Low earth orbit satellite network cross-sat cross-beam fast switching method and system

Abstract

The application provides a low-orbit satellite network cross-satellite cross-beam fast switching method. In view of the frequent switching problem caused by the high-speed movement of satellites in the low-orbit satellite network, a weighted bipartite graph model of users and satellites is established, the channel quality, the residual service time, the satellite load and the power distribution are taken as multi-objective optimization indexes, the information entropy theory is used to objectively weight each index, and a single-objective optimization problem is converted. The method comprehensively considers the link stability, the system load balancing and the resource utilization rate, the user access and the switching process are managed by the earth station, and the optimal service satellite is dynamically calculated. The simulation results show that, compared with the maximum residual service time and the optimal channel quality strategy, the proposed method has significant advantages in reducing the switching times, balancing the satellite load and maintaining the user received signal-to-noise ratio, and is especially suitable for real-time communication scenes under large-scale low-orbit satellite constellation.

Description

Technical Field

[0001] This invention relates to the field of low-Earth orbit satellite communication network technology, specifically to a satellite handover optimization method, and particularly to a cross-satellite and cross-beam handover strategy for low-Earth orbit satellite networks based on multi-objective decision-making. Background Technology

[0002] Low Earth Orbit (LEO) satellite communications have attracted widespread attention due to their advantages of global coverage and low latency. In LEO satellite communication systems, satellites orbit the Earth at high speeds, and the service time of a single satellite for a user is limited. Users need to frequently switch between multiple visible satellites during communication. Therefore, whenever a switch occurs or a new call is initiated, the optimal satellite must be selected from the currently visible satellites for access.

[0003] Existing handover strategies include satellite selection based on methods such as maximum elevation angle, maximum received signal strength, and longest remaining service time. Z. Wu et al. proposed a graph-based handover framework that obtains handover schemes by assigning weights to connecting graph edges and using the shortest path algorithm. However, the above methods are mostly based on single-indicator decisions, making it difficult to consider multiple factors such as channel quality, service duration, and system load balancing. Although some researchers have attempted to weight and synthesize multiple indicators, a unified understanding has not yet been reached regarding the determination of weight coefficients, handover triggering conditions, and the selection of evaluation indicators, limiting the practical application effectiveness of weighted strategies in satellite handover.

[0004] Therefore, how to make objective and efficient switching decisions among multiple indicators is an urgent problem to be solved in current LEO satellite communications. Summary of the Invention

[0005] This invention provides a method and system for rapid cross-satellite and cross-beam switching in low-Earth orbit satellite networks, aiming to solve the above-mentioned problems.

[0006] According to an embodiment of the present invention, a method for rapid cross-satellite and cross-beam handover in low-Earth orbit satellite networks is provided, comprising: The service relationship between each user terminal and each satellite in discrete time slots is obtained. A weighted bipartite graph is constructed based on the service relationship. The edges of the weighted bipartite graph are used to represent the links between the user terminal and the satellite. The weight of the edge is determined by a combination of multiple weighting indicators. The entropy weight method is used to assign values ​​to the multi-weight indicators to obtain the weight coefficients corresponding to each weight indicator; Satellites that meet the visibility conditions within the current discrete time slot are selected as a candidate set. Based on the weight coefficients corresponding to each weight index, the comprehensive evaluation value between each satellite and the user terminal in the candidate set is calculated. The satellite with the highest comprehensive evaluation value is selected to perform user handover.

[0007] Furthermore, the multi-weighted indicators specifically include: Channel quality metrics are used to characterize the stability of downlink transmission between users and satellites; The remaining service time metric is used to characterize the duration for which a satellite can sustainably provide communication services to users; The satellite service user count metric is used to characterize the number of online users currently served by the satellite; Allocable power is a measure of the maximum transmission power that a satellite can allocate to a target user.

[0008] Furthermore, the step of obtaining the service relationship between each user terminal and each satellite in discrete time slots, and constructing a weighted bipartite graph based on the service relationship, specifically includes: The user terminal set and the satellite set are respectively used as the two sets of vertices in the weighted bipartite graph; Discrete time slots are divided based on satellite ephemeris and user location. A user-satellite connection matrix is ​​established within a single time slot. Vertex connection edges are generated when a satellite covers a user. The multi-weighted indicators are transformed into weight parameters for each connecting edge, generating a time-slot dynamic weighted bipartite graph.

[0009] Furthermore, the method for obtaining the channel quality index includes: Based on a uniform planar array antenna configured for satellites, a ray tracing channel modeling method is used to obtain the downlink channel response between the satellite and the user; Channel gain is calculated based on the speed of light, satellite orbital altitude, user elevation angle, and atmospheric fading coefficient. The channel quality metric is determined based on the downlink channel response and channel gain.

[0010] Furthermore, the method for obtaining the remaining service time indicator includes: The ground station calculates the start and end times of coverage for each satellite in each region based on pre-stored satellite ephemeris data, and generates a coverage time matrix; Based on the user's current location, query the coverage end time of each visible satellite from the coverage time matrix; The difference between the current time and the coverage end time is taken as the remaining service time for each satellite to the current user.

