Resource optimization method, device and equipment of satellite hopping beam network, medium and product

Abstract

The invention provides a resource optimization method and device for a satellite beam-hopping network, equipment, a medium and a product. The method comprises the following steps: establishing a communication model under the satellite beam-hopping network; acquiring a user communication demand; determining a multi-dimensional resource joint optimization problem according to the user communication demand and the communication model by joint optimization of beam position division, a hopping beam pattern and a beam forming vector; according to the multi-dimensional resource joint optimization problem, sub-problem alternate iterative optimization is carried out, a multi-dimensional resource optimization scheme is generated, the satellite resource utilization efficiency is improved, and the communication service quality of a satellite beam hopping network is improved.

Description

Technical Field

[0001] This application relates to the field of satellite communication technology, and in particular to a resource optimization method, apparatus, equipment, medium and product for satellite beam-hopping networks. Background Technology

[0002] Satellite beam-hopping networks are an important technical solution for current high-dynamic, wide-coverage communication needs, especially in remote areas, oceans, and polar regions where terrestrial networks are difficult to cover, where they have irreplaceable advantages. With the large-scale deployment of low-Earth orbit (LEO) satellite constellations, satellite communication systems need to dynamically adjust resource allocation to adapt to the spatiotemporal changes in user distribution and the volatility of service demands.

[0003] Existing satellite hopping beam network resource allocation technologies mainly revolve around static beam position allocation, hopping beam pattern design under a single index, and beamforming technology that enhances the target user signal by adjusting the precoding vector.

[0004] However, the above technical solutions lack multi-dimensional joint optimization, which reduces the efficiency of overall resource allocation. Summary of the Invention

[0005] This application provides resource optimization methods, apparatus, devices, media, and products for satellite hopping beam networks, which solve the technical problem in the prior art that the resource utilization rate is limited because the wave position allocation, hopping beam pattern design, and beamforming are not incorporated into a unified optimization framework, thereby improving the resource utilization efficiency and communication service quality of satellite hopping beam networks.

[0006] Firstly, a resource optimization method for satellite beam-hopping networks includes:

[0007] Establish a communication model for satellite hopping beam networks;

[0008] Obtain user communication needs;

[0009] Based on user communication requirements and communication models, the joint optimization of beam position allocation, hopping beam pattern and beamforming vector is used to determine the multi-dimensional resource joint optimization problem.

[0010] Based on the multidimensional resource joint optimization problem, iterative optimization is performed to generate a multidimensional resource optimization solution.

[0011] Furthermore, based on the multidimensional resource joint optimization problem, iterative optimization is performed to generate a multidimensional resource optimization scheme, including:

[0012] Based on the multidimensional resource joint optimization problem, the wave position partitioning sub-problem, the skip beam pattern design sub-problem, and the beamforming sub-problem are determined;

[0013] The subproblems of beam position partitioning, beam skipping pattern design, and beamforming are iteratively optimized sequentially to determine the variables of each subproblem.

[0014] Based on the sub-problem variables and the preset variable threshold requirements, the target sub-problem variables are obtained;

[0015] Based on the target sub-problem variables, a multi-dimensional resource optimization scheme is generated.

[0016] Furthermore, the wave position partitioning subproblem, the hopping beam pattern design subproblem, and the beamforming subproblem are iteratively optimized sequentially to determine the subproblem variables, including:

[0017] Based on the K-means clustering algorithm, the wave position partitioning sub-problem is solved to obtain user affiliation;

[0018] Based on the penalty term relaxation and continuous convex approximation algorithm, the subproblem of hopping beam pattern design is solved to obtain the hopping beam pattern;

[0019] Based on the relaxation variables, the beamforming subproblem is solved to obtain the beamforming vector;

[0020] The sub-problem variables are determined based on user affiliation, hopping beam pattern, and beamforming vector.

[0021] Furthermore, based on the sub-problem variables and preset variable threshold requirements, the target sub-problem variables are obtained, including:

[0022] The changes in each variable in the sub-problem variables are compared with the preset variable threshold requirements to obtain the comparison results;

[0023] If the comparison results indicate that the change in any variable among the variables does not meet the preset variable threshold requirement, then the sub-problem variables are iteratively optimized until the change in each variable among the sub-problem variables meets the preset variable threshold requirement, thus obtaining the target sub-problem variable.

[0024] Furthermore, a communication model under a satellite hopping beam network is established, including:

[0025] Acquire service information, user information, and phased array antenna information under the satellite hopping beam network. The user information includes the number of users and their location information.

[0026] Based on service information and user information, user beam segmentation information and hopping beam pattern information are obtained;

[0027] Beamforming information is obtained based on the phased array antenna information;

[0028] A communication model for a satellite hopping beam network is established based on user beam assignment information, hopping beam pattern information, and beamforming information.

[0029] Furthermore, based on user communication requirements and communication models, the joint optimization of beam position allocation, hopping beam pattern, and beamforming vectors determines a multi-dimensional resource joint optimization problem, including:

[0030] Based on the communication model, the user signal-to-noise ratio is obtained;

[0031] Based on Shannon's formula and the user signal-to-noise ratio, the user communication rate is obtained;

[0032] Based on the user's communication needs and the user's communication rate, a communication optimization objective is obtained;

[0033] Based on the user communication rate and the communication optimization objective, the multi-dimensional resource joint optimization problem is determined by jointly optimizing the beam position partitioning, hopping beam pattern and beamforming vector.

[0034] Furthermore, based on service information and user information, user beamwidth allocation information and hopping beam pattern information are obtained, including:

[0035] Based on the service information, determine the ground service area and service period;

[0036] According to the preset division requirements, the ground service area and service cycle are divided respectively to obtain the wave position and time unit information of the target number;

[0037] Based on the target number of wave positions and user information, user wave position division information and user association matrix are obtained;

[0038] Based on the time unit information and the user association matrix, the hopping beam pattern information is obtained.

