Method for adjusting beam coverage area of low-altitude satellite group

Abstract

The application relates to the technical field of satellite communication, in particular to a beam coverage area adjustment method for a low-altitude satellite group. The method comprises the following steps: step 1: acquiring arrangement information and satellite operation information of the satellite group, and generating satellite communication alternation information of a target area; step 2: collecting user distribution information and communication demand information of the target area in real time, and establishing and updating a communication demand heat map in real time based on the user distribution information and the communication demand information; and step 3: establishing a beam allocation model for each satellite passing through the target area. According to the technical scheme, the effective service time window and the communication coverage range of each satellite in the target area can be accurately acquired by analyzing satellite alternation coverage timing information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite communication, in particular to a method for adjusting the beam coverage area of a low-altitude satellite group. BACKGROUND

[0002] The content of this section only provides background information related to the present application, which may not constitute prior art.

[0003] The core principle of satellite beam communication is to focus signals into directional electromagnetic beams through high-gain antennas, achieve concentrated energy transmission, and generate multiple independent beams covering different areas (such as cities, oceans, or remote areas) on the same satellite using spatial multiplexing technology. Each beam is like an independent "air communication pipeline", which distinguishes users through frequency multiplexing, polarization isolation, or dynamic beam shaping, significantly improving spectral efficiency and anti-interference capability.

[0004] The core advantage of low-orbit satellite communication (LEO) is ultra-low delay and global seamless coverage: low-orbit satellites operate at an altitude of 300-2000 kilometers, with a signal transmission delay of only 20-40 ms (20 times higher than high-orbit satellites), which can support real-time video calls, online games, and other services; through large-scale constellation networking (such as Starlink's over 4000 satellites), dynamic beam switching technology is used to achieve full-coverage coverage on the ground.

[0005] Low-orbit satellites cannot be synchronized with the rotation of the Earth, and they move at high speed relative to the ground. To ensure the continuity of communication, ground users must switch to the next satellite that enters the coverage area before their current service satellite moves out of the effective coverage range. At the same time, the constellation composed of satellites is also continuously orbiting the Earth to provide full-service coverage. In this process, each low-orbit satellite needs to dynamically adjust its beam pointing and coverage area (beam management) to illuminate different ground areas. When the satellite beam rapidly slides on the ground, it also needs to reallocate beam resources according to the specific business needs and channel conditions of users in the beam illuminated area. This dynamic switching mechanism can easily cause a "ping-pong effect" in practice: when a user needs to switch from satellite A to satellite B due to satellite movement, if the beam channel resources of satellite B currently covering the user are highly congested (the occupation ratio is too high), the user will not be able to successfully access satellite B. At this time, the system may attempt to return the user to satellite A, but the signal of satellite A may be rapidly decaying or has moved out of the coverage range, resulting in a connection failure again. This phenomenon of repeatedly attempting to switch between satellite A and satellite B without stable connection not only causes user communication interruption, but also inefficiently occupies valuable channel resources, reducing the overall efficiency of the system. SUMMARY

[0006] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the DETAILED DESCRIPTION. This Summary is not intended to identify key features or essential features of the claimed technology, nor is it intended to be used to limit the scope of the claimed technology.

[0007] Some embodiments of the present application propose a beam coverage area adjustment method for a low-altitude satellite constellation to solve the technical problems mentioned in the background section above.

[0008] As a first aspect of the present application, some embodiments of the present application provide a beam coverage area adjustment method for a low-altitude satellite constellation, comprising the following steps:

[0009] Step 1: Obtain the arrangement information and satellite operation information of the satellite constellation, and generate satellite communication alternation information for the target area;

[0010] Step 2: Collect user distribution information and communication demand information of the target area in real time, and establish and update a communication demand heat map based on the user distribution information and the communication demand information;

[0011] Step 3: Establish a beam allocation model for each satellite passing through the target area;

[0012] Step 4: Generate the order of satellites passing through the target area based on the satellite communication alternation information, and solve the optimal solution of the beam allocation model, so that the high channel capacity area in the beam allocation scheme can correspond to the high demand area in the communication demand heat map when adjacent satellites pass through the target area.

[0013] The technical solution proposed by the present application can accurately obtain the effective service time window and communication coverage range of each satellite in the target area by analyzing the satellite alternation coverage timing information. On this basis, the beam resources of each satellite are optimally allocated in coordination by combining the real-time generated communication demand heat map. It can ensure that the high capacity area in the beam scheme of each adjacent satellite can dynamically adapt to the hotspot distribution of the current ground communication demand when the adjacent satellites cover the target area in turn. This demand-driven dynamic beam adaptation mechanism significantly improves the utilization efficiency of satellite resources. More importantly, it can effectively avoid the problem of switching failure caused by insufficient (congestion) of target beam channel resources of adjacent satellites during satellite switching, thereby fundamentally suppressing the occurrence of "ping-pong effect" and ensuring the continuity of user communication and the overall performance of the system.

[0014] In existing low-Earth orbit (LEO) satellite communication systems, the high-speed movement of satellites necessitates frequent switching of service satellites for ground users. However, traditional beam management methods lack the ability to accurately predict the timing of satellite switching at specific ground locations. Furthermore, static or coarse-grained beam allocation schemes struggle to dynamically adapt to the spatiotemporal changes in ground communication needs. These two shortcomings combined result in the following: when a user triggers a switch due to satellite movement, the beam covering the user's location P(x,y) on the target satellite may not be optimally configured for the current needs (e.g., insufficient capacity or pointing deviation). This can easily lead to user access failures and subsequent "ping-pong switching," severely compromising communication continuity and system efficiency.

[0015] Furthermore, step 1 includes the following steps:

[0016] Step 11: Obtain the satellite constellation layout information S, S = {s1, s2, ... s} i …s n}, s i This represents the communication coverage area on the ground of the i-th satellite in the satellite constellation, where i represents the satellite index and n represents the total number of satellites in the constellation.

[0017] Step 12: Obtain the moving speed of the satellite's communication coverage area on the ground, and calculate the communication coverage area s of each satellite. i The time period T passes through position P(x, y) （x，y）i Where x and y represent the x-coordinate and y-coordinate of position P, respectively, and T （x，y）i This indicates that position P is at point S. i The time period;

[0018] Step 13: Based on each time period T of position P （x，y）i Generate satellite update succession sequence T at position P. P T P ={T （x，y）1 T （x，y）2 ... T （x，y）i ...T （x，y）n};

[0019] Step 14: Collect all satellite update and replacement sequences T P Generate satellite communication alternation information.

