A method and device for determining user switching time of beam-hopping satellite cluster

Abstract

The present application discloses a method and device for determining the switching time of users of a beam-hopping satellite group, wherein the method comprises: obtaining a switching timing diagram for each user in all beam positions within the coverage area of ​​a single satellite; wherein the switching timing diagram indicates that the user determines the switching demand in the current and subsequent time periods according to its own conditions; the switching demand is a target switching satellite within a number of unit times; the switching timing diagrams of each user within the coverage area of ​​a single satellite are merged to obtain a multi-user switching timing diagram; and the multi-user switching timing diagram is solved to obtain the optimal switching time for all users within the coverage area of ​​a single satellite.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method and device for determining the switching time of users of a beam-hopping satellite group. Background Art

[0002] The integrated air-space-ground communication network is an important development trend of the future 6G network. The low-orbit satellite constellation has the characteristics of low latency, low link loss, low cost and wide area coverage. As a supplement to the ground communication network, it can be flexibly and conveniently deployed and networked, and has become an important construction idea for future satellite networks.

[0003] A series of technologies such as new space payloads and high-speed ground communications have effectively promoted the significant improvement of satellite Internet's communication capabilities. First, the development of satellite-borne phased array antennas enables satellites to have wireless resource scheduling capabilities comparable to ground-based MIMO antennas, realizing dynamic beamforming of satellite payloads. Unlike previous satellites, the new generation of satellite payloads can achieve coverage of a large satellite wave position while also achieving finer beam wave position coverage in the large satellite wave position. As a result, on-demand coverage of beam wave positions can be provided for uneven distribution of users in different locations around the world. The introduction of ground-based 5G new air interface communications, such as new communication systems such as OFDM and NOMA, can further enable users in each beam wave position to carry out more fine-grained time-power-frequency multiplexing, further improving the satellite's service capabilities. At the same time, high-speed moving satellites and more complex wireless resource control make the mechanism for users to access satellites more complicated. For example, the satellites of the Starlink low-orbit satellite constellation only pass through the top for a few minutes, and users need to frequently switch to the appropriate satellite during the communication process. Therefore, effectively controlling a large number of users to accurately select access satellites and reducing excessive system overhead caused by incorrect satellite selection have become key issues in improving the efficiency of satellite Internet systems. Summary of the invention

[0004] In view of this, the present application proposes a beam-hopping satellite group user switching timing merging method and device, which effectively controls a large number of users to accurately select access satellites, reduces excessive system overhead caused by incorrect satellite selection, and solves the problem of accurate and reliable switching of multiple users concurrently when actual users are unevenly distributed.

[0005] To achieve the above object, the present application provides a method for determining a user switching time of a beam-hopping satellite group, comprising:

[0006] Obtain a switching timing diagram for each user in all wave positions within the coverage of a single satellite; wherein the switching timing diagram indicates that the user determines the switching demand in the current and subsequent time periods according to its own conditions; the switching demand is a target switching satellite within a number of unit times;

[0007] The switching timing diagrams of each user within the coverage area of ​​a single satellite are merged to obtain a multi-user switching timing diagram;

[0008] The multi-user switching timing diagram is solved to obtain the optimal switching time for all users in the coverage area of ​​a single satellite.

[0009] Preferably, the step of solving the multi-user switching timing diagram comprises:

[0010] According to the multi-user switching timing diagram, an abstract solution space of the multi-user switching timing diagram is determined; wherein each row in the abstract solution space represents the switching demand of each user in the coverage area of ​​a single satellite; each column represents the switching demand of each user in a unit time; and the number of rows in the solution space is the number of users in the coverage area of ​​a single satellite;

[0011] Obtaining the fitness corresponding to each row in the abstract solution space; wherein the fitness is determined based on the switching success rate of users in a single satellite, the switching probability of users in a single satellite, and the global throughput of a single satellite Internet system;

[0012] The wild dog optimization method is used to iteratively update the fitness corresponding to each row in the abstract solution space to obtain the optimal solution of the abstract solution space of the multi-user switching timing diagram.

[0013] Preferably, the step of iteratively updating the fitness corresponding to each row in the abstract solution space by using the wild dog optimization method comprises:

[0014] Each row of the abstract solution space represents the position of a wild dog, and the quality of the wild dog's position is characterized by fitness. The abstract solution space represents a wild dog population, and one of the three behaviors of siege, pursuit, and search is selected to perform the first update on the fitness corresponding to the wild dog's position;

[0015] The maximum fitness value is selected from the updated fitness values ​​as the optimal switching time for all users in the coverage area of ​​a single satellite of the current generation;

[0016] The optimal switching time for all users in the coverage area of ​​a single satellite of the current generation is used to perform iterative calculations, and finally the optimal switching time for all users in the coverage area of ​​a single satellite is obtained.

