Satellite resource pre-allocation method and system and computer readable medium

Abstract

The invention provides a satellite resource pre-allocation method and system and a computer readable medium, and relates to the field of satellite communication, and the method comprises the steps: building a channel model of a downlink during the transit period of a beam-hopping multi-beam satellite based on downlink information from the beam-hopping multi-beam satellite to a user, setting a model constraint for the channel model, modeling is carried out for optimizing the queue length of a waiting queue of the last time slot in the transit period of the beam-hopping multi-beam satellite, and a queue length optimization model is obtained; simplifying the queue length optimization model to obtain a simplified queue length optimization model; and dividing the simplified queue length optimization model into two independent sub-problems, and solving to obtain an optimal solution of time slot allocation, an optimal solution of power allocation and an optimal solution of frequency band allocation of the beam. The resource scheduling difficulty of the satellite can be reduced, the delay can be reduced, the communication efficiency can be improved, the adverse effect of the highly dynamic performance of the channel environment can be reduced, and the real-time communication requirement can be met.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication, and particularly to a method, a system and a computer-readable medium for preallocating satellite resources. Background Art

[0002] Satellite communication systems have become a widely concerned communication method due to their global coverage, anti-damage performance, and communication cost advantages that are not affected by distance. With the development of low-earth orbit satellite technology, mobile direct satellite communication has attracted much attention due to its wide coverage and high reliability. However, the increase in the number of mobile phone users has significantly increased the complexity of resource scheduling. But in the case of limited satellite resources, how to efficiently schedule to meet the diverse needs of mobile phone users has become a key issue. In addition, the spatio-temporal changes in mobile phone user needs and the limitations of satellite computing power have further exacerbated the scheduling difficulty.

[0003] In the prior art, in a communication system based on ground station scheduling, the control center schedules resources by predicting satellite trajectories and monitoring service dynamics. With the increase in the number of mobile phone users, the scheduling and management of system resources become more complex. And transmitting all user service data to the central server for optimization will cause a large delay and cannot effectively meet real-time communication needs. If an on-board scheduling scheme is adopted and each satellite can make decisions according to local real-time service changes, it can adapt to user needs and channel environment changes in real time. However, due to the high dynamicity of the channel environment, satellites still need to continuously adapt to the changing distribution of mobile phone user needs and channel environment. Among them, the channel is the transmission medium between the satellite and the mobile phone, and the channel environment is the environmental conditions for channel transmission. The channel environment refers to the propagation environment including physical environments such as terrain and buildings, electromagnetic environments, and the influence of rain. Changes in the channel environment may cause signal attenuation, distortion, interference, etc., thereby affecting the quality and reliability of the signal. It may also affect performance indicators such as the bandwidth, bit error rate, and delay of the communication system. The high dynamicity of the channel environment refers to the characteristic that the channel environment changes rapidly within a short period of time, such as the fast moving speed of the satellite.

[0004] Therefore, in the scenario of a large number of users with mobile direct satellite connection, how to reduce the resource scheduling difficulty of mobile direct satellite connection, reduce latency, improve communication efficiency, reduce the adverse effects of the high dynamicity of the channel environment, and meet real-time communication needs. Summary of the Invention

[0005] The present invention provides a method, a system and a computer-readable medium for preallocating satellite resources, which can reduce the resource scheduling difficulty of mobile direct satellite connection, reduce latency, improve communication efficiency, reduce the adverse effects of the high dynamicity of the channel environment, and meet real-time communication needs when facing a large number of users with mobile direct satellite connection.

[0006] To achieve the above object, a first aspect of the present invention provides a method for pre - allocating satellite resources, including:

[0007] First step, for the user service where users communicate using a mobile phone directly connected to a satellite, based on the down - link information from a hopping - beam multi - beam satellite to the users, establish a channel model for the down - link during the transit of the hopping - beam multi - beam satellite. The channel model includes the inter - cell interference suffered by bandwidth allocation, the maximum communication rate provided for users in each cell per time slot, and the queue length of the waiting queue.

[0008] Second step, set model constraints for the channel model. The model constraints include: bandwidth constraint, power constraint, beam allocation fairness constraint between cells, and allocation fairness constraint between users.

[0009] Third step, combined with the model constraints and the priorities of users, model the problem of optimizing the queue length of the waiting queue in the last time slot during the transit of the hopping - beam multi - beam satellite to obtain a queue - length optimization model.

[0010] Fourth step, based on the coarse - grained allocation between each time slot of the cell and historical service information, simplify the queue - length optimization model in advance to obtain a simplified queue - length optimization model.

[0011] Fifth step, split the simplified queue - length optimization model into two independent sub - problems: the time - slot allocation sub - problem of the beam, and the joint resource allocation sub - problem of power and frequency band. Solve the time - slot allocation sub - problem of the beam and the joint resource allocation sub - problem of power and frequency band respectively to obtain the optimal solution of the time - slot allocation of the beam, the optimal solution of power allocation, and the optimal solution of frequency - band allocation. The optimal solution of the time - slot allocation of the beam, the optimal solution of power allocation, and the optimal solution of frequency - band allocation are used to provide services for the user services in each cell during the transit of the hopping - beam multi - beam satellite.

[0012] As a second aspect of the present invention, the present invention provides a system for pre - allocating satellite resources, including:

[0013] A channel - model establishment unit, which is used for the user service where users communicate using a mobile phone directly connected to a satellite, based on the down - link information from a hopping - beam multi - beam satellite to the users, establish a channel model for the down - link during the transit of the hopping - beam multi - beam satellite. The channel model includes the inter - cell interference suffered by bandwidth allocation, the maximum communication rate provided for users in each cell per time slot, and the queue length of the waiting queue.

[0014] A model - constraint construction unit, which is used to set model constraints for the channel model. The model constraints include: bandwidth constraint, power constraint, beam allocation fairness constraint between cells, and allocation fairness constraint between users.

[0015] A queue length optimization model construction unit is used to model the problem of optimizing the queue length of the waiting queue in the last time slot during the hopping beam multi-beam satellite transit by combining the model constraints and the user priorities, so as to obtain a queue length optimization model;

[0016] A queue length optimization model simplification unit is used to simplify the queue length optimization model in advance based on the coarse-grained allocation between each time slot of the cell and the historical traffic information, so as to obtain a simplified queue length optimization model;

[0017] A splitting and solving unit is used to split the simplified queue length optimization model into two independent sub-problems: the time slot allocation sub-problem of the beam, and the joint resource allocation sub-problem of power and frequency band, and solve the time slot allocation sub-problem of the beam and the joint resource allocation sub-problem of power and frequency band respectively, so as to obtain the optimal solution of the time slot allocation of the beam, the optimal solution of the power allocation and the optimal solution of the frequency band allocation; the optimal solution of the time slot allocation of the beam, the optimal solution of the power allocation and the optimal solution of the frequency band allocation are used to provide services for the user traffic of each cell during the hopping beam multi-beam satellite transit.

[0018] As the third aspect of the present invention, the present invention provides a computer-readable medium, which stores at least one program, and when the program is executed in a computer device, the computer device can execute the foregoing method for pre-allocating satellite resources.

[0019] As the fourth aspect of the present invention, the present invention provides a computer device, including:

[0020] A processor and a memory, the memory is used to store executable instructions, and the processor executes the foregoing method for pre-allocating satellite resources corresponding to the executable instructions stored in the memory.