[0020] This application accurately predicts satellite service windows and handover times at any location within a target area through location-level satellite coverage time-series modeling. Based on this prediction information, combined with a real-time communication demand heatmap, dynamic optimization allocation of beam resources is completed before the satellite arrives at its coverage location. This scheme significantly improves the accuracy and foresight of beam modulation: on the one hand, it ensures that high-demand areas are always allocated high-capacity beams during the satellite coverage period; on the other hand, it ensures that the beam capacity configuration of adjacent satellites in the handover boundary area is highly matched with real-time demand. This fundamentally avoids handover failures caused by insufficient or mismatched target satellite beam resources, effectively suppresses the "ping-pong effect," guarantees a seamless continuous communication experience for users, and optimizes the overall system spectrum efficiency.

[0021] In low-Earth orbit satellite communication systems, accurately tracking the locations of massive numbers of users (both online and offline) is extremely challenging. Directly constructing a distribution model based on the precise coordinates of individual users would require exponentially increasing computational complexity (reaching millions of users) and continuous updates to the locations of numerous offline users with no immediate communication needs. This not only consumes enormous real-time computing resources but also results in communication demand heatmaps containing significant noise from invalid location updates, making it difficult to accurately and efficiently reflect the true and dynamic distribution of service demands in the target area. Ultimately, this restricts the timeliness and accuracy of beam resource allocation.

[0022] Furthermore, step 2 includes the following steps:

[0023] Step 21: Obtain historical location information of users within the target area and establish a user location probability model;

[0024] Step 22: Obtain the user's average movement speed, and construct a location dynamic model based on the average movement speed and the location probability model;

[0025] Step 23: Obtain the user's average communication rate and render the user's average communication rate to the location dynamic model to generate a communication demand heatmap;

[0026] Step 24: Collect users' average communication rate and average mobility rate in real time to update the communication demand heatmap.

[0027] This application effectively circumvents the computational bottleneck of precise user positioning by employing probabilistic location modeling and demand aggregation techniques: It constructs a user location probability model based on historical location data, predicts group distribution trends using average movement speed (steps 21-22), and maps service demands onto a spatial probability grid using average communication speed (step 23). This scheme naturally filters offline user interference through the probabilistic model, focuses on effective communication demands, and reduces the computational load for describing user locations. It also updates the model using real-time rate data (step 24), ensuring that the heatmap always represents the aggregation demand intensity of currently active users. Thus, it significantly reduces computational overhead while providing high-fidelity, low-latency demand distribution input for beamforming allocation.

[0028] When constructing a large-scale satellite communication user location probability model, the spatial distribution pattern of users within the target area typically exhibits significant regional heterogeneity—users are highly concentrated in urban centers and along major transportation routes (approximately a normal distribution), while users are relatively dispersed in rural areas and wilderness (approximately a uniform distribution). Forcing the use of a single global distribution model (such as a global normal distribution) for fitting will lead to: 1) an underestimation of the distribution density in concentrated areas, failing to accurately characterize hotspots; 2) an overestimation of the distribution density in dispersed areas, introducing false demand signals; and 3) an overall decrease in model fidelity, ultimately causing the generated communication demand heatmap to deviate significantly from the true user distribution characteristics, misleading beam resource optimization and allocation.

[0029] Step 21 includes the following steps:

[0030] Step 211: Divide the target area into several blocks;

[0031] Step 212: Determine whether a user belongs to a uniform random distribution or a normal distribution based on the user's historical location information for each block;

[0032] Step 213: Establish a uniform distribution probability model f1(x, y) for users belonging to blocks with uniform random distribution;

[0033] f1(x, y) = 1 / L, where L represents the number of coordinates within the block, and (x, y) represents the position coordinates within the block;

[0034] Step 214: For blocks where users belong to a normal distribution, establish a normal distribution probability model f2(x, y);

[0035] ;

[0036] Where (x, y) represents the location coordinates within the block. This represents the average location of users within a block along the x-direction. This represents the average location of users within the block along the y-axis. This represents the variance of user locations within a block in the x-direction. This represents the variance of the user's location in the y-direction within the block;

[0037] Step 215: Collect the probability models of all blocks and establish the location probability model.

[0038] This application effectively solves the problem of accurately depicting user distribution in heterogeneous regions through an adaptive block-based hybrid modeling mechanism: First, the target region is dynamically divided into blocks with similar geographical characteristics (step 211), and the dominant distribution type (uniform distribution or normal distribution) of each block is intelligently determined (step 212); then, a suitable local probability model (uniform distribution model or normal distribution model) is constructed for different blocks (steps 213-214). This scheme achieves three core breakthroughs: a leap in spatial resolution: capturing micro-distribution differences through block division, avoiding the "peak smoothing and valley filling" effect of the global model;

[0039] Model matching optimization: providing the highest fidelity mathematical representations for both clustered and dispersed regions;

[0040] Computational controllability is guaranteed: Block-independent modeling supports parallel computing, providing high-precision, fine-grained user distribution input for communication demand heatmaps, laying the data foundation for precise beam scheduling.

[0041] When determining the distribution type (uniform / normal) of user locations within a geographic block, traditional overall location verification methods pose a significant risk of misclassification because they ignore the dimensional independence of spatial coordinates. When users exhibit linear clustering along specific directions (such as roads or coastlines) (one-dimensional normal distribution + one-dimensional uniform distribution), overall verification is prone to misclassifying them as uniform distribution, underestimating the clustering strength. Furthermore, when user distribution is at the borderline between uniform and normal distribution, single hypothesis tests (such as the KS test) are insufficient in power to provide reliable conclusions. These misclassifications will cause the constructed probabilistic model to deviate significantly from the actual spatial structure, thereby compromising the accuracy of communication demand heatmaps.

[0042] Furthermore, step 212 includes the following steps:

[0043] Step 2121: Separate the user's location from the historical location information of the block into horizontal and vertical coordinate data;

[0044] Step 2122: Perform uniform distribution test and normal distribution test on the horizontal axis data in sequence, and perform uniform distribution test and normal distribution test on the vertical axis in sequence;

[0045] When both the horizontal and vertical coordinate data meet the uniform distribution test results, the block is judged as a uniformly distributed block.

