A method for analyzing interference of mega constellation based on CUDA parallel computing

Abstract

A method for analyzing interference in mega-constellations based on CUDA parallel computing is disclosed, relating to the field of satellite system compatibility analysis technology. The technical problem this invention aims to solve is the complexity of interference scenarios between mega-constellations, the large amount of computation involved in interference analysis, the high computational complexity, and the low parallelism and low utilization of computational resources in current algorithms. Key technical points include: CUDA parameter initialization, orbit extrapolation, visibility calculation, earth station satellite selection, visibility calculation of interfering and interfered links, interference index calculation, and after all threads have completed their calculations, transferring the results of the mega-constellation interference analysis from GPU memory to CPU memory, thus ending the calculation. The disclosed method utilizes the independence and parallel computing capabilities of the calculations in each sub-process of mega-constellation compatibility analysis, fully leveraging the large-scale multi-threaded parallel computing and storage capabilities of GPUs to optimize the compatibility analysis process, improve computational efficiency and resource utilization, and enhance the real-time performance of mega-constellation compatibility analysis.

Description

Technical Field

[0001] This invention belongs to the field of inter-satellite system compatibility analysis, and specifically relates to a method for analyzing interference in giant constellations based on CUDA parallel computing. Background Technology

[0002] With the rapid deployment of numerous non-geostationary orbit (NGSO) satellites, interference problems among giant low-Earth orbit (LEO) constellations are becoming increasingly prominent. Effective interference analysis is necessary before satellite launch to support international coordination with other satellite systems. However, the large scale and complex interference scenarios of LEO mega-constellations result in large computational demands and long processing times, failing to meet the timeliness requirements of interference analysis. Therefore, it is imperative to propose a method for interfering with giant constellations that leverages the independence and parallel computing capabilities of each computational stage to optimize the compatibility analysis process and improve computational efficiency and resource utilization.

[0003] Document CN113193901B discloses a method for avoiding interference in large satellite constellations. The method includes: obtaining visibility forecasts for all satellites within the current forecast period for a selected ground station; determining the first traversal period; obtaining visibility forecasts for all satellites within the initial traversal time plus an interval period, obtaining the number of satellites whose signals can be received by the selected ground station within that interval period; when the number of satellites exceeds the number allowed by the technical specifications of the selected ground station, selecting to shut down several satellites; repeatedly obtaining the satellites requiring shutdown for the next interval period, and providing an avoidance scheme for the first traversal period; repeating the above steps to obtain visibility forecasts and avoidance schemes for all satellites within the remaining forecast periods; summarizing the final traversal schemes, and modifying the avoidance strategy to offset for satellites with a shutdown duration less than a predetermined duration. This existing technology can efficiently and quickly solve for the interference time period set based on satellite transit forecasts, autonomously control the satellite payload to power on / off or adjust the side-swing angle, avoiding the complex process of manual intervention to resolve interference. However, no solutions were provided for improving computational efficiency and utilization of computational resources, as well as improving the real-time performance of giant constellation compatibility analysis, in the context of interference analysis. Summary of the Invention

[0004] The technical problem to be solved by this invention is that the interference scenarios between giant low-Earth orbit constellations are complex, the interference analysis computation is large and the computational complexity is high, and the current algorithm has low parallelism and low utilization of computing resources. The invention provides a giant constellation interference analysis method based on CUDA parallel computing (CUDA stands for Computer Unified Device Architecture, a parallel computing architecture).

[0005] The present invention adopts the following technical solution:

[0006] An improved method for analyzing interference in giant constellations based on CUDA parallel computing includes the following steps:

[0007] Step 1, CUDA parameter initialization:

[0008] Import the required orbital parameters, constellation parameters, and earth station location parameters into the device's GPU, and set the thread blocks and the number of threads according to the number of satellites;

[0009] Orbital parameters refer to the six elements of the orbit: semi-major axis, eccentricity, right ascension of the ascending node, inclination, argument of perigee, and argument of true perigee.

[0010] Constellation parameters: number of orbits, number of satellites in each orbit;

[0011] Earth station location parameters: latitude, longitude, and altitude;

[0012] Step 2, Orbit Deduction:

[0013] Using the J2 orbital model, a kernel function kernel0 was written and executed to simulate the satellite trajectory and calculate the satellite's position at each moment.

[0014] Step 3, Visibility Calculation:

[0015] Using the known location of the earth station and the satellite location obtained in step 1, calculate whether the satellite and the earth station are visible to each other. Write and run the kernel function kernel1 according to the following visibility calculation process:

[0016] Calculate the distance D between the satellite and the earth station:

[0017]

[0018] In the above formula, x1, y1, and z1 are the position vectors of the satellite, and x2, y2, and z2 are the position vectors of the earth station.