[0021] The advantageous effects of the present invention: Facing the massive users of mobile phone direct connection to satellite, based on the coarse-grained allocation between each time slot of the cell and the historical traffic information of the user traffic demand before the hopping beam multi-beam satellite transit, the queue length optimization model is simplified to obtain a simplified queue length optimization model, which effectively reduces the computing load of the satellite, can reduce the burden of the satellite in real-time resource allocation, reduces the resource scheduling difficulty of mobile phone direct connection to satellite, and reduces the delay. Splitting the simplified queue length optimization model into two independent sub-problems for solution can ensure joint optimization of satellite resources such as beams, frequencies and powers with very low complexity and overhead to minimize the waiting queue length, so as to achieve on-demand response service under the condition of overloaded on-board computing and meet the real-time communication requirements. Brief Description of the Drawings

[0022] The above and other objectives, features, and advantages of the present application will become more apparent by describing its exemplary embodiments in detail with reference to the accompanying drawings. The following described drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 A flowchart of a method for pre - allocating satellite resources according to an embodiment of the present invention is schematically shown;

[0024] Figure 2 A structural diagram of a system for pre - allocating satellite resources according to an embodiment of the present invention is schematically shown;

[0025] Figure 3 A pre - scheduling model diagram corresponding to the method for pre - allocating satellite resources according to an embodiment of the present invention is schematically shown;

[0026] Figure 4 A schematic diagram of cell distribution and its corresponding service demand diagram when pre - allocating satellite resources according to an embodiment of the present invention is schematically shown. Detailed implementation manners

[0027] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. Like reference numerals in the figures denote like or similar parts, and thus their repeated description will be omitted.

[0028] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well - known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0029] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0030] The flowcharts shown in the accompanying drawings are merely illustrative and not necessarily include all the content and operations / steps, nor are they necessarily executed in the order described. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may change according to the actual situation.

[0031] It should be understood that although terms such as first, second, and third may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Thus, the first component discussed below may be referred to as the second component without departing from the teachings of the concept of the present application. As used herein, the term "and / or" includes any one of the associated listed items and all combinations of one or more of them.

[0032] Those skilled in the art can understand that the drawings are only schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily essential for implementing the present application. Therefore, they cannot be used to limit the protection scope of the present application.

[0033] As Figure 1 shown, in combination with the embodiments of the present invention, a method for pre - allocating satellite resources is provided, including:

[0034] First step, for the user services where users communicate with satellites through mobile phone direct connection, based on the down - link information from the hopping - beam multi - beam satellite to the users, establish a channel model for the down - link during the transit of the hopping - beam multi - beam satellite. The channel model includes the inter - cell interference suffered by bandwidth allocation, the maximum communication rate provided for users in each cell in each time slot, and the queue length of the waiting queue.

[0035] Second step, set model constraints for the channel model. The model constraints include: bandwidth constraint, power constraint, fairness constraint for beam allocation between cells, and fairness constraint for allocation between users.

[0036] Third step, in combination with the model constraints and the priorities of users, model the problem of optimizing the queue length of the waiting queue in the last time slot during the transit of the hopping - beam multi - beam satellite to obtain a queue - length optimization model.

[0037] Fourth step, based on the coarse - grained allocation between each time slot of the cell and the historical service information, simplify the queue - length optimization model in advance to obtain a simplified queue - length optimization model.

[0038] Step 5: Split the simplified queue length optimization model into two independent sub-problems: the beam time slot allocation sub-problem and the joint power and frequency band resource allocation sub-problem. Solve the beam time slot allocation sub-problem and the joint power and frequency band resource allocation sub-problem respectively to obtain the optimal solutions for beam time slot allocation, power allocation, and frequency band allocation. The optimal solutions for beam time slot allocation, power allocation, and frequency band allocation are used to serve the user services in each cell during the transit of the hopping beam multi-beam satellite.

[0039] As Figure 2 shown, in combination with the embodiments of the present invention, a satellite resource pre-allocation system is provided, including:

[0040] A channel model establishment unit 21, configured to establish a downlink channel model during the transit of the hopping beam multi-beam satellite for user services in which users communicate directly with the satellite using mobile phones, based on the downlink information from the hopping beam multi-beam satellite to the users. The channel model includes the inter-cell interference suffered by bandwidth allocation, the maximum communication rate provided for users in the cell in each time slot, and the queue length of the waiting queue.

[0041] A model constraint construction unit 22, configured to set model constraints for the channel model. The model constraints include: bandwidth constraint, power constraint, beam allocation fairness constraint between cells, and allocation fairness constraint between users.

[0042] A queue length optimization model construction unit 23, configured to model the problem of optimizing the queue length of the waiting queue in the last time slot during the transit of the hopping beam multi-beam satellite in combination with the model constraints and the priorities of the users, to obtain a queue length optimization model.

[0043] A queue length optimization model simplification unit 24, configured to simplify the queue length optimization model in advance based on the coarse-grained allocation between each time slot of the cell and the historical service information, to obtain a simplified queue length optimization model.

[0044] A splitting and solving unit 25, configured to split the simplified queue length optimization model into two independent sub-problems: the beam time slot allocation sub-problem and the joint power and frequency band resource allocation sub-problem. Solve the beam time slot allocation sub-problem and the joint power and frequency band resource allocation sub-problem respectively to obtain the optimal solutions for beam time slot allocation, power allocation, and frequency band allocation. The optimal solutions for beam time slot allocation, power allocation, and frequency band allocation are used to serve the user services in each cell during the transit of the hopping beam multi-beam satellite.

[0045] Facing the huge number of users in direct satellite communication with mobile phones, based on the coarse-grained allocation between cells in each time slot and the historical service information of the user service requirements before the multi-beam satellite with hopping beams passes by, the queue length optimization model is simplified to obtain a simplified queue length optimization model, which effectively reduces the computing load of the satellite, can relieve the burden of the satellite in real-time resource allocation, reduces the difficulty of resource scheduling in direct satellite communication with mobile phones, and reduces latency. Splitting the simplified queue length optimization model into two independent sub-problems for solution can ensure joint optimization of resources such as the satellite's beams, frequencies, and powers with very low complexity and overhead to minimize the waiting queue length, so as to achieve on-demand response services under the condition of overloaded on-board computing and meet real-time communication requirements. Therefore, it has great superiority in the scenario of massive connections in direct satellite communication with mobile phones, and can greatly meet the user service requirements while reducing the computing load and complexity of the satellite.

[0046] Preferably, the first step specifically includes:

[0047] For the user service of users communicating with satellites directly through mobile phones, establish the downlink information from the multi-beam satellite with hopping beams to the users; the downlink information includes: a multi-beam satellite with hopping beams simultaneously generates N beam beams, the total power of the N beam beams is P tol ,the total bandwidth of the N beam beams is B tol ,during the passing of the multi-beam satellite with hopping beams, it can provide communication services of direct satellite communication with mobile phones for users distributed in N cell cells, the total communication transmission time of the multi-beam satellite with hopping beams is divided into N slot time slots with a time slot length of T slot ; the index set of the cells is represented as C = {1, 2,... i,..., N cell}, representing the 1st, 2nd,..., i,..., N cell th cells; the index set of the number of users in the i-th cell is represented as U i = {1, 2,..., N i}, indicating that the number of users in the i-th cell is 1, 2,..., or N i , and N i represents the number of users in the i-th cell; the index set of the time slots is represented as T = {1, 2,... t..., N slot}, representing the 1st time slot, the 2nd time slot,..., t..., the N slot th time slot;

[0048] Each beam has N b orthogonal subcarriers, and the bandwidth of each orthogonal subcarrier in the t-th time slot is The total bandwidth serving the user services in the ith cell is The signals on all available orthogonal subcarriers for serving users in the i-th cell can be received simultaneously by each user j in the i-th cell. Each orthogonal subcarrier is allocated to a service. The service is used to meet the user's business, and one user corresponds to one user business.

[0049] use Indicates whether user j in the ith cell is served by the bth orthogonal subcarrier in the tth time slot. If so, it is recorded as If not recorded by the service Denote the index set of available orthogonal subcarriers as B i ={1,2,...,b...,N b}, indicating the 1st, 2nd, ..., b, ..., Nth b orthogonal subcarriers;

[0050] By establishing the downlink information of the beam-hopping multi-beam satellite communication system in low-Earth orbit, it is used to achieve the optimal allocation of joint satellite and ground resources.

