CN115276756A - A low-orbit satellite constellation optimization design method to ensure service quality - Google Patents

Abstract

The invention requests to protect a low earth orbit satellite constellation optimization design method for guaranteeing service quality, and belongs to the technical field of wireless communication. The method comprehensively considers the reliability, effectiveness and completeness of the satellite constellation and provides the definition of the service quality of the satellite constellation. Bit error rate, signal-to-noise ratio and survivability are introduced to express the reliability of a satellite constellation; introducing coverage to indicate the effectiveness of the satellite constellation; the completeness of a user in a satellite constellation is represented by a user matching degree. On the basis, a service quality threshold value is set, a service quality value is calculated, the ratio of the total system capacity of the target area to the constellation construction cost is set as an objective function, the objective function value is iteratively optimized by utilizing the global search capability of the genetic algorithm to obtain an initial constellation solution, and the initial solution is secondarily optimized by utilizing the local search capability of the tabu search algorithm to output the optimal constellation parameter. The invention is oriented to regional users, and optimizes and designs the efficient and economic low-orbit satellite constellation according to the user requirements and the service quality guarantee.

Description

An Optimum Design Method for LEO Satellite Constellation with Guaranteed Quality of Service

technical field

The invention belongs to the technical field of wireless communication. Specifically, the invention relates to a low-orbit satellite constellation optimization design method for guaranteeing service quality.

Background technique

Today's terrestrial networks have certain limitations. For example, base stations are mostly distributed in densely populated areas, and devices in sparsely populated mountainous areas, deserts, and oceans cannot access the Internet. At the same time, terrestrial base stations are vulnerable to irresistible factors such as natural disasters and wars. destruction. Satellite communication, with its advantages of small ground restrictions, flexible networking, wide coverage, and high service quality, can well supplement the insufficiency of ground networks.

Satellite constellation design is the basis of satellite communication design and networking. The satellite constellation design distributes multiple satellites with the same or similar types and functions in similar or complementary orbits, and under the management and control of a centralized or distributed network, they cooperate to complete certain communication tasks. Since a single satellite cannot achieve real-time coverage of the design target area, it is necessary to use multiple satellites to form a satellite constellation to expand the coverage of satellite communications. The satellite constellation composed of multiple satellites forms a satellite network through the construction of inter-satellite links and ground station feeder links, thereby achieving global interconnection and intercommunication. Satellite constellation design is the premise of satellite network deployment and operation, which determines the operation and application level of satellite network. Traditional constellation scheme design methods mainly include geometric analysis method and simulation-based comparative analysis method. These two types of methods are only optimized for the coverage performance of satellites. However, if the constellation design ignores the user requirements and service quality of the designed target area, it will lead to redundancy of constellation resources, resulting in an increase in constellation cost and a lack of constellation service capability.

CN107329146B, a method for optimal design of navigation satellite low-orbit monitoring constellation, fully considers the existing technical basis and future technology development trend, analyzes the design requirements and constraints of navigation satellite low-orbit monitoring constellation, selects Walker-δ constellation and sun Synchronously return to the orbit, and construct the evaluation criterion that comprises monitoring station coverage factor, performance factor and constellation orbit parameter simultaneously, the navigation satellite low-orbit monitoring constellation of optimization design thus has better monitoring performance; According to the navigation satellite low-orbit that the present invention proposes The optimal design method of the monitoring constellation can effectively realize the optimal design of the low-orbit monitoring constellation of navigation satellites. The technical scheme is scientific, optimized, and highly achievable; the designed constellation can use a small number of satellites to achieve a larger coverage factor of monitoring stations and performance factors.