[0011] Furthermore, the method for obtaining the satellite service user number indicator includes: The ground station calculates the sum of the column vector elements corresponding to each satellite in the user-satellite connection matrix in real time to obtain the number of users currently served by each satellite; When the number of users currently served by a satellite reaches its maximum service capacity, the satellite stops accepting new user access.

[0012] Furthermore, the method for obtaining the allocatable power index includes: With the goal of maximizing the total channel capacity of the system, the total satellite transmission power is allocated among the users based on the channel gain between each user and the satellite using a water-filling algorithm. The power value that the satellite is allocated to the current user is used as the allocatable power index.

[0013] Furthermore, the method of assigning values ​​to the multi-weighted indicators using the entropy weight method specifically includes: An evaluation matrix was constructed for each candidate satellite under each indicator, and then normalized. Calculate the information entropy value of each indicator based on the normalized data; The weighting coefficient is determined based on the information entropy value of each indicator. The smaller the information entropy value, the larger the weighting coefficient, and vice versa. The comprehensive evaluation value is the weighted sum of each indicator value and its corresponding weight coefficient.

[0014] Furthermore, the process of selecting satellites that meet the visibility conditions within the current discrete time slot as a candidate set, calculating the comprehensive evaluation value between each satellite in the candidate set and the user terminal based on the weight coefficients corresponding to each weight index, and selecting the satellite with the highest comprehensive evaluation value to perform user handover specifically includes: The user terminal obtains its own geographic coordinates and reports them to the ground station; The ground station queries the coverage time matrix based on the user's location and filters the satellites that cover that location at the current time to form a candidate set; The ground station calculates the comprehensive evaluation value of each satellite in the candidate set and determines the satellite with the highest comprehensive evaluation value as the target satellite; The ground station sends a handover command to the user terminal, and the user terminal performs the access or handover to the target satellite.

[0015] According to an embodiment of the present invention, a rapid cross-satellite and cross-beam switching system for low-Earth orbit satellite networks is provided, comprising: A bipartite graph construction module is used to obtain the service relationship between each user terminal and each satellite in discrete time slots. A weighted bipartite graph is constructed based on the service relationship. The edges of the weighted bipartite graph are used to represent the links between the user terminal and the satellite. The weight of the edge is determined by a combination of multiple weight indicators. The module for obtaining weight coefficients uses the entropy weighting method to assign values ​​to the multi-weight indicators, thereby obtaining the weight coefficients corresponding to each weight indicator. The handover module is used to filter satellites that meet the visibility conditions within the current discrete time slot as a candidate set, calculate the comprehensive evaluation value between each satellite and the user terminal in the candidate set based on the weight coefficients corresponding to each weight index, and select the satellite with the highest comprehensive evaluation value to perform user handover.

[0016] By constructing a weighted bipartite graph containing multiple weighted indicators and objectively determining the weights of each indicator using the entropy weight method, the handover decision comprehensively considers multiple factors such as channel quality, remaining service time, satellite load, and allocable power. This effectively avoids frequent handovers caused by decisions based on a single indicator. Simulation results show that the number of handovers is significantly lower than that of the optimal channel quality strategy and close to that of the maximum remaining service time strategy. At the same time, the variance of each satellite load can quickly converge to an equilibrium state, and the user's received signal-to-noise ratio remains at a relatively good level. While reducing system signaling overhead and the risk of dropped calls, this approach balances system load balancing and user communication quality, providing a scientific and objective quantitative basis for handover decisions in low-Earth orbit satellite networks. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for rapid cross-satellite and cross-beam switching in a low-orbit satellite network according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a low-orbit satellite network cross-satellite and cross-beam rapid switching system according to an embodiment of the present invention. Detailed Implementation

[0018] Satellite communications offer seamless wireless coverage, complementing and extending terrestrial communications. They hold promise for integration into future wireless networks, particularly after 5G. In recent years, low Earth orbit (LEO) satellite communications, with altitudes ranging from 500 km to 1500 km, have garnered significant attention due to their potential to provide global wireless access and higher data rates. Furthermore, LEO satellites exhibit lower signal transmission latency compared to medium and high Earth orbit satellites, enabling real-time communication and higher throughput. The lower orbital altitude also reduces the power requirements for transmission and minimizes signal attenuation, both crucial for reducing the size of terminal devices. Significant progress has been made in projects such as Iridium, Globalstar, and SpaceX.