[0051] For the beam-hopping multi-beam satellite, N beam The total bandwidth of the beam is B tol Divide into N chunk Each beam uses one or more consecutive adjacent frequency band units as the transmission frequency band of the beam. The frequency band scheme of a beam has a maximum of M = 1 + 2 + ... + N. chunk Assuming that one beam serves one cell, the index set of the frequency band scheme of the e-th beam serving the i-th cell is represented as K e i={1,2,...,M}, which means that the frequency band scheme of the e-th beam has 1, 2,..., or M types; in the t-th time slot, the bandwidth allocation scheme of the i-th cell is represented as iF i t ={τ|τ∈K e}, the power of an orthogonal subcarrier b allocated to the i-th cell is recorded as P i t,b , then the bandwidth allocation scheme iF for the i-th cell in the t-th time slot i t The inter-cell interference is:

[0052]

[0053] γ i It represents the multiplicative fading of the channel experienced by the beam of the beam-hopping multi-beam satellite directly irradiating the i-th cell, which obeys the Rice distribution; The bandwidth allocation scheme for the i-th cell is iF i t And the bandwidth allocation scheme for the i'-th cell is iF i ' t The ratio of the overlapping bandwidth, Indicates the gain of the beam of the hopping-beam multi-beam satellite pointing to the i'-th cell in the direction of the i-th cell; G r Indicates the receiving gain of the users in the i-th cell when the beam of the antenna of the hopping-beam multi-beam satellite points to the i-th cell; λ represents the electromagnetic wave wavelength used by the beam of the hopping-beam multi-beam satellite (since there is only one satellite at the same time, the electromagnetic wave wavelength is a known constant value), Represents the distance between user j in the i-th cell and the hopping-beam multi-beam satellite;

[0054] Among them, the unit of interference is dB; when the interference is lower than -10 dB, the interference is small; when the interference is close to or exceeds -10 dB, the interference begins to become significant.

[0055] According to Shannon's theorem, the maximum communication rate provided for user j in the i-th cell in the t-th time slot is obtained

[0056]

[0057] Among them, N0 represents the power spectral density of direct-channel Gaussian white noise, Indicates the gain of the beam of the antenna of the hopping-beam multi-beam satellite pointing to the i-th cell in the direction of the i-th cell, The bandwidth of each orthogonal subcarrier;

[0058] The queue length of the waiting queue of user j in the i-th cell in the t-th time slot Refers to: the queue length of the waiting queue corresponding to the (t - 1)-th time slot Plus the traffic newly entering the waiting queue in the t-th time slot Then subtract the maximum traffic that the hopping-beam multi-beam satellite can provide for the users in the i-th cell in the t-th time slot, but it cannot be less than 0; The queue length of the waiting queue of user j in the i-th cell in the t-th time slot Is expressed as:

[0059]

[0060] Equations (1), (2), and (3) are used together to represent the channel model of the downlink during the transit of the hopping-beam multi-beam satellite.

[0061] The above content specifically included in the first step is also the specific application of the channel model establishment unit 21.

[0062] Preferably, the second step specifically includes:

[0063] By setting an orthogonality constraint for the orthogonal subcarriers among users j, a bandwidth constraint for the i-th cell is formed:

[0064]

[0065] Indicates the situation where the i-th cell is served in the t-th time slot: Indicates that the i-th cell is served in the t-th time slot, Indicates that the i-th cell is not served in the t-th time slot;

[0066] In the t-th time slot, according to the power P of an orthogonal subcarrier b assigned to the i-th cell i t,b , a power constraint set for the i-th cell is obtained:

[0067]

[0068] Among them, Indicates the sum of the powers provided by multiple orthogonal subcarriers for the i-th cell, P i t Indicates the bandwidth allocation scheme iF corresponding to the i-th cell i t The corresponding total power; considering the limited power resources on the hopping beam multi-beam satellite and the problem that using too much power will cause strong interference to other beams, therefore, an upper limit value P of the power for each beam is set beam , such that:

[0069] P i t ≤P beam (6)

[0070] An upper limit value P of the power allocated to all users j of the i-th cell is set max , satisfying:

[0071]

[0072] Among them, Indicates the total power provided by multiple orthogonal subcarriers for the i-th cell, P i t,b Indicates the power of an orthogonal subcarrier b allocated to the i-th cell;

[0073] To ensure the fairness of beam allocation between cells and avoid the situation where cells with low traffic demand are not served during the entire satellite pass, reasonable constraints are needed. Therefore, it is necessary to ensure that each cell receives at least N min service time slots during the entire satellite pass. That is, by ensuring that each cell receives at least N min service time slots during the pass of the hopping beam multi-beam satellite, the fairness constraint of beam allocation between cells is achieved, expressed as:

[0074]

[0075] where, represents the number of time slots serving the i-th cell, represents the service situation of the i-th cell in the t-th time slot, represents that the i-th cell is served in the t-th time slot, represents that the i-th cell is not served in the t-th time slot;

[0076] Meanwhile, to ensure fairness among users j, combined with formula (2), the fairness constraint of allocation among users is set as: the maximum communication rate provided to user j in cell i in the t-th time slot is not less than the minimum communication capacity D allocated to each user j min :

[0077]

[0078] It is expressed using formula (2).

[0079] The above content specifically included in the second step is also the specific application of the model constraint construction unit 22.

[0080] Preferably, the third step specifically includes:

[0081] During the pass of the hopping beam multi-beam satellite, the time slot allocation scheme is expressed as: The subscript is the i-th cell, and the superscript represents the t-th time slot, indicating that the t-th time slot is allocated to the i-th cell; the power allocation scheme is expressed as: The subscript is the i-th cell, and the superscript represents the t-th time slot, indicating the total power allocated to the i-th cell in the t-th time slot; the bandwidth allocation scheme is expressed as: The subscript is the i-th cell, and the superscript represents the t-th time slot, indicating the bandwidth allocation scheme of the i-th cell in the t-th time slot; the allocation scheme of orthogonal subcarriers is expressed as The subscripts successively represent the i-th cell and the number N of users within the i-th cell i, where the superscripts represent the \(t\)-th time slot and the \(b\)-th orthogonal subcarrier in sequence, and it means that the number of users in the \(i\)-th cell during the \(t\)-th time slot is \(N\). i The allocation scheme of the orthogonal subcarriers in the \(i\)-th cell when i ; The power allocation scheme for each user is expressed as The subscript is the \(i\)-th cell, and the superscripts represent the \(t\)-th time slot and the \(b\)-th orthogonal subcarrier in sequence, which means the power of the user corresponding to the \(b\)-th orthogonal subcarrier allocated to the \(i\)-th cell during the \(t\)-th time slot;

[0082] During the hopping beam multi-beam satellite transit, each user has a service request. To meet the requirements for different services (such as satellite phones, Internet access, etc.), users have different priorities, which are determined according to the set rules. For example: user recharge, or vip level, or service emergency (if there is an earthquake in a certain area, the priority of users in that area needs to be higher). According to the preset rules, the corresponding priority \(a\) is set for user \(j\) in the \(i\)-th cell. i,j , \(a\) i,j \(\in A\) i , \(A\) i \(=\{1, 2, \ldots, A\) max}\), and the larger the value of \(A\), the higher the priority; i

[0083] In the real-time downlink, the user service is buffered in the waiting queue. is the queue length of the waiting queue of user \(j\) in the \(i\)-th cell during the \(t\)-th time slot (i.e., time slot \(t\)), and this waiting queue is filled at a rate of , that is represents the traffic volume newly entering the waiting queue in each time slot, and a traffic demand model based on user services is represented through the waiting queue.