The invention establishes a multi-objective optimization model for the middle and low latitude regions of the northern and southern hemispheres, and the optimization objectives include the minimum coverage factor and the coverage performance factor of monitoring stations. This optimization objective only considers the coverage characteristics of the satellite to the ground. If the optimization goal is too single, it will easily lead to the waste of satellite resources. At the same time, the optimal design of constellation parameters in this invention uses traditional simulation plus mathematical analysis to determine the final constellation parameters, and does not use the mainstream intelligent optimization algorithm in current research to solve the problem. As a result, it is easy to cause problems such as excessive workload, limited experience of researchers, and problems that the finally solved constellation is not the optimal constellation. The present invention fully considers multiple service quality indexes, not only limited to constellation coverage performance, but also considers constellation invulnerability, communication bit error rate and signal-to-noise ratio, and user capacity matching degree. By making the above indicators as the constraints of the optimization model, the scope of the search space is limited. At the same time, two intelligent optimization algorithms, genetic algorithm and tabu search algorithm, are combined to solve the model, which greatly reduces the workload and the output constellation meets the service quality requirements.

Contents of the invention

The present invention aims to solve the above problems of the prior art. An optimal design method for low-orbit satellite constellations with guaranteed quality of service is proposed. Technical scheme of the present invention is as follows:

A method for optimizing the design of low-orbit satellite constellations for quality of service, comprising the following steps:

S1, using the method of equal longitude and latitude in the grid point method to divide the target area that the low-orbit satellite constellation needs to cover into N _g areas with equal areas;

S2. Determine the return period and orbital height of the constellation;

S3. Using the global search capability of the genetic algorithm, set the population size of the algorithm to 200 and the number of iterations to 20 generations, and obtain a set of initial solutions of the Walker constellation;

S4. Set thresholds for quality of service parameters of the low-orbit satellite constellation, including reliability, validity, and completion of the constellation. Reliability includes: bit error rate, signal-to-noise ratio, and invulnerability of the constellation; validity includes: The coverage of the constellation to the target area; the completeness of the constellation includes the matching degree of the constellation to the user;

S5. Combining the constellation STK simulation data and corresponding formulas to calculate corresponding service quality values;

S6. Determine whether the calculated service quality value satisfies the set threshold, if so, calculate the objective function value according to the constellation, if not, use the tabu search algorithm to update the optimized constellation parameter solution vector, and continue to return to step S4;

S7. Determine whether the maximum number of iterations is satisfied, if yes, input the optimal optimization target value and corresponding constellation parameters, if not, return to step S4.

Further, in the S1, the target area is divided into N _g areas with equal areas by using the grid point method and the medium longitude method, specifically including:

1) Select the latitude and longitude coordinates of the lower left corner of all grid points according to the target area.

2) Select the basic unit of grid points and divide the target area. The basic units are horizontal and vertical spans.

Further, the step S2 determines the return period and orbit height of the constellation, specifically including:

The return period T _SAT of the satellite is determined according to the earth's rotation period T _e and the required number of low-orbit satellite return cycles n, and its formula is shown in formula (1),

T _e represents the Earth's rotation period. The altitude h of the low-orbit satellite can be calculated by formula (2) using the obtained satellite period;

Where R _e represents the radius of the earth, G represents the gravitational constant, and m _e represents the mass of the earth.

Further, the step S3 uses the genetic algorithm to initialize a set of better initial solution sets. The genetic algorithm has crossover and mutation operations, which makes the next-generation phenotypes generated by a larger population size diverse; apply this to the constellation design , a set of better constellation initial solutions can be generated; the solution vector formed by the parameters of the Walker constellation includes [ _NSAT N _P iP _SAT A _SAT ], which respectively represent the number of single-orbit satellites, the number of constellation orbital planes, the orbital inclination, and the satellite The transmitting power of the antenna and the equivalent area of the satellite antenna.