[0019] While LEO satellite communication systems offer unique advantages over other wireless communication systems, LEO satellites orbit the Earth at high speeds. For users, the service time provided by each LEO satellite is limited, typically less than the duration of a call. During a call, multiple satellites may be needed sequentially to ensure uninterrupted communication. When the currently serving satellite becomes unavailable, the user must switch to another visible satellite. Due to the nature of the LEO satellite constellation, a user is usually within the coverage area of ​​multiple satellites at any given time. Whenever a handover occurs or a new call is initiated, the user needs to select one of the currently visible satellites for access. However, unlike terrestrial wireless networks that rely on ground base stations, users in satellite networks cannot make handover decisions solely based on received signal strength. Other factors must be considered, such as the service time the access satellite can provide and system load balancing. These factors correspond to different handover strategies, such as maximum remaining service time, maximum number of idle channels, and maximum received signal strength. For the system, different choices represent different optimization objectives.

[0020] Regarding satellite handover strategies, A. Bottcher and R. Werner proposed a strategy based on selecting the satellite for handover based on the maximum elevation angle. However, the maximum elevation angle of a satellite does not necessarily reflect the true link quality, so the handover performance of this strategy is not ideal. M. Gkizeli et al. proposed a hard handover scheme and a hybrid channel adaptive handover scheme, both of which use the elevation-related signal strength as the satellite handover standard. E. Papapetrou et al. proposed three different satellite handover factors: maximum elevation angle, longest visibility time, and maximum number of idle channels, and applied these three factors to satellite selection for new calls and handover services, respectively, analyzing the impact of different combinations on system performance. Z. Wu et al. proposed a graph theory-based satellite handover framework, which sets different weights for the edges in the satellite connection graph according to different satellite selection criteria, and then uses the shortest path algorithm to obtain the optimal handover scheme for the user. In LEO communication, the selection of the satellite for handover is more complex, and considering only a single factor is obviously not comprehensive enough. Some researchers have proposed weighting multiple factors to obtain the optimal satellite selection, but there is still no consensus on how to determine the weights, when to trigger the handover, and which indicators to select.

[0021] This invention, referencing existing satellite handover strategies in LEO satellite communication, uses a bipartite graph-based satellite handover model, assigning the weight Q of a certain edge between the user and the satellite in the bipartite graph... ij,tThe objectives are determined by channel quality, the remaining service time of satellite j and user i at time t, the number of users served by satellite j at time t, and the power that satellite j can allocate to user i. For this typical multi-objective problem, we use the entropy weight method to transform it into a single-objective problem, which serves as the basis for user access to the satellite. Simulation results show that, compared with other strategies, this method has certain advantages in terms of handover frequency, user reception signal-to-noise ratio, and system load balancing.

[0022] Method Implementation Examples The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings. However, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments. According to an embodiment of the present invention, a method for rapid cross-satellite and cross-beam switching in low-Earth orbit satellite networks is provided. Figure 1 This is a flowchart of a method for rapid cross-satellite and cross-beam switching in a low-Earth orbit satellite network according to an embodiment of the present invention. Figure 1 As can be seen, the method for rapid cross-satellite and cross-beam switching in low-Earth orbit satellite networks according to embodiments of the present invention specifically includes: S1. Obtain the service relationship between each user terminal and each satellite in discrete time slots, and construct a weighted bipartite graph based on the service relationship. The edges of the weighted bipartite graph are used to represent the links between user terminals and satellites, and the weights of the edges are determined by a combination of multiple weighting indicators, specifically including: In low-Earth orbit (LEO) satellite communication systems, satellites orbit the Earth at high speeds, and the service time of a single satellite for a user is limited. Users need to frequently switch between multiple visible satellites during communication. Due to the characteristics of LEO satellite constellations, users are usually within the coverage area of ​​multiple satellites at any given time. Therefore, whenever a handover occurs or a new call is made, the optimal satellite needs to be selected from the currently visible satellites for access.

[0023] Because the connection between satellites and users changes dynamically over time, this embodiment divides time into several discrete time slots. Within each time slot, the connection between satellites and users can be considered static. Using the user terminal set and the satellite set as two sets of vertices in a weighted bipartite graph, the satellite coverage of users within each time slot is determined based on satellite ephemeris and user location. A user-satellite connection matrix is ​​established, and when a satellite covers a user, a connection edge is generated between the corresponding vertices. The ground station centrally manages the user access and handover process, maintaining and updating the weighted bipartite graph within each time slot.

[0024] To comprehensively evaluate the service level of each satellite to users, this embodiment sets multiple weighted indicators on each connecting edge of the weighted bipartite graph, specifically including: Channel quality metrics are used to characterize the stability of downlink transmission between users and satellites; The remaining service time metric is used to characterize the duration for which a satellite can sustainably provide communication services to users; The satellite service user count metric is used to characterize the number of online users currently served by the satellite; Allocable power is a measure of the maximum transmission power that a satellite can allocate to a target user.

[0025] The above four indicators characterize the satellite's service capabilities from four dimensions: link stability, service continuity, system load balancing, and resource utilization, respectively, providing a data foundation for subsequent multi-target switching decisions.

