Method for optimizing beam dynamic switching of low-orbit satellite beam service importance control

Abstract

The application discloses a kind of low-orbit satellite beam service importance degree control's beam dynamic switch optimization method, belongs to low-orbit satellite resource scheduling, wherein, method includes: the simulation calculation of antenna beam direction intensity distribution is carried out, and the communication traffic of each plot is predicted under dynamic network division, the variable that measures the importance degree of beam service is defined in combination with both, to select the beam that needs to be adjusted, under the premise of guaranteeing coverage, the beam of more important service is preferentially selected, so as to obtain the suboptimal solution of beam switch. Thus, while effectively saving global beam resources, the number of beam adjustments is also effectively reduced, ping-pong switching is reduced, and the algorithm complexity is small, suitable for large-scale user groups.

Description

Technical Field

[0001] This invention relates to the field of low-Earth orbit satellite resource scheduling technology, and in particular to a method for optimizing beam dynamic switching to control the importance of low-Earth orbit satellite beam services. Background Technology

[0002] Low Earth Orbit (LEO) satellite communication networks have attracted widespread attention due to their global coverage, low transmission latency, low power consumption links, and strong resilience, and are an important component of future global mobile communication systems.

[0003] Low Earth Orbit (LEO) satellite mobile communication systems generally refer to satellite constellation communication systems with satellites at an altitude of 500–2000 km and an operational cycle of 2–4 hours. They are currently one of the best solutions for achieving seamless global communication coverage. They have the following advantages:

[0004] 1) Due to their low orbital altitude, LEO satellite communication systems experience less transmission loss, resulting in less signal attenuation and lower propagation delay, making them more advantageous for personal global communication. The path transmission loss of an LEO system is typically several tens of decibels lower than that of a GEO system, and the required transmission power is only a fraction of that of GEO. The propagation delay is only that of GEO. This is highly advantageous for enabling handheld terminals and meeting the latency requirements for voice communication.

[0005] 2) The development of technologies such as cellular communication, multiple access, spot beam, and frequency reuse has provided technical support for LEO satellite mobile communication.

[0006] 3) Because the elevation angle of the ground mobile terminal to the satellite is relatively large, generally between 20° and 56°, the antenna beam is not easily affected by ground reflection, thus avoiding multipath fading.

[0007] However, low-Earth orbit satellite systems have always faced challenges in resource management due to limited communication resources, dynamic network fluctuations, and handover request congestion. Various solutions have been proposed, among which dynamic beam resource management (DRM) is of great significance for coordinating NGSO-GSO interference, rationally allocating onboard power, conserving onboard beam resources, and improving constellation operational performance. Summary of the Invention

[0008] This invention provides a beam dynamic switching optimization method for controlling the importance of low-Earth orbit (LEO) satellite beam services, in order to solve the resource management difficulties that LEO satellite systems always face due to factors such as limited communication resources, dynamic network fluctuations, and handover request congestion.

[0009] The first aspect of this invention provides a beam dynamic switching optimization method for controlling the importance of low-Earth orbit (LEO) satellite beam services, comprising the following steps: simulating the intensity of LEO satellite antenna beam directions, superimposing beam intensities based on the beamforming principle of phased array antennas, and calculating the intensity of beams in different directions; predicting global terrestrial communication traffic volume using a service density network based on dynamic network partitioning to obtain service density; calculating the service importance of multiple beams based on the intensity of the beams in different directions and the service density; determining the beam to be adjusted based on the multiple beam service importance under a preset optimization objective, and turning off the beam to be adjusted.

[0010] Optionally, in one embodiment of the present invention, the method further includes: establishing a beam resource scheduling optimization model and constructing a coverage matrix of beams and terminals; transforming the optimization objective of the beam resource scheduling optimization model according to the coverage matrix; and determining the preset optimization objective under the coverage matrix.

[0011] Optionally, in one embodiment of the present invention, establishing an optimization model for beam resource scheduling includes:

[0012] With the goal of minimizing the number of active beams and the constraint that, when at least one beam is disabled, for any terminal within the service area, there is at least one active beam that can cover that terminal, an optimization model for beam resource scheduling is established. The optimization model for beam resource scheduling is as follows:

[0013]

[0014]

[0015] Where, N s N represents the number of satellites in the system. B The number of beams per satellite, a i，j Let a represent the j-th beam activation factor of the i-th satellite. i，j =1 indicates that the beam is active. Let K be the set of all users within the constellation network coverage area, and c be the total number of users. i，j，k c represents the coverage factor of terminal k and the j-th beam of the i-th satellite. i，j，k =1 indicates that the beam covers terminal k.