Further, the setting of the quality of service threshold in step S4 is to set the threshold of signal-to-noise ratio, bit error rate, invulnerability, coverage, and user matching degree, and the specific symbols are expressed as

Further, the specific calculation method of each quality of service index in the step 5 is as follows:

(1) When the threshold BER ₀ of the bit error rate of the low-orbit satellite network is given, the signal-to-noise ratio of the system is calculated by formula (3);

erfc(·) represent the complementary error function, and E _b /N ₀ represents the signal-to-noise ratio of the system. E _b represents the average bit energy, and N ₀ represents the noise power spectral density.

(2) The invulnerability is quantified by referring to the natural connectivity of the complex network, and the periodic dynamic natural connectivity is used to quantify and optimize the invulnerability of the constellation, as shown in formula (4);

T_SAT、N_T、T_i、P(·)分别表示连接概率矩阵、周期动态的自然连通度、卫星回归周期、对卫星周期划为的时间片数、每个时间片的长度、求对应矩阵的自然连通度。A_T(G)中的元素表示在卫星网络中的动态拓扑周期内两个节点的保持连接的概率，

是A_T(G)的第i个特征根，在一个回归周期内，星座的任意两节点的连接概率可以通过获取STK星间链路建链数据求出。Among them, A _T (G),

T _SAT , NT , _{T i} , P(·) respectively represent the connection probability matrix, the natural connectivity degree of periodic dynamics, the satellite return cycle, the number of time slices divided into satellite cycles, the length of each time slice, and the corresponding matrix natural connectivity. The elements in A _T (G) represent the probability of two nodes staying connected during the dynamic topology period in the satellite network,

is the i-th characteristic root of A _T (G). In a regression period, the connection probability of any two nodes of the constellation can be obtained by obtaining the STK inter-satellite link establishment data.

(3) In the constellation regression period, the coverage rate CV of the constellation to the target area is the weighted statistics of the coverage of all grid points in the target area by the satellite constellation, and its specific calculation formula is shown in formula (5).

Where N _g is the number of grid points on the ground, L is the number of divided time slots, if at time t, the constellation covers grid point i, then y _it =1, otherwise y _it =0, the above calculation is obtained by passing in the constellation parameters and obtaining It is calculated from the coverage data of the constellation on the ground grid points in STK.

Further, in the step S5 (4), the user matching degree S is defined as the ratio of satellite resources to the capacity requirements of users in different grid point areas on the ground on time slots 0, 1, 2, ..., L-1 Matching degree, its value is between 0 and 1, the greater the user matching degree, that is, the more the satellite constellation matches the user needs in different areas on the ground, the calculation steps are as follows;

Bring the signal-to-noise ratio into formula (6) to calculate the downlink rate R of a single satellite;

_{PSAT is the transmit power of the satellite, G SAT is} the satellite antenna gain, G _r is the antenna gain of the user, L _f is the path loss, L _M is the link margin, T is the noise temperature of the system, and K is the Boltzmann constant , the gain of the antenna is calculated by equation (7);

Wherein η _SAT represents the efficiency of each antenna, A _SAT represents the equivalent area of the antenna, f represents the operating frequency of the system, and c represents the speed of light;

The capacity of a single satellite, that is, the number of users that can be served, is expressed as formula (8);

R is the downlink data rate of a single satellite, η _MAE is the efficiency of multiple access modulation of the satellite antenna, R _user is the data rate of the user, and its value is 1.554Mbps according to the T1 service standard set by the ITU;

Thus, the user matching degree is calculated by formula (9);

Among them, N _g is the number of grid points divided by the ground, STF _tn is the capacity matching situation of the nth grid point in the tth time slot; in any time slot t(t=0,1,2,... ., L-1), if the satellite constellation provides the capacity of the grid point N greater than or equal to the total satellite communication population of the grid point, STF _tn = 1, otherwise, STF _tn = 0, such as formula (10) shown;

以及卫星用户的比例

的乘积计算，即第n个网格点的卫星通信用户数为

C_t(n)为t时隙内，网格点n中可见卫星数量提供的容量，根据单星容量和从STK获取的地面网格点可见卫星数计算。D(n) represents the number of satellite communication users of the ground grid point, the population N(n) who can pass through this grid point, and the proportion of communication users

and the proportion of satellite users

The product of is calculated, that is, the number of satellite communication users at the nth grid point is

C _t (n) is the capacity provided by the number of visible satellites in the grid point n in time slot t, calculated according to the single-satellite capacity and the number of visible satellites in the ground grid point obtained from STK.