[0026] The methods for obtaining channel quality indicators are as follows: A single satellite needs to serve multiple single-antenna users simultaneously. The satellite is configured with a uniform planar array (UPA) consisting of M = Mx × My antennas, where Mx and My represent the number of antennas along the x-axis and y-axis, respectively. Without loss of generality, the antenna spacing is assumed to be half a wavelength. Since different users are typically separated by several wavelengths, the channels between the satellite and different users can be considered uncorrelated. Based on the ray tracing channel modeling method, the downlink channel response of low-Earth orbit satellite j and user i at time t and frequency f can be expressed as: (1); In the formula, Indicates the number of propagation paths in the user channel. , , v and v represent the channel gain, Doppler shift, time delay, and downlink channel array response vector, respectively.

[0027] Among them, channel gain It can be given by the following formula: (2); Where c is the speed of light and h is the orbital altitude. The elevation angle is A(d), and A(d) represents the coefficient related to atmospheric decay.

[0028] Assuming the above parameters remain constant during the coherence time, these parameters will change when the relative positions of the satellite and the user change significantly.

[0029] The remaining service time metric is obtained as follows: Because satellites orbit the Earth at high speeds (far exceeding the speeds of most ground users), the handover process is primarily caused by satellite motion. The service time a satellite can provide to users is a key indicator, directly impacting the number of handovers during service delivery. In this study, the ground station, based on the predictability of the satellite constellation, is responsible for calculating the coverage of each satellite within its managed area. When a user needs to access the communication system or perform a satellite handover, they can send location information to the ground station, which will then return a coverage time matrix. : (3); In this 2×N dimensional matrix, the first row elements Indicates the start time of satellite j's coverage of the target area, the next row element Let represent the coverage end time (1≤i≤N and i∈Z, where N is the total number of satellites in the constellation). Therefore, the remaining service time of satellite j for user i at time t can be expressed as: (4); The method for obtaining the number of satellite service users is as follows: When selecting a satellite for access, users should consider the current number of users served by the satellite as an important reference indicator to avoid the unreasonable situation where some satellites are overloaded while others are underloaded, thereby improving the utilization rate of satellite resources. This embodiment of the invention uses the following formula to calculate the number of users served by satellite j at time t: (5); In the formula, E represents the connection matrix between the user and the satellite. This represents the maximum user capacity that a satellite can serve. The calculation method accurately reflects the instantaneous load status of each satellite by summing the column vector elements of the connection matrix in real time, providing a quantitative basis for load balancing decisions.

[0030] The method for obtaining the allocatable power index is as follows: Satellite transmission power is a finite resource, and its allocation efficiency directly affects system capacity. This embodiment employs a water-filling algorithm to adaptively allocate the total satellite transmission power based on the real-time gain of each user channel. When channel conditions are favorable, more power is allocated to increase the transmission rate; when channel conditions deteriorate, less power is allocated to conserve resources, thereby maximizing the total system channel capacity.

[0031] (6); (7); N is the total channel capacity of the system, and N is the number of channels. It is the power that satellite j allocates to user i at time t. It is the channel gain of user i and satellite j at time t. It is the noise power spectral density. This represents the total transmission power. The global optimal solution can be obtained using Lagrange multiplication.

[0032] Based on the limitations of the above indicators, in this embodiment of the invention, the service quality provided by satellite j to user i at time t is quantified as Qij,t. The service level of each satellite to the user is comprehensively evaluated based on the service quality.

[0033] (8); (9); (10); (11); (12); in, This represents the channel quality between user i and satellite j at time t; This represents the remaining service time of satellite j for user i at time t; This represents the number of users served by satellite j at time t; This represents the power that satellite j can allocate to user i at time t.

[0034] S2. The entropy weight method is used to assign values ​​to the multi-weight indicators to obtain the weight coefficients corresponding to each weight indicator. In low-Earth orbit satellite communication systems, the main reasons for triggering inter-satellite handover include three aspects: Firstly, when a satellite moves beyond the user's line of sight and the service becomes unavailable, the user must switch to another visible satellite. Secondly, inter-satellite channels have time-varying characteristics. When the channel quality deteriorates to the point that it cannot meet the user's minimum communication requirements, a handover must be performed to ensure service quality. Third, due to the limited channel capacity of a single satellite, when some satellites are overloaded while others are lightly loaded due to unreasonable access strategies or uneven distribution of users, users need to be switched from high-load satellites to low-load satellites in order to optimize the allocation of system resources.

[0035] The three handover triggering conditions mentioned above correspond to multi-dimensional indicators such as channel quality, remaining service time, satellite load, and allocable power. Decisions based on a single indicator are difficult to simultaneously take into account all optimization objectives.

[0036] Therefore, this embodiment employs the entropy weight method to objectively assign values ​​to the multi-weighted indicators. The basic idea of ​​the entropy weight method originates from information theory: information entropy is an indicator that measures the degree of disorder in a system. The smaller the information entropy of an indicator, the greater its data variation (i.e., dispersion), and the more information it can provide in comprehensive evaluation; therefore, it should be assigned a higher weight. Conversely, the larger the information entropy, the smaller the weight. The entropy weight method achieves objective quantification of weights by analyzing the inherent discrete characteristics of each indicator's data, avoiding the bias caused by subjective weighting in traditional weighting methods.

