Same-frequency multi-beam resource allocation simulation system and method for high-orbit satellites

Abstract

The invention discloses a same-frequency multi-beam resource allocation simulation system and method for a high-orbit satellite, belongs to the technical field of satellite communication, and solves the problem of how to allocate beams with different frequencies to users with strong interference in a high-orbit satellite communication system. According to the method, strong interference windows between low-orbit satellites with strong interference are calculated, a strong interference window library is constructed, and the interference window library is periodically updated to adapt to dynamic changes of the orbit; same-frequency beam resources are allocated based on a priority heuristic strategy, different frequency points are preferentially allocated for users with different priorities, and the resource utilization efficiency is optimized; core indexes are respectively defined for different types of services, system performance indexes are evaluated according to a link calculation result, a beam allocation strategy is further optimized according to an evaluation result, system beam resources can be fully utilized, the user service quality is improved, beams of different frequencies are allocated to users with strong interference, allocation of beams of the same frequency is avoided, and the user experience is improved. The transmission success rate is improved.

Description

Technical Field

[0001] This invention belongs to the field of satellite communication technology and relates to a simulation system and method for resource allocation of multiple beams at the same frequency for high-orbit satellites. Background Technology

[0002] In high-orbit satellite communication systems, the returning beams formed by high-orbit phased array antennas exhibit spatial division effects. When user spacecraft using the same frequency become too close due to orbital movement, their transmitted returning signals will interfere with each other, leading to reception conflicts and preventing normal acquisition. Therefore, when user spacecraft request data transmission resources, the resource acceptance center needs to perform beam scheduling management. Based on the strong interference windows between user spacecraft orbits, different frequency beams are allocated to users experiencing strong interference, avoiding the allocation of the same frequency beam and improving the overall throughput of the high-orbit satellite communication system. When only beams of the same frequency exist, priority is given to allocating idle windows unaffected by interference from other users to ensure the service quality of high-priority users.

[0003] Existing technologies, such as the invention patent with publication number CN116156631A, disclose an adaptive allocation method for multi-beam interference power in satellite communication. This method calculates cross-beam interference power information by analyzing the target interference terminal set and communication channel parameters, generating cross-beam interference signals to interfere with the communication signals of the target interference terminals. By adaptively and accurately controlling the interference power, it cancels the isolation effects of adjacent beams, improving interference effectiveness. However, this method has the following drawbacks: additional power compensation is required after interference occurs, resulting in low efficiency; furthermore, this method only cancels the isolation effects and does not optimize the beam resource allocation strategy, still leading to the phenomenon of allocating the same frequency beam. Summary of the Invention

[0004] The technical solution of this invention is used to solve the problem of how to allocate different frequency beams to users with strong interference in high-orbit satellite communication systems.

[0005] The present invention solves the above-mentioned technical problems through the following technical solutions:

[0006] A simulation system for co-frequency multi-beam resource allocation for high-orbit satellites includes:

[0007] The user distribution simulation module is used to simulate the number and location distribution of user satellites.

[0008] The beam resource request module is used to establish a resource request model and simulate the arrival time and message parameters of user satellite resource requests.

[0009] The Star-to-Ground Channel Simulation Module is used to simulate all user channels based on user location and link conditions, and calculate the received power of user satellite signals at different locations reaching the high-orbit satellite.

[0010] The user orbit strong interference calculation module is used to calculate the strong interference window between user satellites based on the user satellite orbits.

[0011] The resource management module allocates co-frequency beam resources based on a priority-based heuristic strategy.

[0012] The performance evaluation module is used to collect statistics on system performance indicators and verify the effectiveness of resource allocation strategies.

[0013] Furthermore, the user distribution simulation module specifically determines the number of users N and the orbital parameters of each user based on the construction requirements of the low-Earth orbit user constellation configuration;

[0014] The position and velocity of the user satellite are dynamically modeled in the simulation system. Based on the mobility of the user satellite, the position of the user satellite changes with time and moves along its orbit.

[0015] Furthermore, the orbital parameters in the beam resource request module include the semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, and true anomaly.

[0016] Furthermore, the message parameters include user identifier, requested resource type, service data rate, service duration, earliest service start time, and latest service end time.

[0017] Furthermore, the specific calculation of the received power of user satellite signals at different locations reaching the high-orbit satellite in the star-to-ground channel simulation module is as follows:

[0018] Using the signal power Pr received by the high-orbit satellite from the nth low-orbit satellite n The received power of the signal from the nth user satellite reaching the high-orbit satellite is represented by the following logic: Pr n :

[0019] Pr n =Pt n ·Gt n ·Gr·L p ·L n

[0020] Among them, Pt n Let Gt be the launch power of the nth low-Earth orbit satellite. n Let G be the transmit antenna gain of the nth low-Earth orbit satellite, Gr be the receive antenna gain of the high-Earth orbit satellite, and L be the transmit antenna gain of the nth low-Earth orbit satellite. n For other losses, L p The path loss is expressed as λ′ is the signal wavelength, d n Let be the distance from the nth low-orbit satellite to the high-orbit satellite.

