the direction and coverage range of the beams according to real-time service demands, achieve flexible scheduling and efficient utilization of resources. At the same time, reasonable cluster division helps to reduce inter-cluster interference because adjacent clusters can be assigned different frequency or time resources, thereby improving the overall performance of the system.

[0097] Based on the same inventive concept, an embodiment of the present disclosure also provides a hopping beam satellite clustering device as described in the following embodiments. Since the principle of solving problems by this device embodiment is similar to that of the above method embodiment, the implementation of this device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be described again.

[0098] Figure 4 FIG. shows a schematic diagram of a hopping beam satellite clustering device in an embodiment of the present disclosure, as Figure 4 shown. The device includes: a candidate solution acquisition module 401, an evaluation index update module 402, and a clustering scheme determination module 403.

[0099] Among them, the candidate solution acquisition module 401 is used to obtain one or more candidate solutions, where each candidate solution corresponds to a clustering scheme; the evaluation index update module 402 is used to iteratively calculate the fitness values of each candidate solution based on a preset optimization function, and update the preset parameters during the iteration, where the preset optimization function is used to minimize the difference in user demand between each cluster group, and the preset parameters are used to optimize the clustering scheme corresponding to each candidate solution; the clustering scheme determination module 403 is used to determine the target candidate solution when the preset iteration number is reached, and use the clustering scheme corresponding to the target candidate solution as the target clustering scheme.

[0100] As can be seen from the above, after obtaining the initial number of candidate solutions, based on the preset optimization function, the fitness values of each candidate solution are iteratively calculated, and the preset parameters are updated during the iteration. When the preset iteration number is reached, the iteration is stopped and the target candidate solution is determined, and the clustering scheme corresponding to this candidate solution is used as the final clustering scheme. The present disclosure can meet dynamic resource requirements and improve the overall throughput of the system.

[0101] In an embodiment of the present disclosure, the above evaluation index update module 402 is further configured to calculate the fitness values of each candidate solution based on the preset optimization function, and obtain the current target candidate solution; determine whether the current iteration number reaches the preset iteration number; if the current iteration number does not reach the preset iteration number, update the preset parameters according to the current target candidate solution; perform the next round of iterative update on the clustering scheme corresponding to each candidate solution according to the updated preset parameters; calculate the fitness values of each candidate solution during the next round of iteration according to the updated candidate solutions.

[0102] In an embodiment of the present disclosure, the above evaluation index update module 402 is further configured to calculate the fitness values of each candidate solution based on the preset optimization function; sort the fitness values of each candidate solution in descending order, and use the candidate solution corresponding to the minimum fitness value as the current target candidate solution.

[0103] In an embodiment of the present disclosure, the preset optimization function can be represented by the above formula (1). By finding the optimal candidate solution that makes the function value (i.e., the fitness value) the minimum, that is, finding a clustering scheme that can make the difference in user demand between each cluster group as small as possible, so as to more effectively utilize resources and improve the overall performance of the system.

[0104] In an embodiment of the present disclosure, the above evaluation index update module 402 is further configured to perform the next round of iterative update on the clustering scheme corresponding to each candidate solution according to the updated preset parameters in combination with the whale optimization algorithm.

[0105] In an embodiment of the present disclosure, the preset parameters at least include: a shrinkage enclosure coefficient and an execution probability.

[0106] In addition, the preset parameters may further include: a linearly decreasing weight coefficient, a random number scaling factor, a random number for controlling the variation range of the spiral shape control parameter, etc. It should be noted that the specific updated parameters can be determined according to the position update formula adopted in the actual situation. The above parameter types only serve as examples, and the embodiments of the present disclosure do not make specific limitations thereto.

[0107] In an embodiment of the present disclosure, the whale optimization algorithm is a meta-heuristic optimization algorithm that searches for the optimal solution in the search space. It mainly explores the search space by simulating the following three behaviors: encircling prey, bubble-net hunting, and random search. Among them, bubble-net hunting may include: a shrinking encirclement and a spiral update. The position update process of encircling prey can be represented by the above formula (2), the position update process of bubble-net hunting can be represented by the above formula (3), and the position update process of spiral update can be represented by the above formula (4).