[0037] Specifically, the method of assigning values ​​to the multi-weighted indicators using the entropy weight method includes the following sub-steps: S21, Calculation; Ideally, users expect satellites to simultaneously offer optimal channel quality, maximum remaining service time, minimum number of users served, and maximum allocated power. However, in real-world systems, a single satellite cannot meet all these conditions at the same time, necessitating a multi-objective trade-off optimization scheme.

[0038] In a bi-objective optimization problem, let... (x) and (x) represents the optimization objective. When only considering... When (x), its minimum value is b; considering only When (x) is true, the minimum value is a. Clearly, neither can be optimal simultaneously. Using a weighted summation method, the multi-objective problem can be quantified as: (13); in This represents the weight coefficient for the i-th objective, and its physical meaning is: when it needs to be optimized... At that time, a larger amount should be given A smaller value can be used if the value is larger; conversely, a smaller value can be used if the value is smaller. Weighting ratio / Directly reflects Compared to The importance of.

[0039] While weighted methods are an effective means of solving multi-objective problems, scientifically determining the weights remains challenging. This paper innovatively employs the entropy weight method, which achieves objective quantification of weights by analyzing the data dispersion characteristics of each indicator.

[0040] S22. Determine the weight by the entropy value.

[0041] According to the basic principles of information theory, information entropy is an indicator that measures the degree of disorder in a system (corresponding to the amount of information characterizing the degree of order in a system). When the information entropy of an indicator is low, it indicates a high degree of data variation, providing more information in the comprehensive evaluation and thus deserving higher weight; conversely, the higher the information entropy, the lower the weight. It should be noted that "information amount" here specifically refers to the degree of data dispersion—the greater the dispersion, the higher its contribution to the evaluation results. The formula for calculating information entropy is: When all states have equal probability of occurring, that is... At this point, the entropy value reaches its maximum, emax = lnm. The weights determined by the entropy weight method are characterized by strong objectivity and good interpretability. Based on the above analysis, the comprehensive evaluation index for satellite services... It can be represented as: (14); In the formula (n=1,2,3,4) represent the entropy weights of each evaluation index.

[0042] It should be noted that the entropy weight calculation described above is performed uniformly by the ground station. When a user triggers a handover request, the ground station acquires real-time indicator data for all candidate satellites at the current moment and calculates the entropy weight of each indicator in real time. Since the ephemeris data of the low-Earth orbit satellite constellation is predictable, and the indicator data can be collected and calculated in a relatively short time, the entropy weight method described above can meet the real-time requirements of handover decisions. Furthermore, during system operation, the ground station can also periodically update the entropy weight of each indicator based on changes in network status to adaptively respond to changes in optimization priorities under different scenarios.

[0043] Through the above steps, this embodiment achieves objective weighting of four indicators: channel quality, remaining service time, satellite load, and allocable power. It transforms the originally mutually constraining multi-objective optimization problem into a single-objective decision-making problem, providing a scientific and objective weighting basis for the calculation of the comprehensive evaluation value in the subsequent step S3.

[0044] S3. Select satellites that meet the visibility conditions within the current discrete time slot as a candidate set. Calculate the comprehensive evaluation value between each satellite and the user terminal in the candidate set based on the weight coefficients corresponding to each weight index. Select the satellite with the highest comprehensive evaluation value to perform user handover.

[0045] During the access phase, each user first determines their geographic coordinates using their own positioning system and sends them to the ground station. The ground station determines the user's location based on the received information and then returns the satellite coverage matrix for that area to the user. The user then combines the coverage information sent by the ground station with the current time (which must meet certain conditions). (Conditions), determine the available satellite queue, and calculate the parameters of each satellite in the queue. The value is then selected, and a connection is established with the satellite corresponding to the maximum value.

[0046] Considering that a single satellite cannot continuously provide service, and that factors such as low elevation angles can affect channel quality, this paper adopts a periodic active handover mechanism to avoid the impact of long-term channel degradation on communication experience and system load balancing. The handover process is similar to the access phase, but since the service range of a single satellite is hundreds of kilometers, and the short-term displacement of users is negligible relative to the satellite beam range, there is no need to reacquire coverage information during the handover phase. The target satellite selection strategy is consistent with that of the access phase.

[0047] For ease of description and calculation, the time granularity is set to the duration required for a single handover / access, i.e., the handover / access operation is initiated at time t and completed at time t+1.

[0048] The performance of the proposed satellite handover strategy was evaluated through simulation experiments. Simulation parameters were set based on Globalstar satellite parameters and in conjunction with future trends in low-Earth orbit satellite development. The satellite uses an 8×8 uniform planar array with an antenna spacing of half a wavelength.