[0021] Furthermore, the user track strong interference calculation module specifically comprises:

[0022] Define the angle θ between two low-Earth orbit satellites i and j using the same frequency and a high-Earth orbit satellite. ij Less than 1.2 times the beamwidth θ 3dB When, the relation θ is satisfied ij <1.2·θ 3dB If there is strong interference between low-Earth orbit satellite i and low-Earth orbit satellite j, then the beam angle is defined as the strong interference angle θ. I ;

[0023] For all user satellites within the system, a user orbit library is constructed based on orbital parameters obtained from the user distribution simulation module. Based on these orbital parameters, the orbital information of the user satellites over a future period can be obtained. The angle between two user satellites and the high-orbit satellite is then calculated, and it is determined whether this angle is less than the strong interference angle θ. I The included angle is smaller than the strong interference angle θ. I The time period is used as the strong interference time window between two user satellites. All user satellites are traversed, and the existence of a strong interference time window between each pair of user satellites is calculated and counted to build a strong interference window library.

[0024] Furthermore, the priority-based heuristic strategy for allocating co-frequency beam resources in the resource management module specifically involves:

[0025] The first step is that within the time window T, the resource acceptance center receives N user return data transmission request tasks;

[0026] The second step involves the resource processing center calculating the task priorities for N user data transfer requests within the current time period. Specifically, this involves categorizing and quantifying the attributes, and then using a weighted method to calculate the task priority p for a single user's data transfer request.

[0027]

[0028] Where, N I ω represents the number of attributes. m Let m be the weight coefficient of the m-th attribute, where m ∈ N. I d m Quantize the size of the attribute value;

[0029] The third step is for the resource acceptance center to sort the N data transmission application tasks within the current time window T according to their priority from high to low, forming a priority list. The resource acceptance center then allocates beam resources in the order of priority from high to low in the priority list.

[0030] Fourth, the resource processing center first allocates beam resources to the user ranked first in the priority list. Specifically, based on the earliest service start time, latest service end time, service duration required, and current beam resource usage in the user's resource request message, it checks whether there is an interference-free beam in the unoccupied beam window. If so, it allocates the interference-free beam first and proceeds to step five. If not, it checks whether there is a weak interference beam. If so, it allocates the weak interference beam and proceeds to step five. If not, it checks whether there is a strong interference beam. If so, it allocates the strong interference beam and proceeds to step five. If not, the resource allocation fails, the resource processing center rejects the user's application, and proceeds to step six.

[0031] Step 5: Resource allocation successful. Resource processing center approves user application, updates strong interference window library, and proceeds to step 6.

[0032] Step 6: The resource processing center updates the priority list, removing users who were successfully or unsuccessfully assigned from the priority list;

[0033] Step 7: For the remaining users to be assigned in the priority list, return to step 4 and continue until the priority list is cleared of all users to be assigned.

[0034] Furthermore, the interference-free beam refers to a beam that is not used by any other user during the service duration; the weakly interfering beam refers to a beam that is used by other users during the service duration, but there is no strong interference between users; the strongly interfering beam refers to a beam that is used by other users during the service duration, and there is strong interference between users.

[0035] Furthermore, the system performance indicators in the effect evaluation module include algorithm running time, task completion rate, and throughput;

[0036] The algorithm runtime specifically refers to the runtime of the resource management module from the start of allocation to the end of the simulation; the task completion rate specifically refers to the ratio of the total number of planned tasks completed to the total number of requested tasks; and the throughput specifically refers to the number of data successfully transmitted per unit time.

[0037] This invention also provides a simulation method for resource allocation of high-orbit satellites at the same frequency and with multiple beams, comprising the following steps:

[0038] S1 simulates the number and location distribution of user satellites;

[0039] S2, Establish a resource request model to simulate the arrival time and message parameters of user satellite resource requests;

[0040] S3. Based on the user's location and link conditions, simulate all user channels and calculate the received power of user satellite signals at different locations reaching the high-orbit satellite.

[0041] S4, calculate the strong interference window between user satellites based on the user satellite orbits;

[0042] S5, allocates co-frequency beam resources based on a priority-based heuristic strategy;

[0043] S6, statistical system performance indicators, to verify the effectiveness of resource allocation strategies.