[0016] Optionally, in one embodiment of the present invention, the construction of the beam and terminal coverage matrix, and the transformation of the optimization objective of the beam resource scheduling optimization model based on the coverage matrix to determine the preset optimization objective under the coverage matrix, include:

[0017] The coverage matrix is ​​constructed by representing beams as rows and terminals as columns. in, Let be the nth row vector of the coverage matrix D, representing the coverage information of the nth beam, wherein the coverage matrix satisfies:

[0018]

[0019] Among them, [D] n，k N represents the element in the nth row and kth column of matrix D. B，tot This represents the total number of satellite beams.

[0020] Let all row vectors form a set. The optimization objective of the beam resource scheduling optimization model is converted into the preset optimization objective:

[0021]

[0022]

[0023] Here, card|·| represents the number of elements in the set.

[0024] Optionally, in one embodiment of the present invention, the intensity of beams in different directions is calculated by simulating the intensity of beam directions of low-orbit satellite antennas and superimposing beam intensities according to the beamforming principle of phased array antennas. This includes: using the azimuth and elevation information of multiple beams of a single satellite to calculate the beam direction vector of the phased array antenna, and projecting the intensity of the superimposed beam direction vectors onto the Earth's latitude and longitude coordinate system through mathematical transformation to obtain the intensity of beams in different directions.

[0025] Optionally, in one embodiment of the present invention, before predicting global terrestrial communication traffic volume using a service density network to obtain service density, the method includes: drawing a communication traffic heatmap of a preset dataset and performing grayscale processing on the heatmap to obtain a training dataset; training a constructed convolutional long short-term memory neural network using the training dataset to obtain the service density network, wherein the convolutional long short-term memory neural network is formed by adding convolutional operations for extracting spatial features to the long short-term memory network and replacing fully connected operations in the long short-term memory network with convolutional operations.

[0026] Optionally, in one embodiment of the present invention, the service density is obtained by predicting the global terrestrial communication traffic using the service density network, including: inputting selected sample images into the service density prediction network to obtain prediction result images; and performing binarization processing on the prediction result images to obtain the service density.

[0027] Optionally, in one embodiment of the present invention, the importance of the services of the plurality of beams calculated based on the intensity of the beams in different directions and the service density is as follows:

[0028]

[0029] Where, ρ n (x,y) represents the service density of a single grid, e n (x,y) represents the signal strength of a single grid cell, A n For the importance of the nth beam service, m n This represents the number of grid cells.

[0030] A second aspect of the present invention provides a beam dynamic switching optimization device for controlling the importance of low-Earth orbit (LEO) satellite beam services, comprising: a calculation module for calculating the intensity of beams in different directions by simulating the intensity of LEO satellite antenna beam directions and superimposing beam intensities according to the beamforming principle of phased array antennas; a prediction module for predicting global terrestrial communication traffic based on dynamic network partitioning and using a service density network to obtain service density; and an optimization module for calculating the importance of multiple beam services based on the intensity of the beams in different directions and the service density, determining the beam to be adjusted based on the multiple beam service importance under a preset optimization objective, and turning off the beam to be adjusted.

[0031] A third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform a beam dynamic switching optimization method for controlling the importance of low-Earth orbit satellite beam services as described in the above embodiments.

[0032] The beam dynamic switching optimization method and apparatus for controlling the importance of low-Earth orbit satellite beam services according to embodiments of the present invention have the following beneficial effects:

[0033] 1) The algorithm utilizes beam service importance control to effectively reduce beam resource waste in high-latitude regions and beam overlap and ping-pong handover issues in mid- and low-latitude regions. Simultaneously, the adjustable coverage threshold ensures rapid convergence of the algorithm under high dynamic conditions, making it suitable for the communication scenario of low-Earth orbit satellite constellations.

[0034] 2) Spatiotemporal sequence prediction of communication traffic heatmap is performed to obtain a more accurate distribution of future communication traffic. Based on the predicted traffic distribution, the satellite beam switch is set in advance, which effectively reduces the number of beam adjustments and beam switch algorithm executions, and saves on-board power.

[0035] 3) The grid partitioning method is used to transform the communication needs of a single user into the communication traffic of a user group within the grid, which effectively controls the size of the matrix, reduces the complexity of the algorithm, and ensures that the speed of the algorithm meets the requirements when the user scale is large.

[0036] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0037] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0038] Figure 1 A flowchart of a beam dynamic switching optimization method for controlling the importance of low-Earth orbit satellite beam services according to an embodiment of the present invention;

[0039] Figure 2(a) is a schematic diagram of the intensity of 48 beams of a single-satellite phased array antenna provided according to an embodiment of the present invention;

[0040] Figure 2(b) is a simulation diagram of the superposition of the 48 beam strengths of a single satellite phased array antenna according to an embodiment of the present invention;

[0041] Figure 3(a) is a heat map of communication traffic provided according to an embodiment of the present invention;

[0042] Figure 3(b) is a schematic diagram of the prediction results of terrestrial communication traffic density using Conv-LSTM according to an embodiment of the present invention;