[0084] While considering the demand differences of user \(j\), minimize the queue length of the priority-weighted waiting queue of the mobile direct connection hopping beam multi-beam satellite communication system to ensure communication reliability. Combining the user's priority \(a\) i,j , according to the queue length of the waiting queue of user \(j\) in the \(i\)-th cell during the \(t\)-th time slot, obtain the queue length of the waiting queue in the last time slot during the hopping beam multi-beam satellite transit. For the problem of optimizing the queue length of the waiting queue in the last time slot during the hopping beam multi-beam satellite transit, a queue length optimization model is established:

[0085]

[0086]

[0087] Fi t = {τ | τ ∈ K i}.(10c)

[0088] Wherein, P1 represents the abbreviation of problem1 that optimizes the queue length of the waiting queue for the last time slot, and s.t. represents combining equations (4), (5), (6), (7), (8) and (9).

[0089] The above - mentioned content specifically included in the third step is also the specific application of the queue - length optimization model construction unit 23.

[0090] Preferably, the fourth step specifically includes:[[]]

[0091] Considering the spatio - temporal characteristics of user requirements and using the historical service information of user service requirements. Before the hopping - beam multi - beam satellite transit, the ground station estimates the average cell demand by using the collected historical service information (historical average number of users, user traffic volume corresponding to users with the same priority), and performs beam - level scheduling across time slots and cells. During the hopping - beam multi - beam satellite transit, considering real - time user services, fine - grained scheduling is carried out. Thus, on - demand service response is achieved, while making the spatial calculation slightly overloaded and greatly reducing the computational overhead.

[0092] Wherein, for beam - level coarse - grained scheduling, first assume that the power of each beam is evenly distributed among orthogonal sub - carriers, thereby obtaining the coarse - grained allocation problem between time slots. Subsequently, during the hopping - beam multi - beam satellite transit, the priority service queue based on historical service information (historical average number of users, user traffic volume corresponding to users with the same priority) replaces the real - time service queue to simplify the problem, so as to better meet the requirements of different services and reduce the spatial computational overhead.

[0093] For beam - level coarse - grained scheduling, first assume that the power of each beam is evenly distributed among orthogonal sub - carriers, thereby obtaining the coarse - grained allocation problem between time slots; specifically: based on the assumption that the power of each beam is evenly distributed to each orthogonal sub - carrier, approximate the communication capacity of the i - th cell, and express the power of each beam evenly distributed to each orthogonal sub - carrier as:

[0094]

[0095] Substitute equation (13) into equation (1). Therefore, the frequency - band allocation scheme iF of the i - th cell in the t - th time slot i t The interference of can be obtained from equation (14):

[0096]

[0097] According to the superposition of the communication capacity of each cell and the communication capacity of each user: The maximum transmission rate provided for the i-th cell in each time slot is:

[0098]

[0099] The communication capacity approximation of the i-th cell is represented by formula (15), and the coarse-grained allocation problem between each time slot of the cell is obtained;

[0100] Based on the historical average number of users The dynamic approximation of the queue length of the waiting queue of the i-th cell is advanced: assuming that the allocation under one priority does not affect the allocation under other priorities, at the t-th time slot, the queue length of the total waiting queue with the same priority a is denoted as

[0101]

[0102] Using the historical average number of users To approximately calculate the real-time number of users with priority a in the i-th cell, and assuming that the user traffic corresponding to the users with priority a in the i-th cell in each time slot is all Then the user traffic in the waiting queue of the i-th cell at the t-th time slot is converted to:

[0103]

[0104] Among them, before the hopping beam multi-beam satellite passes by, the historical average number of users in the i-th cell is obtained The historical average number of users refers to the average value of the number of users with priority a in multiple time slots in history, which is obtained through;

[0105] The queue length of the waiting queue of the user traffic corresponding to the users with priority a in the i-th cell in each time slot is estimated as:

[0106]

[0107] The dynamic approximation of the queue length of the real-time waiting queue of the i-th cell is represented by formula (18); after approximation, the user traffic corresponding to the higher user priority in the i-th cell will be preferentially transmitted.

[0108] The user index set with priority a in the i-th cell is represented as Then P1 corresponding to formula (10) is equivalent to:

[0109]

[0110] By only considering N beamThe total power of the beams is distributed among cells without considering the distribution among users, and the formula (9) is relaxed to obtain:

[0111]

[0112] where is the maximum transmission rate of the user service corresponding to the user with priority a in the i-th cell, is the number of users with priority a in the i-th cell, D min represents the minimum communication capacity allocated to each user j in the i-th cell by each hopping beam multi-beam satellite in the t-th time slot;

[0113] By approximately calculating the communication capacity of the i-th cell and dynamically approximating the queue length of the real-time waiting queue in the i-th cell, the maximum transmission rate of the user service corresponding to the user with priority a in the i-th cell in formula (12) is expressed as:

[0114]

[0115] By approximately calculating the communication capacity of the i-th cell and dynamically approximating the queue length of the waiting queue in the i-th cell, P corresponding to formula (11) 1a is transformed into a priority weighted utility function in beam-level coarse-grained scheduling:

[0116]

[0117] represents the queue length of the waiting queue corresponding to the user with priority a in the i-th cell in the last time slot;

[0118] Adopt the queue length optimization model simplified by formula (20).

[0119] However, P2 faces the complex challenge of simultaneously optimizing the time domain, frequency domain, and power domain, which is manifested as a MINLP problem. These variables are tightly coupled, so P2 in formula (20) cannot be directly solved by convex optimization tools.

[0120] The above content specifically included in the fourth step is also the specific application of the queue length optimization model simplification unit 24.

[0121] Preferably, the fifth step specifically includes:

[0122] First, utilize the independence, large solution space, and tightly coupled variables of the MINLP problem to decouple X, P, and F in P2. To reduce the complexity of the optimization problem, P2 is divided into two independent sub-problems: the beam time slot allocation sub-problem, which means evenly distributing the beams among cells in each time slot, and the joint power and frequency band resource allocation sub-problem, which means jointly allocating power and frequency band resources among the selected cells in a given time slot. Utilize the rich computing resources of the ground station and adopt the Cross Entropy (CE) resource allocation algorithm and the Quantum Particle Swarm Optimization (QPSO) resource allocation algorithm to solve these two sub-problems.

[0123] Solve the beam time slot allocation sub-problem through the resource allocation algorithm based on cross entropy. The specific implementation of time slot allocation among cells includes:

[0124] Assume that there is an optimal solution X* that minimizes the queue length of the waiting queue for user services. Define the time slot allocation sub-problem as:

[0125]

[0126] In formula (21), n = {1,..., N}, where N is the number of samples and X is the solution space. represents that the i-th cell in the n-th sample cell group is served in the t-th time slot. Use the Cross Entropy (CE) algorithm to solve the time slot allocation sub-problem. Randomly generate candidate solution samples according to the parameterized probability distribution. Evaluate the candidate solutions using the objective function and update the parameters of the sampling distribution to increase the probability of the optimal solution appearing in the next iteration.

[0127] According to the resource allocation algorithm based on cross entropy, the probability density distribution function of the time slot allocation sub-scheme can be defined as:

[0128]

[0129] Among them, P i t ∈[0, 1] represents the probability of . For a given probability P, obtain the probability of any solution f(X n , P), and update P in the next iteration:

[0130]

[0131] Among them, indicates that the n-th solution satisfies the constraint condition: or does not satisfy: that is ) The right side of equation (23) represents the ratio of the number of cells selected in the t-th time slot to the number of solutions that satisfy the constraint conditions in the previous iteration result;

[0132] Update X according to the probability matrix P n value: For each sample n of X n Sort all P i t from large to small, and randomly select N beam cells from the top 1.5N beam cells, and then assign to belong to X n ;

[0133] When P iterates to the convergence value, the optimal solution X* of the time slot allocation is obtained.

[0134] The above content specifically included in the fifth step is also the specific application of the splitting and solving unit 25.