[0044] The advantages of this invention are:

[0045] This invention provides a simulation system for co-frequency beam resource allocation for high-orbit satellites. It simulates and models user satellites based on user characteristics, dynamically simulating the constellation configuration of low-orbit satellites. By calculating the strong interference windows between low-orbit satellites with strong interference, it constructs a strong interference window library and periodically updates the library to adapt to dynamic orbital changes. A priority-based heuristic strategy allocates co-frequency beam resources. By classifying and quantifying the relevant task attributes of various user return data transmission requests, it assigns user priorities and prioritizes users with different priorities by allocating beams of different interference types, avoiding the allocation of beams at the same frequency. This fully utilizes system beam resources, optimizes resource utilization efficiency, and effectively reduces co-frequency allocation conflicts. When the high-orbit satellite system has a large workload and short task planning and preparation time, it exhibits shorter algorithm runtime and a higher task completion rate.

[0046] This invention is applicable to high-orbit satellite communication systems, specifically to scenarios involving multi-beam resource allocation when multiple user spacecraft are transmitting back to share the same spectrum due to limited inter-satellite frequency resources. Through co-frequency beam scheduling and management, different frequency beams can be allocated to users experiencing strong interference, thereby improving the quality of service. The simulation system can evaluate the effectiveness of the multi-beam resource allocation strategy and optimize the strategy based on the evaluation results. The parameters of the simulation system can be dynamically configured according to the system design, making it a general-purpose simulation system with significant application value. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of a high-orbit satellite co-frequency multi-beam transmission scenario according to Embodiment 1 of the present invention;

[0048] Figure 2 This is a structural diagram of the high-orbit satellite co-frequency multi-beam resource allocation simulation system according to Embodiment 1 of the present invention;

[0049] Figure 3 This is a flowchart of the heuristic strategy for allocating co-frequency beam resources according to Embodiment 1 of the present invention;

[0050] Figure 4 This is a schematic diagram illustrating the allocation of co-frequency beam resources to user A in the first scenario of Embodiment 1 of the present invention;

[0051] Figure 5This is a schematic diagram illustrating the allocation of co-frequency beam resources to user A under the second scenario of Embodiment 1 of the present invention;

[0052] Figure 6 This is a flowchart of the simulation method for high-orbit satellite co-frequency multi-beam resource allocation in Embodiment 1 of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0055] Example 1

[0056] like Figure 1 The diagram shown is a schematic of a high-orbit satellite co-frequency multi-beam transmission scenario in this embodiment. Multiple low-orbit spacecraft users may use the same frequency beam to send return data transmission service signals to the high-orbit satellite. The high-orbit satellite will transparently forward the signals to the ground station for acquisition and demodulation.

[0057] like Figure 2 As shown, specifically, a simulation system for co-frequency multi-beam resource allocation of high-orbit satellites is disclosed, including a user distribution simulation module, a beam resource request module, a star-ground channel simulation module, a resource management module, and an effect evaluation module connected in sequence;

[0058] Furthermore, it also includes a user track strong interference calculation module; the output end of the user track strong interference calculation module is connected to the control end of the resource management module.

[0059] The user distribution simulation module is used to simulate the number and location distribution of user satellites. Specifically, it determines the number of users N and the orbital parameters of each user based on the construction requirements of the low-Earth orbit user constellation configuration. In the simulation system, it dynamically models the position and velocity of user satellites and simulates the movement of user satellites along their orbits over time, based on the mobility of user satellites.

[0060] Optionally, the constellation configuration is the Walker constellation, and the orbital parameters include the semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, and true anomaly.

[0061] In this embodiment, a user satellite refers to a satellite that serves end users, such as a low-Earth orbit remote sensing satellite, which is responsible for receiving and transmitting user data, or acts as an intermediary between ground stations and satellite networks to realize signal transmission.

[0062] The beam resource request module is used to establish a resource request model and simulate the arrival time and message parameters of user satellite resource requests.

[0063] In this embodiment, the resource request model adopts the Poisson distribution model as an example for explanation. For the arrival time of simulated resource requests from all users, the time interval of the arrival time of resource requests from all users follows an exponential distribution with parameter λ, where λ is the distribution probability parameter, representing the average number of times the event occurs per unit time.

[0064] The arrival time of the user's satellite resource request is specifically as follows:

[0065] Within time T, the arrival time of each user's satellite resource request is t. n Let n = 1, ..., N, and t1 = 0. Then the arrival time t of the nth user is... n The calculation formula is:

[0066] t n =t n-1 +Δt

[0067] Where Δt represents the arrival time interval between two adjacent user satellite resource requests, t n-1 This indicates the arrival time of the (n-1)th user resource request.

[0068] Furthermore, using the Poisson distribution model as an example, the arrival time of resource requests from user satellites follows a Poisson distribution model. Therefore, the interval between the arrival times of resource requests from two user satellites follows an exponential distribution with parameter λ, where λ is the distribution probability parameter, representing the average number of times the event occurs per unit time. Thus, Δt follows an exponential distribution with parameter λ. In this embodiment, an inverse transformation sampling method can be used to generate exponentially distributed random numbers using uniformly distributed random numbers. The following logic represents Δt:

[0069] Δt=-log(1-rand) / λ

[0070] Here, rand means to generate uniformly distributed random numbers in the range [0,1), and 1-rand means to restrict the range to the range [0,1) to avoid infinity when taking the logarithm.