[0039] Secondly, this application provides a resource optimization device for satellite beam-hopping networks, comprising:

[0040] The communication model building module is used to build communication models under satellite hopping beam networks;

[0041] The communication requirement acquisition module is used to acquire user communication requirements;

[0042] The module for obtaining the multidimensional resource joint optimization problem is used to jointly optimize beam position allocation, hopping beam pattern and beamforming vector according to user communication requirements and communication model to obtain the multidimensional resource joint optimization problem.

[0043] The multi-dimensional resource optimization solution generation module is used to perform iterative optimization of the multi-dimensional resource joint optimization problem and generate a multi-dimensional resource optimization solution.

[0044] Thirdly, embodiments of this application provide a resource optimization device for a satellite beam-hopping network, including: a memory and a processor;

[0045] The memory stores the instructions that the computer executes;

[0046] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0047] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0048] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0049] The resource optimization method, apparatus, equipment, medium, and product for satellite hopping beam networks provided in this application include: establishing a communication model under the satellite hopping beam network; obtaining user communication requirements; jointly optimizing beam position allocation, hopping beam pattern, and beamforming vector based on user communication requirements and the communication model to determine a multi-dimensional resource joint optimization problem; and performing iterative optimization of the problem based on the multi-dimensional resource joint optimization problem to generate a multi-dimensional resource optimization scheme, thereby improving satellite resource utilization efficiency and enhancing the communication service quality of the satellite hopping beam network. Attached Figure Description

[0050] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0051] Figure 1 A flowchart illustrating an embodiment of the resource optimization method for satellite beam-hopping networks provided in this application;

[0052] Figure 2 A flowchart illustrating Embodiment 2 of the resource optimization method for satellite beam-hopping networks provided in this application;

[0053] Figure 3 A flowchart illustrating Embodiment 3 of the resource optimization method for satellite beam-hopping networks provided in this application;

[0054] Figure 4 A schematic diagram of the communication model under the satellite hopping beam network provided in this application;

[0055] Figure 5 A schematic diagram illustrating the first scenario of the communication optimization objective provided in this application;

[0056] Figure 6A schematic diagram illustrating the second optimization scenario for the communication optimization objective provided in this application;

[0057] Figure 7 A schematic diagram of the resource optimization device for the satellite beam-hopping network provided in this application;

[0058] Figure 8 A schematic diagram of the resource optimization device for the satellite beam-hopping network provided in this application.

[0059] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0060] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0061] The specific application scenario of this application is applicable to low Earth orbit (LEO) satellite hopping beam communication networks. Its core architecture includes a network architecture where a multi-beam LEO satellite generates multiple spot beams through a phased array antenna, covering users in ground-based beam positions. Each beam position contains multiple users, who are dynamically assigned to different beam positions based on their geographical location and needs. Communication scenario: The satellite divides its service period into multiple time slots, dynamically switching beam coverage areas using hopping beam technology, and optimizing signal transmission direction using beamforming technology. Resource constraints: The satellite needs to dynamically allocate resources to meet user communication needs while suppressing inter-beam interference, given limited transmit power, number of time slots, and number of beams.

[0062] Based on the above scenarios, it is evident that existing technologies in satellite hopping beam networks suffer from a disconnect between resource allocation and demand due to static beam allocation. Traditional methods allocate beams based on K-means clustering of user geographic locations, but fail to consider the differences in user demand (e.g., video streaming users require high bandwidth, while IoT users require low power consumption). The non-convexity of hopping beam pattern design, particularly the binary constraints (beam-beam position-time slot correlation), results in a highly non-convex optimization problem, making it difficult for existing relaxation methods (such as converting binary variables into continuous variables) to guarantee solution feasibility. Beamforming interference suppression is insufficient, failing to effectively suppress inter-beam interference. Furthermore, multi-dimensional resource optimization is fragmented, with existing schemes optimizing beam allocation, hopping beam patterns, and beamforming separately, lacking a joint optimization framework.

[0063] The core technical concept of this application is to dynamically coordinate beam assignment, hopping beam pattern design, and beamforming through a multi-dimensional resource joint optimization framework, aiming to maximize the minimum user communication demand satisfaction rate and achieve efficient resource reuse and interference suppression in satellite hopping beam networks. This includes: dynamically adjusting beam assignment using an improved K-means algorithm based on user geographic location and demand, avoiding simplistic assumptions about user needs in traditional methods; transforming the binary variables (beam-beam position-time slot correlation) into a continuous optimization problem through penalty term relaxation and Continuous Convex Approximation (SCA) algorithms, and solving the non-convexity problem of hopping beam design by iteratively approximating the binary solution; introducing relaxation variables and SCA techniques to separate the beamforming vector from the signal-to-noise ratio (SINR) constraint, and transforming the non-convex problem into a solvable convex optimization problem through a first-order Taylor expansion approximation of a fractional function; and incorporating beam assignment, hopping beam pattern design, and beamforming into a unified optimization framework, gradually approximating the global suboptimal solution by iteratively solving subproblems.

[0064] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0065] Figure 1 This is a flowchart illustrating an embodiment of the resource optimization method for satellite beam-hopping networks provided in this application. Figure 1 As shown, the method includes:

[0066] S101. Establish a communication model under a satellite hopping beam network.

[0067] This step involves constructing a communication scenario model between a multi-beam low-Earth orbit (LEO) satellite and ground users to determine the system architecture and signal relationships.

[0068] Specifically, satellites use... The phased array antenna of the array element generates K point beams, each covering M ground positions. The user set is denoted as... The geographical location and business demand of each user are used as modeling inputs.