[0017] Preferably, the step of iteratively updating the fitness corresponding to each row in the abstract solution space by using the wild dog optimization method also includes:

[0018] Determine the survival rate value of the wild dogs using the fitness of each wild dog in the wild dog population after the first update;

[0019] The fitness corresponding to the position of the wild dog whose survival rate value is lower than the threshold is updated for the second time.

[0020] Preferably, the step of selecting one of the three behaviors of siege, pursuit and search to update the fitness corresponding to the position of the wild dog includes:

[0021] Determine whether to hunt; if so, clarify the hunting strategy, and select one of the two behaviors, siege behavior and pursuit behavior, to update the fitness corresponding to the position of the wild dog; otherwise, select the search behavior to update the fitness corresponding to the position of the wild dog.

[0022] Preferably, the self-conditions include received signal power value, relative distance, remaining service time, and nearby satellite ephemeris information.

[0023] Preferably, the handover success rate includes a handover success rate in a case where users are non-uniformly distributed and a handover success rate in a case where users are evenly distributed.

[0024] Preferably, the switching probability is the average number of switching times of a single user in a single satellite coverage area per unit time.

[0025] Preferably, the global throughput is determined by using the ratio of the amount of data transmitted in a single satellite Internet system per unit time to the time.

[0026] To achieve the above-mentioned purpose, the embodiment of the present application further provides a device for determining user switching time of a beam-hopping satellite group, the device comprising a memory and a processor; wherein:

[0027] The memory is used to store computer program instructions;

[0028] The processor is used to execute the computer program instructions to implement the above-mentioned method for determining the user switching time of the beam-hopping satellite group.

[0029] This disclosure mainly aims at the characteristics of uneven user distribution in actual scenarios, which leads to the performance of the switching scheme based on the switching timing diagram being sometimes good and sometimes bad and having poor stability, and proposes a switching decision scheme suitable for uneven user distribution under different time and space conditions. The satellite counts and abstracts the switching needs of users in all wave positions, and uses an optimization algorithm to calculate and predict the optimal switching time for the abstract multi-user switching solution space, and finally completes the efficient and reliable determination of the user switching timing of the beam-hopping satellite group based on the optimization of the wild dog hunting survival trend, and completes the switching control of the low-orbit satellite Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 This is a schematic diagram of the switching of beam hopping scenarios for low-orbit satellites;

[0032] Figure 2 Schematic diagram of user distribution in different time and space;

[0033] Figure 3 A schematic diagram for analyzing the switching requirements of users in different wave positions;

[0034] Figure 4 This application discloses a flow chart of a method for determining user switching time of a beam-hopping satellite group;

[0035] Figure 5 It is a schematic diagram of the combined timing diagram of multi-user switching on the satellite side;

[0036] Figure 6 A schematic diagram of a process flow for solving the multi-user switching timing diagram;

[0037] Figure 7 This is a schematic diagram of beam hopping for low-orbit satellites;

[0038] Figure 8 Schematic diagram of a device for determining the switching time of users in a beam-hopping satellite cluster. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] In the satellite Internet scenario, the access network is mainly composed of low-orbit satellites with base station functions and users on the ground segment. The low-orbit satellite constellation can be expressed as A = {a1, a2, ..., a n}, a constellation composed of n satellites is connected to the ground Internet and completes wide-area coverage of ground users around the world, thereby realizing satellite Internet services for ground terminals.

[0041] When switching between different cells, general communication networks mainly judge based on the strength of the wireless signals transmitted by different base stations received by the terminal. Because the cell coverage area is small, a series of switching strategies based on the received signal strength can effectively solve the problem of switching errors caused by changes in terminal position and building obstructions. However, unlike the terrestrial network, the area covered by a single satellite is much larger than the terrestrial cell, making the received signal power at the center of the satellite beam cell similar to the received signal power at the edge of the beam cell. Therefore, it is difficult to determine whether the terminal is leaving the current cell or the signal strength is different due to changes in the channel state by monitoring the received signal strength.

[0042] Since the satellite constellation moves according to the predetermined orbit, the terminal can accurately estimate the satellites currently serving the area and the satellites that will serve in the future based on the ephemeris information, which can provide an important reference for accurately selecting the access satellite. The switching strategy based on ephemeris assistance has become the mainstream of current satellite Internet switching. Its main ideas include converting the satellite service time into a directed graph and conducting a multi-factor comprehensive analysis with ephemeris as the main factor. Among them, the directed graph of satellite service time mainly draws the service time and service duration of different satellites in a certain area based on the differences in the service satellites at different times in a certain area. The main problem of this series of methods is that a beam is regarded as a whole, that is, all users use the same switching time, and due to the uneven distribution of users, especially when the user distribution is concentrated at the edge of the beam, it is easy to cause some users to switch prematurely or delayed. Multi-factor comprehensive analysis is carried out with ephemeris as the main factor, and the movement position of the satellite is calculated through the ephemeris. When there are fewer users, the switching time can be accurately adjusted based on the predicted satellite position. However, as the number of users increases, the factors of different users are quite different, and the error of the switching time of each user will increase under a fixed benchmark.