[0071] Furthermore, the message parameters include elements such as user identifier, requested resource type, service data rate, service duration, earliest service start time, and latest service end time.

[0072] The star-to-ground channel simulation module is used to simulate all user channels based on user location and link conditions, and calculate the received power of user satellite signals at different locations reaching the high-orbit satellite; wherein, the signal power Pr received by the high-orbit satellite from the nth low-orbit satellite is used. n The received power of the signal from the nth user satellite reaching the high-orbit satellite is represented by the following logic: Pr n :

[0073] Pr n =Pt n ·Gt n ·Gr·L p ·L n

[0074] Among them, Pt n Gt represents the launch power of the nth low-Earth orbit satellite. n Gr is the transmit antenna gain of the nth low-Earth orbit satellite; Gr is the receive antenna gain of the high-Earth orbit satellite. λ′ is the path loss, d is the signal wavelength, and d is the path loss. n L represents the distance from the nth low-Earth orbit satellite to the high-Earth orbit satellite. n Other losses, including atmospheric attenuation and pointing error, can be set based on engineering experience.

[0075] Specifically, the link conditions include the operating frequency band, the gain Gr of the high-orbit satellite receiving antenna, and the path loss L. p Other losses L n wait.

[0076] In this embodiment, all user channels are simulated and link calculations are performed accordingly, which serve as the basis for calculating performance evaluation indicators such as throughput.

[0077] The user orbit strong interference calculation module is used to calculate the strong interference window between user satellites based on the user satellite orbits.

[0078] First, define the angle θ between two low-orbit satellites i and j using the same frequency and a high-orbit satellite. ij Less than 1.2 times the beamwidth θ 3dB When, the relation θ is satisfied ij <1.2·θ 3dB If there is strong interference between low-Earth orbit satellite i and low-Earth orbit satellite j, then the beam angle is defined as the strong interference angle θ. I .

[0079] Secondly, for all user satellites within the system, a user orbit library is constructed based on the orbital parameters obtained from the user distribution simulation module. Based on these orbital parameters, the orbital information of the user satellites over a future period can be obtained. The angle between two user satellites and the high-orbit satellite is then calculated, and it is determined whether this angle is less than the strong interference angle θ. I The included angle is smaller than the strong interference angle θ. I The time period is used as the strong interference time window between two user satellites. All user satellites are traversed, and the existence of a strong interference time window between each pair of user satellites is calculated and counted. A strong interference window library is constructed as the basis for multi-beam resource allocation. The library is also periodically updated by simulating the arrival, failure, and departure of actual user satellites.

[0080] The resource management module allocates co-frequency beam resources based on a priority-based heuristic strategy.

[0081] like Figure 3 As shown, the resource acceptance center allocates co-frequency beam resources based on the message parameters of the user's resource request, combined with a strong interference window library, using a priority-based heuristic strategy. Specifically, the priority-based heuristic strategy for allocating co-frequency beam resources is as follows:

[0082] In the first step, within the time window T, the resource acceptance center receives N user return data transmission request tasks.

[0083] The second step is for the resource acceptance center to calculate the priority of the N user data transmission request tasks within the current time period.

[0084] In this embodiment, a higher task priority indicates a more important task. For low-Earth orbit remote sensing satellite return data transmission tasks, the tasks can be categorized and quantified according to attributes such as remote sensing purpose (including military emergency observation, civilian emergency observation, military routine observation, civilian routine observation, etc.), data transmission method (including live image transmission, store-and-forward, etc.), and data type (including optical, radar, electronic, etc.). A weighted method is then used to calculate the task priority p of a single user's return data transmission request.

[0085]

[0086] Where, N I ω represents the number of attributes. m Let m be the weight coefficient of the m-th attribute, where m ∈ N. I d m This quantifies the size of the attribute value.

[0087] The third step involves the resource acceptance center sorting the N data transmission application tasks within the current time window T according to their priority from high to low, forming a priority list. The resource acceptance center then allocates beam resources in the order of priority from high to low in the priority list.

[0088] Fourth, the resource processing center first allocates beam resources to the user ranked first in the priority list. Specifically, based on the earliest service start time, latest service end time, service duration required, and current beam resource usage in the user's resource request message, it checks whether there is an interference-free beam in the unoccupied beam window. If so, it allocates the interference-free beam first and proceeds to step five. If not, it checks whether there is a weak interference beam. If so, it allocates the weak interference beam and proceeds to step five. If not, it checks whether there is a strong interference beam. If so, it allocates the strong interference beam and proceeds to step five. If not, the resource allocation fails, the resource processing center rejects the user's application, and proceeds to step six.