Further, the calculation method of the objective function value in step S6 is as follows.

Define the set of satellites that the satellite constellation provides services to grid point n in time slot t as S _t = {m|θ _nm ≥ θ _min }, where θ _nm is the elevation angle of grid point n to satellite m, and θ _min is to achieve The lowest elevation angle of good communication conditions, then C _t (n) in formula (10) is expressed as shown in formula (11);

Therefore, in the whole period, the capacity provided by the satellite constellation for the target area is the sum of the capacities provided in each time slot;

Then the objective function value-the cost-effectiveness ratio of the network is expressed as the ratio of the sum of the above capacities to the cost of constructing the network, as shown in formula (13);

Further, said step S6 using the tabu search algorithm to optimize the constellation parameters refers to:

1) Obtain the output solution of the genetic algorithm as the current solution of the tabu search algorithm and set the quality of service threshold;

2) Judging whether the optimization goal is satisfied remains unchanged, if so, then output the result; if not, then enter the next step;

3) Perform neighborhood operations on the current solution to generate neighborhood solutions, and determine candidate solutions from the neighborhood according to the quality of service constraints and the objective function value Ψ;

4) Judging whether the defiance principle is satisfied for the candidate solution, if so, replace the current solution with the best state solution that despises the principle criterion; and replace the object that first entered the taboo list with the best state solution;

5) Judge the taboo state of each object corresponding to the candidate solution, select the best state corresponding to the non-taboo object in the candidate solution set as the current new solution, and replace the taboo object that entered the taboo table the earliest with the corresponding taboo object;

6) Determine whether the optimization target value in the algorithm changes, if so, end the algorithm and output the optimized constellation parameters [ _NSAT N _P i P _SAT A _SAT ] and the maximum objective function value Ψ; otherwise, go to step 3).

Advantage of the present invention and beneficial effect are as follows:

The invention proposes a low-orbit satellite constellation optimization design method that guarantees service quality, and defines the service quality indicators of low-orbit satellite constellation design as reliability, effectiveness and completeness. Reliability means that the constellation provides users with small errors and high-quality communication performance, and the specific quantitative indicators include bit error rate, signal-to-noise ratio, and invulnerability of the constellation. Effectiveness refers to the ability of the network composed of low-orbit satellite constellations to provide services to all users in the target area, and the specific quantitative index is the coverage of the constellation. Completion refers to how the network matches the needs of users in different regions, and the specific indicator is the degree of user matching. In the quality of service constraint, within a satellite return cycle, the user matching degree compares the actual satellite communication population of the grid point in the target area with the number of users that can be accommodated by the visible satellite of the grid point, through the relationship between the two and the capacity of a single satellite Its calculation formula is defined, which belongs to a unique innovation of the present invention. The main innovation of the present invention is to use the satellite simulation software STK to set service quality constraints according to actual needs, and combine the algorithm to optimize the cost-effectiveness ratio of the constellation to design a low-cost constellation with high resource utilization. In the existing studies, the constellation optimization design always considers the area-oriented coverage performance, and there are few optimization designs that combine satellite-ground user links and inter-satellite links to establish optimization constraints. The constellation optimization design of the present invention considers the inter-satellite and satellite-ground characteristics of the low-orbit satellite constellation at the same time, and thus designs the service quality index. Therefore, the quality of service constraint design system proposed by the present invention is not easy to think of by those skilled in the art. The invention introduces the natural connectivity of the invulnerability index in the complex network by considering the establishment of the inter-satellite link in the satellite return period. In order to adapt to the dynamic nature of the satellite network, a periodic dynamic natural connectivity is designed to represent the invulnerability index of the satellite constellation. At the same time, by considering the satellite-ground user link, introducing the bit error rate, signal-to-noise ratio and designing the user matching degree, using the above indicators to establish the relationship between the constellation and the ground user requirements. Therefore, the present invention fully considers the inherent invulnerability of the constellation and the resource matching between satellite and ground users, and the designed constellation has higher cost performance. In terms of algorithms, in order to avoid the limitations of a single algorithm in the traditional constellation optimization design, this method combines the global search ability of the genetic algorithm and the local search ability of the tabu search algorithm to achieve the goal of not falling into the local solution and the solution space of the constellation parameters. full search purposes. Therefore, the optimal low-orbit satellite constellation that meets the service quality index is designed.