[0043] In summary, in view of the high overall switching failure rate under the non-uniform distribution of users during beam-hopping satellite Internet switching, considering the significant spatial correlation of satellite Internet user distribution, the satellite service start and end times corresponding to different users under the same satellite beam are calculated separately, thereby converting a single switching timing diagram into a multi-user comprehensive switching timing diagram to solve the problem of low switching adaptability and high failure rate under the non-uniform distribution of users in different spaces. When the number of users increases, the differences in users in terms of region and business will lead to low efficiency of the fusion switching timing diagram. The user regional distribution and business behavior can be fused and classified to make each switching scheme gradually approach the optimal switching scheme.

[0044] Unlike traditional satellite communications where the satellite beams are relatively fixed, low-orbit satellite Internet pays more attention to the location of satellites because of the fast relative movement of satellites and the ground. Multi-factor comprehensive analysis is carried out with ephemeris as the main factor, and the movement position of the satellite is calculated through ephemeris. When there are fewer users, the switching time can be accurately adjusted based on the predicted satellite position. It mainly includes the fusion of ephemeris and user received signal strength, the fusion of ephemeris and user position, etc.

[0045] Qi Dongyuan and others from Nanjing Kairuide Information Technology Co., Ltd. proposed an efficient access and switching scheme for beam-hopping satellite communication systems. The beam-hopping system switches the beam position according to the changes in the movement positions of the terminal and the satellite. The specific process is as follows: the satellite calculates whether the terminal is about to enter the target beam position from the source beam position based on the terminal's reported position and the satellite's position, allocates beam-hopping time slot resources in the target beam position, and generates a time slot resource allocation table that is sent to the terminal through beam hopping. After the terminal moves to the target beam position, the time slot resource allocation table is updated to continue communication.

[0046] Zhang Chen and others from Nanjing University of Posts and Telecommunications proposed a handover method for satellite mobile terminals in beam-hopping scenarios. The method constructs a handover probability model, analyzes the arrival rate of new call requests and the arrival rate of handover requests, and calculates the handover failure rate and new call blocking rate.

[0047] Wang Yu and others from Huawei provided a design method for a beam-hopping satellite system, which achieves dynamic transformation of communication and positioning beams through flexible beam hopping, realizes UE positioning under the communication beam through multi-satellite passive positioning, and realizes UE positioning through switching of positioning beams. At the same time, mobility management such as switching is performed based on the UE's own location information.

[0048] At present, most of the research on switching schemes in low-orbit satellite beam-hopping scenarios simply applies the switching schemes in traditional fixed-beam scenarios to beam-hopping scenarios, or changes the RRM measurement to a position reporting process. The switching schemes are not adapted according to the beam operation mechanism of the beam-hopping scenario. Moreover, as the number of users increases, the factors of different users vary greatly, which will increase the error of the switching time of each user under a fixed benchmark.

[0049] The existing inter-satellite switching scheme has been studied in the fixed beam and staring beam scenarios. The directed graph of satellite service time mainly draws the service time and service duration of different satellites in a certain area based on the differences in the service satellites at different times. Then, the time when users switch satellites is supported by recording the rules of multiple cycles. The schematic diagram of inter-satellite switching of low-orbit satellites based on the calculation of the switching time is shown in the figure. Figure 1 shown.

[0050] Wang Xinling and others from Hytera Communications Co., Ltd. determined the switching time of the satellite switching gateway. Before the switching time arrives, the configuration information required for the switching and the UE context information are sent to the target gateway to establish the bearer. Qualcomm P. Purkayastha and others controlled the terminal to request the switching information from the ground network. The information includes the satellite position and switching start time calculated by the ephemeris, the satellite-to-ground transmission delay, satellite information, etc. The terminal selects an available satellite in the switching information list to initiate a switching request.

[0051] Huawei's Lin Meixin and others proposed that the terminal obtains satellite ephemeris information to determine the target satellite information and switching time, completes the subsequent switching process, and thus reduces the measurement process.

[0052] Liu Feng and others from the Beijing University of Aeronautics and Astronautics proposed a plan for switching the target spacecraft between two relay satellites. The overlapping area of ​​the two satellites is calculated based on the satellite ephemeris as the preset switching area, and the distance from the preset switching area is determined based on the ephemeris information of the spacecraft, and the time when the switching should occur is calculated.

[0053] Wang Liming and others from the Institute of Information Engineering of the Chinese Academy of Sciences used ground signal gateways to perform regular ephemeris calculations, update satellite overpass status, and pre-calculate overpass satellites, relay satellites, and the time to initiate switching.