[0089] Step 5: Resource allocation successful. Resource processing center approves user application, updates strong interference window library, and proceeds to step 6.

[0090] Step 6: The resource processing center updates the priority list and removes users who were successfully or unsuccessfully assigned from the priority list.

[0091] Step 7: For the remaining users to be assigned in the priority list, return to step 4 and continue until the priority list is cleared of all users to be assigned.

[0092] In this embodiment, the interference-free beam refers to a beam that is not used by any other user during the service duration; the weakly interfering beam refers to a beam that is used by other users during the service duration, but there is no strong interference between users; and the strongly interfering beam refers to a beam that is used by other users during the service duration, and there is strong interference between users.

[0093] like Figure 4 As shown, in Scenario 1, the resource processing center allocates co-frequency beam resources to user A based on a priority-based heuristic strategy. User A's earliest service start time is t. s The latest service end time is t l It is necessary to t s and t l The beam window is searched within the range to meet the required service duration. At this point, beams 1 and 2, both at the same frequency, are used at t... s and t l There is no interference-free window within the area, and beams 3 and 4, using the same frequency 2, are used at t. s and t l Within a non-interference window, beam 4 is allocated. To reduce latency, the service start time coincides with the earliest service start time, and the actual service time allocation starts from t. s To t e .

[0094] like Figure 5As shown, in scenario 2, beam resources are allocated to user A, and the earliest service start time for user A is t. s The latest service end time is t l Beams 1-4 in t s to t l There is no interference-free window within the range. Given the required service duration, beam 1 with a weak interference window is allocated. Service starts at time t0, and the actual allocated service time is from t0 to t... e′ .

[0095] The effect evaluation module is used to statistically analyze system performance indicators and verify the effectiveness of resource allocation strategies.

[0096] Specifically, the system performance metrics include algorithm running time, task completion rate, and throughput.

[0097] The specific performance metrics of the statistical system are: algorithm running time, task completion rate and throughput over a period of time, which are used to compare with other resource allocation strategies, help designers evaluate resource allocation strategies from different dimensions, and ultimately select a strategy suitable for a specific system.

[0098] In this embodiment, the algorithm running time specifically refers to the running time of the resource management module from the start of allocation to the end of the simulation, which is used to evaluate the strategy complexity. The algorithm running time cannot exceed the task planning preparation time reserved by the system. The task completion rate specifically refers to the ratio of the total number of planned tasks to the total number of requested tasks. The throughput specifically refers to the number of data successfully transmitted per unit time, which can be calculated using the power value output by the Star-Ground Channel Simulation Module and the Shannon Formula.

[0099] Based on the evaluation results, the beam allocation strategy is optimized. When the high-orbit satellite system has a large workload and a short mission planning and preparation time, a heuristic resource allocation strategy is generally suitable, with a short algorithm running time and a high mission completion rate. However, when the workload is small and the mission planning and preparation time is long, optimization allocation strategies such as genetic algorithms can also be used to achieve a higher mission completion rate.

[0100] In summary, the co-frequency multi-beam resource allocation simulation system of this embodiment is applied to high-orbit satellite communication systems. Through co-frequency beam scheduling and management, it can allocate different frequency beams to users with strong interference, thereby improving the quality of service for users. The system can evaluate the effectiveness of the multi-beam resource allocation strategy and optimize the resource allocation strategy based on the evaluation results. The parameters of the simulation system can be dynamically configured according to the system design. As a general-purpose simulation system, it has good application value.

[0101] like Figure 6As shown, this implementation addresses the issue of co-frequency multi-beam resource allocation in high-orbit satellite communication systems. A generalized simulation for co-frequency multi-beam resource allocation in high-orbit satellites is established, including the following steps:

[0102] S1 simulates the number and location distribution of user satellites. Specifically, based on the construction requirements of the low-Earth orbit user constellation configuration, the number of users N and the orbital parameters of each user are determined. The position and velocity of the user satellites are dynamically modeled in the simulation system. Based on the mobility of user satellites, the position of the user satellites is simulated to change along the orbit over time.

[0103] Optionally, the constellation configuration is the Walker constellation, and the orbital parameters include the semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, and true anomaly.

[0104] In this embodiment, a user satellite refers to a satellite that serves end users, such as a low-Earth orbit remote sensing satellite, which is responsible for receiving and transmitting user data, or acts as an intermediary between ground stations and satellite networks to realize signal transmission.

[0105] S2, establish a resource request model to simulate the arrival time and message parameters of user satellite resource requests.

[0106] In this embodiment, the resource request model adopts the Poisson distribution model as an example for explanation. For the arrival time of simulated resource requests from all users, the time interval of the arrival time of resource requests from all users follows an exponential distribution with parameter λ, where λ is the distribution probability parameter, representing the average number of times the event occurs per unit time.