[0135] Preferably, the fifth step specifically includes;

[0136] Based on the optimal solution of the time slot allocation sub-problem of the beam, solve the power and frequency band joint resource allocation sub-problem using the resource allocation algorithm based on quantum particle swarm optimization, and realize the joint resource allocation of power and bandwidth, specifically including:

[0137] Based on the optimal solution X* obtained from the time slot allocation sub-problem of the beam, use the resource allocation algorithm QPSO of quantum particle swarm to perform joint resource allocation of power and frequency band among the selected cells in a given time slot. The resource allocation algorithm QPSO of quantum particle swarm makes good use of the DELTA potential from the perspective of quantum mechanics. QPSO is a meta-heuristic algorithm based on the movement of a swarm of particles randomly distributed in the search space. Each particle tracks its own coordinates and is associated with the best solution (fitness) obtained so far. Another optimal value tracked by the particle swarm optimizer is the overall optimal value and its position obtained by any particle in the particle swarm so far.

[0138] According to the optimal solution X * of the time slot allocation sub-problem of the beam, define the power and bandwidth corresponding to the i-th cell selected in the t-th time slot corresponding to the v-th particle as v ∈ {1, 2,..., N s}, i ∈ {1, 2,..., N beam}, N s is the number of particles, and N beam is the number of cells. Through and respectively represent the bandwidth vector and power vector associated with the v-th particle. Through and represent the bandwidth and power allocation selected by N beam cells;

[0139] Given , substitute F t , P t into equation (20) corresponding to P2, and convert P2 into a joint power and frequency band resource allocation sub-problem:

[0140]

[0141] For equation (24), in the u-th iteration, the historical optimal joint power and frequency band resource allocation scheme corresponding to the v-th particle's individual is expressed as ε ∈ {F, P}; in the u-th iteration, the position vector of the v-th particle's power and frequency band is f ∈ {B, S}. The optimal utility function value in the u-th iteration is expressed as α best v (u), and the historical optimal utility function of each particle is:

[0142]

[0143] Each particle will update its individual solution:

[0144]

[0145] Define the globally historical optimal joint power and frequency band resource allocation scheme as g best,ε (u), ε ∈ {F, P}:

[0146]

[0147] Among them, Then define the attractor of the v-th particle that determines the movement direction in the next iteration process:

[0148]

[0149] Through the Monte Carlo method, the position update of the v-th particle in the (u + 1)-th iteration is as follows:

[0150]

[0151] Among them, r2 is a random factor, satisfying r2 ∼ U(0, 1), is the mean of all individual optimal positions. α is an innovation factor that increases the diversity of new particles;

[0152] Terminate after N iter rounds to obtain the optimal solution P of power allocation* Optimal solution F for sum frequency band allocation * 。

[0153] The above content specifically included in the fifth step is also the specific application of the splitting and solving unit 25.

[0154] The following is the specific application of satellite resource pre - allocation for mobile - to - satellite direct connection.

[0155] Step 1: Establish the downlink of the hopping - beam multi - beam satellite system to achieve optimal allocation of space - to - ground resources.

[0156] For the downlink of the hopping - beam multi - beam satellite system, a low - Earth - orbit satellite simultaneously generates 10 beams with a total power of 1000W. The total bandwidth of each beam is 10MHz, providing mobile - to - satellite direct - connection services for users distributed in 25 cells. The transmission time is divided into 1000 time slots, each slot being 30ms. N i represents the number of users in the i - th cell. The number of users in each cell per time slot is in the range of 1 - 60. When no comparison simulation is performed, the default number is 10 - 20.

[0157] Step 2: Establish a traffic model based on service requirements to achieve efficient and orderly service.

[0158] Under the settings of Step 1, each user has a service request with different priorities. a i,j ∈A i , where A i ={0.0001, 0.0002,..., 1}. The larger the value, the higher the priority. is the length of the queue of user j in cell i at time slot t, and this queue is filled at a rate of . The service demand of each cell is shown in Table 1. The cell distribution schematic and its corresponding service demand are as shown in Figure 4 .

[0159] Table 1 Service demand of each cell

[0160]

[0161] Step 3: Establish a channel model for the downlink to achieve multi - beam joint scheduling.

[0162] It is assumed that there are a total of N i orthogonal sub - carriers, and the bandwidth of each carrier is for transmission to users in the i - th cell. The downlink carrier frequency is 4GHz. Each user can simultaneously receive signals on all available sub - carriers. Each user's service is allowed to use one or more sub - carriers, and each sub - carrier can only be allocated to one service. Binary variable It means that the jth user in the i-th cell is served by the b-th subcarrier in the t-th time slot, otherwise not.

[0163] The total bandwidth of B is 10MHz. tol It is divided into 4 frequency band units on average, and it is assumed that each beam can use one or more continuous frequency band units as the transmission band of this beam. In time slot t, according to the power P of subcarrier b allocated to cell i, i t,b Obtain the bandwidth allocation solution iF for cell i i t Inter-cell interference in G r =40dBi, The maximum communication rate of user j in cell i at time slot t is obtained by Shannon's theorem: N0 is set to -174dbm / Hz. The traffic volume in the waiting queue in time slot t is calculated by adding the traffic volume in the queue at time slot t-1 to the traffic volume in the new queue at time slot t minus the maximum traffic volume that the satellite can provide to the user in this time slot, but it cannot be less than 0.

[0164]

[0165] Step 4: Set system constraints to achieve fair and reasonable allocation of resources.

[0166] (1) Bandwidth constraint: Orthogonality constraints are imposed on carriers between users.

[0167]

[0168] (2) Power constraint: The power allocated to each user is constrained so that the sum of the powers allocated to all users in each cell is less than or equal to the total power P provided by the beam-hopping satellite to illuminate the i-th cell in time slot t. i t,b

[0169]

[0170] Considering the limited power resources on the satellite and the problem that using too much power will cause strong interference to other beams, an upper limit value P is set for the power used by each beam. beam ,Right now

[0171] P i t ≤P beam

[0172] Where P beam =150W. The power allocated to a single user is set to an upper limit value Pmax = 20W,

[0173]

[0174] (3) Fairness constraint: To ensure the fairness of beam allocation between cells and avoid the situation where cells with low traffic demand are not served during the entire satellite pass, reasonable constraints are needed to ensure that each cell receives at least 10 service time slots during the entire satellite pass:

[0175]

[0176] At the same time, to ensure fairness among users, the minimum communication capacity D to be allocated to each user is set min ,

[0177]

[0178] Step Five: Optimize problem modeling to achieve optimization of the waiting queue length.

[0179] While considering the differences in user requirements, minimize the weighted waiting queue length of the system's priority as much as possible to ensure communication reliability. According to obtain which represents the unmet demand capacity of the j-th user during satellite transmission, i.e., the final length of the queue. The optimization problem is modeled as

[0180]

[0181] s.t. (4), (5), (6), (7), (8), (9),

[0182]

[0183] F i t ={τ∣τvK i}.

[0184] Step Six: Model splitting, pre-allocation scheduling problem modeling.

[0185] Pre-allocation scheduling problem modeling. Let the user index set with priority a in cell i be denoted as U i a ={j|a i,j = a}, then P1 is equivalent to:

[0186]

[0187] s.t. (4), (5), (6), (7), (8), (9),

[0188] Since only the allocation between cells is considered now, constraint (9) can be relaxed to make it independent of users. Thus, we have

[0189]

[0190] where is the maximum transmission rate of cell i with priority a, is the number of users in cell i with priority a,

[0191] (1) Cell capacity approximation:

[0192] Assume that the power is evenly distributed to each sub - carrier, then we have Therefore, the frequency band allocation scheme iF of the i - th cell can be obtained i t The interference of... According to the sum of the capacities of each cell and the superposition of each user, that is Find the maximum transmission rate of the i - th cell in each time slot

[0193]

[0194] (2) Queue dynamics approximation: The length of the service waiting queue of cell i with priority a in each time slot can be estimated as

[0195]

[0196] (3) Problem transformation: Through the above steps of cell capacity and queue dynamics approximation, P 1a can be

[0197] transformed into a priority - weighted utility function in coarse - grained scheduling,

[0198]

[0199] s.t. (6), (8), (10b), (10c), (12).