[0043] Figure 4 A schematic diagram illustrating the execution process of beam dynamic switching optimization for low-Earth orbit satellite beam service importance control according to an embodiment of the present invention;

[0044] Figure 5 An example diagram of a beam dynamic switching optimization device for controlling the importance of low-orbit satellite beam services according to an embodiment of the present invention;

[0045] Figure 6 A schematic diagram of the structure of an electronic device provided for an embodiment of the invention. Detailed Implementation

[0046] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0047] The following describes a dynamic beam switching optimization method for controlling the importance of low-Earth orbit (LEO) satellite beam services, based on embodiments of the present invention, with reference to the accompanying drawings. Addressing the resource management difficulties inherent in LEO satellite systems, as mentioned in the background section, due to limited communication resources, network fluctuations, and handover request congestion, this invention provides a dynamic beam switching optimization method for controlling the importance of LEO satellite beam services. In this method, the antenna beam directional intensity distribution is simulated and calculated. Under dynamic network partitioning, Conv-LSTM is used to predict the communication traffic volume of each area. Combining these two methods, variables are defined to measure the importance of beam services. Based on this, beams requiring adjustment are selected. While ensuring coverage, beams with more important services are prioritized, thus obtaining a suboptimal solution for beam switching. By utilizing beam service importance control, beam resource waste in high-latitude regions and problems such as beam overlap and ping-pong handover in mid- and low-latitude regions can be effectively reduced.

[0048] Before introducing the embodiments of the present invention, the parameters used in the embodiments of the present invention will be explained first.

[0049] Table 1. Definition of Satellite Communication System Symbols

[0050]

[0051] The number of satellites in a low-Earth orbit satellite constellation system is denoted as N. s The satellites within the constellation can be represented as S in sequence. i i = 1, 2, ..., N s The number of beams for each satellite is denoted as N. B b i，j Indicates satellite S i If the j-th beam is a given beam, then the total number of beams in the system is N. B，tot =N s ·N B .

[0052] With the satellite platform's center of mass as the origin O, the direction from the origin to the Earth's center as the positive Z-axis, and the direction from the origin O to the direction of the satellite's tangent as the positive X-axis, the Y-axis is obtained according to the right-hand rule.

[0053] For beam b i，j In satellite S i Define its spatial orientation in the body coordinate system. Among them, azimuth angle Az i，j Elevation angle El is defined as the angle from the positive X-axis clockwise to the projection of the beam pointing onto the XOY plane. i，j Defined as the angle between the beam direction and the XOZ plane.

[0054] From the spatial correspondence, we can know that spatial vectors The corresponding OXYZ representation for:

[0055]

[0056] Each satellite beam adopts a circular beam model, and the corresponding beam angle (BW) is denoted as... For beam b i，j Terminal g k Specifically, when the spatial angle between the satellite terminal's pointing direction and the beam's pointing direction is less than the beam's half-width, the terminal is considered to be covered by the beam; otherwise, the terminal is considered not to be within the beam's coverage area. Therefore, a terminal-beam related coverage factor c is defined. i，j，k Characterizing the terminal g k Is it beamed? i，j Coverage, its expression is:

[0057]

[0058] Define beam activation / deactivation factor a i，j The expression for whether the beam is active or off is:

[0059]

[0060] Specifically, Figure 1 This is a flowchart illustrating a method for optimizing beam dynamic switching to control the importance of low-Earth orbit satellite beam services, according to an embodiment of the present invention.

[0061] like Figure 1 As shown, the beam dynamic switching optimization method for controlling the importance of low-Earth orbit satellite beam services includes the following steps:

[0062] In step S101, the intensity of the beam in different directions is calculated by simulating the intensity of the low-orbit satellite antenna beam direction and superimposing the beam intensity according to the beamforming principle of the phased array antenna.

[0063] As a specific implementation method, the azimuth and elevation information of multiple beams of a single satellite can be used to calculate the beam direction vector of the phased array antenna. The intensity of the superimposed beam direction vectors can be projected onto the Earth's latitude and longitude coordinate system through mathematical transformation to obtain the intensity of beams in different directions.

[0064] To better measure the standard of beam dynamic switching, the beam directional intensity under a single satellite was simulated, as shown in Figure 2, which illustrates the relationship between beam intensity distribution and latitude / longitude under a single satellite. Given the azimuth and elevation angles of the 48 beams of a single satellite in the satellite coordinate system, the beam directional vector of the phased array antenna was calculated. Then, through mathematical transformation, the intensity of the superimposed 48 beams was projected onto the Earth's latitude / longitude coordinate system, thus obtaining the intensity of beams in different directions.

[0065] In general, in multi-antenna systems, this is considered as far-field plane wave transmission. Assuming a uniformly spaced two-dimensional array with N×M elements, where N is the number of elements along the x-axis and M is the number of elements along the y-axis, with an interval of d between adjacent elements. The origin of the array's Cartesian coordinate system is set at the (0, 0)-th element, and all N×M elements are located in the first quadrant of the xoy plane. Two angles are defined in the array's Cartesian coordinate system, where θ∈[0, π / 2], representing the angle between the beam direction and the z-axis. The angle between the beam direction and the xoz plane.