[0046] When both the horizontal and vertical axis data meet the normality test results, the block is judged as a normally distributed block;

[0047] When the uniform distribution test and the normal distribution test cannot determine whether the data belongs to the uniform distribution model or the normal distribution model, the AIC information criterion is used to determine whether the horizontal and vertical coordinates belong to the uniform distribution model or the normal distribution model.

[0048] This application achieves high-precision discrimination of block distribution types through a dual-dimensional separation test and intelligent decision-making mechanism: First, the horizontal coordinate (X) and vertical coordinate (Y) data of the user's location are independently decoupled (step 2121), and uniform distribution and normal distribution tests are performed on the X and Y sequences respectively (step 2122). This design can accurately capture directional clustering characteristics (such as distribution along the coastline). For blocks with clear test results (X / Y uniform → uniform distribution; X / Y normal → normal distribution), the type is directly determined; for critically ambiguous blocks, the AIC information criterion is introduced to select the model (uniform model vs. normal model), and the distribution type with the highest goodness of fit is selected by quantitative indicators. This scheme completely solves the two major problems of "linear clustering misjudgment" and "critical state uncertainty", ensuring that a suitable probability model is assigned to each block, significantly improving the fidelity and robustness of user distribution modeling, and providing reliable spatial demand input for downstream beam optimization.

[0049] In satellite communication user location prediction, traditional static location probability models (such as the basic normal distribution) have a fundamental flaw: they assume that user locations are independent and identically distributed in the time dimension, completely ignoring the temporal correlation and motion inertia of user movement (such as speed maintenance and direction persistence). This makes it impossible to capture the dynamic evolution of user speed and direction (such as acceleration, turning, and cruising); and as the prediction time step increases, the location estimation error amplifies exponentially.

[0050] Furthermore, step 22 includes the following steps:

[0051] Step 221: Extract the normal distribution model from the location probability model;

[0052] Step 222: For the normal distribution model, generate the initial state based on the user's historical location information;

[0053] The initial state includes: the user's initial position X(t0), Y(t0), initial velocity v(t0), initial direction γ(t0) at time t0, and fixed parameters;

[0054] Fixed parameters include: , , , , ;

[0055] This indicates that the velocity approximates the mean. Indicates the direction asymptotically approximating the mean. Indicates the standard deviation of velocity. Indicates the standard deviation of direction; Represents the autoregressive coefficient of velocity. Indicates the direction of autoregression coefficient;

[0056] Noise signals are generated based on the normal distribution model. ;

[0057] ;

[0058] p represents the index of the time interval, where the time interval is the interval between user location updates. Represents the velocity variance. Indicates directional variance;

[0059] For speed noise, For directional noise, Represents a normal distribution;

[0060] Step 223: Establish a dynamic model of velocity and direction;

[0061] ;

[0062] ;

[0063] Where, v(t) p ) represents the velocity of the p-th time interval, γ(t) p ) represents the direction of the p-th time interval, v(t) p-1 ) represents the velocity in the (p-1)th time interval, γ(t) p-1 () indicates the direction of the (p-1)th time interval;

[0064] Step 224: Calculate the conditional mean and conditional variance;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] in, ;

[0070] ;

[0071] ;

[0072] ;

[0073] This represents the conditional mean in the X direction. This represents the conditional mean in the Y direction. Represents the conditional variance in the X direction. Represents the conditional variance in the X direction; This represents the conditional expectation of the velocity in the (p-1)th time interval. This represents the conditional expectation in the direction of the (p-1)th time interval. This represents the conditional variance of the velocity in the (p-1)th time interval. t represents the conditional variance in the direction of the (p-1)th time interval. step Indicates the size of the time interval;

[0074] Step 225: Calculate the current location distribution parameters based on the conditional mean and conditional variance. Generate a location probability density model based on the current location distribution parameters. ;

[0075] ;

[0076] ;

[0077] ;

[0078] Represents a bivariate normal distribution. This indicates the user's position in the X direction during the (p-1)th time interval. The time interval represents the user's position in the Y direction during the (p-1)th time interval;

[0079] Step 226: Replace the normal distribution model in the location probability model with the location probability density model to generate a location dynamic model.

[0080] This application overcomes the temporal blind spot of static probability models by performing refined modeling of user mobility: user state and dynamic parameters are initialized based on historical location data (step 222); a joint autoregressive model of velocity and direction is constructed, incorporating a physically driven noise term to represent random disturbances (step 223); then, the location probability density function is updated in real time by recursively calculating the conditional mean and conditional variance (steps 224-225) (step 226). This scheme dynamically reflects the prediction confidence through conditional variance, thus providing a high-fidelity, strongly temporally correlated user distribution evolution input for communication demand heatmaps, enabling precise pre-scheduling of beam resources.

[0081] Furthermore, step 3 includes the following steps:

[0082] Step 31: Pre-set initialization parameters, including the satellite's communication range, the satellite's initial beam range, and the initial beam allocation method;

[0083] Step 32: Establish signal power models for ground user k and satellite i based on the initial beam range. ;

[0084] ;

[0085] Where B is the bandwidth, N0 is the noise power spectral density, k is the user index, and i is the satellite index. The signal power received by user k;

[0086] Step 33: Establish signal power models for all satellites i Generate the communication power of satellite systems at each location in the target area and generate a beam allocation model.

[0087] In the technical solution provided in this application, by constructing a signal power model of each location and the covered satellites in the target area, a power allocation scheme under the current conditions can be obtained, thereby accurately generating model information describing the power distribution of users.

[0088] Further:

[0089] ;

[0090] in, G represents the transmit antenna gain. r P represents the user's antenna gain (a constant). t Let λ be the output power of the satellite transmitter, and λ be the wavelength of the carrier signal. Here, h represents the satellite altitude, and the comprehensive power attenuation factor is a constant.

[0091] ;

[0092] J1 represents the maximum transmit gain of the antenna along the main beam axis, J3 is a Bessel function of the first kind used to model the radiation mode of a circular beam, and J4 is a Bessel function of the third kind. This represents the off-axis angle of user k to the principal axis of satellite i antenna;

[0093] ;

[0094] This represents the efficiency of a phased array antenna, describing the effectiveness of the antenna's radiated energy. N represents the radiating element in the phased array antenna, and π represents pi. This indicates the 3dB gain angle of the beam;

[0095] ;

[0096] This represents an intermediate function used for antenna gain calculation, which depends on the sine values ​​of the off-axis angle and the 3dB gain angle;

[0097] ;

[0098] The beam's coverage radius is represented by h, the satellite altitude is represented by m, and the beam index is represented by m.