[0107] The arrival time of the user's satellite resource request is specifically as follows:

[0108] Within time T, the arrival time of each user's satellite resource request is t. n Let n = 1, ..., N, and t1 = 0. Then the arrival time t of the nth user is... n The calculation formula is:

[0109] t n =t n-1 +Δt

[0110] Where Δt represents the arrival time interval between two adjacent user satellite resource requests, t n-1 This indicates the arrival time of the (n-1)th user resource request.

[0111] Furthermore, using the Poisson distribution model as an example, the arrival time of resource requests from user satellites follows a Poisson distribution model. Therefore, the interval between the arrival times of resource requests from two user satellites follows an exponential distribution with parameter λ, where λ is the distribution probability parameter, representing the average number of times the event occurs per unit time. Thus, Δt follows an exponential distribution with parameter λ. In this embodiment, an inverse transformation sampling method can be used to generate exponentially distributed random numbers using uniformly distributed random numbers. The following logic represents Δt:

[0112] Δt=-log(1-rand) / λ

[0113] Here, rand means to generate uniformly distributed random numbers in the range [0,1), and 1-rand means to restrict the range to the range [0,1) to avoid infinity when taking the logarithm.

[0114] Furthermore, the message parameters include elements such as user identifier, requested resource type, service data rate, service duration, earliest service start time, and latest service end time.

[0115] S3, based on the user's location and link conditions, simulate all user channels and calculate the received power of the user satellite signal reaching the high-orbit satellite at different locations; among which, the signal power Pr received by the high-orbit satellite from the nth low-orbit satellite is used. n The received power of the signal from the nth user satellite reaching the high-orbit satellite is represented by the following logic: Pr n :

[0116] Pr n =Pt n ·Gt n ·Gr·L p ·L n

[0117] Among them, Pt n Gt represents the launch power of the nth low-Earth orbit satellite. n Gr is the transmit antenna gain of the nth low-Earth orbit satellite; Gr is the receive antenna gain of the high-Earth orbit satellite. λ′ is the path loss, d is the signal wavelength, and d is the path loss. n L represents the distance from the nth low-Earth orbit satellite to the high-Earth orbit satellite. n Other losses, including atmospheric attenuation and pointing error, can be set based on engineering experience.

[0118] Specifically, the link conditions include the operating frequency band, the gain Gr of the high-orbit satellite receiving antenna, and the path loss L. p Other losses L n wait.

[0119] In this embodiment, all user channels are simulated and link calculations are performed accordingly, which serve as the basis for calculating performance evaluation indicators such as throughput.

[0120] S4 calculates the strong interference window between user satellites based on their orbits.

[0121] First, define the angle θ between two low-orbit satellites i and j using the same frequency and a high-orbit satellite. ij Less than 1.2 times the beamwidth θ 3dB When, the relation θ is satisfied ij <1.2·θ 3dB If there is strong interference between low-Earth orbit satellite i and low-Earth orbit satellite j, then the beam angle is defined as the strong interference angle θ. I .

[0122] Secondly, for all user satellites within the system, a user orbit library is constructed based on the orbital parameters obtained from step S1. The orbital parameters allow for the acquisition of orbital information for user satellites over a future period. This information is used to calculate the angle between two user satellites relative to a high-orbit satellite, and to determine whether the angle is less than the strong interference angle θ. I The included angle is smaller than the strong interference angle θ. I The time period is used as the strong interference time window between two user satellites. All user satellites are traversed, and the existence of a strong interference time window between each pair of user satellites is calculated and counted. A strong interference window library is constructed as the basis for multi-beam resource allocation. The library is also periodically updated by simulating the arrival, failure, and departure of actual user satellites.

[0123] S5 allocates co-frequency beam resources using a priority-based heuristic strategy.

[0124] In this embodiment, the resource acceptance center allocates co-frequency beam resources based on a priority-based heuristic strategy, according to the message parameters of the user's resource request and in conjunction with a strong interference window library. Specifically, the priority-based heuristic strategy for allocating co-frequency beam resources involves:

[0125] In the first step, within the time window T, the resource acceptance center receives N user return data transmission request tasks.

[0126] The second step is for the resource acceptance center to calculate the priority of the N user data transmission request tasks within the current time period.

[0127] In this embodiment, a higher task priority indicates a more important task. For low-Earth orbit remote sensing satellite return data transmission tasks, the tasks can be categorized and quantified according to attributes such as remote sensing purpose (including military emergency observation, civilian emergency observation, military routine observation, civilian routine observation, etc.), data transmission method (including live image transmission, store-and-forward, etc.), and data type (including optical, radar, electronic, etc.). A weighted method is then used to calculate the task priority p of a single user's return data transmission request.

[0128]

[0129] Where, N I ω represents the number of attributes. m Let m be the weight coefficient of the m-th attribute, where m ∈ N. I d m This quantifies the size of the attribute value.