[0069] In addition, the channel between the satellite and the user adopts the LOS (Line-of-Sight) model, and the channel vector is defined as:

[0070]

[0071] in, and These represent the satellite transmitting antenna gain and the satellite user receiving antenna gain, respectively, where c represents the speed of light. Indicates the center frequency. This represents the distance between the satellite and the l-th user.

[0072] By establishing a multi-dimensional integrated communication model, the logical mapping between wave positions, beams, and users was realized, providing a unified data structure for subsequent signal-to-noise ratio calculation and optimization constraints.

[0073] S102. Obtain user communication requirements.

[0074] In this step, the communication requirements of each user are collected through satellite-to-ground backhaul links or user terminal reporting. This includes metrics such as speed, latency, and power consumption levels.

[0075] Specifically, this step introduces multi-dimensional demand parameters, enabling the communication model to achieve a "demand-aware" resource input mechanism. This allows subsequent beam allocation and beam distribution to dynamically match user service characteristics, improving the fairness of resource allocation and system efficiency.

[0076] S103. Based on the communication model, obtain the user signal-to-noise ratio.

[0077] In this step, the signal-to-noise ratio for each user in time slot t can be calculated using the following formula:

[0078]

[0079] Among them, w k For beamforming vectors, This represents noise power.

[0080] This step introduces time slot dimension and beam position scheduling variables, making subsequent beamforming and scheduling decisions more accurate. It solves the technical problem that traditional signal-to-noise ratio models do not consider the dynamic changes in time slots caused by beam switching and cannot reflect the impact of instantaneous interference between different beams.

[0081] S104. Based on Shannon's formula and the user signal-to-noise ratio, the user communication rate is obtained.

[0082] In this step, the user's communication rate in time slot t can be calculated using Shannon's law and the user's signal-to-noise ratio, as shown in the following formula:

[0083]

[0084] Where B represents the system bandwidth. The communication rate of the l-th user within a service cycle is:

[0085]

[0086] Through the above calculation process, the rate can be smoothly averaged over multiple time slots, which is closer to the actual communication experience. At the same time, it provides accurate performance evaluation for multi-dimensional joint optimization. It solves the technical problem that traditional methods only calculate the rate in a single time slot, do not consider the dynamics of hopping beams in the time domain, and are prone to overestimating or underestimating long-term service capabilities.

[0087] S105. Based on user communication needs and user communication rates, obtain communication optimization targets.

[0088] Based on demand information and user communication rates, the optimization objective is determined as maximizing the minimum user communication demand satisfaction rate, defined as:

[0089]

[0090] in, Let l be the average communication rate of user l.

[0091] By defining communication optimization objectives, resource allocation not only pursues maximizing the overall rate but also ensures the minimum service quality for weak users. This solves the problem that existing optimization strategies ignore some edge users and fail to obtain basic communication quality, making the network more robust in multi-user scenarios.

[0092] S106. Based on the communication optimization objective, jointly optimize the beam position division, hopping beam pattern and beamforming vector to determine the multi-dimensional resource joint optimization problem.

[0093] In this step, beamforming, hopping beam pattern, and beamforming vector are jointly optimized to construct a unified multidimensional optimization framework. The optimization problem can be expressed as the following optimization model:

[0094]

[0095] In addition, the above optimization model needs to satisfy the following constraints: Constraint C1 ensures that every user is covered and that each user can only belong to one beam position; Constraint C2 indicates that at any given time, a beam can serve at most one beam position; Constraint C3 indicates that at any given time, a beam position can only be served by one beam; Constraint C4 indicates that at any given time, at most K beams can be lit; Constraint C5 indicates that the sum of the transmit power of each beam of the satellite must not exceed the maximum transmit power; Constraint C6 indicates that the elements in the beam position selection matrix are zero-one variables; Constraint C7 indicates that the elements in the hopping beam pattern are zero-one variables.

[0096] This step enables the coordination between wave positions, beams, and time slots through a unified joint optimization model, achieving true multi-dimensional collaborative allocation, significantly improving the overall network throughput and scheduling flexibility. It solves the technical problem that traditional solutions often optimize the three types of resources independently, leading to local optima and resource waste, and lacking global coordination.

[0097] S107. Based on the multi-dimensional resource joint optimization problem, perform iterative optimization of the problem to generate a multi-dimensional resource optimization scheme.

[0098] Finally, an alternating optimization strategy is employed to solve the joint model. The process involves adjusting beam assignment based on user demand as weight in the beam assignment stage; relaxing the penalty term to allow the binary scheduling variables to become continuous variables, and iteratively approximating the binary solution using the Continuous Convex Approximation (SCA) algorithm in the hopping beam pattern stage; and linearizing the SINR constraint using Taylor expansion under a fixed scheduling matrix, transforming the problem into a beamforming stage, a convex optimization problem solvable by CVX. After each iteration, the three types of variables are updated, and convergence occurs when the change is below a preset threshold. The final output includes the beam assignment scheme, the hopping beam pattern, and the beamforming vector.

[0099] This method achieves controllable solution using the idea of ​​"decomposition-relaxation-approximation-iteration". Each round is a convex optimization subproblem, with fast and stable convergence speed, ensuring that a high-quality feasible solution is obtained within a finite number of steps. It can maximize resource utilization while ensuring service fairness, and solves the problem of solution difficulty caused by non-convexity and convergence instability caused by multi-dimensional coupling in the existing technology.

[0100] This application establishes a complete link from channel modeling to resource allocation, solving problems such as static beam partitioning, difficulty in optimizing hopping beam scheduling, and insufficient beamforming interference suppression in the prior art, and significantly improving the throughput and user demand satisfaction rate of satellite hopping beam communication systems.

[0101] Figure 2 This is a flowchart illustrating a second embodiment of the resource optimization method for satellite beam-hopping networks provided in this application. Figure 2 As shown, based on Example 1, according to the multi-dimensional resource joint optimization problem, iterative optimization is performed to generate a multi-dimensional resource optimization scheme, including:

[0102] S201. Based on the multidimensional resource joint optimization problem, determine the wave position partitioning sub-problem, the skip beam pattern design sub-problem, and the beamforming sub-problem.