[0200] Step 7: Decompose the pre - allocation scheduling problem to achieve efficient solution.

[0201] First, utilize the independence, large solution space and tightly - coupled variables of the MINLP problem to decouple X, P, and F in P2. To reduce the complexity of the optimization problem, P2 is divided into two independent sub - problems: hopping time - slot allocation and joint optimization of power and frequency band. Utilize the rich computing resources of the ground station and adopt the CE and QPSO algorithms to solve these two sub - problems.

[0202] Step 8: Resource allocation algorithm based on cross - entropy to achieve time - slot allocation between cells.

[0203] Suppose there is an optimal solution X* that minimizes the waiting queue length of user service requirements. Define the time slot allocation sub-problem as

[0204]

[0205] Use the CE algorithm to solve the time slot allocation sub-problem. Randomly generate candidate solution samples according to a parameterized probability distribution. Evaluate the candidate solutions using the objective function and update the parameters of the sampling distribution, thereby increasing the probability of the optimal solution appearing in the next iteration. The probability density distribution function of the time slot allocation scheme can be defined as For a given probability p, the probability of any solution f(X n , p) can be obtained. Update p in the next iteration. When p iterates to the convergence value, X* is the optimal solution. Table 2 shows the number of time slots allocated to each cell by the cross-entropy algorithm.

[0206] Table 2 Number of time slots allocated to each cell

[0207]

[0208]

[0209] Step Nine: Based on the resource allocation algorithm of quantum particle swarm, realize the joint resource allocation of power and bandwidth.

[0210] Based on the optimal solution X* obtained from the time slot allocation, this step uses QPSO to perform joint resource allocation of power and frequency band among the selected cells in the given time slot. P2 can be transformed into a sub-problem

[0211]

[0212] Each particle will update its individual solution, and the entire algorithm terminates after 1000 iterations. Use F* and P* as the optimal allocation schemes of bandwidth and power in the pre-scheduling. Table 3 shows the results of the allocated bandwidth of the cells served in a single time slot.

[0213] Table 3 Allocation bandwidth of the cells served in a single time slot

[0214]

[0215] The pre-scheduling model corresponding to the pre-allocation is as Figure 3 shown.

[0216] Combined with the embodiments of the present invention, a computer-readable medium is provided. The computer-readable medium stores at least one program, and when the program is executed in a computer device, the computer device can execute any one of the pre-allocation methods of the satellite resources.

[0217] In combination with the embodiments of the present invention, a computer device is provided, including:

[0218] A processor and a memory, the memory is used to store executable instructions, and the processor executes any one of the pre-allocation methods of the satellite resources corresponding to the executable instructions stored in the memory.

[0219] The beneficial technical effects obtained by the embodiments of the present invention are as follows:

[0220] Considering the spatio-temporal characteristics of user requirements and using the historical service information of user service requirements. Before the hopping beam multi-beam satellite transits, the ground station estimates the average cell demand by using the collected historical service information (historical average number of users, user traffic volume corresponding to users with the same priority), and performs beam-level scheduling across time slots and cells. Using ground scheduling can reduce the burden on the satellite in real-time resource allocation. The control center performs traffic prediction and pre-scheduling.

[0221] During the hopping beam multi-beam satellite transit, for user services, the satellite performs fine-grained scheduling of real-time service scheduling: it can perform efficient allocation within a reduced action space, so as to achieve on-demand response of services. At the same time, the space calculation is slightly overloaded, greatly reducing the satellite's computing load. It has great superiority in the massive connection scenario of mobile phone direct connection to satellite, and can greatly meet the user service requirements while reducing the satellite's computing load and complexity.

[0222] It can ensure joint optimization of satellite resources such as beams, frequencies, and powers with very low complexity and overhead to minimize the waiting queue length, so as to achieve on-demand response services in the case of overloaded on-board computing.

[0223] 1. Compared with the traditional centralized scheduling (a unified scheduling by a single scheduling center) resource allocation method, the embodiments of the present invention use historical service information for resource pre-allocation, effectively reducing the satellite's computing load and realizing on-demand response services. Through resource pre-allocation, it is possible to handle load fluctuations more smoothly, avoid resource shortages during peak periods or emergencies, and thus effectively reduce the length of the waiting queue.

[0224] 2. Compared with the traditional resource allocation method, in the embodiments of the present invention, through user service quality guarantee, it is possible to effectively match the priorities of users and services to ensure the utility function. At the same time, this solution reserves resources for different types of services according to different priorities to ensure that critical services or high-priority services can still obtain sufficient resource support when resources are scarce. It not only ensures a certain degree of fairness but also maximally meets the diverse service needs of users.

[0225] 3. Compared with traditional resource allocation methods, in the embodiments of the present invention, through various joint allocations of resources, it can be independent of pre-scheduling and real-time scheduling, and different optimization algorithms are used to solve the problem, transforming the problem into an iterative solution of an integer linear programming problem and a product optimization problem. Among them, the cross-entropy and quantum particle swarm algorithms are adopted to achieve the optimal allocation of time slots in each cell, as well as the bandwidth and power allocation of each time slot with low complexity.

[0226] The above specific description further details the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above is only a specific embodiment of the present invention and is not used to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention. Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described here can be implemented by software or by a combination of software and necessary hardware.

[0227] According to the technical solution of the embodiments of the present invention, it can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.) or on the network, including several instructions to enable a computing device (which can be a personal computer, server, or network device, etc.) to execute the above method according to the embodiments of the present invention. The software product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. 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 can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or component. The program code contained on the readable storage medium can be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0228] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's 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 can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0229] The above computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to implement the following functions: after receiving a satellite instruction, the satellite confirms the structure of the satellite data frame and the structure mapping rule driving function; during satellite mission scheduling, according to the structure of the data frame and the structure mapping rule driving function, the satellite converts the operation data into a satellite data frame; the satellite data frame is sent to the measurement, operation and control service platform; the measurement, operation and control service platform parses the satellite data frame and sends the parsed data to the test platform.

[0230] Those skilled in the art can understand that the above-mentioned modules can be distributed in the device according to the description of the embodiments, or can be correspondingly changed and distributed in one or more devices that are different from the present embodiment. The modules of the above embodiments can be combined into one module, or can be further split into multiple sub-modules.

[0231] 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 can be implemented by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0232] The above specifically shows and describes the exemplary embodiments of the present invention. It should be understood that the present invention is not limited to the detailed structure, setting method or implementation method described herein; on the contrary, the present invention is intended to cover various modifications and equivalent settings included in the spirit and scope of the appended claims.

Claims

1. A method for pre - allocating satellite resources, characterized in that, Including: First step, for the user service where users communicate using a mobile phone directly connected to a satellite, based on the downlink information from a hopping beam multi-beam satellite to the users, establish a channel model for the downlink during the transit of the hopping beam multi-beam satellite. The channel model includes the inter-cell interference suffered by bandwidth allocation, the maximum communication rate provided for users in each cell in each time slot, and the queue length of the waiting queue. Second step, set model constraints for the channel model. The model constraints include: bandwidth constraint, power constraint, beam allocation fairness constraint between cells, and allocation fairness constraint between users. Third step, in combination with the model constraints and the priorities of the users, model the problem of optimizing the queue length of the waiting queue in the last time slot during the transit of the hopping beam multi-beam satellite to obtain a queue length optimization model. Fourth step, based on the coarse-grained allocation between each time slot of the cell and the historical service information, simplify the queue length optimization model in advance to obtain a simplified queue length optimization model. Fifth step, divide the simplified queue length optimization model into two independent sub-problems: the time slot allocation sub-problem of the beam, and the joint resource allocation sub-problem of power and frequency band. Solve the time slot allocation sub-problem of the beam and the joint resource allocation sub-problem of power and frequency band respectively to obtain the optimal solution of the time slot allocation of the beam, the optimal solution of the power allocation, and the optimal solution of the frequency band allocation. The optimal solution of the time slot allocation of the beam, the optimal solution of the power allocation, and the optimal solution of the frequency band allocation are used to serve the user services of each cell during the transit of the hopping beam multi-beam satellite.