[0054] Zhang Yasheng and others from the 54th Institute of China Electronics Technology Group Corporation proposed a terminal autonomous switching control method. The satellite only needs to broadcast its own ephemeris information. After receiving the ephemeris information from the satellite, the user calculates the target cell to be switched to and the time of switching, and then reports the obtained information to the satellite, which makes relevant preparations for the switching.

[0055] Liu Ziqi and others from the Guizhou Aerospace Metrology and Testing Technology Research Institute confirmed the terminal's switching time through periodic satellite movement, completing the routine switching process and authentication process.

[0056] In view of the high-speed mobility and predictability of satellite networks, Tang Lun and others from Chongqing University of Posts and Telecommunications designed an inter-beam pre-switching method based on terminal location information. They predicted the switching time through ephemeris, terminal location, and coverage model, and designed a complete pre-switching solution as well as signaling and processes.

[0057] Guo Zunli and others from Beijing Unisoc obtained the ephemeris information of the low-orbit satellite, and obtained the target beam to be switched and the target time of switching based on the ephemeris information and the geographical location information of the satellite terminal. At the same time, the solution flexibly configures the switching parameters based on the location information and ephemeris information of the satellite terminal, making it easier for the beam to become the target beam.

[0058] The switching time prediction based on ephemeris and satellite motion cycle has low reliability and large prediction error, and cannot calculate precise and accurate switching time. At the same time, the user model used in the traditional inter-satellite switching scheme is a uniform distribution model. This model is simple, subjective, and static, and cannot accurately describe the user distribution in different time and space in the actual ground environment. The main problem with this strategy is that it treats a beam as a whole, that is, all users use the same switching time. However, due to the uneven distribution of users, especially when the user distribution is concentrated at the edge of the beam, it is easy to cause some users to switch prematurely or delayed.

[0059] Satellite Internet for beam hopping urgently needs to solve the problem of reliable user switching under high-speed satellite movement. Existing solutions are mainly based on converting satellite service time into a directed graph and conducting multi-factor comprehensive analysis based on ephemeris as the main factor, but none of them can solve the problem of accurate and reliable switching of multiple users concurrently when actual users are unevenly distributed. Therefore, the present invention focuses on solving the following two problems:

[0060] Problem 1: Modeling of uneven user distribution in beam-hopping scenarios and fusion of multi-user switching timing diagrams. Beam-hopping low-orbit satellite Internet has a more detailed wireless resource scheduling capability, forming a large satellite beam and hundreds of small beam beams contained in the satellite beam when covering the ground. Because the satellite beam is large and the global user distribution varies greatly, users in each satellite beam are spatially non-uniformly distributed. Schematic diagram of user distribution in different time and space is shown below. Figure 2 As shown. The greater the non-uniformity of user distribution, especially when the user distribution is concentrated at the edges of different beams, it is easy to cause some users to switch prematurely or delayed. Considering that there is a significant spatial correlation in the distribution of satellite Internet users, the satellite service start time and end time corresponding to different users under the same satellite beam are calculated separately. Therefore, this disclosure will convert a single switching timing diagram into a multi-user comprehensive switching timing diagram to solve the problem of low switching adaptability and high failure rate under the non-uniform distribution of users in different spaces.

[0061] Problem 2: Efficient integration of switching time of satellite Internet users in beam-hopping with regional and business differences. When there are multiple users in a beam and the number of users is large, a switching time diagram can be formed according to the different services and location points of the users during the switching judgment. For users with different services and different locations, multiple switching timing diagrams can be formed, such as Figure 3As shown. This will lead to low efficiency of fusion switching judged by the network side based on the comprehensive user switching timing diagram. Considering that the user's switching time has certain similarities according to the service, user location and satellite wave position switching time, according to the similarity distribution of user conditions under a certain satellite wave position, that is, the proportion of users with greater similarity, different switching times can be efficiently fused. Therefore, this disclosure will be based on the bionic optimization classification and processing configuration algorithm, so that the switching scheme can gradually approach the optimal switching scheme according to the user and regional conditions.

[0062] Based on the above two problems, a switching decision method suitable for non-uniform distribution of users under different space-time conditions is proposed. The satellite counts and abstracts the switching needs of users in all wave positions, and uses an optimization algorithm to calculate and predict the optimal switching time for the abstract multi-user switching solution space, and finally completes an efficient and reliable user switching time determination scheme for a beam-hopping satellite group based on survival-driven optimization. The disclosed scheme is illustrated using a star in a low-orbit satellite constellation as an example. In the satellite Internet scenario, beam-hopping technology is used to achieve efficient frequency utilization and power utilization of satellites. Figure 4 As shown, this application discloses a flow chart of a method for determining user switching time in a beam-hopping satellite group.

[0063] include:

[0064] Step 401): Obtain a switching timing diagram for each user in all wave positions within the coverage of a single satellite; wherein the switching timing diagram indicates that the user determines the switching demand in the current and subsequent time periods based on its own conditions; the switching demand is a target switching satellite within a number of unit times.