[0049] Analysis of constellation coverage characteristics using STK11 software shows that users in the target area can be covered by more than four satellites simultaneously. The number of handovers for the three strategies increases linearly with call duration, with the maximum remaining service time strategy showing the slowest increase, the optimal channel quality strategy showing the fastest increase, and the weighted strategy proposed in this paper falling between the two and closer to the former. For short calls, the difference in handover numbers between the weighted strategy and the maximum remaining service time strategy is small. The number of handovers directly affects user experience and system signaling overhead; frequent handovers not only increase the number of link establishment / teardowns, leading to a higher call drop rate, but also waste resources.

[0050] The signal-to-noise ratio (SNR) of the weighted strategy during communication falls between that of the optimal channel quality strategy and the maximum remaining service time strategy. Although satellites with suboptimal channel quality may be selected to maintain load balance, resulting in a slight decrease in SNR at certain times, it is generally beneficial for system load balance. The satellite load variance of the optimal channel quality and maximum remaining service time strategies is relatively large, which can easily lead to resource waste and increased call drop rate; while the strategy in this paper, although having a larger load variance in the initial stage of the system, can quickly converge to fluctuate near zero, effectively ensuring load balance among satellites.

[0051] By constructing a weighted bipartite graph containing multiple weighted indicators and objectively determining the weights of each indicator using the entropy weight method, the handover decision comprehensively considers multiple factors such as channel quality, remaining service time, satellite load, and allocable power. This effectively avoids frequent handovers caused by decisions based on a single indicator. Simulation results show that the number of handovers is significantly lower than that of the optimal channel quality strategy and close to that of the maximum remaining service time strategy. At the same time, the variance of each satellite load can quickly converge to an equilibrium state, and the user's received signal-to-noise ratio remains at a relatively good level. While reducing system signaling overhead and the risk of dropped calls, this approach balances system load balancing and user communication quality, providing a scientific and objective quantitative basis for handover decisions in low-Earth orbit satellite networks.

[0052] System Implementation Examples According to embodiments of the present invention, a rapid cross-satellite and cross-beam switching system for low-Earth orbit satellite networks is provided. Figure 2 This is a schematic diagram of a low-Earth orbit satellite network cross-satellite and cross-beam fast switching system according to an embodiment of the present invention. Figure 2 As can be seen, the low-orbit satellite network cross-satellite and cross-beam fast switching system of this invention specifically includes: A bipartite graph construction module 20 is used to obtain the service relationship between each user terminal and each satellite in discrete time slots, and to construct a weighted bipartite graph based on the service relationship. The edges of the weighted bipartite graph are used to represent the links between user terminals and satellites, and the weights of the edges are determined by a combination of multiple weighting indicators. Specifically, the bipartite graph construction module 20 is used for: The user terminal set and the satellite set are respectively used as the two sets of vertices in the weighted bipartite graph; Discrete time slots are divided based on satellite ephemeris and user location. A user-satellite connection matrix is ​​established within a single time slot. Vertex connection edges are generated when a satellite covers a user. The multi-weighted indicators are transformed into weight parameters for each connecting edge, generating a time-slot dynamic weighted bipartite graph.

[0053] The weight coefficient acquisition module 22 assigns values ​​to the multi-weight indicators using the entropy weight method to obtain the weight coefficients corresponding to each weight indicator; the weight coefficient acquisition module 22 is specifically used for: An evaluation matrix was constructed for each candidate satellite under each indicator, and then normalized. Calculate the information entropy value of each indicator based on the normalized data; The weighting coefficient is determined based on the information entropy value of each indicator. The smaller the information entropy value, the larger the weighting coefficient, and vice versa. The comprehensive evaluation value is the weighted sum of each indicator value and its corresponding weight coefficient.

[0054] The multi-weighted indicators specifically include: Channel quality metrics are used to characterize the stability of downlink transmission between the user and the satellite; the steps for obtaining the channel quality metrics include: Based on a uniform planar array antenna configured for satellites, a ray tracing channel modeling method is used to obtain the downlink channel response between the satellite and the user; Channel gain is calculated based on the speed of light, satellite orbital altitude, user elevation angle, and atmospheric fading coefficient. The channel quality metric is determined based on the downlink channel response and channel gain.

[0055] The remaining service time index is used to characterize the duration for which a satellite can sustainably provide communication services to users. The steps for obtaining the remaining service time index include: The ground station calculates the start and end times of coverage for each satellite in each region based on pre-stored satellite ephemeris data, and generates a coverage time matrix; Based on the user's current location, query the coverage end time of each visible satellite from the coverage time matrix; The difference between the current time and the coverage end time is taken as the remaining service time for each satellite to the current user.

[0056] The satellite service user count index is used to characterize the number of online users currently supported by the satellite. The steps for obtaining the satellite service user count index include: The ground station calculates the sum of the column vector elements corresponding to each satellite in the user-satellite connection matrix in real time to obtain the number of users currently served by each satellite; When the number of users currently served by a satellite reaches its maximum service capacity, the satellite stops accepting new user access.