2. The pre-allocation method of satellite resources according to claim 1, wherein The first step specifically includes: For the user service where users communicate using a mobile phone directly connected to a satellite, establish the downlink information from a hopping beam multi-beam satellite to the users. The downlink information includes: a hopping-beam multi-beam satellite simultaneously generates N beam beams, the total power of the N beam beams is P tol and the total bandwidth of the N beam beams is B tol . During the transit of the hopping-beam multi-beam satellite, it can provide mobile direct satellite communication services for users distributed in N cell cells. The total communication transmission time of the hopping-beam multi-beam satellite is divided into N slot time slots with a time slot length of T slot . The index set of the cells is represented as C = {1, 2,... i,..., N cell}; the index set of the number of users in the i-th cell is represented as U i = {1, 2,..., N i}, indicating that the number of users in the i-th cell is 1, 2,..., or N i . Let N i represent the number of users in the i-th cell; the index set of the time slots is represented as T = {1, 2,..., t..., N slot}; Each beam has N b orthogonal subcarriers. In the t-th time slot, the bandwidth of each orthogonal subcarrier is The total bandwidth for serving the user traffic of the i-th cell is For the signals on all available orthogonal subcarriers serving the users of the i-th cell to be simultaneously received by each user j in the i-th cell, each orthogonal subcarrier is correspondingly assigned to a service, where the service is for satisfying the user traffic, and one user corresponds to one user traffic; Adopt Indicate whether user j in the i-th cell is served by the b-th orthogonal subcarrier in the t-th time slot. If served, it is denoted as If not served, it is denoted as Represent the index set of available orthogonal subcarriers as B i ={1, 2,..., b,..., N b} indicating the 1st, 2nd,..., b-th,..., N-th b orthogonal subcarriers; For the beam-hopping multi-beam satellite, N beam The total bandwidth of the beam is B tol Divide into N chunk Each beam uses one or more consecutive adjacent frequency band units as the transmission frequency band of the beam. The frequency band scheme of a beam has a maximum of M = 1 + 2 + ... + N. chunk Assuming that one beam serves one cell, the index set of the frequency band scheme of the e-th beam serving the i-th cell is represented as K e i={1,2,...,M}, which means that the frequency band scheme of the e-th beam has 1, 2,..., or M types; in the t-th time slot, the bandwidth allocation scheme of the i-th cell is represented as iF i t ={τ|τ∈K e }, the power of an orthogonal subcarrier b allocated to the i-th cell is recorded as P i t,b , then in the tth time slot, the bandwidth allocation scheme for the i-th cell iF i t The inter-cell interference is: where γ i represents the channel multiplicative fading experienced when the beam of the hopping beam multi-beam satellite directly hits the i-th cell, and follows the Rice distribution; is the bandwidth allocation scheme iF for the i-th cell i t and the ratio of the overlapping bandwidth with the bandwidth allocation scheme of the i'-th cell, represents the gain of the beam of the hopping beam multi-beam satellite pointing to the i'-th cell in the direction of the i-th cell; G r represents the receiving gain of the users in the i-th cell when the beam of the antenna of the hopping beam multi-beam satellite points to the i-th cell; λ represents the electromagnetic wave wavelength used by the beam of the hopping beam multi-beam satellite, represents the distance between user j in the i-th cell and the hopping beam multi-beam satellite; According to Shannon's theorem, the maximum communication rate provided for user j in the i-th cell at the t-th time slot is obtained. where N0 represents the power spectral density of the direct channel Gaussian white noise, represents the gain of the beam of the hopping beam multi-beam satellite pointing to the i-th cell in the direction of the i-th cell, the bandwidth of each orthogonal sub-carrier; The queue length of the waiting queue of user j in the i-th cell in the t-th time slot Refers to: the queue length of the waiting queue corresponding to the (t - 1)-th time slot Plus the traffic volume newly entering the waiting queue in the t-th time slot And then subtract the maximum traffic volume that the hopping beam multi-beam satellite can provide for the users in the i-th cell in the t-th time slot; Denote the queue length of the waiting queue of user j in the i-th cell in the t-th time slot As: Use Equation (1), Equation (2), and Equation (3) together to represent the channel model of the downlink during the transit of the hopping beam multi-beam satellite.

3. The pre-allocation method of satellite resources according to claim 2, characterized in that The second step specifically includes: By setting the orthogonality constraint of the orthogonal subcarriers between users j, form the bandwidth constraint for the i-th cell: Indicates the situation where the \(i\)-th cell is served in the \(t\)-th time slot: Indicates that the \(i\)-th cell is served in the \(t\)-th time slot, Indicates that the \(i\)-th cell is not served in the \(t\)-th time slot; At the t-th time slot, based on the power P of an orthogonal sub-carrier b allocated to the i-th cell i t,b , the power constraint set for the i-th cell is obtained: Among them, represents the sum of the powers provided by multiple orthogonal subcarriers for the i-th cell, P i t represents the bandwidth allocation scheme iF corresponding to the i-th cell i t the corresponding total power; set an upper limit value P for the power of each beam beam , such that: P i t ≤P beam (6) The upper limit value P of the power allocated to all users j in the i-th cell max , satisfying: Among them, represents the total power provided by multiple orthogonal subcarriers for the i-th cell, P i t,b represents the power of an orthogonal subcarrier b allocated to the i-th cell; By ensuring that each cell receives at least N service time slots during the transit of a hopping beam multi-beam satellite, fairness constraints on beam allocation between cells are achieved, expressed as: min service time slots, fairness constraints on beam allocation between cells are achieved, expressed as: Among them, represents the number of time slots serving the i-th cell, represents the serving situation of the i-th cell in the t-th time slot, means that the i-th cell is served in the t-th time slot, means that the i-th cell is not served in the t-th time slot; Combined with formula (2), the fairness constraint for user allocation is set as: the maximum communication rate provided to user j in the i-th cell at the t-th time slot is not less than the minimum communication capacity D allocated to each user j min :

4. The pre-allocation method of satellite resources according to claim 3, characterized in that The third step includes: During the transit of the hopping beam multi-beam satellite, the allocation scheme of time slots is expressed as: It means that the t-th time slot is allocated to the i-th cell; the allocation scheme of power is expressed as: It means the total power allocated to the i-th cell in the t-th time slot; the bandwidth allocation scheme is expressed as: It means the bandwidth allocation scheme of the i-th cell in the t-th time slot; the allocation scheme of orthogonal subcarriers is expressed as It means that when the number of users in the i-th cell in the t-th time slot is N i the allocation scheme of orthogonal subcarriers of the i-th cell. The power allocation scheme for each user is represented as It refers to the power of the user corresponding to the b-th orthogonal subcarrier allocated to the i-th cell in the t-th time slot; Set the corresponding priority level a for user j in the i-th cell according to a preset rule i,j , a i,j ∈A i , A i ={1,2,..., A max}, where the larger the value of A i , the higher the priority level; Combined with the user's priority a i,j , according to the queue length of the waiting queue of user j in the i-th cell in the t-th time slot Obtain the queue length of the waiting queue in the last time slot during the hopping beam multi-beam satellite transit Model the problem of optimizing the queue length of the waiting queue in the last time slot during the hopping beam multi-beam satellite transit to obtain a queue length optimization model: s.t. (4), (5), (6), (7), (8), (9), F i t = {τ | τ ∈ K i}. (10c) where P1 represents the abbreviation of problem1 for optimizing the queue length of the waiting queue in the last time slot, and s.t. means in combination with Equation (4), Equation (5), Equation (6), Equation (7), Equation (8), and Equation (9).