[0019] The distance R1 from the satellite to the Earth's center is: The distance R2 from the Earth station to the Earth's center is: The distance D of the satellite and earth station from the horizon i :

[0020]

[0021] In the above formula, R e The radius of the Earth;

[0022] When the distance D between the satellite and the earth station is less than D1+D2, the satellite and the earth station are visible to each other.

[0023] Step 4, Earth Station Star Selection:

[0024] The Earth station writes and runs the kernel function kernel2 according to the satellite selection strategy, and selects one of the visible satellites obtained in step 3 to establish a link;

[0025] Step 5, Visibility calculation of the interfering link and the interfered link:

[0026] After both the earth station of the jamming system and the earth station of the jammed system complete the satellite selection and link establishment in step 4, the visibility calculation method in step 3 is used to analyze whether the transmitter of the jamming system and the receiver of the jammed system are visible to each other.

[0027] If it is not visible, the simulation time changes from T1 to T2, and returns to step 1 to start calculating the interference situation at time T2; if it is visible, jump to step 6 to calculate the interference index.

[0028] Step 6, Calculation of interference index:

[0029] Write a kernel function kernel3 to calculate interference indicators;

[0030] Step 7: After all threads have completed their calculations, the calculation results of the giant constellation interference analysis are transferred from the GPU memory to the CPU memory, and the calculation ends.

[0031] Furthermore, in step 4, the satellite selection strategy includes the longest tracking time, the maximum communication angle, the shortest communication distance, and the maximum separation angle.

[0032] Furthermore, in step 6, the interference metrics calculated include C / N, C / I, I / N, C / (I+N), ΔT / T, PFD, and EPFD. C / N is the signal-to-noise ratio, C / I is the signal-to-interference ratio, I / N is the interference-to-noise ratio, C / (I+N) is the signal-to-interference-to-noise ratio, ΔT / T is the noise temperature change rate, PFD is the power flux density, and EPFD is the equivalent power flux density.

[0033] The beneficial effects of this invention are:

[0034] The method disclosed in this invention utilizes the independence and parallel computing characteristics of the calculations in each sub-process of the giant constellation compatibility analysis, makes full use of the large-scale multi-threaded parallel computing and storage capabilities of GPUs, optimizes the compatibility analysis process, performs parallel computing of the compatibility analysis process on GPUs, improves computing efficiency and utilization of computing resources, reduces the compatibility analysis time, and improves the real-time performance of the giant constellation compatibility analysis. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the compatibility analysis process optimization.

[0036] Figure 2This is a schematic diagram of the optimized compatibility analysis process.

[0037] Figure 3 This is a flowchart based on CUDA parallel computing. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0039] like Figure 1 As shown, the compatibility analysis of a mega-constellation is executed sequentially, following the steps of orbit extrapolation, visibility calculation, earth station satellite selection, visibility calculation of interfering and interfered links, and interference index calculation. Each sub-process contains numerous independent, parallelizable computational steps, which can be further accelerated using GPUs. For example, orbit extrapolation requires performing an orbit extrapolation equal to the number of satellites at each moment to determine their current positions. By decomposing the steps of the mega-low-Earth orbit constellation compatibility analysis into multiple parallel blocks executed sequentially, and treating the satellite calculation process in each module as a separate thread, the CPU calls the kernel function, which is then executed on the GPU. The optimized compatibility analysis process is as follows: Figure 2 As shown.

[0040] Example 1 discloses a method for analyzing interference from giant constellations based on CUDA parallel computing, such as... Figure 3 As shown, it includes the following steps:

[0041] Step 1, CUDA parameter initialization:

[0042] Import the required orbital parameters, constellation parameters, and earth station locations into the device's GPU. Set the number of thread blocks and threads according to the number of satellites to make each thread as active as possible and fully utilize computing resources.

[0043] Step 2, Orbit Deduction:

[0044] Using the J2 orbital model, a kernel function kernel0 was written and executed to simulate the satellite trajectory and calculate the satellite's position at each moment.

[0045] Step 3, Visibility Calculation:

[0046] Using the known location of the earth station and the satellite location obtained in step 1, calculate whether the satellite and the earth station are visible to each other. Write and run the kernel function kernel1 according to the following visibility calculation process:

[0047] Calculate the distance D between the satellite and the earth station:

[0048]

[0049] In the above formula, x1, y1, and z1 are the position vectors of the satellite, and x2, y2, and z2 are the position vectors of the earth station.

[0050] The distance R1 from the satellite to the Earth's center is: The distance R2 from the Earth station to the Earth's center is: The distance D of the satellite and earth station from the horizon i :

[0051]

[0052] In the above formula, R e The radius of the Earth;

[0053] When the distance D between the satellite and the earth station is less than D1+D2, that is, D<D1+D2, the satellite and the earth station are visible to each other.