[0103] Specifically, in this step, the multidimensional resource joint problem is decomposed into a beam position partitioning subproblem, a hopping beam pattern design subproblem, and a beamforming subproblem, which are solved separately. By giving an initial solution and the optimization variables involved in the other three subproblems, the three subproblems are solved in sequence.

[0104] S202. Iteratively optimize the wave position partitioning subproblem, the skip beam pattern design subproblem, and the beamforming subproblem in sequence to determine the subproblem variables.

[0105] In this step, the wave position sub-problem is first solved to obtain user affiliation. The specific wave position design formula is as follows:

[0106]

[0107] It should be noted that in satellite communications, the ground location of satellite users is usually considered to be known. Therefore, this application determines the waveband allocation based on the distance between satellite users and the demand.

[0108] Specifically, this application proposes an improved K-means-based beamwidth partitioning algorithm, which adjusts user affiliation during resource allocation based on K-means clustering, considering user demand satisfaction rate rather than just geographical distance. The specific steps are as follows: (1) Initialize geographical clustering, using the traditional K-means clustering algorithm, and iterate based on satellite user coordinates to minimize the sum of squared errors of each sample's distance from its cluster center point, thereby realizing beamwidth partitioning. Initial beamwidth centers are generated through weighted calculation based on user demand. (2) Optimize resource allocation, performing hop beam pattern design and beamforming optimization. (3) Dynamic handover decision, evaluating the actual demand satisfaction rate of each user in its assigned beamwidth, prioritizing the handover of users with low demand satisfaction rates, and if a beamwidth exists that makes the current user's demand satisfaction rate better than the original beamwidth, then the user is handed over. (4) Update cluster centers, updating beamwidth centers based on the new beamwidth partitioning when no users need to be handed over or when the maximum number of iterations is reached.

[0109] After solving the wavefront allocation subproblem, the hopping beam pattern design subproblem is solved to obtain the hopping beam pattern. Specifically, based on a given bandwidth allocation scheme, wavefront design scheme, and beamforming scheme, the design scheme for the hopping beam pattern can be obtained through the following formula:

[0110]

[0111] Furthermore, this application proposes a Penalized Relaxation Beam Hopping (BH-PR) scheme, which relaxes the binary variables and then uses a convex optimization algorithm with a penalty factor to obtain a suboptimal solution to the problem.

[0112] Therefore, to simplify the expression, the above problem can be rewritten as the following formula:

[0113]

[0114] It should be noted that C7 is a difficult binary constraint to handle, which is equivalently converted to the following in the embodiments of this application:

[0115]

[0116] Then, C7 can be converted into a continuously optimized variable that is easy to handle between 0 and 1.

[0117] By introducing multipliers By adding penalty constraints to the objective function, we can obtain an optimization problem with penalty terms, where... .

[0118] The specific optimization formula is as follows:

[0119]

[0120] in, Is A penalty term that penalizes the objective function when it is not equal to 0 or 1. Constraints were imposed on C9 when hour Equivalent to .

[0121] Additionally, it should be noted that at the very beginning... It initializes a small value, and by gradually increasing its value, it gradually approaches and strictly satisfies C7, which guarantees convergence to a feasible solution of the original problem.

[0122] At this time, due to Includes Since skipped beam patterns exist in both the numerator and denominator, this problem remains a non-convex optimization problem.

[0123] Therefore, the following formula is defined:

[0124]

[0125] in,

[0126]

[0127]

[0128] At this point, the problem is non-convex. It is a concave function, we use it in Approximating this with a first-order Taylor expansion of the point, we get:

[0129]

[0130] therefore,

[0131]

[0132] You will then receive:

[0133]

[0134] In addition, at point There are ,then There is a lower bound:

[0135]

[0136] Therefore, the original problem is transformed into:

[0137]

[0138] This objective function is a lower bound of the original objective function.

[0139] The outer ring of round N+1 is given We obtain a suboptimal solution through inner loop iteration. To reduce violations of binary constraints, the following formula is used for updating. The formula is as follows:

[0140]

[0141] At this point, the step size update ensures Incrementing in iterations, to a sufficiently large value. It can strictly satisfy the constraint of binary variables and obtain a feasible solution to the original problem.

[0142] Define variables ,when When Q is close to 0, it means that Q can strictly satisfy the binary constraint of the variable.

[0143] Finally, the beamforming problem is solved to obtain the beamforming vector, as shown in the following formula:

[0144]

[0145] Among them, due to Includes Since beamforming vectors exist in both the numerator and denominator, the problem is a highly non-convex optimization problem. In this embodiment, relaxation variables are introduced to separate the beamforming vectors from the numerator and denominator. Continuous convex approximation is used to transform the non-convex constraints into convex constraints. The transformed problem is then solved using MATLAB or CVX.

[0146] Specifically, firstly, we introduce a The equivalent expression is shown in the following formula:

[0147]

[0148] Here, e is an auxiliary variable introduced. To address the non-convex constraint C11, a relaxation variable is further introduced. At this point, the problem This is equivalent to the following formula:

[0149]

[0150] Then, C12 is obtained based on the SINR expression, due to the problem. The equality holds when the optimal solution is obtained, therefore Equivalent to .at this time The nonconvexity of derives from C13, which we rewrite as:

[0151]

[0152] At this point, because the quadratic linear fractional function on the right is The function is convex within the interval, and this application uses it in... The first-order Taylor expansion of the point is approximated by the following formula:

[0153]

[0154] Where n represents the nth SCA iteration, and then C13 is rewritten as:

[0155]

[0156] By replacing C13 with C14, we can obtain:

[0157]

[0158] S203. Determine the sub-problem variables based on user affiliation, hopping beam pattern, and beamforming vector.