5. The pre-allocation method of satellite resources according to claim 1, wherein The fourth step specifically includes: Based on the assumption of evenly distributing the power of each beam to each orthogonal subcarrier, approximate the communication capacity of the i-th cell, and represent the evenly distributing the power of each beam to each orthogonal subcarrier as: Substitute equation (13) into equation (1). Therefore, the frequency band allocation scheme iF of the i-th cell in the t-th time slot i t The interference can be obtained from equation (14): According to the superposition of the communication capacity of each cell and the communication capacity of each user: The maximum transmission rate provided for the i-th cell in each time slot is: Represent the approximation of the communication capacity of the i-th cell through Equation (15) to obtain the coarse-grained allocation problem between each time slot of the cell. Based on the historical average number of users Dynamic approximation of the queue length of the waiting queue for the i-th cell in advance: Assuming that the allocation under one priority does not affect the allocation under other priorities, at the t-th time slot, the queue length of the total waiting queue with the same priority a is denoted as Use the historical average number of users to approximately calculate the number of real-time users with priority a in the i-th cell, and assume that the user traffic corresponding to users with priority a in the i-th cell in each time slot is all Then convert the user traffic in the waiting queue of the i-th cell in the t-th time slot to: Among them, before the hopping beam multi-beam satellite passes by, obtain the historical average number of users in the i-th cell The historical average number of users refers to the average value of the number of users with priority a in multiple time slots in history, which is obtained through; Estimate the queue length of the waiting queue of the user service corresponding to the user with priority a in the i-th cell in each time slot as: The dynamic approximation of the queue length of the real-time waiting queue for the i-th cell is represented by formula (18); Let the user index set with the priority of the \(i\)-th cell being \(a\) be represented as \(U\) i a =\(\{j|a\) i,j =a\}\), then \(P1\) corresponding to formula \((10)\) is equivalent to: s.t. (4), (5), (6), (7), (8), (9), By only considering the total power of N beam beams for distribution among cells without considering the distribution among users, the formula (9) is relaxed, and after relaxation, the formula (9) becomes: Among them, is the maximum transmission rate of the user service corresponding to the user with priority a in the i-th cell, is the number of users with priority a in the i-th cell, D min represents the minimum communication capacity allocated by each hopping beam multi-beam satellite to each user j in the i-th cell at the t-th time slot; By approximately calculating the communication capacity of the \(i\)-th cell and dynamically approximating the queue length of the real-time waiting queue of the \(i\)-th cell, the maximum transmission rate of the user service corresponding to the user with priority \(a\) in the \(i\)-th cell of formula (12) is expressed as: By approximately calculating the communication capacity of the \(i\)-th cell and dynamically approximating the queue length of the waiting queue of the \(i\)-th cell, the \(P\) corresponding to formula (11) 1a is converted into a priority weighted utility function in beam-level coarse-grained scheduling: Denote the queue length of the waiting queue corresponding to the user with priority a in the i-th cell at the last time slot; Use Equation (20) for the simplified queue length optimization model.

6. The pre-allocation method of satellite resources according to claim 1, wherein The fifth step specifically includes: Solve the time slot allocation sub - problem of the beam through a cross - entropy - based resource allocation algorithm, specifically including: Suppose there is an optimal solution X* that minimizes the queue length of the waiting queue of user services. Define the time slot allocation sub - problem as: In the formula (21), N is the number of samples, and X is the solution space, represents that the i-th cell in the n-th sample sub-block is served at the t-th time slot; According to the cross - entropy - based resource allocation algorithm, the probability density distribution function of the time slot allocation sub - solution can be defined as: Among them, P i t ∈ [0, 1] represents probability; for a given probability P, the probability of obtaining any solution f(X n , P) is used to update P in the next iteration: Among them, indicates that the nth solution satisfies the constraint condition: or does not satisfy: that is ), the right side of formula (23) represents the ratio of the number of cells selected in the tth time slot to the number of solutions that satisfy the constraint condition in the previous iteration result; Update X according to the probability matrix P n value: For each sample n of X n sort all P i t from large to small, and randomly select N beam cells from the top 1.5N beam cells, and then assign to belong to X n ; When P iterates to the convergence value, the optimal solution X of time slot allocation is obtained * .

7. The pre-allocation method of satellite resources according to claim 1, characterized in that, The fifth step specifically includes; According to the optimal solution of the time slot allocation sub - problem of the beam, solve the power - rate and frequency - band joint resource allocation sub - problem based on the solution of the quantum particle swarm - based resource allocation algorithm, specifically including: According to the optimal solution X of the sub-problem of beam time slot allocation * , the power and bandwidth corresponding to the i-th cell selected in the t-th time slot for the v-th particle are respectively defined as N s is the number of particles, and N beam is the number of cells; through and respectively represent the bandwidth vector and power vector related to the v-th particle; through and represent the bandwidth and power allocations selected by the N beam cells; Given , bring F t , P t into the corresponding P2 in formula (20), and transform P2 into a joint power and frequency band resource allocation sub-problem: For the arithmetic expression (24), in the $u$-th iteration, the historical optimal power and frequency band joint resource allocation scheme corresponding to the $v$-th particle is expressed as In the $u$-th iteration, the position vector of power and frequency band of the $v$-th particle is The optimal utility function value in the $n$-th iteration is expressed as $\alpha$ best v (u), and the historical optimal utility function of each particle is: α best v (u + 1)= min{α best v (u), α best v (u + 1)} (25) Each particle updates its individual solution: Define the scheme of joint resource allocation of power and frequency band for the globally historical optimal as g best,ε (u), ε ∈ {F, P}: Among them, Then define the attractor of the v-th particle that determines the movement direction in the next iteration process: Through the Monte Carlo method, the position update of the v - th particle in the (u + 1) - th iteration is as follows: where r2 is a random factor satisfying r2 ~ U(0, 1), is the mean of all individual optimal positions, and α is an innovation factor that increases the diversity of new particles; Terminate after iteration N iter to obtain the optimal solution P for power allocation * and the optimal solution F for frequency band allocation * .

8. A pre-allocation system for satellite resources, characterized in that, Including: A channel model establishment unit, which is used to establish a channel model of the downlink during the transit of the hopping - beam multi - beam satellite for user services where users communicate with satellites through mobile - phone direct connection. Based on the downlink information from the hopping - beam multi - beam satellite to the user, the channel model includes the inter - cell interference suffered by bandwidth allocation, the maximum communication rate provided for users in the cell in each time slot, and the queue length of the waiting queue; A model constraint construction unit, which is used to set model constraints for the channel model. The model constraints include: bandwidth constraint, power constraint, beam allocation fairness constraint between cells, and allocation fairness constraint between users; A queue - length optimization model construction unit, which is used to combine the model constraints and the priorities of users to model the problem of optimizing the queue length of the waiting queue in the last time slot during the transit of the hopping - beam multi - beam satellite, and obtain a queue - length optimization model; A queue - length optimization model simplification unit, which is used to simplify the queue - length optimization model in advance based on the coarse - grained allocation between time slots in each cell and historical service information, and obtain a simplified queue - length optimization model; A splitting and solving unit, which is used to split the simplified queue - length optimization model into two independent sub - problems: the time slot allocation sub - problem of the beam and the power - frequency - band joint resource allocation sub - problem, and solve the time slot allocation sub - problem of the beam and the power - frequency - band joint resource allocation sub - problem respectively to obtain the optimal solution of the time slot allocation of the beam, the optimal solution of power allocation, and the optimal solution of frequency - band allocation. The optimal solution of the time slot allocation of the beam, the optimal solution of power allocation, and the optimal solution of frequency - band allocation are used to provide services for user services in each cell during the transit of the hopping - beam multi - beam satellite.

9. A computer-readable medium, characterized in that, The computer - readable medium stores at least one program, and when the program is executed in a computer device, the computer device can execute the pre - allocation method of satellite resources described in any one of claims 1 - 7.

10. A computer device, characterized in that, Including: A processor and a memory. The memory is used to store executable instructions, and the processor executes the pre - allocation method of satellite resources described in any one of claims 1 - 7 corresponding to the executable instructions stored in the memory.

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