[0065] In this step, the user's switching timing diagram refers to the user's comprehensive judgment of the switching needs in the current and subsequent period of time based on his own conditions, including the received signal power value, relative distance, remaining service time, nearby satellite ephemeris information, etc. The switching demand is the target switching satellite within a certain unit time.

[0066] Step 402): Combine the switching timing diagrams of each user within the coverage of a single satellite to obtain a multi-user switching timing diagram.

[0067] In this step, the satellite collects the switching timing diagrams of all users and merges the switching timing diagrams of multiple users on the satellite side. Figure 5 This is an example of merging the multi-user switching timing diagram on the satellite side. There are some relatively dense timing diagram lines in the multi-user switching timing diagram, indicating that the users in this area are relatively densely distributed in several wave positions.

[0068] Step 403): Solve the multi-user switching timing diagram to obtain the optimal switching time for all users in the coverage area of ​​a single satellite.

[0069] The solution process is further described in detail, such as Figure 6 As shown, the step of solving the multi-user switching timing diagram includes:

[0070] Step 601): According to the multi-user switching timing diagram, determine the abstract solution space of the multi-user switching timing diagram; wherein each row in the abstract solution space represents the switching demand of each user in the coverage area of ​​a single satellite; each column represents the switching demand of each user per unit time; the number of rows in the solution space is the number of users in the coverage area of ​​a single satellite.

[0071] In this step, the satellite abstracts the collected multi-user switching timing diagram into a set of solution vectors. The solution space obtained after abstraction is shown in Table 1. In Table 1, the solution space consists of ∞ and satellite ID, which respectively indicate that the current user has no switching demand at the current moment and the switching target satellite of the current user at the current moment. Each row vector represents the switching demand of each user, and the number of users in the area is the number of rows. Each column vector is the switching demand of each user per unit time. The switching timing diagram within a period of time is composed of several column vectors. The number of column vectors depends on the overall duration of the switching timing diagram and the duration of the unit time.

[0072] Table 1

[0073] T1 T2 T3 T4 T5 T6 T7 UE1 ∞ ∞ Sat1 ∞ ∞ ∞ ∞ UE2 ∞ ∞ ∞ ∞ ∞ ∞ Sat3 UE3 ∞ Sat1 ∞ ∞ ∞ ∞ ∞ UE4 ∞ ∞ ∞ ∞ Sat2 ∞ ∞ UE5 ∞ ∞ Sat1 ∞ ∞ ∞ ∞

[0074] Step 602): Obtain the fitness corresponding to each row in the abstract solution space; wherein the fitness is determined based on the switching success rate of users in a single satellite, the switching probability of users in a single satellite, and the global throughput of a single satellite Internet system.

[0075] The fundamental purpose of studying inter-satellite switching solutions for low-orbit satellite Internet beam hopping scenarios is to improve the performance of satellite systems and improve user experience. j The throughput should be maximized, and the average switching success rate of all users should be maximized and the switching probability should be minimized; therefore, the objective function of the wild dog optimization algorithm is defined as the fitness function. Therefore, the fitness function is the global throughput Tra of the low-orbit satellite Internet system. total , the average switching success rate of users And the average user switching probability P_HO ave Determined, the expression is:

[0076]

[0077] Among them, k1, k2, and k3 are weight coefficients. It is obtained by dividing the sum of the handover success rates of all users in the coverage area of ​​a single satellite by the number of all users in the coverage area of ​​a single satellite. ave It is obtained by dividing the switching probability of all users in the coverage area of ​​a single satellite by the number of all users in the coverage area of ​​a single satellite.

[0078] Each satellite can cover hundreds of wave positions, which can be expressed as B = {b1, b2, ..., b m The satellite dispatches wireless resources according to the service needs of ground users. m , according to its business volume, a specific beam is allocated to it in units of time slots for scanning and service. When the satellite can still provide services to the current ground area, or the beam inclination does not exceed the maximum antenna elevation angle of the ground terminal, the satellite beam needs to scan between all beam positions in the current ground area according to the corresponding beam scheduling strategy. Figure 7 shown.

[0079] There are different distributions of ground users in satellite Internet scenarios. To simplify the design process, most studies take uniform distribution as a premise and adopt inter-satellite switching schemes based on satellite periodicity and ephemeris information to predict the switching time. When users are uniformly distributed, the probability density function is expressed as:

[0080]

[0081] In the formula, (x, y) represents the user's location coordinate information, S is the area of ​​coverage of a single satellite, and Area is the system coverage area. Since users are evenly distributed around the world, Area here can represent a certain wave position b in the coverage range of a single satellite. m , can also represent the set B of all wave positions within the satellite coverage.