[0159] After solving the preceding subproblems, we obtain the user attribution (wave position division), hopping beam pattern, and beamforming vector. These three types of results can be summarized to form the "subproblem variables" set for this iteration, which will serve as the basis for subsequent comparisons and iterations.

[0160] It should be noted that this set includes user-wave position associations obtained from wave position partitioning, time slot-beam-wave position service relationships obtained from hopping beam patterns, and beamforming vectors for each beam obtained from beamforming. At the same time, derived quantities related to the target (such as user rate / satisfaction rate) can be calculated based on this for subsequent comparison.

[0161] S204. Compare the changes of each variable in the sub-problem variables with the preset variable threshold requirements to obtain the comparison results.

[0162] In this step, the changes in the "sub-problem variables" determined in S203 are verified by thresholding. Specifically, for the user affiliation matrix V, it is necessary to check whether the partitioning constraint such as "each user belongs to only one beam position" is satisfied; for the hopping beam pattern Q, it is necessary to check whether the time slot / scheduling constraint such as "each beam serves only one beam position per time slot and each beam position is served by only one beam per time slot" is satisfied; for Q from "binary relaxation", a binary feasibility check is performed (i.e., whether it reaches the near / binary state corresponding to the "preset variable threshold requirement"); for the beamforming vector W, it is necessary to check whether it meets physical constraints such as the upper limit of transmit power; for user rate and satisfaction rate, it is necessary to check whether the threshold requirements related to the optimization objective are met.

[0163] By using a unified thresholding comparison in this step, the complex joint optimization process can be transformed into verifiable and recordable interim results, facilitating project implementation and process control.

[0164] S205. If the comparison results indicate that the change of any variable among the variables does not meet the preset variable threshold requirement, then continue to iteratively optimize the sub-problem variables until the change of each variable in the sub-problem variables meets the preset variable threshold requirement, and obtain the target sub-problem variable.

[0165] In this step, if the comparison result obtained in S205 shows that any item does not meet the preset threshold, the iteration optimization continues. For example, for a hopping beam pattern, under the framework of "binary relaxation + penalty term", the penalty factor is gradually increased, and the fractional term in the rate is approximated by continuous convex approximation (SCA) in combination. The solution is then solved again in the next iteration, so that the solution gradually approaches the binary feasible solution.

[0166] For beamforming, we continue to use the method of introducing relaxation variables and first-order approximation to transform the "non-convex constraint" into a "convex constraint that can be solved by the solver" and update W in the next iteration.

[0167] For user affiliation, the result remains the same as the current round, and the process proceeds to the next round of alternating iterations.

[0168] Continue to iterate through the above process of "solving - determining subproblem variables - threshold comparison" until all variables meet the preset threshold requirements, thereby obtaining the target subproblem variables.

[0169] Through the closed-loop logic of "threshold-continue iteration-re-comparison" in this step, it can be ensured that the final variable group (i.e. target sub-problem variables) meets all requirements such as binaryity, physical constraints, and target-related thresholds, providing qualified input for the subsequent "generation of multi-dimensional resource optimization scheme".

[0170] S206. Generate a multi-dimensional resource optimization scheme based on the target sub-problem variables.

[0171] This step generates a complete multidimensional resource optimization scheme based on the target sub-problem variables.

[0172] Specifically, based on the final user assignment results obtained from the beam position partitioning subproblem, the beam position to which each user belongs is determined; based on the final hop beam scheduling matrix obtained from the hop beam pattern design subproblem, the service beam position corresponding to each beam in each time slot is determined; and based on the final beamforming vector obtained from the beamforming subproblem, the directionality and amplitude allocation of the satellite transmitted signal are determined.

[0173] By comprehensively mapping the above three types of target variables, a multi-dimensional resource optimization scheme under the satellite hopping beam network can be formed. This scheme includes resource allocation results in the spatial domain (wave position), time domain (time slot), and power and direction domain (beamforming), which are used for subsequent communication task scheduling and system deployment. This solves the problem of scattered optimization results and inability to output them in a unified manner in the past, and realizes the mapping transformation from mathematical solution results to engineering executable schemes.

[0174] This embodiment incorporates three sub-problems—beam hopping, beam pattern design, and beamforming—into a unified iterative optimization framework, achieving collaborative allocation and dynamic coordination of multi-dimensional resources. It solves the problems of existing technologies, such as beam hopping not considering user needs, difficulty in optimizing beam hopping scheduling, and insufficient suppression of inter-beam interference. While ensuring user communication fairness and system stability, it significantly improves resource utilization and overall network throughput, achieving high efficiency and convergence in resource allocation within satellite beam hopping networks.

[0175] Figure 3 This is a flowchart illustrating Embodiment 3 of the resource optimization method for satellite beam-hopping networks provided in this application. Figure 3 As shown, based on Example 1, a communication model under a satellite hopping beam network is established, including:

[0176] S301. Obtain service information, user information, and phased array antenna information under the satellite hopping beam network. The user information includes user quantity information and location information.

[0177] In this step, three types of basic data directly related to modeling are first collected: (1) service information, including ground service area and service period T, system bandwidth B, and noise power. Number of concurrent beams K, maximum transmit power P max (2) User information, including the total number of users L and the geographical location of each user; (3) Phased array antenna information, including the array element arrangement. Element spacing d, operating wavelength Transmit antenna gain G s User-side antenna gain G l This information is used to generate array steering vectors and beamforming constraints.

[0178] S302. Based on the service information and user information, obtain the user beam position division information and hopping beam pattern information.

[0179] Specifically, based on service information, the ground service area and service period are determined.