[0099] ;

[0100] ;

[0101] This represents the straight-line distance from user k to the center of beam m. This represents the geographic coordinates of user k. This represents the geographic coordinates of the center point of beam m of satellite i.

[0102] The technical solution provided in this application constructs a closed-loop calculation framework that accurately characterizes the special features of satellite communication and directly relates to beam resource parameters by tightly coupling geometric location, antenna radiation characteristics, and channel physical models. It calculates the off-axis angle based on the distance between the user and the beam center and the satellite altitude, accurately models the gain attenuation characteristics of a circular beam using Bessel functions, and explicitly introduces the impact of beam size on peak gain and edge user performance. Furthermore, it integrates path loss and transmit parameters using the Fries formula to solve for the received power, and finally outputs the rate using the Shannon capacity formula. This chain not only efficiently quantifies the dynamic effect of beam resources (such as the beam coverage radius determining the geometric relationship between the number of beams and user allocation) on the rate, but also provides an important basis for subsequently adjusting the remaining bandwidth and transmit power within the beam.

[0103] Furthermore, step 33 includes the following steps:

[0104] Step 331: Satellite i generates an initial beam allocation scheme based on user communication requests within its current coverage area;

[0105] Step 332: Satellite i calculates the signal power model for each location on each beam based on the remaining bandwidth. ;

[0106] Step 333: Develop the initial beam assignment scheme and the signal power model for each location on each beam. This serves as the beam assignment model for satellite i.

[0107] In the technical solution provided in this application, the beam allocation model includes an initial beam allocation scheme and a signal power model for each location. Therefore, the maximum communication rate that can be uploaded at each location can be determined in the beam allocation model. Based on the maximum communication rate, the actual handover success rate of users at these locations can be judged, and the beam bandwidth and transmission power can be adjusted accordingly.

[0108] Step 4 includes the following steps:

[0109] Step 41: Obtain the satellite update and replacement sequence T for all locations P. P T P ={T （x，y）1 T （x，y）2 ... T （x，y）i ...T （x，y）n}, based on satellite update and replacement sequence T P Obtain all positions P that satellite i needs to pass through, generate the satellite cruise area and the time it takes for satellite i to pass through each position in the satellite cruise area;

[0110] Step 42: Obtain a heat map of communication demand in the satellite cruise area;

[0111] Step 43: Set a fixed beam size and number of beams for satellite i to generate the beam matrix for satellite i;

[0112] Step 44: Obtain the time it takes for each beam in the beam matrix to pass through each location in the satellite's cruising area. Based on the real-time updated communication demand heatmap, adjust the bandwidth and transmission power of each beam so that the beams closest to the side of the satellite's travel have the maximum communication redundancy.

[0113] The technical solution provided in this application can accurately allocate the transmission power and bandwidth of each beam in the satellite based on the heat map of communication demand and the time when the satellite passes through the target area, thereby increasing the success rate of users switching satellites. Attached Figure Description

[0114] Figure 1 A flowchart illustrating the method for adjusting the beam coverage area of ​​a low-altitude satellite constellation.

[0115] Figure 2 This is a beam distribution diagram of the satellite.

[0116] Figure 3 This is a simplified diagram of the beam's centerline.

[0117] Figure 4 This is a bandwidth distribution diagram. Detailed Implementation

[0118] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments. The same reference numerals in the accompanying drawings represent the same components. It should be noted that the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the described embodiments of this application without creative effort are within the scope of protection of this application.

[0119] Compared to the embodiments shown in the accompanying drawings, feasible embodiments within the scope of this application may have fewer components, other components not shown in the drawings, different components, differently arranged components, or components with different connections, etc. Furthermore, two or more components in the drawings may be implemented in a single component, or a single component shown in the drawings may be implemented as multiple separate components.

[0120] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” and similar terms used in this specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “upper” and “lower” are used only to indicate relative positional relationships, and these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0121] refer to Figure 1 Example 1: The method for adjusting the beam coverage area of ​​a low-altitude satellite constellation includes the following steps:

[0122] Step 1: Obtain satellite constellation layout information and satellite operation information, and generate satellite communication alternation information for the target area.

[0123] Step 1 includes the following steps:

[0124] Step 11: Obtain the satellite constellation layout information S, S = {s1, s2, ... s} i …s n}, s i This represents the communication coverage area on the ground of the i-th satellite in the satellite constellation, where i represents the satellite index and n represents the total number of satellites in the constellation.

[0125] Low Earth orbit (LEO) satellites are relatively close to the Earth's surface, and each satellite can only cover a limited signal range. Furthermore, LEO satellites rotate rapidly and cannot synchronize with the Earth's rotation. Therefore, LEO satellites are typically arranged in a constellation of multiple satellites, which can provide communication services to the entire Earth's surface. This scheme assumes that the number of satellites in the constellation is n, and i is the index of the constellation.

[0126] Step 12: Obtain the moving speed of the satellite's communication coverage area on the ground, and calculate the communication coverage area s of each satellite. i The time period T passes through position P(x, y) （x，y）i Where x and y represent the x-coordinate and y-coordinate of position P, respectively, and T （x，y）i This indicates that position P is at point S. i The time period;

[0127] Each satellite cannot be synchronized with the ground and will move relative to the ground. Therefore, it is necessary to obtain the speed at which the satellite's communication coverage area moves relative to the ground. This speed is related to the number of satellites and their direction of rotation around the Earth. Once the satellite's altitude and rotation direction are determined, the speed of the satellite's communication coverage area relative to the ground is a fixed value. Knowing the speed of the satellite's communication coverage area relative to the ground, we can determine the time the satellite's communication coverage area remains at each location. This allows us to determine which specific satellites are communicating at each location during different time periods, thus determining the time period of each satellite.

[0128] Step 13: Based on each time period T of position P （x，y）i Generate satellite update succession sequence T at position P. P T P ={T （x，y）1 T （x，y）2 ... T （x，y）i ...T （x，y）n};

[0129] Step 14: Collect all satellite update and replacement sequences T P Generate satellite communication alternation information.

[0130] Satellite communication switching information is essentially the time when each satellite on the ground communicates with each location on the ground. Based on the satellite communication switching information, it can be determined at which time period each location P needs to switch communication satellites.