[0082] In this disclosure, the handover success rate, handover probability and system throughput are mainly considered, and these three performance indicators are used as the optimization targets of the inter-satellite handover solution. Among them, the handover performance analysis under different user distributions is as follows:

[0083] During satellite operation, there is a possibility of handover failure or ping-pong handover of users at the edge of the area during inter-satellite handover. It is assumed that users within the x1% area on the left side of the area Area will experience wireless link failure due to the distance of satellite 1 being too far, and the probability of wireless link failure is P1. Users within the x2% area on the right side of the area Area will experience ping-pong handover or handover failure due to the distance of satellite 2 being too far, and the probability of handover failure is P2, and the probability of ping-pong handover is P3. Under the premise of uniform distribution of users, the user percentage and area percentage can be directly converted, and the handover success rate at this time is:

[0084] f(x)=1-x1·P1 / 100-x2·P2 / 100-x2·P3 / 100

[0085] In the case of non-uniform user distribution, it can be assumed that the proportion of users within the area x1% on the left side of the area Area is y1%, and the proportion of users within the area x2% on the right side of the area Area is y2%. The handover success rate is:

[0086] f(y)=1-y1·P1 / 100-y2·P2 / 100-y2·P3 / 100

[0087] Comparing the switching success rates under the premise of uniform and non-uniform user distribution, when x1=y1 and x2=y2, the switching success rates based on the timing diagram are equal, but there is a possibility of clustered distribution when users are unevenly distributed.

[0088] When users are densely distributed within the area x1% on the left side of the area Area or within the area x2% on the right side of the area Area, the handover success rate will be greatly reduced.

[0089] The switching probability can be equivalent to the average number of switching times per unit time, and the expression is as follows:

[0090]

[0091] According to the expression, it can be seen that the switching probability is related to the switching success rate, ping-pong switching rate, and switching failure rate. Since ping-pong switching will cause the user to switch to the target satellite and then switch back to the source satellite, the user will have two invalid switches, which will increase the signaling load, computing overhead and interruption time of the system, and will cause the overall performance of the communication system to deteriorate. Similarly, a switching failure will lead to several repeated switching attempts, which will also reduce system performance. Under the premise of different user distributions, the switching probability in the inter-satellite switching scheme based on switching time prediction will also vary greatly with the user distribution, resulting in poor system performance stability.

[0092] The system throughput can be expressed as the ratio of the amount of data transmitted in the system per unit time to the time:

[0093]

[0094] The amount of data transmitted in the system is composed of the probability of users sending data, the amount of data sent, and the duration of continuous transmission. However, due to the inter-satellite switching caused by satellite movement, data transmission is interrupted, so the interruption time caused by the switching needs to be considered. Therefore, under the premise of different user distribution, in the inter-satellite switching scheme based on switching time prediction, the system throughput is mainly determined by the interruption time, and the interruption time depends on the average number of switching times and the switching success rate.

[0095] Step 603): using the wild dog optimization method to iteratively update the fitness corresponding to each row in the abstract solution space, and obtain the optimal solution of the abstract solution space of the multi-user switching timing diagram.

[0096] In this step, the step of iteratively updating the fitness corresponding to each row in the abstract solution space by using the wild dog optimization method includes:

[0097] Each row of the abstract solution space represents the position of a wild dog, and the quality of the wild dog's position is characterized by fitness. The abstract solution space represents a wild dog population, and one of the three behaviors of siege, pursuit, and search is selected to perform the first update on the fitness corresponding to the wild dog's position;

[0098] The maximum fitness value is selected from the updated fitness values ​​as the optimal switching time for all users in the coverage area of ​​a single satellite of the current generation;

[0099] The optimal switching time for all users in the coverage area of ​​a single satellite of the current generation is used to perform iterative calculations, and finally the optimal switching time for all users in the coverage area of ​​a single satellite is obtained.

[0100] In this embodiment, after the satellite side obtains the multi-user switching timing diagram, it is also necessary to calculate the optimal switching time through a decision algorithm. In the present disclosure, the wild dog optimization algorithm is used to solve the multi-user switching timing diagram, and finally the optimal switching time for all users in the area is obtained. Among them, the wild dog optimization algorithm selects the best solution from many solutions under certain conditions to meet certain performance indicators of the system to reach the maximum or minimum value. In the present disclosure, many solutions represent multiple single-user switching timing diagrams, and the optimal solution represents the comprehensive switching timing diagram of all users in the area. The optimal solution obtained can make the average switching rate in the communication system lower, while the switching success rate and system throughput are higher. The wild dog optimization algorithm simulates all solutions as wild dog populations, and each solution is simulated as the position of a wild dog. The pros and cons of the wild dog position are calculated by the fitness function fitness. In this optimization algorithm, each wild dog will choose one of the three behaviors of siege, pursuit and search. In the beam-hopping satellite intersatellite switching scheme, the optimization algorithm will guide the population switching timing diagram to perform survival-driven optimization. The single switching scheme simulates the three behaviors of wild dogs: siege, pursuit, and search, and finally converges to the optimal switching scheme.