[0066] Using spherical coordinates, we can obtain:

[0067] Direction vector:

[0068]

[0069] Weight vector:

[0070]

[0071] The specific expression for the pattern function formed by a uniform linear array is:

[0072]

[0073] Given the azimuth and elevation information of a single satellite with 48 beams in the satellite body coordinate system, as shown in Figure 2(a), coordinate transformation is performed using the following two sets of formulas. First, it is converted to the definition of phased array antenna coordinates. Then, the 48 directional beams of the phased array are superimposed, and the result is projected onto the Earth's latitude and longitude coordinates, as shown in Figure 2(b). This represents the relationship between the beam directional intensity distribution and latitude and longitude of a single satellite.

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] In step S102, based on dynamic network partitioning, the global terrestrial communication traffic volume is predicted using the service density network to obtain the service density.

[0081] Optionally, before using the service density network to predict global terrestrial communication traffic and obtain the service density, the process includes: drawing a heatmap of communication traffic for a preset dataset and performing grayscale processing on the heatmap to obtain a training dataset; using the training dataset to train the constructed convolutional long short-term memory neural network to obtain the service density network, wherein the convolutional long short-term memory neural network is formed by adding convolutional operations for extracting spatial features to the long short-term memory network and replacing the fully connected operations in the long short-term memory network with convolutional operations.

[0082] In this embodiment of the invention, the service density is predicted by a service density network to obtain the service density, including: inputting selected sample images into the service density prediction network to obtain prediction result images; and performing binarization processing on the prediction result images to obtain the service density.

[0083] To better measure beam switching standards, global terrestrial communication traffic volume is needed. Based on dynamic network partitioning, a traffic density network is trained using a Convolutional Long Short-Term Memory Neural Network (Conv-LSTM) to predict global terrestrial communication traffic volume. The process is as follows:

[0084] The definition of terrestrial communication traffic volume here is the traffic intensity in the Erlang-C formula, i.e., the call request rate: λ(calls / second); average call duration: T. s Traffic density (call intensity): u = λ·T s .

[0085] Based on data from the MILAN communication dataset, a heatmap of communication traffic was drawn on a 3D Earth and then converted to grayscale. This grayscale image was then transformed into a standard 640×640 image and used as training samples for a Conv-LSTM. A batch of training samples was input into the training network of the neural network to obtain the desired weight values. Afterwards, sample images were selected and input into the prediction network of the neural network to obtain the prediction result images. To make the prediction results more prominent, the prediction result images were binarized (with an adjustable threshold).

[0086] Specifically, taking Milan, Italy, as an example, this dataset consists of telecommunications and weather data aggregated from November 1, 2013, to January 1, 2014. Telecommunications data includes aggregated information on received and sent SMS messages, incoming and outgoing calls, and internet activity, measured using Call Detail Records (CDRs), a method for measuring cellular traffic. Weather data includes humidity information (measured as a percentage), temperature (measured in degrees Celsius), and wind speed information (measured in meters per second). The data is aggregated hourly and arranged within a 300 square kilometer area of ​​the city. The city is divided into 1×1 km grids, and a binary adjacency matrix is ​​used to represent the connectivity between grids within the city. Data related to user connection requests is used to create a 640×640 two-dimensional planar heatmap, which is then converted to grayscale. The dataset contains internet connections from users to base stations (user requests are associated with their grid IDs, and the time of the request).

[0087] Conv-LSTM adds convolutional operations for extracting spatial features to the LSTM network, replacing the fully connected operations in LSTM with convolutional operations. The starting point is to use weight sharing to represent spatial correlations. That is:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] Two thousand 640×640 two-dimensional planar grayscale thermal images were used as samples. Due to the special nature of the spatiotemporal sequence, no individual labels were created for the samples. Instead, they were arranged in chronological order, with each group of four frames serving as a set. The first three frames were used as input samples, and the fourth frame was used as the sample label for training.

[0094] To save computing resources and reduce training time, the original samples were first preprocessed, reducing the 640×640 samples to 128×128 as input. Therefore, the parameters of the 5D tensor are [2000, 4, 128, 128, grayscale value].

[0095] The training epochs were set to 50. Multiple experiments revealed that too many epochs would cause the loss to decrease at a very slow rate, potentially leading to fitting errors and feature loss. Therefore, a suitable number of training epochs was chosen.