[0131] Step 2: Collect user distribution information and communication demand information in the target area in real time, and build and update the communication demand heat map in real time based on the user distribution information and communication demand information.

[0132] The target area is the region that needs optimization. In principle, low-Earth orbit satellites need to provide communication services to a large ground area, which is described as the target area in this scheme. Within the target area, the location, number of users, and communication needs are not fixed, but they generally conform to a distribution pattern. Based on this distribution pattern, a communication demand heatmap can be accurately described and updated in a timely manner.

[0133] Step 2 includes the following steps:

[0134] Step 21: Obtain the historical location information of users within the target area and establish a location probability model for the users.

[0135] When creating a communication demand heatmap, it is necessary to first determine the location distribution of users and then determine the average communication demand of users. Only in this way can a communication demand heatmap for the entire target area be obtained. The average communication demand of users is relatively stable, but the distribution of users can fluctuate significantly. Therefore, a probabilistic model is used to describe the location of users.

[0136] Furthermore, step 21 includes the following steps:

[0137] Step 211: Divide the target area into several blocks;

[0138] Historical location information refers to a user's location information within a certain time period. For example, within 5 minutes, the locations of all users within the entire target area are considered as one piece of historical location information.

[0139] The more time periods collected, the more historical location information is available, and each piece of historical location information can be used to calculate a location probability model.

[0140] The target area can be divided into multiple blocks. In practice, a block can be set as a rectangular window of fixed size, which can then divide the target area into several non-overlapping blocks.

[0141] Step 212: Determine whether a user belongs to a uniform random distribution or a normal distribution based on the user's historical location information for each block.

[0142] Blocks are primarily built based on probability models of user clustering. Users within a block generally follow one of two distributions: random or normal. A random distribution means that the probability of a user appearing at any location within the entire block is equal, while a normal distribution means that users tend to cluster around a certain clustering point.

[0143] Step 212: Includes the following steps:

[0144] Step 2121: Separate the user's location from the historical location information of the block into horizontal and vertical coordinate data;

[0145] Step 2122: Perform uniform distribution test and normal distribution test on the horizontal axis data in sequence, and perform uniform distribution test and normal distribution test on the vertical axis in sequence;

[0146] When both the horizontal and vertical coordinate data meet the uniform distribution test results, the block is judged as a uniformly distributed block.

[0147] When both the horizontal and vertical axis data meet the normality test results, the block is judged as a normally distributed block;

[0148] When the uniform distribution test and the normal distribution test cannot determine whether the data belongs to the uniform distribution model or the normal distribution model, the AIC information is used to determine whether the horizontal and vertical coordinates belong to the uniform distribution model or the normal distribution model.

[0149] The determination of uniform distribution tests, normal distribution tests, and AIC information criteria all have existing techniques, which will not be described further here. For ease of understanding, the determination principles of uniform distribution tests, normal distribution tests, and AIC information criteria will be briefly explained below:

[0150] The principle of the uniform distribution test is to determine whether observed data are equally likely to occur within a certain interval, without obvious clustering or biased regions. The Kolmogorov-Smirnov test (KS test) is generally used. The KS test determines this by calculating the maximum vertical distance (D statistic) between the cumulative distribution function of the sample and the cumulative distribution function of the theoretical uniform distribution. If this maximum distance is too large (exceeding the critical value), the data is considered to have significantly deviated from a uniform distribution.

[0151] The principle of the normality test is to determine whether observed data conforms to the characteristics of a "bell curve," meaning that the data is symmetrically distributed around a central value, and the probability of data moving away from the central value decreases according to a specific pattern. The Shapiro-Wilk test (SW test) is generally used. The Shapiro-Wilk test assesses normality by calculating the correlation (W statistic) between the sample data and the quantiles of the theoretical normal distribution. The stronger the correlation (the closer the W value is to 1), the more likely the data follows a normal distribution.

[0152] AIC (Akaike Information Criterion) is a model selection criterion. Its core idea is to seek the best balance between model goodness of fit (ability to interpret data) and model complexity (number of parameters) in order to find the "optimal" approximate model.

[0153] The formula for calculating the AIC value is: AIC = 2k - 2ln(L), where k is the number of model parameters and L is the maximum likelihood value of the model (reflecting the degree to which the model fits the data). The smaller the AIC value, the better the model.

[0154] In step 212, when neither the uniform distribution test nor the normal distribution test can definitively determine the distribution, a uniform distribution model (with fewer parameters, such as the interval range) and a normal distribution model (with more parameters, such as the mean and standard deviation) are constructed to fit the x-axis or y-axis data. Then, the AIC values ​​of the two models are calculated. The distribution type (uniform or normal) corresponding to the model with the smaller AIC value is selected as the judgment result. AIC prioritizes the model with a good fit but greater simplicity to avoid overfitting.

[0155] Step 213: Establish a uniform distribution probability model f1(x, y) for users belonging to blocks with uniform random distribution;

[0156] f1(x, y) = 1 / L, where L represents the number of coordinates within the block, and (x, y) represents the position coordinates within the block;

[0157] Step 214: For blocks where users belong to a normal distribution, establish a normal distribution probability model f2(x, y);

[0158] ;

[0159] Where (x, y) represents the location coordinates within the block. This represents the average location of users within a block along the x-direction. This represents the average location of users within the block along the y-axis. This represents the variance of user locations within a block in the x-direction. This represents the variance of the user's location in the y-direction within the block;

[0160] Step 215: Collect the probability models of all blocks and establish the location probability model.

[0161] The location probability model describes the probability of a user appearing at each location, and thus can accurately describe the distribution of users within the target area.

[0162] Since each region has an independent probability distribution model, the location probability model of the entire target region is actually a model matrix formed by arranging multiple probability distribution models according to the location relationship of the blocks.

[0163] Step 22: Obtain the user's average movement rate, and construct a location dynamic model based on the average movement rate and the location probability model.

[0164] Satellite communication users are not fixed, but rather mobile. To accurately describe the distribution of users, their movement speed needs to be taken into account.