[0180] Then, according to the preset division requirements, the ground service area and service period are divided to obtain the target number of wavelets and time unit information. For example, the ground service area is divided into the target number of wavelets M (spatial cells / wavelet grids) according to preset rules; the service period is divided into the target number of time units (time slots) according to preset rules.

[0181] Based on the target number of wave positions and user information, user wave position segmentation information and user association matrix are obtained. Specifically, user wave position segmentation information is formed, that is, which wave position each user belongs to.

[0182] Suppose the satellite is paired with the first The signal sent by each user is Then it satisfies Define a binary variable. This is used to represent the association between the l-th user and the m-th wavelength. If user l is in the m-th wavelength, then... ,otherwise .

[0183] Then, generate the user association matrix. The specific formula is as follows:

[0184]

[0185] Wherein, at time t, the transmitted signal of the beam to the wave position m is .

[0186] Then, based on the time unit information and the user association matrix, the hopping beam pattern information is obtained. That is, the hopping beam pattern is defined. ,include:

[0187]

[0188] The satellite's transmitted signal in time slot t is:

[0189]

[0190] The signal received by the l-th user is:

[0191]

[0192] in, The mean of the data received by the l-th user is 0, and the variance is . Additive white Gaussian noise.

[0193] S303. Based on the phased array antenna information, obtain beamforming information.

[0194] Among them, the satellite is set to the number The direction of a user can be represented as Then the satellite reaches the 1st The guidance vector for each user can be represented as...

[0195]

[0196] in, For wavelength, Let be the distance between antenna elements in the x and y directions. This represents the Kronecker product.

[0197] Then, define Time of the first The precoding vector of each beam is Define the beamformer as... The following formula exists:

[0198]

[0199] S304. Based on user beam position division information, hopping beam pattern information, and beamforming information, establish a communication model under the satellite hopping beam network.

[0200] In the preceding steps, user beam assignment information, hopping beam pattern information, and beamforming information have been obtained respectively. This step uses these three types of information as input to comprehensively establish a communication model under the satellite hopping beam network.

[0201] Specifically, based on user position allocation information, the position to which each user belongs is determined, thereby establishing a spatial mapping relationship of "user-position"; based on hopping beam pattern information, the correspondence between each beam and position in each time slot is determined, thereby establishing a time-beam-position timing mapping; based on beamforming information, the transmit gain and phase weighting of each beam in different directions are determined, thereby establishing the physical transmission characteristics of the signal.

[0202] Based on this, by combining the above three types of information, a complete description of the satellite's communication process with various ground positions and users at different times and under different beams can be achieved, forming a comprehensive communication model with time-domain jump characteristics, spatial beam characteristics, and signal propagation characteristics. This model can characterize the dynamic communication behavior of the satellite system under multi-beam service, position switching, and time slot scheduling, providing theoretical support and computational foundation for subsequent resource allocation and optimization solutions.

[0203] This application's embodiments, by comprehensively acquiring satellite service information, user distribution information, and phased array antenna parameters, construct a complete communication model encompassing beamwidth partitioning, hopping beam patterns, and beamforming, achieving a unified description of spatial, temporal, and signal domain resources in satellite hopping beam networks. This model accurately reflects the dynamic coverage relationships and signal transmission characteristics of satellite multi-beams in different time slots, providing a precise data foundation and constraints for subsequent multi-dimensional resource joint optimization. This effectively improves the realism and computability of the modeling, solving the technical problem that traditional models cannot simultaneously consider the coupling relationship between spatial scheduling and beamforming.

[0204] Figure 4 This is a schematic diagram of the communication model under the satellite hopping beam network provided in this application. Figure 4 As shown, the "satellite" transmits multiple beams to the ground through a phased array antenna. Each beam covers a region of the ground, which is called a beam position (such as beam position 1, beam position 2, beam position 3, beam position 4, and beam position 5).

[0205] Each wavelength contains several "satellite terminals" (also known as user terminals), which receive downlink signals from the satellite or send uplink signals to the satellite.

[0206] Specifically, the satellite establishes communication with satellite terminals within each band via beams. Due to limited satellite resources, it does not continuously cover all bands simultaneously, but instead provides services to different bands in turn through beam skipping, thereby achieving efficient bandwidth utilization and more flexible service scheduling.

[0207] Figure 5 This is a schematic diagram illustrating the first scenario of the communication optimization objective provided in this application. (See diagram below.) Figure 5 As shown in Example 1, the variation of minimum user demand satisfaction rate with maximum satellite transmission power is demonstrated under different methods.

[0208] The graph contains five curves, each representing a different algorithm or design method. By comparing these curves, the performance of each method under varying power conditions can be clearly observed.

[0209] Specifically, the green curve (marked with a triangle) represents the result of the method proposed in this application. This method performs best at all power levels, with the curve consistently at the top and exhibiting the largest growth slope. This indicates that the combined beamforming, skip beam pattern, and beamforming design strategy proposed in this invention is most effective in improving the user demand satisfaction rate.

[0210] The blue curve (circular marker) represents the result of "demand load hopping beam pattern design - pointing vector beamforming". The overall performance of this method is low, and the curve is almost horizontal, indicating that its performance improvement is limited under different transmit powers and it is difficult to effectively improve the user demand satisfaction rate.

[0211] The black curve (marked with a star) represents "Demand Load Jump Beamformation - SCA Beamforming". The performance of this method is slightly higher than that of the blue curve, but still much lower than that of the method in this application, indicating that the traditional convex optimization (SCA) beamforming strategy has limitations in resource utilization.

[0212] The pink curve (diamond mark) indicates that the "demand load hopping beam pattern design - pointing vector beamforming" has moderate performance. As the power increases, the demand satisfaction rate improves to some extent, but the increase is not as significant as that of the method of this invention.

[0213] The red curve (marked with a plus sign) indicates "the method proposed in this application - inaccurate user location". This curve is lower than the green curve at all power levels, but is significantly better than the three traditional methods, indicating that even when there are errors in the user location information, the method of the present invention still maintains high robustness and superior performance.