[0130] The third step involves the resource acceptance center sorting the N data transmission application tasks within the current time window T according to their priority from high to low, forming a priority list. The resource acceptance center then allocates beam resources in the order of priority from high to low in the priority list.

[0131] Fourth, the resource processing center first allocates beam resources to the user ranked first in the priority list. Specifically, based on the earliest service start time, latest service end time, service duration required, and current beam resource usage in the user's resource request message, it checks whether there is an interference-free beam in the unoccupied beam window. If so, it allocates the interference-free beam first and proceeds to step five. If not, it checks whether there is a weak interference beam. If so, it allocates the weak interference beam and proceeds to step five. If not, it checks whether there is a strong interference beam. If so, it allocates the strong interference beam and proceeds to step five. If not, the resource allocation fails, the resource processing center rejects the user's application, and proceeds to step six.

[0132] Step 5: Resource allocation successful. Resource processing center approves user application, updates strong interference window library, and proceeds to step 6.

[0133] Step 6: The resource processing center updates the priority list and removes users who were successfully or unsuccessfully assigned from the priority list.

[0134] Step 7: For the remaining users to be assigned in the priority list, return to step 4 and continue until the priority list is cleared of all users to be assigned.

[0135] In this embodiment, the interference-free beam refers to a beam that is not used by any other user during the service duration; the weakly interfering beam refers to a beam that is used by other users during the service duration, but there is no strong interference between users; and the strongly interfering beam refers to a beam that is used by other users during the service duration, and there is strong interference between users.

[0136] S6, statistical system performance indicators, to verify the effectiveness of resource allocation strategies.

[0137] Specifically, the system performance metrics include algorithm running time, task completion rate, and throughput.

[0138] The specific performance metrics of the statistical system are: algorithm running time, task completion rate and throughput over a period of time, which are used to compare with other resource allocation strategies, help designers evaluate resource allocation strategies from different dimensions, and ultimately select a strategy suitable for a specific system.

[0139] In this embodiment, the algorithm running time specifically refers to the running time from the start of allocation to the end of the simulation in step S5, which is used to evaluate the strategy complexity. The algorithm running time cannot exceed the task planning preparation time reserved by the system. The task completion rate specifically refers to the ratio of the total number of planned tasks to the total number of requested tasks. The throughput specifically refers to the number of data successfully transmitted per unit time, which can be calculated using the power value output in step S3 and based on the Shannon formula.

[0140] Based on the evaluation results, the beam allocation strategy is optimized. When the high-orbit satellite system has a large workload and a short mission planning and preparation time, a heuristic resource allocation strategy is generally suitable, with a short algorithm running time and a high mission completion rate. However, when the workload is small and the mission planning and preparation time is long, optimization allocation strategies such as genetic algorithms can also be used to achieve a higher mission completion rate.

[0141] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulation system for co-frequency multi-beam resource allocation for high-orbit satellites, characterized in that, include: The user distribution simulation module is used to simulate the number and location distribution of user satellites. The beam resource request module is used to establish a resource request model and simulate the arrival time and message parameters of user satellite resource requests. The Star-to-Ground Channel Simulation Module is used to simulate all user channels based on user location and link conditions, and calculate the received power of user satellite signals at different locations reaching the high-orbit satellite. The user orbit strong interference calculation module is used to calculate the strong interference window between user satellites based on the user satellite orbits. The resource management module allocates co-frequency beam resources based on a priority-based heuristic strategy. The performance evaluation module is used to collect statistics on system performance indicators and verify the effectiveness of resource allocation strategies.

2. The simulation system for co-frequency multi-beam resource allocation of high-orbit satellites according to claim 1, characterized in that, The user distribution simulation module specifically determines the number of users N and the orbital parameters of each user based on the construction requirements of the low-Earth orbit user constellation configuration. The position and velocity of the user satellite are dynamically modeled in the simulation system. Based on the mobility of the user satellite, the position of the user satellite changes with time and moves along its orbit.

3. The simulation system for co-frequency multi-beam resource allocation of high-orbit satellites according to claim 2, characterized in that, The orbital parameters in the beam resource request module include semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, and true anomaly.

4. The simulation system for co-frequency multi-beam resource allocation of high-orbit satellites according to claim 1, characterized in that, The message parameters include user identifier, requested resource type, service data rate, service duration, earliest service start time, and latest service end time.

5. A simulation system for co-frequency multi-beam resource allocation for high-orbit satellites according to claim 1, characterized in that, The specific calculation of the received power of user satellite signals at different locations reaching the high-orbit satellite in the star-to-ground channel simulation module is as follows: Using the signal power Pr received by the high-orbit satellite from the nth low-orbit satellite n The received power of the signal from the nth user satellite reaching the high-orbit satellite is represented by the following logic: Pr n : Pr n =Pt n ·Gt n ·Gr·L p ·L n Among them, Pt n Let Gt be the launch power of the nth low-Earth orbit satellite. n Let G be the transmit antenna gain of the nth low-Earth orbit satellite, Gr be the receive antenna gain of the high-Earth orbit satellite, and L be the transmit antenna gain of the nth low-Earth orbit satellite. n For other losses, L p The path loss is expressed as λ′ is the signal wavelength, d n Let be the distance from the nth low-orbit satellite to the high-orbit satellite.