[0165] The uniform distribution model primarily describes the probability distribution of users within the entire block, so it doesn't need to consider the impact of movement speed. The normal distribution model, on the other hand, mainly describes the clustering effect of users. This clustering effect is affected by user movement, therefore the movement speed needs to be superimposed on the normal distribution model. The specific solution is as follows:

[0166] Step 22 includes the following steps:

[0167] Step 221: Extract the normal distribution model from the location probability model;

[0168] Step 222: For the normal distribution model, generate the initial state based on the user's historical location information;

[0169] The initial state includes: the user's initial position X(t0), Y(t0), initial velocity v(t0), initial direction γ(t0) at time t0, and fixed parameters; the fixed parameters include: , , , , ;

[0170] This indicates that the velocity approximates the mean. Indicates the direction asymptotically approximating the mean. Indicates the standard deviation of velocity. Indicates the directional standard deviation. Represents the autoregressive coefficient of velocity. Indicates the direction of autoregression coefficient;

[0171] Noise signals are generated based on the normal distribution model. ;

[0172] ;

[0173] p represents the index of the time interval, where the time interval is the interval between user location updates. Represents the velocity variance. Indicates directional variance; For speed noise, For directional noise, Represents a normal distribution;

[0174] Step 223: Establish a dynamic model of velocity and direction;

[0175] ;

[0176] ;

[0177] Where, v(t) p) represents the velocity of the p-th time interval, γ(t) p ) indicates the direction of the p-th time interval; v(t) p-1 ) represents the velocity in the (p-1)th time interval, γ(t) p-1 () indicates the direction of the (p-1)th time interval;

[0178] Step 224: Calculate the conditional mean and conditional variance;

[0179] ;

[0180] ;

[0181] ;

[0182] ;

[0183] Among them, among them, ;

[0184] ;

[0185] ;

[0186] ;

[0187] This represents the conditional mean in the X direction. This represents the conditional mean in the Y direction. Represents the conditional variance in the X direction. Represents the conditional variance in the X direction; This represents the conditional expectation of the velocity in the (p-1)th time interval. This represents the conditional expectation in the direction of the (p-1)th time interval. This represents the conditional variance of the velocity in the (p-1)th time interval. t represents the conditional variance in the direction of the (p-1)th time interval. step Indicates the size of the time interval;

[0188] Step 225: Calculate the current location distribution parameters based on the conditional mean and conditional variance. Generate a location probability density model based on the current location distribution parameters. :

[0189] ;

[0190] ;

[0191] ;

[0192] Represents a bivariate normal distribution. This indicates the user's position in the X direction during the (p-1)th time interval. The time interval represents the user's position in the Y direction during the (p-1)th time interval.

[0193] Step 226: Replace the normal distribution model in the location probability model with the location probability density model to generate a location dynamic model.

[0194] The location probability density model generated in step 226 further incorporates the user's speed and direction of movement, thereby enabling a more accurate description of the user's movement.

[0195] Step 23: Obtain the user's average communication rate and render the user's average communication rate to the location dynamic model to generate a communication demand heatmap;

[0196] Step 24: Collect users' average communication rate and average mobility rate in real time to update the communication demand heatmap.

[0197] After obtaining the distribution information of users, the users in the distribution information are replaced with the corresponding average communication rate, and then a communication rate distribution map can be obtained. The communication rate distribution map is actually the communication demand that the satellite needs to carry. To this end, historical location information is continuously collected, and the communication demand heat map can be continuously updated.

[0198] Step 3: Establish a beam assignment model for each satellite passing through the target area.

[0199] Step 3 includes the following steps:

[0200] Step 31: Pre-set initialization parameters, including the satellite's communication range, the satellite's initial beam range, and the initial beam allocation method;

[0201] refer to Figure 2 The beam configuration is generally a circular beam. The size and number of beams are fixed, but the frequency range, power and bandwidth of each beam are different. Therefore, by adjusting the power and frequency bandwidth of each beam, the communication resources of each beam can be adjusted. In this way, more communication resources can be reserved to facilitate users to quickly switch communication satellites.

[0202] Step 32: Establish signal power models for ground user k and satellite i based on the initial beam range. ;

[0203] ;

[0204] Where B is the bandwidth, N0 is the noise power spectral density, k is the user index, and i is the satellite index. Let be the signal power received by user k.

[0205] The signal power model is essentially the communication rate between the user and the satellite. The higher the value of the signal power model, the more communication resources are available at the user's location, and the more efficient the switching of satellites.

[0206] refer to Figure 3 Furthermore:

[0207] ;

[0208] in, G represents the transmit antenna gain. r P represents the user's antenna gain (a constant). t Let λ be the output power of the satellite transmitter, and λ be the wavelength of the carrier signal. This is the comprehensive power attenuation factor (fixed value);

[0209] ;

[0210] J1 represents the maximum transmit gain of the antenna along the main beam axis, J3 is a Bessel function of the first kind used to model the radiation mode of a circular beam, and J4 is a Bessel function of the third kind. This represents the off-axis angle from user k to the main axis of satellite i antenna, and m represents the beam index;

[0211] ;

[0212] This represents the efficiency of a phased array antenna, describing the effectiveness of the antenna's radiated energy. N represents the radiating element in the phased array antenna, and π represents pi. This indicates the 3dB gain angle of the beam;

[0213] ;

[0214] This represents an intermediate function used for antenna gain calculation, which depends on the sine values ​​of the off-axis angle and the 3dB gain angle;

[0215] ;

[0216] The beam's coverage radius is represented by h, the satellite altitude is represented by m, and the beam index is represented by m.

[0217] ;

[0218] ;

[0219] This represents the straight-line distance from user k to the center of beam m. This represents the geographic coordinates of user k. This represents the geographic coordinates of the center point of beam m of satellite i.

[0220] Step 33: Establish signal power models for all satellites i Generate the communication power of satellite systems at each location in the target area and generate a beam allocation model.

[0221] When calculating the signal power model, the main variables are actually: the straight-line distance from user k to the center of beam m, the wavelength of the carrier signal, and the output power of the satellite transmitter. The remaining parameters are either fixed or difficult to adjust. The wavelength of the carrier signal actually corresponds to the communication frequency, which is the bandwidth of the beam. The output power of the satellite transmitter corresponds to the power of the beam. The straight-line distance from user k to the center of beam m corresponds to the positional relationship between the user's location and the beam.

[0222] Step 33 includes the following steps:

[0223] Step 331: Satellite i generates an initial beam allocation scheme based on user communication requests within its current coverage area;

[0224] The initial beam allocation scheme is designed to ensure that all users within the communication range of satellite i can communicate with the satellite at the required communication power.

[0225] Step 332: Satellite i calculates the signal power model for each location on each beam based on the remaining bandwidth. ;

[0226] Step 333: Develop the initial beam assignment scheme and the signal power model for each location on each beam. This serves as the beam assignment model for satellite i.