[0057] The allocatable power index is used to characterize the upper limit of transmission power that a satellite can allocate to a target user. The steps for obtaining the allocatable power index include: With the goal of maximizing the total channel capacity of the system, the total satellite transmission power is allocated among the users based on the channel gain between each user and the satellite using a water-filling algorithm. The power value that the satellite is allocated to the current user is used as the allocatable power index.

[0058] The execution switching module 24 is used to filter satellites that meet the visibility conditions in the current discrete time slot as a candidate set, calculate the comprehensive evaluation value between each satellite in the candidate set and the user terminal based on the weight coefficients corresponding to each weight index, and select the satellite with the highest comprehensive evaluation value to perform user handover.

[0059] The execution switching module is specifically used for: The user terminal obtains its own geographic coordinates and reports them to the ground station; The ground station queries the coverage time matrix based on the user's location and filters the satellites that cover that location at the current time to form a candidate set; The ground station calculates the comprehensive evaluation value of each satellite in the candidate set and determines the satellite with the highest comprehensive evaluation value as the target satellite; The ground station sends a handover command to the user terminal, and the user terminal performs the access or handover to the target satellite.

[0060] This system embodiment is an embodiment that corresponds one-to-one with the above method embodiments. For specific implementation, please refer to the specific content of the above method embodiments, which will not be repeated here.

[0061] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0062] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0066] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for rapid cross-satellite and cross-beam switching in low-Earth orbit satellite networks, characterized in that, include: The service relationship between each user terminal and each satellite in discrete time slots is obtained. A weighted bipartite graph is constructed based on the service relationship. The edges of the weighted bipartite graph are used to represent the links between the user terminal and the satellite. The weight of the edge is determined by a combination of multiple weighting indicators. The entropy weight method is used to assign values ​​to the multi-weight indicators to obtain the weight coefficients corresponding to each weight indicator; Satellites that meet the visibility conditions within the current discrete time slot are selected as a candidate set. Based on the weight coefficients corresponding to each weight index, the comprehensive evaluation value between each satellite and the user terminal in the candidate set is calculated. The satellite with the highest comprehensive evaluation value is selected to perform user handover.

2. The method according to claim 1, characterized in that, The multi-weighted indicators specifically include: Channel quality metrics are used to characterize the stability of downlink transmission between users and satellites; The remaining service time metric is used to characterize the duration for which a satellite can sustainably provide communication services to users; The satellite service user count metric is used to characterize the number of online users currently served by the satellite; Allocable power is a measure of the maximum transmission power that a satellite can allocate to a target user.

3. The method according to claim 1, characterized in that, The step of obtaining the service relationship between each user terminal and each satellite in discrete time slots, and constructing a weighted bipartite graph based on the service relationship, specifically includes: The user terminal set and the satellite set are respectively used as the two sets of vertices in the weighted bipartite graph; Discrete time slots are divided based on satellite ephemeris and user location. A user-satellite connection matrix is ​​established within a single time slot. Vertex connection edges are generated when a satellite covers a user. The multi-weighted indicators are transformed into weight parameters for each connecting edge, generating a time-slot dynamic weighted bipartite graph.

4. The method according to claim 2, characterized in that, The method for obtaining the channel quality index includes: Based on a uniform planar array antenna configured for satellites, a ray tracing channel modeling method is used to obtain the downlink channel response between the satellite and the user; Channel gain is calculated based on the speed of light, satellite orbital altitude, user elevation angle, and atmospheric fading coefficient. The channel quality metric is determined based on the downlink channel response and channel gain.

5. The method according to claim 2, characterized in that, The method for obtaining the remaining service time indicator includes: The ground station calculates the start and end times of coverage for each satellite in each region based on pre-stored satellite ephemeris data, and generates a coverage time matrix; Based on the user's current location, query the coverage end time of each visible satellite from the coverage time matrix; The difference between the current time and the coverage end time is taken as the remaining service time for each satellite to the current user.

6. The method according to claim 2, characterized in that, The methods for obtaining the satellite service user number indicator include: The ground station calculates the sum of the column vector elements corresponding to each satellite in the user-satellite connection matrix in real time to obtain the number of users currently served by each satellite; When the number of users currently served by a satellite reaches its maximum service capacity, the satellite stops accepting new user access.

7. The method according to claim 2, characterized in that, The method for obtaining the allocatable power index includes: With the goal of maximizing the total channel capacity of the system, the total satellite transmission power is allocated among the users based on the channel gain between each user and the satellite using a water-filling algorithm. The power value that the satellite is allocated to the current user is used as the allocatable power index.

8. The method according to claim 1, characterized in that, The process of assigning values ​​to the multi-weighted indicators using the entropy weight method specifically includes: An evaluation matrix was constructed for each candidate satellite under each indicator, and then normalized. Calculate the information entropy value of each indicator based on the normalized data; The weighting coefficient is determined based on the information entropy value of each indicator. The smaller the information entropy value, the larger the weighting coefficient, and vice versa. The comprehensive evaluation value is the weighted sum of each indicator value and its corresponding weight coefficient.