[0054] Step 4, Earth Station Star Selection:

[0055] The Earth station writes and runs a kernel function kernel2 based on the satellite selection strategy, and selects one of the visible satellites obtained in step 3 to establish a link; the satellite selection strategy includes the longest tracking time, the maximum communication angle, the shortest communication distance, and the maximum separation angle, etc.

[0056] Step 5, Visibility calculation of the interfering link and the interfered link:

[0057] If system A interferes with system B in a scenario, then system A is the interfering system and system B is the interfered system.

[0058] After both the earth station of the jamming system and the earth station of the jammed system complete the satellite selection and link establishment in step 4, the visibility calculation method in step 3 is used to analyze whether the transmitter of the jamming system and the receiver of the jammed system are visible to each other.

[0059] If it is not visible, the simulation time changes from T1 to T2, and returns to step 1 to start calculating the interference situation at time T2; if it is visible, jump to step 6 to calculate the interference index.

[0060] Step 6, Calculation of interference index:

[0061] Write a kernel function kernel3 according to the requirements to calculate interference indicators specified by ITU, such as C / N, C / I, I / N, C / (I+N), ΔT / T, PFD, EPFD, etc.

[0062] Step 7: After all threads have completed their calculations, the calculation results of the giant constellation interference analysis are transferred from the GPU memory to the CPU memory, and the calculation ends.

[0063] The Starlink and OneWeb constellations are selected as the interference simulation objects to analyze the downlink interference of the Starlink constellation to the OneWeb constellation. The simulation period is 1 day and the simulation step size is 1 second. The orbital parameters of the OneWeb constellation are shown in Table 1 below.

[0064] Table 1: Oneweb Constellation Orbit Parameters

[0065]

[0066] The system parameters of Oneweb constellation are shown in Table 2 below.

[0067] Table 2: Oneweb Constellation System Parameters

[0068] parameter numerical values Satellite transmitting antenna peak gain / dBi 28 Satellite transmitting antenna half-power beamwidth / (°) 4.9 Ground station receiving antenna peak gain / dBi 38 Ground station receiving antenna half-power beamwidth / (°) 1.6 Satellite transmit power / dBW 3 Communication frequency / GHz 17.8 Communication bandwidth / MHz 250 System noise temperature / K 300 Ground station location / (°E, °N) (120,40)

[0069] The orbital parameters of the Starlink constellation are shown in Table 3 below.

[0070] Table 3: Starlink constellation orbital parameters

[0071]

[0072] The system parameters of the Starlink constellation are shown in Table 4 below.

[0073] Table 4: Starlink Constellation System Parameters

[0074]

[0075] Step 1: CUDA parameter initialization. The required orbital parameters, constellation parameters, and earth station positions are copied to the device's (GPU) video memory. To improve overall computing efficiency and avoid unnecessary data copying between memory and video memory, the required data is copied to the GPU video memory in one go. Operations such as orbital extrapolation, visibility calculation, earth station star selection, visibility calculation of interfering links and interfered links, and interference index calculation are all performed within the GPU.

[0076] Step 2: Use the J2 orbit model to write and run the kernel function kernel0 to deduce the satellite trajectory and calculate the satellite position at each moment. In this scenario, the number of satellites is 1969 + 11908 = 13877. Set block and thread. The maximum number of threads, i.e. the total number of orbit deductions required, is: 13877 (satellites) × 24 × 60 × 60 (times).

[0077] Step 3: Using the known location of the earth station and the satellite location obtained in Step 1, calculate whether the satellite and the earth station are visible to each other. Write and run the kernel function kernel1 according to the following visibility calculation process:

[0078] Calculate the distance D between the satellite and the earth station:

[0079]

[0080] In the above formula, x1, y1, and z1 are the position vectors of the satellite, and x2, y2, and z2 are the position vectors of the earth station.

[0081] The distance R1 from the satellite to the Earth's center is: The distance R2 from the Earth station to the Earth's center is: The distance D of the satellite and earth station from the horizon i :

[0082]

[0083] In the above formula, R e The radius of the Earth;

[0084] When the distance D between the satellite and the earth station is less than D1+D2, that is, D<D1+D2, the satellite and the earth station are visible to each other.

[0085] Step 4: Earth Station Satellite Selection: The earth station writes and runs the kernel function kernel2 according to the satellite selection strategy, and selects one of the visible satellites obtained in Step 3 to establish a link. Specific satellite selection strategies include: longest tracking time, maximum communication angle, shortest communication distance, and maximum separation angle, etc.

[0086] Step 5: Visibility calculation of the interfering link and the interfered link: After both the earth station of the interfering system and the earth station of the interfered system have completed satellite selection and link establishment in Step 4, the visibility calculation method in Step 3 is used to analyze whether the transmitter of the interfering system and the receiver of the interfered system are visible to each other.