[0101] Siege behavior means that the optimization algorithm randomly selects a certain number of single switching solutions to search near the global optimal position and obtain a new position. Its behavior is as follows:

[0102]

[0103] in, is the new position of the single switching solution search agent (indicating the movement of the single switching solution); na is a random function generated in the reverse order of [2, SizePop / 2], where SizePop is the size of the switching solution population, is a subset of the search agent (switching scheme simulating wild dog attacks), where X is a randomly generated switching scheme group; is the current search agent; is the best search agent found in the previous iteration; β1 is a random number uniformly generated in [-2,2] and is a scaling factor that changes the movement trajectory of the switching scheme.

[0104] The hunting behavior refers to the switching scheme individuals in the group searching near the global optimal position and obtaining a new position. The following equation simulates this behavior:

[0105]

[0106] in, represents the motion of the switching scheme, is the current search agent, is the best search agent found in the previous iteration, the value of β1 is the same as in the siege attack expression, β2 is a random number generated uniformly in the interval [-1,1], r1 is a random number generated in the interval from 1 to the maximum search agent (switching scheme) size, is the randomly selected r1th search agent, where i≠r1. It is used in the hunting behavior expression to represent the trajectory of the switching scheme when simulating hunting prey.

[0107] Search behavior refers to the switching scheme individuals in the group searching near any other individuals in the global scope and obtaining a new position.

[0108] The following simulates this behavior:

[0109]

[0110] Among them, x l (t+1) represents the movement of a single switching solution in the switching group space, the value of β2 is the same as the value in the hunting behavior expression, r1 is a random number generated in the interval from 1 to the maximum search agent (switching solution group) size, is the randomly selected r1th search agent, is the current search agent, where i≠r1, σ is a binary number randomly generated by Algorithm 2, σ∈{0,1}.

[0111] For multi-user switching schemes, it is also necessary to calculate the pros and cons of each single user's switching scheme through the fitness function fitness. In each iteration cycle, each switching scheme is updated through siege, pursuit, or search in the optimization algorithm, so that each switching scheme gradually approaches the optimal switching scheme. Among them, the execution of behaviors and strategies in the optimization algorithm needs to be configured at the beginning of the program, and the system parameters P and Q are set to determine whether the population hunts and the hunting strategy, respectively. The hunting strategy is siege or pursuit. Therefore, the step of selecting one of the three behaviors of siege, pursuit, and search to update the fitness corresponding to the position of the wild dog includes: judging whether to hunt; if so, clarifying the hunting strategy, and selecting one of the two behaviors of siege and pursuit to update the fitness corresponding to the position of the wild dog; otherwise, selecting the search behavior to update the fitness corresponding to the position of the wild dog.

[0112] Set the system parameter n to determine the maximum number of iterations of the optimization algorithm. According to the comparison between the random number generated in the current iteration cycle and the system parameters P and Q, different strategies are implemented for the individuals in the population, that is, different solution vectors in the solution space. The population individuals are updated and the best individual is calculated in a loop until the number of iterations exceeds the maximum number of iterations. The best individual after the maximum number of iterations is the optimal solution. After multiple rounds of iterative updates, all single-user switching schemes will converge to the optimal switching scheme.

[0113] For better iterative updating, the step of iteratively updating the fitness corresponding to each row in the abstract solution space by using the wild dog optimization method also includes:

[0114] Determine the survival rate value of the wild dogs using the fitness of each wild dog in the wild dog population after the first update;

[0115] The fitness corresponding to the position of the wild dog whose survival rate value is lower than the threshold is updated for the second time.

[0116] Specifically, after simulating the above-mentioned predation behaviors, the switching scheme still faces the problem of survival, and the survival rate of each individual is calculated by the fitness function. Individuals with too low survival rates need to be updated to move near the optimal individual to increase their survival probability.

[0117] In the optimization algorithm, the survival rate value of the switching scheme is given by:

[0118]

[0119] Among them, fitness max and fitness minare the worst and best fitness values ​​in the current generation, respectively, and fitness(i) is the current fitness value of the i-th search agent. The survival vector in the survival rate calculation formula contains the normalized fitness in the interval [0,1]. The update process formula is as follows, which is applied to low survival rates through the algorithm, for example, the survival rate value is equal to or less than 0.3.

[0120]

[0121] in, is the search agent with a lower survival rate to be updated, r1 and r2 are random numbers generated in the interval from 1 to the maximum size of the search agent (switching scheme group), r1≠r2, and is to randomly select the r1th and r2th search agents, is the best search agent found in the previous iteration, and σ is a randomly generated binary number in Algorithm 2, σ∈{0,1}. Here, the update formula is the addition or subtraction of the vector, defined by the random value of σ.