[0108] In an embodiment of the present disclosure, the above evaluation index update module 402 is further configured to determine whether the execution probability is less than a preset probability threshold; if the execution probability is less than the preset probability threshold, then determine whether the shrinking encirclement coefficient is greater than a preset shrinking threshold; if the shrinking encirclement coefficient is greater than the preset shrinking threshold, then adopt a random search method to perform the next-round iterative update on the clustering scheme corresponding to each candidate solution; if the shrinking encirclement coefficient is less than or equal to the preset shrinking threshold, then adopt a shrinking encirclement method to perform the next-round iterative update on the clustering scheme corresponding to each candidate solution.

[0109] In an embodiment of the present disclosure, the above evaluation index update module 402 is further configured to, if the execution probability is greater than the preset probability threshold, then adopt a spiral update method to perform the next-round iterative update on the clustering scheme corresponding to each candidate solution.

[0110] In an embodiment of the present disclosure, the preset probability threshold can be set to 0.5, and the preset shrinking threshold can be set to 1. When the probability p < 0.5 and the absolute value of the shrinking encirclement coefficient |A| > 1, a random search method is adopted to update the position of the whale individual; when the probability p < 0.5 and the absolute value of the shrinking encirclement coefficient |A| ≤ 1, a shrinking encirclement method is adopted to update the position of the whale individual; when the probability p ≥ 0.5, a spiral update method is adopted to update the position of the whale individual.

[0111] It should be noted that the numerical values of the above preset probability threshold and preset shrinking threshold only serve as examples and can be adjusted according to the actual situation. The embodiments of the present disclosure do not make specific limitations thereto.

[0112] In one embodiment of the present disclosure, an optimization problem is established with the goal of making the user demand between clusters as equal as possible. The whale optimization algorithm is introduced to solve the optimization problem. According to the optimal solution obtained by the whale optimization algorithm, non-uniform clustering of the hopping beam satellite based on the whale optimization algorithm is completed, and a non-uniform clustering scheme of the hopping beam satellite is obtained, so that the demand between clusters is close, enabling the hopping beam satellite to meet dynamic service request changes and improving the overall system throughput.

[0113] Those skilled in the art of the present technology can understand that various aspects of the present disclosure can be implemented as a system, method, or program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "system" here.

[0114] The following refers to Figure 5 to describe the electronic device 500 according to this embodiment of the present disclosure. Figure 5 The displayed electronic device 500 is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0115] As Figure 5 shown, the electronic device 500 is presented in the form of a general computing device. The components of the electronic device 500 may include, but are not limited to: at least one of the above-mentioned processing units 510, at least one of the above-mentioned storage units 520, and a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510).

[0116] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 510, so that the processing unit 510 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 510 may execute the following steps of the above method embodiment: obtaining one or more candidate solutions, where each candidate solution corresponds to a clustering scheme; based on a preset optimization function, iteratively calculating the fitness values of each candidate solution, and updating the preset parameters during the iteration, where the preset optimization function is used to minimize the difference in user demand between each cluster, and the preset parameters are used to optimize the clustering scheme corresponding to each candidate solution; when reaching the preset number of iterations, determining the target candidate solution and taking the clustering scheme corresponding to the target candidate solution as the target clustering scheme.

[0117] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 5201 and / or a cache storage unit 5202, and may further include a read-only storage unit (ROM) 5203.

[0118] The storage unit 520 may also include a program / utilities 5204 having a set (at least one) of program modules 5205. Such program modules 5205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0119] The bus 530 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0120] The electronic device 500 may also communicate with one or more external devices 540 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or may communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through the input / output (I / O) interface 550. Moreover, the electronic device 500 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 560. As shown in the figure, the network adapter 560 communicates with other modules of the electronic device 500 through the bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0121] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by the way of software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0122] Based on the same inventive concept, embodiments of the present disclosure also provide a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, it implements the hopping beam satellite clustering method of any one of the above. Since the principle of solving problems in the embodiment of the computer-readable storage medium is similar to that of the above method embodiment, the implementation of the embodiment of the computer-readable storage medium can refer to the implementation of the above method embodiment, and the repeated parts will not be described again.

[0123] More specific examples of the computer-readable storage medium in the present disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0124] In the present disclosure, the computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, and this readable medium may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0125] Optionally, the program code contained on the computer-readable storage medium may be transmitted by any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0126] In specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, and the programming languages include object-oriented programming languages - such as Java, C++, etc., and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0127] Based on the same inventive concept, embodiments of the present disclosure also provide a computer program product, including: a computer program or instructions, which, when executed by a processor, implement the hopping beam satellite clustering method according to any one of the above method embodiments. Since the principle of solving problems in this computer program product embodiment is similar to that of the above method embodiment, the implementation of this computer program product embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be described again.