Description of drawings

Fig. 1 is the overall flowchart of the preferred embodiment provided by the present invention;

Fig. 2 is a flowchart of genetic algorithm initialization constellation;

Fig. 3 is a schematic diagram of the secondary optimization of the tabu search algorithm.

Detailed ways

The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

The technical scheme that the present invention solves the problems of the technologies described above is:

A low-orbit satellite constellation optimization design scheme that guarantees service quality. The optimal satellite constellation is obtained by setting service quality constraints and combining genetic algorithms and tabu search algorithms. Constellation's quality of service indicators include reliability, effectiveness, and completeness. The specific quantitative indicators are bit error rate, signal-to-noise ratio, invulnerability, coverage rate, and user matching degree. The above indicators are closely related to the performance of the constellation, so setting the constraints on the above indicators is to make certain constraints on the solution space of the constellation parameters. By designing the constraint conditions of the above indicators in the algorithm program, combined with the optimization goal that is the maximum cost-effectiveness ratio of the constellation, iteratively solves it, and finally outputs the satellite constellation parameters that satisfy the service quality constraints and the maximum cost-effectiveness ratio.

Specific steps are as follows:

Step 1: When setting the target number of regions, we will automatically divide the Chinese region into n regions with the STK software (the specific number of divisions shall be based on the results of the division of the selected longitude and latitude intervals, usually 3°). It is used to calculate the population of each grid point according to the population distribution map.

The second step: according to the target area, determine the basic constellation configuration as the Walker constellation, determine the return period T _S of each satellite in the satellite constellation, and thus determine the constellation satellite orbit height h. After the orbital height is determined, the parameters to be optimized for the Walker constellation are the number of single-orbit satellites, the number of satellite orbits, the orbital inclination, the transmit power of a single-satellite antenna, and the effective area of the antenna. The symbols are expressed as [ _NSAT N _P i P _SAT A _SAT ] .

The third step: use the genetic algorithm to initialize a group of global optimal solutions, the purpose is to avoid the search target from falling into the local optimal solution.

并结合STK软件中仿真的优化参数组成的星座性能数据和服务质量指标计算公式计算相应的服务质量值。Step 4: Set the threshold of service quality indicators, including signal-to-noise ratio, bit error rate, invulnerability, coverage, and user matching degree. The symbols are expressed as

And combined with the constellation performance data composed of optimized parameters simulated in the STK software and the calculation formula of the service quality index, the corresponding service quality value is calculated.

Step 5: The tabu search algorithm seeks the optimal solution from the field of the solution vector, so through the local search ability of the tabu search algorithm, the initial solution set is searched twice. In the algorithm, the calculation method described in the fourth step is used to judge whether the service quality threshold is met, and through loop iterations, when the optimization target Ψ is satisfied, the optimal constellation parameter configuration and the largest optimization target are output.

Preferably, in the second step, the calculation method for determining satellite period and orbit is as follows:

The return period T _SAT of the satellite is determined according to the earth rotation period T _e and the required number of low-orbit satellite return cycles n, and its formula expression is shown in formula (1).