[0096] Since the original output of Conv-LSTM is relatively blurry in grayscale, making feature extraction difficult, the original output image is binarized in the final output. This facilitates the observation of the features of the predicted image. Figure 3(a) shows the distribution of communication traffic in Milan, Italy at a certain time. This is achieved by drawing a heatmap based on geographical information data and communication traffic data for each location, and then processing it in grayscale to obtain the dataset sample. Figure 3(b) shows the final prediction result obtained after inputting the above image into the constructed Conv-LSTM network and binarizing the output. The feature extraction effect of the main traffic distribution area is good, and the MSE remains at a low level, indicating that Conv-LSTM performs well in communication traffic prediction.

[0097] In step S103, the importance of multiple beam services is calculated based on the intensity and service density of beams in different directions. Under the preset optimization objective, the beam to be adjusted is determined based on the importance of multiple beam services, and the beam to be adjusted is turned off.

[0098] By combining the obtained beam directional strength and service density, variables are defined to measure the importance of beam services. Based on this, the beams that need to be adjusted are selected. Under the premise of ensuring full coverage, the beams with more important services are prioritized, thus obtaining a suboptimal solution for beam switching.

[0099] Understandably, the service importance of a unit grid is defined as the ratio of service density to signal strength within that grid. Subsequently, the service importance of the nth beam is defined as the ratio of the sum of service importance of each unit grid within that beam to the number of grids; beams with smaller ratios are more likely to be prioritized for shutdown. For beams, beams with lower service density should be shut down more frequently. For terminals, beams corresponding to terminals with higher total superimposed beam signal strength will be prioritized for shutdown.

[0100] To characterize the importance of beamforming services, the following variables are defined:

[0101] (Unit grid) service density ρ n (x,y), (unit grid) signal strength e n (x,y), (unit grid) business importance α n (x,y)(=Service Density / Signal Strength):

[0102]

[0103] The importance of the nth beam service (= the number of grids under this beam / m) n ):

[0104]

[0105] Optionally, in one embodiment of the present invention, the method further includes: establishing a beam resource scheduling optimization model and constructing a coverage matrix of beams and terminals; transforming the optimization objective of the beam resource scheduling optimization model according to the coverage matrix; and determining a preset optimization objective under the coverage matrix.

[0106] Specifically, an optimization model for beam resource scheduling is established, including: taking minimizing the number of active beams as the optimization objective, and taking the constraint that, when at least one beam is turned off, for any terminal within the service area, there is at least one active beam that can cover any terminal, as the constraint condition, an optimization model for beam resource scheduling is established, wherein the optimization model for beam resource scheduling is:

[0107]

[0108]

[0109] Where, N s N represents the number of satellites in the system. B The number of beams per satellite, a i，j Let a represent the j-th beam activation factor of the i-th satellite. i，j =1 indicates that the beam is active. Let K be the set of all users within the coverage area of ​​the constellation network, where K is the total number of users, and c is the number of users. i，j，k c represents the coverage factor of terminal k and the j-th beam of the i-th satellite. i，j，k =1 indicates that the beam covers terminal k.

[0110] Construct a coverage matrix for beams and terminals, and transform the optimization objective of the beam resource scheduling optimization model based on the coverage matrix to determine the preset optimization objective under the coverage matrix, including:

[0111] By representing beams as rows and terminals as columns, a coverage matrix is ​​constructed. in, Let be the nth row vector of the coverage matrix D, representing the coverage information of the nth beam. The coverage matrix satisfies:

[0112]

[0113] Among them, [D] n，k N represents the element in the nth row and kth column of matrix D. B，tot N is the total number of satellite beams. B，tot =N B N S ;

[0114] Let all row vectors form a set. The optimization objective of the beam resource scheduling optimization model is transformed into a preset optimization objective:

[0115]

[0116]

[0117] Here, card|·| represents the number of elements in the set.

[0118] The objective of establishing the beam resource scheduling optimization model is to minimize the number of beams operating in the constellation network. The constraint is that when some beams within the constellation network are disabled, coverage of the service area must be guaranteed; that is, even with several beams disabled, for any terminal within the service area, there must be at least one active beam that can cover that terminal. This optimization problem is NP-complete and difficult to solve directly; therefore, a heuristic algorithm is used. Thus, the dynamic beam-off optimization problem can be expressed as the beam resource scheduling optimization model described above.

[0119] The optimization problem described above is a double summation problem, with the objective of minimizing the number of active beams, while the constraint is to ensure continuous coverage and that beams with service transmission are not turned off. Therefore, the double summation problem can be transformed into a single summation problem based on beams.

[0120] By constructing a coverage matrix related to the beam and the terminal, the above optimization problem is transformed into minimizing the number of row vectors in the matrix under the premise of satisfying the constraints. The optimization problem is then simplified to the preset optimization objective expressed above.

[0121] like Figure 4 As shown, the algorithm process is as follows:

[0122] Step 1

[0123] Using Conv-LSTM to calculate ρ for communication service prediction i，j .

[0124] Step 2

[0125] Intensity simulation calculations were performed on 48 beams of a single satellite. i，j .

[0126] Step 3

[0127] Using formula Calculate the service importance of the nth beam.