[0214] As can be observed from the figure, the method proposed in this invention achieves the highest minimum user demand satisfaction rate under all satellite maximum transmit power conditions, indicating that this method has significant advantages in communication resource allocation efficiency, beam directivity, and the rationality of beam position allocation. When the transmit power gradually increases from 38dBW to 54dBW, the improvement of the traditional method is limited, while the improvement of the method of this invention is significant, proving that it can make fuller use of the increased power resources.

[0215] Figure 6This is a schematic diagram illustrating the second optimization scenario for the communication optimization objective provided in this application. (See diagram below.) Figure 6 As shown in Example 1, the changes in the minimum user demand satisfaction rate with the maximum user demand under several different methods are demonstrated. This reflects the system's performance when facing increasing user business demands and can intuitively demonstrate the resource scheduling capability and robustness of each algorithm.

[0216] Specifically, the green curve (marked with a triangle) represents the method proposed in this application, which maintains the highest minimum demand satisfaction rate across all user demand levels. When user demand is low (range 12), the satisfaction rate reaches approximately 53%, indicating that the system can fully utilize satellite power and beam resources to achieve efficient communication. Even under high demand conditions (user demand of 5), it outperforms other methods, demonstrating the powerful performance of this invention in the joint optimization of beam allocation, hopping beam scheduling, and beamforming.

[0217] Although the red curve (marked with a plus sign) is slightly lower than the green curve, it is still significantly better than other traditional methods. This indicates that the algorithm proposed in this invention maintains good performance even when there are errors in user location information, and has high robustness and practical application value.

[0218] The pink curve (diamond mark) indicates "Demand Load Jump Beamforming Design - SCA Beamforming", which has moderate performance and gradually declines as user demand increases.

[0219] The black curve (marked with a star) indicates "Demand Load Jump Beamform Design - Pointing Vector Beamforming," which performs slightly worse than the pink curve and descents faster.

[0220] The blue curve (circular marker) indicates that "demand-load hopping beamformation design - pointing vector beamforming" has the worst overall performance, with the curve almost touching the horizontal axis, indicating that this method can hardly meet the rate requirements of most users when user demand is high.

[0221] In summary, the method proposed in this invention is significantly superior to traditional fixed hop beamforming and SCA beamforming schemes under all demand levels. When user demand increases, the performance of traditional methods drops sharply, while the performance of the method proposed in this invention drops more gradually, demonstrating stronger resource allocation flexibility and adaptability.

[0222] Figure 7 This is a schematic diagram of the resource optimization device for the satellite beam-hopping network provided in this application. Figure 7 As shown, the resource optimization device 70 for satellite beam hopping networks provided in this embodiment includes:

[0223] Communication model establishment module 701 is used to establish a communication model under a satellite hopping beam network;

[0224] The communication requirement acquisition module 702 is used to acquire user communication requirements;

[0225] The multidimensional resource joint optimization problem is obtained by module 703, which is used to jointly optimize beam position allocation, hopping beam pattern and beamforming vector according to user communication requirements and communication model, thus obtaining the multidimensional resource joint optimization problem.

[0226] The multidimensional resource optimization scheme generation module 704 is used to perform iterative optimization of the multidimensional resource joint optimization problem and generate a multidimensional resource optimization scheme.

[0227] In one possible implementation, the multidimensional resource optimization scheme generation module 704 is further specifically used for:

[0228] Based on the multidimensional resource joint problem, the sub-problems of beam position partitioning, beam skipping pattern design, and beamforming are identified.

[0229] The subproblems of beam position partitioning, beam skipping pattern design, and beamforming are iteratively optimized sequentially to determine the variables of each subproblem.

[0230] Based on the sub-problem variables and the preset variable threshold requirements, the target sub-problem variables are obtained;

[0231] Based on the target sub-problem variables, a multi-dimensional resource optimization scheme is generated.

[0232] Furthermore, the multi-dimensional resource optimization scheme generation module 704 is also specifically used for:

[0233] Based on the K-means clustering algorithm, the wave position partitioning sub-problem is solved to obtain user affiliation;

[0234] Based on the penalty term relaxation and continuous convex approximation algorithm, the subproblem of hopping beam pattern design is solved to obtain the hopping beam pattern;

[0235] Based on the relaxation variables, the beamforming subproblem is solved to obtain the beamforming vector;

[0236] The sub-problem variables are determined based on user affiliation, hopping beam pattern, and beamforming vector.

[0237] Furthermore, the multi-dimensional resource optimization scheme generation module 704 is also specifically used for:

[0238] The changes in each variable in the sub-problem variables are compared with the preset variable threshold requirements to obtain the comparison results;

[0239] If the comparison results indicate that the change in any variable among the variables does not meet the preset variable threshold requirement, then the sub-problem variables are iteratively optimized until the change in each variable among the sub-problem variables meets the preset variable threshold requirement, thus obtaining the target sub-problem variable.

[0240] Furthermore, the communication model establishment module 701 is also specifically used for:

[0241] Acquire service information, user information, and phased array antenna information under the satellite hopping beam network. The user information includes the number of users and their location information.

[0242] Based on service information and user information, user beam segmentation information and hopping beam pattern information are obtained;

[0243] Beamforming information is obtained based on the phased array antenna information;

[0244] A communication model for a satellite hopping beam network is established based on user beam assignment information, hopping beam pattern information, and beamforming information.

[0245] Furthermore, module 703, which addresses the multidimensional resource joint optimization problem, is specifically used for:

[0246] Based on the communication model, the user signal-to-noise ratio is obtained;

[0247] The user communication rate is obtained based on Shannon's formula and the user signal-to-noise ratio;

[0248] Based on user communication needs and user communication rates, the multi-dimensional resource joint optimization problem is determined by jointly optimizing beam position partitioning, hopping beam pattern, and beamforming vector.