The altitude h of the low-orbit satellite can be calculated by formula (2) using the obtained satellite period.

Preferably, in the third step, a group of global optimal solutions is initialized using a genetic algorithm. Genetic algorithms have crossover and mutation operations, which make the phenotypes of the next generation generated by larger population sizes diverse. Applying this to constellation design can produce a set of better constellation initial solutions. The solution vector formed by the parameters of the Walker constellation includes [N _SAT N _P i P _SAT A _SAT ], which respectively represent the number of single-orbit satellites, the number of constellation orbital planes, the orbital inclination, the transmitting power of the satellite antenna, and the equivalent area of the satellite antenna.

The specific steps to initialize the Walker constellation parameters are:

1) Set the population size to 200, and the number of iterations to 20 generations.

2) Calculate the constellation optimization objective function value Ψ.

3) Determine the termination condition.

4) If yes, generate an initial constellation parameter solution set. If not, perform selection, crossover, and mutation operations, and return to the second step. (The crossover probability is set to 0.8, and the mutation probability is set to 0.1).

The specific process is shown in Figure 2.

Preferably, in the fourth step, when the optimization algorithm calculates the three service quality indicators of invulnerability, coverage, and user matching degree, the constellation parameters in the optimization need to be passed in to STK, and then obtained in the satellite return period respectively, Inter-satellite link establishment data, satellite coverage data, and ground grid-to-constellation visibility data.

Preferably, the fourth step is to define and calculate signal-to-noise ratio, bit error rate, invulnerability, coverage rate, and user matching degree respectively. Include the following calculation formulas:

1) When the threshold BER of the bit error rate of the low-orbit satellite network is given BER ₀ , the signal-to-noise ratio (SNR) of the system can be calculated by formula (3).

2) Invulnerability is quantified by referring to the natural connectivity of complex networks. The index natural connectivity in complex networks is strictly monotonic. It represents the sum of the number of closed loops of each node in the network, which can measure the redundancy of the network. Natural connectivity can be used to measure the redundancy of alternative paths existing in a network. Its formula is shown in formula (4).

Among them, λ _i is the i-th characteristic root of the adjacency matrix A(G) of the graph G(V,E), thus, the natural connectivity of a network is the characteristic spectrum of the adjacency matrix of the network and then taking the natural logarithm average value. However, due to the fast and dynamic changes of the topology of the LEO satellite network, the natural connectivity of the static network is not suitable for the satellite network.

The periodic dynamic natural connectivity is used to quantify and optimize the invulnerability of the constellation. As shown in formula (5).

Among them, A _T (G) represents the connection probability matrix. The elements in A _T (G) represent the probability of two nodes staying connected during the dynamic topology period in the satellite network. In a regression period, the connection probability of any two nodes of the constellation can be calculated by obtaining the STK inter-satellite link establishment data.

3) During the constellation regression period, the coverage rate of the constellation to the target area is the weighted statistics of the coverage of all grid points of the target area by the satellite constellation. Its specific calculation formula is shown in formula (6).

Among them, N _g is the number of ground grid points, and L is the number of divided time slots. If the constellation covers grid point i at time t, then y _it =1, otherwise y _it =0. The above calculation is calculated by passing in the constellation parameters and obtaining the coverage data of the constellation on the ground grid points in STK.

4) Divide the single-satellite return period T _S into different small time slots ΔT. The divided time slot number L can be obtained by the single-satellite return period T _S and the ratio of ΔT. In each time slot, the position of the satellite can be considered as constant. The user matching degree is defined as the matching degree of satellite resources to the capacity requirements of users in different grid point areas on the ground at time slots 0, 1, 2, ..., L-1, and its value is between 0 and 1. The greater the user matching degree, the more the satellite constellation matches the user requirements of different areas on the ground. Its calculation steps are as follows:

First, bring the signal-to-noise ratio into equation (7) to calculate the downlink rate R of a single satellite.