[0128] It should be noted that although several modules or units of devices for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0129] In addition, although the steps of the methods in the present disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be executed in that specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0130] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0131] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the appended claims.

Claims

1. A hopping beam satellite clustering method, characterized in that, Including: Obtain one or more candidate solutions, where each candidate solution corresponds to a clustering scheme; Based on a preset optimization function, iteratively calculate the fitness values of each candidate solution, and update the preset parameters during the iteration, where the preset optimization function is used to minimize the difference in user demand between each cluster group, and the preset parameters are used to optimize the clustering scheme corresponding to each candidate solution; When the preset number of iterations is reached, determine the target candidate solution, and use the clustering scheme corresponding to the target candidate solution as the target clustering scheme.

2. The beam-hopping satellite clustering method according to claim 1, wherein Based on a preset optimization function, iteratively calculate the fitness values of each candidate solution, and update the preset parameters during the iteration, including: Based on the preset optimization function, calculate the fitness values of each candidate solution, and obtain the current target candidate solution; Judge whether the current number of iterations has reached the preset number of iterations; If the current number of iterations has not reached the preset number of iterations, update the preset parameters according to the current target candidate solution; According to the updated preset parameters, perform the next round of iterative update on the clustering scheme corresponding to each candidate solution; According to the updated candidate solutions, calculate the fitness values of each candidate solution during the next round of iteration.

3. The hopping beam satellite clustering method according to claim 2, characterized in that Based on the preset optimization function, calculate the fitness values of each candidate solution, and obtain the current target candidate solution, including: Based on the preset optimization function, calculate the fitness values of each candidate solution; Sort the fitness values of each candidate solution in descending order, and use the candidate solution corresponding to the minimum fitness value as the current target candidate solution.

4. The hopping beam satellite clustering method according to claim 2, wherein According to the updated preset parameters, perform the next round of iterative update on the clustering scheme corresponding to each candidate solution, including: According to the updated preset parameters, combined with the whale optimization algorithm, perform the next round of iterative update on the clustering scheme corresponding to each candidate solution.

5. The hopping beam satellite clustering method according to any one of claims 1 to 4, characterized in that The preset parameters at least include: a shrinking encircling coefficient and an execution probability.

6. The hopping beam satellite clustering method according to claim 5, characterized in that, According to the updated preset parameters, combined with the whale optimization algorithm, perform the next round of iterative update on the clustering scheme corresponding to each candidate solution, including: Judge whether the execution probability is less than the preset probability threshold; If the execution probability is less than the preset probability threshold, judge whether the shrinking encircling coefficient is greater than the preset shrinking threshold; If the shrinking encircling coefficient is greater than the preset shrinking threshold, use the random search method to perform the next round of iterative update on the clustering scheme corresponding to each candidate solution; If the shrinking encircling coefficient is less than or equal to the preset shrinking threshold, use the shrinking encircling method to perform the next round of iterative update on the clustering scheme corresponding to each candidate solution.

7. The hopping beam satellite clustering method according to claim 6, wherein The method further includes: If the execution probability is greater than the preset probability threshold, use the spiral update method to perform the next round of iterative update on the clustering scheme corresponding to each candidate solution.

8. A hopping beam satellite clustering device, characterized in that, Including: A candidate solution acquisition module, used to obtain one or more candidate solutions, where each candidate solution corresponds to a clustering scheme; An evaluation index update module, used to iteratively calculate the fitness values of each candidate solution based on a preset optimization function, and update the preset parameters during the iteration, where the preset optimization function is used to minimize the difference in user demand between each cluster group, and the preset parameters are used to optimize the clustering scheme corresponding to each candidate solution; A clustering scheme determination module, configured to determine a target candidate solution when a preset number of iterations is reached, and use the clustering scheme corresponding to the target candidate solution as the target clustering scheme.

9. An electronic device, characterized in that, Comprising: A processor; And A memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the hopping beam satellite clustering method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, implements the hopping beam satellite clustering method according to any one of claims 1 to 7.

11. A computer program product, comprising: A computer program or instruction, characterized in that the computer program or instruction, when executed by a processor, implements the hopping beam satellite clustering method according to any one of claims 1 to 7.

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