[0087] If it is not visible, the simulation time changes from T1 to T2, and returns to step 1 to start calculating the interference situation at time T2; if it is visible, jump to step 6 to calculate the interference index.

[0088] Step 6: Interference index calculation: Write the kernel function kernel3 according to the requirements to calculate the interference indexes specified by ITU, such as C / N, C / I, I / N, C / (I+N), ΔT / T, PFD, EPFD, etc.

[0089] Step 7: After all threads have completed their calculations, transfer the results of the giant constellation interference analysis from the GPU memory to the CPU memory, and the calculation ends.

[0090] Verification has shown that the method proposed in this invention solves the technical problem raised in this invention, and practical application has verified the technical effects and practicality claimed in this invention.

[0091] The method described in this invention has been verified through simulation experiments and practical applications, demonstrating the technical effects claimed by this invention.

[0092] The algorithm (method) proposed in this invention is the underlying technical core of this invention, and various products can be derived based on the algorithm.

[0093] Based on the algorithm (method) proposed in this invention, a CUDA-based parallel computing-based giant constellation interference analysis system is developed using a programming language. This system has program modules corresponding to the steps of the above-mentioned technical solution, and executes the steps in the above-mentioned CUDA-based parallel computing-based giant constellation interference analysis method when running.

[0094] The developed system (software) computer program is stored on a computer-readable storage medium, and the computer program is configured to implement the steps of the above-described method for analyzing interference in a giant constellation based on CUDA parallel computing when called by a processor. In other words, the invention is materialized on a carrier, becoming a computer program product.

[0095] A device for analyzing giant constellation interference based on CUDA parallel computing is disclosed. The device includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the aforementioned method for analyzing giant constellation interference based on CUDA parallel computing, thereby realizing the analysis of giant constellation interference.

[0096] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] The computational programs (also referred to as programs, software, software applications, or code) of this invention include machine instructions of a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0098] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing interference in giant constellations based on CUDA parallel computing, characterized in that, The implementation process of the method is as follows: Step 1: CUDA parameter initialization: Import the required orbital parameters, constellation parameters, and earth station location parameters into the device's GPU, and set the thread blocks and the number of threads according to the number of satellites; Step 2, Orbit Deduction: Using the J2 orbital model, a kernel function kernel0 was written and executed to simulate the satellite trajectory and calculate the satellite's position at each moment. Step 3, Visibility Calculation: Using the known location of the earth station and the satellite location obtained in step 2, calculate whether the satellite and the earth station are visible to each other. Write and run the kernel function kernel1 according to the following visibility calculation process: Calculate the distance D between the satellite and the earth station: In the above formula, x1, y1, and z1 are the position vectors of the satellite, and x2, y2, and z2 are the position vectors of the earth station. The distance R1 from the satellite to the Earth's center is: The distance R2 from the Earth station to the Earth's center is: Distance of satellites and earth stations from the horizon : In the above formula, The radius of the Earth; When the distance D between the satellite and the earth station is less than D1+D2, the satellite and the earth station are visible to each other. Step 4, Earth Station Star Selection: The Earth station writes and runs the kernel function kernel2 according to the satellite selection strategy, and selects one of the visible satellites obtained in step 3 to establish a link; Step 5: Calculate the visibility of the interfering link and the interfered link: After both the earth station of the jamming system and the earth station of the jammed system complete the satellite selection and link establishment in step 4, the visibility calculation method in step 3 is used to analyze whether the transmitter of the jamming system and the receiver of the jammed system are visible to each other. If it is not visible, then the calculation time is... Become And return to step 1 to begin calculation. The interference situation at any given time; if visible, skip to step 6 to calculate the interference index; Step 6: Calculation of Interference Indicators Write a kernel function kernel3 to calculate interference indicators; Step 7: After all threads have completed their calculations, transfer the results of the giant constellation interference analysis from the GPU memory to the CPU memory, and the calculation ends.

2. The method for analyzing giant constellation interference based on CUDA parallel computing according to claim 1, characterized in that, In step 4, the satellite selection strategy includes the longest tracking time, the maximum communication angle, the shortest communication distance, and the maximum separation angle.

3. The method for analyzing giant constellation interference based on CUDA parallel computing according to claim 2, characterized in that, In step 6, the interference indices calculated include C / N, C / I, I / N, C / (I+N), ΔT / T, PFD, and EPFD.

4. A giant constellation interference analysis system based on CUDA parallel computing, characterized in that: The system has a program module corresponding to the steps of any one of the claims 1-3 above, and executes the steps in the giant constellation interference analysis method based on CUDA parallel computing when it runs.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program configured to, when invoked by a processor, implement the steps of the CUDA-based parallel computing-based giant constellation interference analysis method as described in any one of claims 1-3.

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