[0122] To summarize, the scheme disclosed in the present invention first constructs a switching problem model based on beam-hopping satellite Internet, and describes the switching performance under beam-hopping resources and non-uniform user distribution; then proposes a scheme optimization and configuration algorithm based on the survival tendency of animal hunting, and successively approaches the optimal scheme by establishing siege, pursuit, search and survival models. Based on the fusion optimization scheme and the new multi-user fusion timing diagram, the switching control of the low-orbit satellite Internet is completed, and the problem of unstable inter-satellite switching performance when users are unevenly distributed in the satellite Internet beam-hopping scenario is solved.

[0123] like Figure 8 As shown, it is a schematic diagram of a device for determining the switching time of a user in a beam-hopping satellite group. The device includes a memory and a processor; wherein,

[0124] The memory is used to store computer program instructions;

[0125] The processor is configured to execute the computer program instructions to implement Figure 4 The method for determining the switching time of users of the beam-hopping satellite group.

[0126] It can be known from the description of the above implementation modes that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various implementation modes of the present application or certain parts of the implementation modes.

[0127] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the implementation of the client and the server, the introduction of the implementation of the aforementioned method can be referred to for comparative explanation.

[0128] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

Claims

1. A method for determining the switching time of a beam-hopping satellite group user, characterized in that: include: Obtain a switching timing diagram for each user in all wave positions within the coverage of a single satellite; wherein the switching timing diagram indicates that the user determines the switching demand in the current and subsequent time periods according to its own conditions; the switching demand is a target switching satellite within a number of unit times; The switching timing diagrams of each user within the coverage area of ​​a single satellite are merged to obtain a multi-user switching timing diagram; Solving the multi-user switching timing diagram to obtain the optimal switching time for all users in the coverage area of ​​a single satellite; The step of solving the multi-user switching timing diagram comprises: According to the multi-user switching timing diagram, an abstract solution space of the multi-user switching timing diagram is determined; wherein each row in the abstract solution space represents the switching demand of each user in the coverage area of ​​a single satellite; each column represents the switching demand of each user in a unit time; and the number of rows in the solution space is the number of users in the coverage area of ​​a single satellite; Obtaining the fitness corresponding to each row in the abstract solution space; wherein the fitness is determined based on the switching success rate of users in a single satellite, the switching probability of users in a single satellite, and the global throughput of a single satellite Internet system; Iteratively updating the fitness corresponding to each row in the abstract solution space by using a wild dog optimization method, so as to obtain an optimal solution of the abstract solution space of the multi-user switching timing diagram; The step of iteratively updating the fitness corresponding to each row in the abstract solution space by using the wild dog optimization method comprises: Each row of the abstract solution space represents the position of a wild dog, and the quality of the wild dog's position is characterized by fitness. The abstract solution space represents a wild dog population, and one of the three behaviors of siege, pursuit, and search is selected to perform the first update on the fitness corresponding to the wild dog's position; The maximum fitness value is selected from the updated fitness values ​​as the optimal switching time for all users in the coverage area of ​​a single satellite of the current generation; The optimal switching time for all users in the coverage area of ​​a single satellite of the current generation is used to perform iterative calculations, and finally the optimal switching time for all users in the coverage area of ​​a single satellite is obtained.

2. The method according to claim 1, characterized in that The step of iteratively updating the fitness corresponding to each row in the abstract solution space by using the wild dog optimization method also includes: Determine the survival rate value of the wild dogs using the fitness of each wild dog in the wild dog population after the first update; The fitness corresponding to the position of the wild dog whose survival rate value is lower than the threshold is updated for the second time.

3. The method according to claim 1, characterized in that The step of selecting one of the three behaviors of siege, pursuit, and search to update the fitness corresponding to the position of the wild dog includes: Determine whether to hunt; if so, clarify the hunting strategy, and select one of the two behaviors, siege behavior and pursuit behavior, to update the fitness corresponding to the position of the wild dog; otherwise, select the search behavior to update the fitness corresponding to the position of the wild dog.

4. The method according to claim 1, characterized in that The self-conditions include received signal power value, relative distance, remaining service time, and nearby satellite ephemeris information.

5. The method according to claim 1, characterized in that The switching success rate includes a switching success rate in a case where users are non-uniformly distributed and a switching success rate in a case where users are evenly distributed.

6. The method according to claim 1, characterized in that The switching probability is the average number of switching times for a single user within a single satellite coverage area per unit time.

7. The method according to claim 1, characterized in that The global throughput is determined by the ratio of the amount of data transmitted in a single satellite Internet system per unit time to the time.

8. A device for determining user switching time of a beam-hopping satellite group, characterized in that: The device comprises a memory and a processor; wherein, The memory is used to store computer program instructions; The processor is used to execute the computer program instructions to implement the method for determining the user switching time of the beam-hopping satellite cluster as described in any one of claims 1 to 7.

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