_{PSAT is the transmit power of the satellite, G SAT is} the satellite antenna gain, G _r is the antenna gain of the user, L _f is the various transmission losses, L _M is the link margin, T is the noise temperature of the system, and K is Boltz Mann constant. The gain of the antenna is calculated by formula (8).

Where η _SAT represents the efficiency of each antenna, A _SAT represents the equivalent area of the antenna, f represents the operating frequency of the system, and c represents the speed of light.

Secondly, by single satellite downlink data rate R, user's data rate R _user (according to the T1 service standard that ITU sets, its value is 1.554Mbps.) and the efficiency η _MAE of the multiple access modulation of satellite antenna calculates the single satellite capacity. The capacity of a single satellite is the number of users that can be served, expressed as formula (9).

Finally, user matching degree is calculated by formula (10).

Among them, N _g is the number of grid points divided by the ground, and STF _tn is the capacity matching situation of the nth grid point in the tth time slot. In any time slot t(t=0,1,2,....,L-1), if the capacity of the satellite constellation to the grid point n provides more than or equal to the total satellite communication population of the grid point, STF _tn =1, otherwise, STF _tn =0. As shown in formula (11).

以及卫星用户的比例

的乘积计算，即第n个网格点的卫星通信用户数为

C_t(n)为t时隙内，网格点n中可见卫星数量提供的容量。它可以根据单星容量和从STK获取的地面网格点可见卫星数计算。D(n) represents the number of satellite communication users of the ground grid point, the population N(n) who can pass through this grid point, and the proportion of communication users

and the proportion of satellite users

The product of is calculated, that is, the number of satellite communication users at the nth grid point is

C _t (n) is the capacity provided by the number of visible satellites in grid point n in time slot t. It can be calculated according to the single satellite capacity and the number of visible satellites of the ground grid points obtained from STK.

Preferably, the fourth step uses a tabu search algorithm to perform local optimization, which is characterized in that the solution optimized by the genetic algorithm is used as the initial solution input by the tabu search algorithm, and the local search capability of the neighborhood operation of the tabu search algorithm is used to perform Local quadratic optimization. The specific steps are as follows:

7) Obtain the output solution of the genetic algorithm as the current solution of the tabu search algorithm and set the quality of service threshold.

8) Judging whether the optimization objective is satisfied remains unchanged, and if so, output the result. If not, go to the next step.

9) Perform neighborhood operations on the current solution to generate neighborhood solutions, and determine candidate solutions from the neighborhood according to the quality of service constraints and the objective function value Ψ.

10) Judging whether the candidate solution satisfies the defiance principle, if so, replace the current solution with the best state solution under the defiance principle criterion. And replace the object that enters the tabu list earliest with the solution of the best state.

11) Determine the taboo state of each object corresponding to the candidate solution, select the best state corresponding to the non-taboo object in the candidate solution set as the current new solution, and replace the tabu object that entered the tabu list first with the corresponding tabu object.

12) Determine whether the optimization target value in the algorithm changes, if so, end the algorithm and output the optimized constellation parameters [ _NSAT N _P i P _SAT A _SAT ] and the maximum objective function value Ψ Otherwise go to step 3)

Its specific process is shown in Figure 3.

The concepts and models involved in the content of the present invention are as follows:

1. Network model

The main scenario of the present invention is the constellation coverage for users willing to join the satellite communication in the Chinese region. The space segment consists of a low-orbit satellite broadband network composed of low-orbit satellites, which can provide users with a data rate of at least 1.554Mbps. The terrestrial segment consists of terrestrial users and terrestrial broadband networks. Satellite broadband network can well make up for the lack of ground network coverage in remote areas and extreme environment areas, and can also provide communication services for communication interruptions caused by natural disasters in base stations. Because the population in China is extremely uneven, if the distribution characteristics of users are not considered in the constellation design, it will cause waste of satellite resources and increase the design cost of satellite constellations. This model is based on the actual low-orbit satellite broadband network service quality setting and the user distribution in the ground grid points to design a low-orbit satellite constellation that guarantees user needs and service quality.