[0128] Step 4

[0129] Let D′ = D;

[0130] Step 5

[0131] Select a row vector d from D′. n Turn off the beam.

[0132] d n The selection criteria are as follows:

[0133] n = arg min n A n

[0134] (Note: Beams with lower service importance should be prioritized for shutdown. For beams, beams with lower traffic density should be shut down more frequently. For terminals, beams corresponding to terminals with higher total superimposed beam signal strength should be prioritized for shutdown.)

[0135] d n Delete (turn off the corresponding beam).

[0136] Step 6

[0137] If D′ satisfies the constraints of problem Q, update D = D′.

[0138] Otherwise, return to Step 5;

[0139] If the importance level A of beam services is met... n <A th Skip to Step 7.

[0140] (Note: A th (This represents a threshold indicating the importance of beam services, used to control redundant beams.)

[0141] Otherwise, return to Step 5.

[0142] Step 7

[0143] The solution D for the output beam shut-off algorithm is terminated.

[0144] The beam dynamic switching optimization method for controlling the importance of low-Earth orbit satellite beam services proposed in this embodiment of the invention first simulates and calculates the antenna beam directional intensity distribution to measure beam switching standards. Then, under dynamic network partitioning, a service density network trained by Conv-LSTM is used to predict the communication traffic volume of each area. Combining these two methods, variables for measuring the importance of beam services are defined, and beams that need to be adjusted are selected based on this. Under the premise of ensuring coverage, beams with more important services are prioritized, thus obtaining a suboptimal solution for beam switching. Simulation verification shows that this algorithm effectively saves global beam resources while reducing the number of beam adjustments and ping-pong handovers. Furthermore, the algorithm has low complexity and is suitable for large-scale user groups.

[0145] Next, referring to the accompanying drawings, a beam dynamic switching optimization device for controlling the importance of low-orbit satellite beam services according to an embodiment of the present invention is described.

[0146] Figure 5This is an example diagram of a beam dynamic switching optimization device for controlling the importance of low-orbit satellite beam services according to an embodiment of the present invention.

[0147] like Figure 5 As shown, the beam dynamic switching optimization device 10 for controlling the importance of low-orbit satellite beam services includes: a calculation module 100, a prediction module 200, and an optimization module 300.

[0148] The calculation module 100 is used to calculate the intensity of beams in different directions by simulating the intensity of beams in the direction of low-orbit satellite antennas and superimposing beam intensity according to the beamforming principle of phased array antennas; the prediction module 200 is used to predict the global terrestrial communication traffic based on dynamic network partitioning and using the service density network to obtain the service density; the optimization module 300 is used to calculate the service importance of multiple beams based on the intensity of beams in different directions and the service density, and under the preset optimization target, determine the beams to be adjusted based on the service importance of multiple beams and turn off the beams to be adjusted.

[0149] Optionally, in one embodiment of the present invention, the beam dynamic switching optimization device 10 for controlling the importance of low-orbit satellite beam services further includes: a generation module, used to establish a beam resource scheduling optimization model, construct a coverage matrix of beams and terminals, transform the optimization objective of the beam resource scheduling optimization model according to the coverage matrix, and determine the preset optimization objective under the coverage matrix.

[0150] Optionally, in one embodiment of the present invention, establishing an optimization model for beam resource scheduling includes:

[0151] With the objective of minimizing the number of active beams and the constraint that, for any terminal within the service area, at least one active beam can cover any terminal when at least one beam is disabled, an optimization model for beam resource scheduling is established. The optimization model for beam resource scheduling is as follows:

[0152]

[0153]

[0154] Where, N s N represents the number of satellites in the system. B The number of beams per satellite, a i，j Let a represent the j-th beam activation factor of the i-th satellite. i，j =1 indicates that the beam is active. Let K be the set of all users within the constellation network coverage area, and c be the total number of users. i，j，k c represents the coverage factor of terminal k and the j-th beam of the i-th satellite. i，j，k=1 indicates that the beam covers terminal k.

[0155] Optionally, in one embodiment of the present invention, a beam and terminal coverage matrix is ​​constructed, and the optimization objective of the beam resource scheduling optimization model is transformed according to the coverage matrix to determine a preset optimization objective under the coverage matrix, including:

[0156] By representing beams as rows and terminals as columns, a coverage matrix is ​​constructed. in, Let be the nth row vector of the coverage matrix D, representing the coverage information of the nth beam. The coverage matrix satisfies:

[0157]

[0158] Among them, [D] n，k N represents the element in the nth row and kth column of matrix D. B，tot Let N be the total number of satellite beams. B，tot =N B N S ;

[0159] Let all row vectors form a set. The optimization objective of the beam resource scheduling optimization model is transformed into a preset optimization objective:

[0160]

[0161]

[0162] Here, card|·| represents the number of elements in the set.