[0227] In step 331, a beam allocation scheme is generated, meaning all users within the communication range can communicate with the satellite. However, the bandwidth of each beam cannot be fully utilized, leaving some bandwidth unused. At this point, the signal power model of the beam for each possible user location within the satellite's communication range is calculated; that is, the maximum communication power between user k's location and the satellite. Thus, using the signal power model for each location, the remaining communication resources of the satellite can be determined. Therefore, the beam allocation model is essentially the current satellite beam allocation scheme and the remaining communication resources.

[0228] Step 4: Generate the order in which satellites pass through the target area based on the alternating information of satellite communication, and solve the optimal solution of the beam allocation model so that when adjacent satellites pass through the target area, the high channel capacity area in the beam allocation scheme can correspond to the high demand area in the communication demand heatmap.

[0229] Step 4 includes the following steps:

[0230] Step 41: Obtain the satellite update and replacement sequence T for all locations P. P T P ={T （x，y）1 T （x，y）2 ... T （x，y）i ...T （x，y）n}, based on satellite update and replacement sequence T P Obtain all the positions P that satellite i needs to pass through, generate the satellite cruise area and the time it takes for satellite i to pass through each position in the satellite cruise area.

[0231] The satellite's patrol area and the time it takes to pass through each location within that area are essentially the points in time that need to be considered for satellite handover. In practice, the higher the communication demand at a location, the more communication headroom should be reserved. Communication demand is obtained from the information in the communication demand heatmap.

[0232] Step 42: Obtain a heat map of communication demand in the satellite cruise area.

[0233] Step 43: Set a fixed beam size and number of beams for satellite i to generate the beam matrix for satellite i.

[0234] In this scheme, the beam size and number of beams are not adjusted. The main adjustment is to the beam bandwidth and beam transmit power. After adjusting the beam bandwidth and transmit power, the signal power model at each location will change.

[0235] Step 44: Obtain the time it takes for each beam in the beam matrix to pass through each location in the satellite's cruising area. Based on the real-time updated communication demand heatmap, adjust the bandwidth and transmission power of each beam so that the beams closest to the side of the satellite's travel have the maximum communication redundancy.

[0236] Step 44 includes the following steps:

[0237] Step 441: Obtain the communication requirements of each location in the satellite cruise area based on the communication requirements heatmap;

[0238] Step 442: Obtain the current beam assignment model to get the beam assignment scheme and the signal power model at each position on each beam. ;

[0239] Step 443: Obtain the communication requirements of the new locations that the edge beams of satellite i need to pass through in the future, and adjust the current beam allocation scheme so that the beams close to the side of the satellite's travel have the maximum communication redundancy.

[0240] In step 443, the communication power is adjusted first. When adjusting the communication power fails to ensure that the communication redundancy of the beam closest to the satellite's travel side does not exceed the threshold, the bandwidth of each beam is then adjusted.

[0241] refer to Figure 4 When adjusting the bandwidth of each beam, starting with the last beam in the direction of satellite movement relative to the ground, the bandwidth is gradually reduced, and the reduced bandwidth is allocated to the previous beam. The amount of bandwidth reduction is related to the signal power model at the edge of each beam. Related, signal power model The larger the value, the greater the bandwidth reduction. The surplus bandwidth is gradually collected from back to front, and the final surplus bandwidth is allocated to the beam closest to the direction of satellite travel.

[0242] The reason for obtaining surplus bandwidth from the back to the front is that when allocating bandwidth, the interference between adjacent beams needs to be considered. Reducing bandwidth from the back to the front can avoid large overlap in the bandwidth of adjacent beams and increase the rationality of bandwidth allocation.

[0243] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for adjusting the beam coverage area of ​​a low-altitude satellite constellation, characterized in that, include: Step 1: Obtain satellite constellation layout and operation information, and generate satellite communication alternation information for the target area; Step 2: Collect user distribution information and communication demand information in the target area in real time, and build and update the communication demand heat map in real time based on the user distribution information and communication demand information; Step 3: Establish a beam assignment model for each satellite passing through the target area; Step 4: Generate the order in which satellites pass through the target area based on the alternating information of satellite communication, and solve for the optimal solution of the beam allocation model so that when adjacent satellites pass through the target area, the high channel capacity area in the beam allocation scheme can correspond to the high demand area in the communication demand heatmap. Step 1 includes the following steps: Step 11: Obtain the satellite constellation layout information S, S = {s1, s2, ... s} i …s n }, s i This represents the communication coverage area on the ground of the i-th satellite in the satellite constellation, where i represents the satellite index and n represents the total number of satellites in the constellation. Step 12: Obtain the moving speed of the satellite's communication coverage area on the ground, and calculate the communication coverage area s of each satellite. i The time period T passes through position P(x, y) （x，y）i Where x and y represent the x-coordinate and y-coordinate of position P, respectively, and T （x，y）i This indicates that position P is at point S. i The time period; Step 13: Based on each time period T of position P （x，y）i Generate satellite update succession sequence T at position P. P T P ={T （x，y）1 T （x，y）2 ... T （x，y）i ...T （x，y）n }; Step 14: Collect all satellite update and replacement sequences T P Generate satellite communication alternation information; Step 4 includes the following steps: Step 41: Obtain the satellite update and replacement sequence T for all locations P. P T P ={T （x，y）1 T （x，y）2 ... T （x，y）i ...T （x，y）n }, based on satellite update and replacement sequence T P Obtain all positions P that satellite i needs to pass through, generate the satellite cruise area and the time it takes for satellite i to pass through each position in the satellite cruise area; Step 42: Obtain a heat map of communication demand in the satellite cruise area; Step 43: Set a fixed beam size and number of beams for satellite i to generate the beam matrix for satellite i; Step 44: Obtain the time it takes for each beam in the beam matrix to pass through each location in the satellite's cruising area. Based on the real-time updated communication demand heatmap, adjust the bandwidth and transmission power of each beam so that the beams closest to the side of the satellite's travel have the maximum communication redundancy.

2. The method for adjusting the beam coverage area of ​​a low-altitude satellite constellation according to claim 1, characterized in that, Step 2 includes the following steps: Step 21: Obtain historical location information of users within the target area and establish a user location probability model; Step 22: Obtain the user's average movement speed, and construct a location dynamic model based on the average movement speed and the location probability model; Step 23: Obtain the user's average communication rate and render the user's average communication rate to the location dynamic model to generate a communication demand heatmap; Step 24: Collect users' average communication rate and average mobility rate in real time to update the communication demand heatmap.