9. The method according to claim 1, characterized in that, The process of selecting satellites that meet the visibility criteria within the current discrete time slot as a candidate set, calculating the comprehensive evaluation value between each satellite in the candidate set and the user terminal based on the weight coefficients corresponding to each weight index, and selecting the satellite with the highest comprehensive evaluation value for user handover specifically includes: The user terminal obtains its own geographic coordinates and reports them to the ground station; The ground station queries the coverage time matrix based on the user's location and filters the satellites that cover that location at the current time to form a candidate set; The ground station calculates the comprehensive evaluation value of each satellite in the candidate set and determines the satellite with the highest comprehensive evaluation value as the target satellite; The ground station sends a handover command to the user terminal, and the user terminal performs the access or handover to the target satellite.

10. A rapid cross-satellite and cross-beam switching system for low-Earth orbit satellite networks, characterized in that, include: A bipartite graph construction module is used to obtain the service relationship between each user terminal and each satellite in discrete time slots. A weighted bipartite graph is constructed based on the service relationship. The edges of the weighted bipartite graph are used to represent the links between the user terminal and the satellite. The weight of the edge is determined by a combination of multiple weight indicators. The module for obtaining weight coefficients uses the entropy weighting method to assign values ​​to the multi-weight indicators, thereby obtaining the weight coefficients corresponding to each weight indicator. The handover module is used to filter satellites that meet the visibility conditions within the current discrete time slot as a candidate set, calculate the comprehensive evaluation value between each satellite and the user terminal in the candidate set based on the weight coefficients corresponding to each weight index, and select the satellite with the highest comprehensive evaluation value to perform user handover.

11. The system according to claim 10, characterized in that, The multi-weighted indicators specifically include: Channel quality metrics are used to characterize the stability of downlink transmission between users and satellites; The remaining service time metric is used to characterize the duration for which a satellite can sustainably provide communication services to users; The satellite service user count metric is used to characterize the number of online users currently served by the satellite; Allocable power is a measure of the maximum transmission power that a satellite can allocate to a target user.

12. The system according to claim 10, characterized in that, The bipartite graph construction module is specifically used for: The user terminal set and the satellite set are respectively used as the two sets of vertices in the weighted bipartite graph; Discrete time slots are divided based on satellite ephemeris and user location. A user-satellite connection matrix is ​​established within a single time slot. Vertex connection edges are generated when a satellite covers a user. The multi-weighted indicators are transformed into weight parameters for each connecting edge, generating a time-slot dynamic weighted bipartite graph.

13. The system according to claim 11, characterized in that, The steps for obtaining the channel quality indicators include: Based on a uniform planar array antenna configured for satellites, a ray tracing channel modeling method is used to obtain the downlink channel response between the satellite and the user; Channel gain is calculated based on the speed of light, satellite orbital altitude, user elevation angle, and atmospheric fading coefficient. The channel quality metric is determined based on the downlink channel response and channel gain.

14. The system according to claim 11, characterized in that, The steps for obtaining the remaining service time indicator include: The ground station calculates the start and end times of coverage for each satellite in each region based on pre-stored satellite ephemeris data, and generates a coverage time matrix; Based on the user's current location, query the coverage end time of each visible satellite from the coverage time matrix; The difference between the current time and the coverage end time is taken as the remaining service time for each satellite to the current user.

15. The system according to claim 11, characterized in that, The steps for obtaining the satellite service user number indicator include: The ground station calculates the sum of the column vector elements corresponding to each satellite in the user-satellite connection matrix in real time to obtain the number of users currently served by each satellite; When the number of users currently served by a satellite reaches its maximum service capacity, the satellite stops accepting new user access.

16. The system according to claim 11, characterized in that, The steps for obtaining the allocatable power index include: With the goal of maximizing the total channel capacity of the system, the total satellite transmission power is allocated among the users based on the channel gain between each user and the satellite using a water-filling algorithm. The power value that the satellite is allocated to the current user is used as the allocatable power index.

17. The system according to claim 10, characterized in that, The module for obtaining weight coefficients is specifically used for: An evaluation matrix was constructed for each candidate satellite under each indicator, and then normalized. Calculate the information entropy value of each indicator based on the normalized data; The weighting coefficient is determined based on the information entropy value of each indicator. The smaller the information entropy value, the larger the weighting coefficient, and vice versa. The comprehensive evaluation value is the weighted sum of each indicator value and its corresponding weight coefficient.

18. The system according to claim 1, characterized in that, The execution switching module is specifically used for: The user terminal obtains its own geographic coordinates and reports them to the ground station; The ground station queries the coverage time matrix based on the user's location and filters the satellites that cover that location at the current time to form a candidate set; The ground station calculates the comprehensive evaluation value of each satellite in the candidate set and determines the satellite with the highest comprehensive evaluation value as the target satellite; The ground station sends a handover command to the user terminal, and the user terminal performs the access or handover to the target satellite.

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