6. The simulation system for high-orbit satellite co-frequency multi-beam resource allocation according to claim 1, characterized in that, The user track strong interference calculation module is specifically as follows: Define the angle θ between two low-Earth orbit satellites i and j using the same frequency and a high-Earth orbit satellite. ij Less than 1.2 times the beamwidth θ 3dB When, the relation θ is satisfied ij <1.2·θ 3dB If there is strong interference between low-Earth orbit satellite i and low-Earth orbit satellite j, then the beam angle corresponding to this is defined as the strong interference angle θ. I ; For all user satellites within the system, a user orbit library is constructed based on the orbit parameters obtained from the user distribution simulation module; Based on the orbital parameters, the orbital information of the user satellites over a future period can be obtained. This information is then used to calculate the angle between the two user satellites and the high-orbit satellite, and to determine whether the angle is less than the strong interference angle θ. I The included angle is smaller than the strong interference angle θ. I The time period is used as the strong interference time window between two user satellites. All user satellites are traversed, and the existence of a strong interference time window between each pair of user satellites is calculated and counted to build a strong interference window library.

7. A simulation system for co-frequency multi-beam resource allocation of high-orbit satellites according to claim 1, characterized in that, The priority-based heuristic strategy for allocating co-frequency beam resources in the resource management module specifically involves: The first step is that within the time window T, the resource acceptance center receives N user return data transmission request tasks; The second step involves the resource processing center calculating the task priorities for N user data transfer requests within the current time period. Specifically, this involves categorizing and quantifying the attributes, and then using a weighted method to calculate the task priority p for a single user's data transfer request. Where, N I ω represents the number of attributes. m Let m be the weight coefficient of the m-th attribute, where m ∈ N. I d m Quantize the size of the attribute value; The third step is for the resource acceptance center to sort the N data transmission application tasks within the current time window T according to their priority from high to low, forming a priority list. The resource acceptance center then allocates beam resources in the order of priority from high to low in the priority list. Fourth, the resource processing center first allocates beam resources to the user ranked first in the priority list. Specifically, based on the earliest service start time, latest service end time, service duration required, and current beam resource usage in the user's resource request message, it checks whether there is an interference-free beam in the unoccupied beam window. If so, it allocates the interference-free beam first and proceeds to step five. If not, it checks whether there is a weak interference beam. If so, it allocates the weak interference beam and proceeds to step five. If not, it checks whether there is a strong interference beam. If so, it allocates the strong interference beam and proceeds to step five. If not, the resource allocation fails, the resource processing center rejects the user's application, and proceeds to step six. Step 5: Resource allocation successful. Resource processing center approves user application, updates strong interference window library, and proceeds to step 6. Step 6: The resource processing center updates the priority list, removing users who were successfully or unsuccessfully assigned from the priority list; Step 7: For the remaining users to be assigned in the priority list, return to step 4 and continue until the priority list is cleared of all users to be assigned.

8. A simulation system for co-frequency multi-beam resource allocation for high-orbit satellites according to claim 7, characterized in that, The interference-free beam refers to a beam that is not used by any other user during the service duration; the weakly interfering beam refers to a beam that is used by other users during the service duration, but there is no strong interference between users; the strongly interfering beam refers to a beam that is used by other users during the service duration, and there is strong interference between users.

9. A simulation system for co-frequency multi-beam resource allocation of high-orbit satellites according to claim 1, characterized in that, The system performance indicators in the effect evaluation module include algorithm running time, task completion rate, and throughput. The algorithm runtime specifically refers to the runtime of the resource management module from the start of allocation to the end of the simulation; the task completion rate specifically refers to the ratio of the total number of planned tasks completed to the total number of requested tasks; and the throughput specifically refers to the number of data successfully transmitted per unit time.

10. A simulation method for resource allocation of high-orbit satellites using multiple beams at the same frequency, characterized in that, Includes the following steps: S1 simulates the number and location distribution of user satellites; S2, Establish a resource request model to simulate the arrival time and message parameters of user satellite resource requests; S3. Based on the user's location and link conditions, simulate all user channels and calculate the received power of user satellite signals at different locations reaching the high-orbit satellite. S4, calculate the strong interference window between user satellites based on the user satellite orbits; S5, allocates co-frequency beam resources based on a priority-based heuristic strategy; S6, statistical system performance indicators, to verify the effectiveness of resource allocation strategies.

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