[0249] Furthermore, the communication model establishment module 701 is also specifically used for:

[0250] Based on the service information, determine the ground service area and service period;

[0251] According to the preset division requirements, the ground service area and service cycle are divided respectively to obtain the wave position and time unit information of the target number;

[0252] Based on the target number of wave positions and user information, user wave position division information and user association matrix are obtained;

[0253] Based on the time unit information and the user association matrix, the hopping beam pattern information is obtained.

[0254] The device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0255] Figure 8This application provides a schematic diagram of the structure of a resource optimization device for a satellite beam-hopping network, as illustrated in the embodiments provided herein. Figure 8 As shown in this embodiment, the resource optimization device 80 for a satellite beam-hopping network provided in this application includes at least one processor 801 and a memory 802. Optionally, the device 80 further includes a communication component 803. The processor 801, memory 802, and communication component 803 are connected via a bus 804.

[0256] In a specific implementation, at least one processor 801 executes computer execution instructions stored in memory 802, causing at least one processor 801 to perform the above-described method.

[0257] The specific implementation process of processor 801 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0258] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0259] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0260] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0261] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0262] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0263] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0264] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0265] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0266] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0267] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0268] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0269] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0270] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A resource optimization method for satellite beam-hopping networks, characterized in that, include: Establish a communication model for satellite hopping beam networks; Obtain user communication needs; Based on the user communication requirements and the communication model, the multi-dimensional resource joint optimization problem is determined by jointly optimizing the beam position partitioning, hopping beam pattern and beamforming vector. Based on the multidimensional resource joint optimization problem, iterative optimization is performed to generate a multidimensional resource optimization scheme.

2. The resource optimization method according to claim 1, characterized in that, Based on the aforementioned multidimensional resource joint optimization problem, iterative optimization is performed to generate a multidimensional resource optimization scheme, including: Based on the multidimensional resource optimization problem, the wave position partitioning sub-problem, the hopping beam pattern design sub-problem, and the beamforming sub-problem are determined. The sub-problem of wave position partitioning, the sub-problem of skip beam pattern design, and the sub-problem of beamforming are iteratively optimized in sequence to determine the sub-problem variables; Based on the sub-problem variables and the preset variable threshold requirements, the target sub-problem variables are obtained; Based on the target sub-problem variables, a multi-dimensional resource optimization scheme is generated.

3. The resource optimization method according to claim 2, characterized in that, The wave position partitioning subproblem, the hopping beam pattern design subproblem, and the beamforming subproblem are iteratively optimized sequentially to determine the subproblem variables, including: The wave position partitioning sub-problem is solved based on the K-means clustering algorithm to obtain user affiliation; Based on the penalty term relaxation and continuous convex approximation algorithm, the hopping beam pattern design subproblem is solved to obtain the hopping beam pattern; Based on the relaxation variables, the beamforming subproblem is solved to obtain the beamforming vector; The sub-problem variables are determined based on the user affiliation, the hopping beam pattern, and the beamforming vector.

4. The resource optimization method according to claim 2, characterized in that, Based on the sub-problem variables and preset variable threshold requirements, the target sub-problem variables are obtained, including: The change in each variable in the sub-problem variables is compared with the preset variable threshold requirements to obtain the comparison results; If the comparison result indicates that the change of any variable among the variables does not meet the preset variable threshold requirement, then the sub-problem variable is iteratively optimized until the change of each variable among the sub-problem variables meets the preset variable threshold requirement, and the target sub-problem variable is obtained.

5. The resource optimization method according to any one of claims 1 to 4, characterized in that, Establish a communication model for satellite hopping beam networks, including: Acquire service information, user information, and phased array antenna information under a satellite hopping beam network, wherein the user information includes user quantity information and location information; Based on the service information and the user information, user beam segmentation information and hopping beam pattern information are obtained; Beamforming information is obtained based on the phased array antenna information; A communication model for a satellite hopping beam network is established based on the user beam segmentation information, the hopping beam pattern information, and the beamforming information.

6. The resource optimization method according to any one of claims 1 to 4, characterized in that, Based on the user communication requirements and the communication model, the multi-dimensional resource joint optimization problem is determined by jointly optimizing beam position allocation, hopping beam pattern, and beamforming vector, including: Based on the communication model, the user signal-to-noise ratio is obtained; Based on Shannon's formula and the user signal-to-noise ratio, the user communication rate is obtained; Based on the user's communication needs and the user's communication rate, a communication optimization objective is obtained; Based on the aforementioned communication optimization objectives, the multi-dimensional resource joint optimization problem is determined by jointly optimizing beam position partitioning, hopping beam pattern, and beamforming vector.

7. The resource optimization method according to claim 5, characterized in that, Based on the service information and the user information, user beamwidth division information and beam skipping pattern information are obtained, including: Based on the service information, determine the ground service area and service period; According to the preset division requirements, the ground service area and the service cycle are divided respectively to obtain the target number of wave positions and time unit information; Based on the target number of wave positions and the user information, user wave position division information and user association matrix are obtained; Based on the time unit information and the user association matrix, the hopping beam pattern information is obtained.

8. A resource optimization device for satellite beam-hopping networks, characterized in that, include: The communication model building module is used to build communication models under satellite hopping beam networks; The communication requirement acquisition module is used to acquire user communication requirements; The multidimensional resource joint optimization problem module is used to jointly optimize beam position allocation, hopping beam pattern and beamforming vector according to the user communication requirements and the communication model to obtain the multidimensional resource joint optimization problem. The multidimensional resource optimization scheme generation module is used to perform iterative optimization of the problem based on the multidimensional resource joint optimization problem, and generate a multidimensional resource optimization scheme.

9. A resource optimization device for satellite beam-hopping networks, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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