[0163] Optionally, in one embodiment of the present invention, the calculation module is further used to calculate the beam direction vector of the phased array antenna using the azimuth and elevation information of multiple beams of a single satellite, and to project the intensity of the superimposed beam direction vectors onto the Earth's latitude and longitude coordinate system through mathematical transformation to obtain the intensity of beams in different directions.

[0164] Optionally, in one embodiment of the present invention, the beam dynamic switching optimization device 10 for controlling the importance of low-orbit satellite beam services further includes: a training module, used to draw a heatmap of communication traffic of a preset dataset and perform grayscale processing on the heatmap before predicting global terrestrial communication traffic using the service density network to obtain the service density, thereby obtaining a training dataset; and to train a constructed convolutional long short-term memory neural network using the training dataset to obtain the service density network, wherein the convolutional long short-term memory neural network is formed by adding convolution operations for extracting spatial features to the long short-term memory network and replacing the fully connected operations in the long short-term memory network with convolution operations.

[0165] Optionally, in one embodiment of the present invention, the prediction module is further configured to input the selected sample image into the service density prediction network to obtain the prediction result image; and to perform binarization processing on the prediction result image to obtain the service density.

[0166] Optionally, in one embodiment of the present invention, the service importance of multiple beams calculated based on the intensity and service density of beams in different directions is as follows:

[0167]

[0168] Where, ρ n (x,y) represents the service density of a single grid, e n (x,y) represents the signal strength of a single grid cell, A n For the importance of the nth beam service, m n This represents the number of grid cells.

[0169] It should be noted that the explanation of the aforementioned embodiment of the beam dynamic switching optimization method for controlling the importance of low-orbit satellite beam services also applies to the beam dynamic switching optimization device for controlling the importance of low-orbit satellite beam services in this embodiment, and will not be repeated here.

[0170] The beam dynamic switching optimization device for controlling the importance of low-Earth orbit satellite beam services proposed in this embodiment of the invention first simulates and calculates the directional intensity distribution of the antenna beam to measure beam switching standards. Then, under dynamic network partitioning, it uses a service density network trained by Conv-LSTM to predict the communication traffic volume of each area. Combining these two methods, it defines variables to measure the importance of beam services. Based on this, it selects the beams that need to be adjusted. Under the premise of ensuring coverage, it prioritizes beams with more important services, thus obtaining a suboptimal solution for beam switching. Simulation verification shows that this algorithm effectively saves global beam resources while reducing the number of beam adjustments and ping-pong handovers. Furthermore, the algorithm has low complexity and is suitable for large-scale user groups.

[0171] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. The electronic device may include:

[0172] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0173] When the processor 602 executes the program, it implements the beam dynamic switching optimization method for controlling the importance of low-orbit satellite beam services provided in the above embodiments.

[0174] Furthermore, electronic devices also include:

[0175] Communication interface 603 is used for communication between memory 601 and processor 602.

[0176] The memory 601 is used to store computer programs that can run on the processor 602.

[0177] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0178] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0179] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0180] Processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0181] This embodiment also provides a computer-readable storage medium storing a computer program, characterized in that, when the program is executed by a processor, it implements the above-mentioned beam dynamic switching optimization method for controlling the importance of low-orbit satellite beam services.

[0182] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0183] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0184] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0185] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0186] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method for beam dynamic switching optimization for LEO satellite beam traffic importance control, characterized in that, The method comprises the following steps: The intensity of different direction beams is calculated by simulating the intensity of low-orbit satellite antenna beam direction, superimposing beam intensity according to the beam forming principle of phased array antenna, and calculating the intensity of different direction beams; The global ground communication traffic is predicted by using the traffic density network based on dynamic network division to obtain the traffic density; The traffic importance of multiple beams is calculated according to the intensity of different direction beams and the traffic density, an optimization model of beam resource scheduling is established, a coverage matrix of beams and terminals is constructed, the optimization target of the optimization model of beam resource scheduling is converted according to the coverage matrix, a preset optimization target under the coverage matrix is determined, and a to-be-adjusted beam is determined according to the traffic importance of multiple beams under the preset optimization target, and the to-be-adjusted beam is closed; The traffic importance of multiple beams calculated according to the intensity of different direction beams and the traffic density is as follows: wherein, is a single grid service density, is a single grid signal strength, is a first beam service importance, is a grid number; The optimization model of beam resource scheduling comprises: The optimization model of beam resource scheduling is established by taking the number of activated beams as the optimization target and taking the constraint condition that, when at least one beam is closed, there is at least one activated beam that can cover any terminal in the service area, wherein the optimization model of beam resource scheduling is as follows: wherein, is the number of satellites of the system, is the number of beams per satellite, denotes the activation factor of the th beam of the th satellite, denotes that the beam is in an active state, is the set of all users within the coverage area of the constellation network, is the total number of users, denotes the coverage factor of the terminal with the th beam of the th satellite, denotes that the beam covers the terminal ; The coverage matrix of beams and terminals is constructed, and the optimization target of the optimization model of beam resource scheduling is converted according to the coverage matrix to determine the preset optimization target under the coverage matrix, which comprises: The coverage matrix is constructed by representing beams as rows and terminals as columns wherein, is a coverage matrix is a first row vector of the coverage matrix representing coverage information of a first beam, the coverage matrix satisfying: wherein denotes the element of the matrix in the i-th row and the j-th column, in the i-th row and the j-th column, in the i-th row and the j-th column, is the total number of satellite beams; Let all row vectors constitute a set The optimization target of the optimization model of the beam resource scheduling is converted into the preset optimization target. wherein denotes the number of elements in the set.