3. The method for adjusting the beam coverage area of ​​a low-altitude satellite constellation according to claim 2, characterized in that, Step 21 includes the following steps: Step 211: Divide the target area into several blocks; Step 212: Determine whether a user belongs to a uniform random distribution or a normal distribution based on the user's historical location information for each block; Step 213: Establish a uniform distribution probability model f1(x, y) for users belonging to blocks with uniform random distribution; f1(x,y)= l / L, where L represents the number of coordinates within the block, and (x, y) represents the position coordinates within the block. l This indicates the average number of users within a block. Step 214: For blocks where users belong to a normal distribution, establish a normal distribution probability model f2(x, y); ； Where (x, y) represents the location coordinates within the block. This represents the average location of users within a block along the x-direction. This represents the average location of users within the block along the y-axis. This represents the variance of user locations within a block in the x-direction. This represents the variance of the user's location in the y-direction within the block; Step 215: Collect the probability models of all blocks and establish the location probability model.

4. The method for adjusting the beam coverage area of ​​a low-altitude satellite constellation according to claim 3, characterized in that, Step 212: Includes the following steps: Step 2121: Separate the user's location from the historical location information of the block into horizontal and vertical coordinate data; Step 2122: Perform uniform distribution test and normal distribution test on the horizontal axis data in sequence, and perform uniform distribution test and normal distribution test on the vertical axis in sequence; When both the horizontal and vertical coordinate data meet the uniform distribution test results, the block is judged as a uniformly distributed block. When both the horizontal and vertical axis data meet the normality test results, the block is judged as a normally distributed block; When the uniform distribution test and the normal distribution test cannot determine whether the data belongs to the uniform distribution model or the normal distribution model, the AIC information is used to determine whether the horizontal and vertical coordinates belong to the uniform distribution model or the normal distribution model.

5. The method for adjusting the beam coverage area of ​​a low-altitude satellite constellation according to claim 4, characterized in that, Step 22 includes the following steps: Step 221: Extract the normal distribution model from the location probability model; Step 222: For the normal distribution model, generate the initial state based on the user's historical location information; The initial state includes: the user's initial position X(t0), Y(t0), initial velocity v(t0), initial direction γ(t0) at time t0, and fixed parameters; the fixed parameters include: , , , , ; This indicates that the velocity approximates the mean. Indicates the direction asymptotically approximating the mean. Indicates the standard deviation of velocity. Indicates the directional standard deviation. Represents the autoregressive coefficient of velocity. Indicates the direction of autoregression coefficient; Noise signals are generated based on the normal distribution model. ; ； p represents the index of the time interval, where the time interval is the interval between user location updates. Represents the velocity variance. Indicates directional variance; For speed noise, For directional noise, Represents a normal distribution; Step 223: Establish a dynamic model of velocity and direction; ； ； Where, v(t) p ) represents the velocity of the p-th time interval, γ(t) p ) represents the direction of the p-th time interval, v(t) p-1 ) represents the velocity in the (p-1)th time interval, γ(t) p-1 () indicates the direction of the (p-1)th time interval; Step 224: Calculate the conditional mean and conditional variance; ； ； ； ； in, ; ； ； ； This represents the conditional mean in the X direction. This represents the conditional mean in the Y direction. Represents the conditional variance in the X direction. Represents the conditional variance in the X direction; This represents the conditional expectation of the velocity in the (p-1)th time interval. This represents the conditional expectation in the direction of the (p-1)th time interval. This represents the conditional variance of the velocity in the (p-1)th time interval. t represents the conditional variance in the direction of the (p-1)th time interval. step Indicates the size of the time interval; Step 225: Calculate the current location distribution parameters based on the conditional mean and conditional variance. Generate a location probability density model based on the current location distribution parameters. ; ; ; ； Represents a bivariate normal distribution. This indicates the user's position in the X direction during the (p-1)th time interval. This indicates the user's position in the Y direction during the (p-1)th time interval; Step 226: Replace the normal distribution model in the location probability model with the location probability density model to generate a location dynamic model.

6. The method for adjusting the beam coverage area of ​​a low-altitude satellite constellation according to claim 1, characterized in that, Step 3 includes the following steps: Step 31: Pre-set initialization parameters, including the satellite's communication range, the satellite's initial beam range, and the initial beam allocation method; Step 32: Establish signal power models for ground user k and satellite i based on the initial beam range. ; ； Where B is the bandwidth, N0 is the noise power spectral density, k is the user index, and i is the satellite index. The signal power received by user k; Step 33: Establish signal power models for all satellites i Generate the communication power of satellite systems at each location in the target area and generate a beam allocation model.

7. The method for adjusting the beam coverage area of ​​a low-altitude satellite constellation according to claim 6, characterized in that, ； in, G represents the transmit antenna gain. r P represents the user's antenna gain. t Let λ be the output power of the satellite transmitter, and λ be the wavelength of the carrier signal. The comprehensive power attenuation factor is given, and h represents the satellite altitude. ； J1 represents the maximum transmit gain of the antenna along the main beam axis, J3 is a Bessel function of the first kind used to model the radiation mode of a circular beam, and J4 is a Bessel function of the third kind. This represents the off-axis angle of user k to the principal axis of satellite i antenna; ； This represents the efficiency of a phased array antenna, describing the effectiveness of the antenna's radiated energy. N represents the radiating element in the phased array antenna, and π represents pi. This indicates the 3dB gain angle of the beam; ； This represents an intermediate function used for antenna gain calculation, which depends on the sine values ​​of the off-axis angle and the 3dB gain angle; ； The beam's coverage radius is represented by h, the satellite altitude is represented by m, and the beam index is represented by m. ； ； This represents the straight-line distance from user k to the center of beam m. This represents the geographic coordinates of user k. This represents the geographic coordinates of the center point of beam m of satellite i.

8. The method for adjusting the beam coverage area of ​​a low-altitude satellite constellation according to claim 7, characterized in that, Step 33 includes the following steps: Step 331: Satellite i generates an initial beam allocation scheme based on user communication requests within its current coverage area; Step 332: Satellite i calculates the signal power model for each location on each beam based on the remaining bandwidth. ; Step 333: Develop the initial beam assignment scheme and the signal power model for each location on each beam. This serves as the beam assignment model for satellite i.

Images