2. The method of claim 1, wherein, The intensity of different direction beams is calculated by simulating the intensity of low-orbit satellite antenna beam direction, superimposing beam intensity according to the beam forming principle of phased array antenna, and calculating the intensity of different direction beams, which comprises: The beam direction vectors of the phased array antenna are calculated by using the azimuth and elevation information of multiple beams of a single satellite, and the intensity of the superimposed multiple beam direction vectors is projected in the earth longitude and latitude coordinate system to obtain the intensity of different direction beams.

3. The method of claim 1, wherein, Before predicting the global ground communication traffic by using the traffic density network to obtain the traffic density, the following steps are included: A communication traffic heat map of a preset data set is drawn, and the heat map is subjected to gray scale processing to obtain a training data set; The constructed convolutional long short-term memory neural network is trained by using the training data set to obtain the traffic density network, wherein the convolutional long short-term memory neural network is a convolutional operation that extracts spatial features added to a long short-term memory network, and a full connection operation in the long short-term memory network is replaced by a convolutional operation.

4. The method of claim 1, wherein, The global ground communication traffic is predicted by using the traffic density network to obtain the traffic density, which comprises: The selected sample image is input into the traffic density prediction network to obtain a prediction result picture; The prediction result picture is subjected to binaryzation processing to obtain the traffic density. 5.A device for optimizing beam dynamic switching of low earth orbit satellite beam traffic importance control, characterized in that, The method comprises the following steps: The intensity of different direction beams is calculated by simulating the intensity of low-orbit satellite antenna beam direction, superimposing beam intensity according to the beam forming principle of phased array antenna, and calculating the intensity of different direction beams; The global ground communication traffic is predicted by using the traffic density network based on dynamic network division to obtain the traffic density; An optimization module is configured to calculate a plurality of beam service importance degrees according to the intensity of the different directional beams and the service density, establish a beam resource scheduling optimization model, construct a coverage matrix of beams and terminals, convert an optimization objective of the beam resource scheduling optimization model according to the coverage matrix, determine a preset optimization objective under the coverage matrix, and determine a to-be-adjusted beam according to the plurality of beam service importance degrees under the preset optimization objective, and shut down the to-be-adjusted beam. The plurality of beam service importance degrees calculated according to the intensity of the different directional beams and the service density are as follows: wherein, is the service density of a single grid, is the signal strength of a single grid, is the service importance of the th beam, is the number of grids The establishing of the beam resource scheduling optimization model comprises: The optimization objective is to minimize the number of activated beams, and the constraint condition is that, when at least one beam is closed, there is at least one activated beam that can cover any terminal in a service area, and the optimization model of the beam resource scheduling is as follows: wherein, is the number of satellites of the system, is the number of beams per satellite, denotes the activation factor of the th beam of the th satellite, denotes that the beam is in an active state, is the set of all users within the coverage area of the constellation network, is the total number of users, denotes the coverage factor of the terminal with the th beam of the th satellite, denotes that the beam covers the terminal ; The constructing of the coverage matrix of beams and terminals, the conversion of the optimization objective of the beam resource scheduling optimization model according to the coverage matrix, and the determination of the preset optimization objective under the coverage matrix comprise: The coverage matrix is constructed by representing beams as rows and terminals as columns wherein, is a coverage matrix is a first row vector of the coverage matrix representing coverage information of a first beam, the coverage matrix satisfying: wherein represents the element in the i-th row and j-th column of matrix , represents the element in the i-th row and j-th column of matrix , is the total number of satellite beams; Let all row vectors constitute a set The optimization target of the optimization model of the beam resource scheduling is converted into the preset optimization target. wherein denotes the number of elements in a set.

6. An electronic device, comprising: The optimization objective is to minimize the number of activated beams, and the constraint condition is that, when at least one beam is closed, there is at least one activated beam that can cover any terminal in a service area, and the optimization model of the beam resource scheduling is as follows: The optimization objective is to minimize the number of activated beams, and the constraint condition is that, when at least one beam is closed, there is at least one activated beam that can cover any terminal in a service area, and the optimization model of the beam resource scheduling is as follows: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the beam dynamic switching optimization method for low-orbit satellite beam service importance degree control according to any one of claims 1-4.

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