2. The technical scheme of the present invention is as follows:

The invention proposes a low-orbit satellite constellation optimization design method that guarantees service quality. First, a system model including the LEO satellite constellation and ground users is established. Then, an optimal design method of low-orbit satellite constellation that guarantees service quality includes constellation design from three aspects: constellation reliability, effectiveness, and completeness. Among them, reliability takes into account bit error rate, signal-to-noise ratio and invulnerability. Validity takes into account constellation coverage. Completion takes into account user fit. By setting the thresholds of the above indicators and defining their calculation methods, the quality of service constraints are established to limit the optimal solution space of the constellation. Finally, the constellation parameters are initialized through the genetic algorithm, and the tabu search algorithm is optimized twice, and the optimal constellation parameters and the maximum objective function value Ψ are output.

It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes Other elements not expressly listed, or elements inherent in the process, method, commodity, or apparatus are also included. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

The above embodiments should be understood as only for illustrating the present invention but not for limiting the protection scope of the present invention. After reading the contents of the present invention, skilled persons can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims (9)

1. A low-orbit satellite constellation optimization design method for guaranteeing service quality is characterized by comprising the following steps:

s1, dividing a target area to be covered by a low-orbit satellite constellation into N areas with equal areas by adopting an equal longitude and latitude method in a grid point method_gAn area;

s2, determining a regression cycle and an orbit height of the constellation;

s3, using the global search capability of the genetic algorithm, setting the population scale of the algorithm to be 200 and the iteration times to be 20, and obtaining a group of Walker constellation initial solutions;

s4, setting a threshold value of the service quality parameter of the low-orbit satellite constellation, wherein the threshold value comprises reliability, effectiveness and constellation completeness, and the reliability comprises the following steps: bit error rate, signal-to-noise ratio, and constellation survivability; the effectiveness includes: coverage rate of the constellation to the target area; the constellation completeness comprises the matching degree of the constellation to the user;

s5, calculating a corresponding service quality value by combining the constellation STK simulation data and a corresponding formula;

s6, judging whether the calculated service quality value meets a set threshold value, if so, calculating a target function value according to the constellation, if not, updating an optimized constellation parameter solution vector by adopting a tabu search algorithm, and continuing to return to the operation of the step S4;

and S7, judging whether the maximum iteration times are met, if so, inputting an optimal optimization target value and a corresponding constellation parameter, and if not, returning to the step S4.

2. The method for optimally designing a low earth orbit satellite constellation for guaranteeing service quality according to claim 1, wherein the step S1 of dividing the target area into n areas with equal areas by using an equal longitude and latitude method in a grid point method specifically comprises the steps of:

1) Selecting longitude and latitude coordinates of the lower left corners of all grid points according to the target area;

2) A basic unit of grid points, i.e. the lateral and longitudinal span, is selected and the target area is divided.

3. The method for optimally designing a low-earth-orbit satellite constellation for guaranteeing the service quality according to claim 1, wherein the step S2 of determining the regression cycle and the orbit height of the constellation specifically comprises the following steps:

according to the period of rotation of the earth T_eAnd the required regression cycle number n of the low-orbit satellite to determine the regression cycle T of the satellite_SATThe formula expression is shown as formula (1),

T_eshowing the rotation period of the earth. Calculating the height h of the low-orbit satellite by using the obtained satellite period according to the formula (2);

wherein R is_eRepresenting the radius of the earth, G representing the constant of gravity, m_eRepresenting the mass of the earth.

4. The method for optimizing design of low-earth-orbit satellite constellation for guaranteeing service quality as claimed in claim 1, wherein the step S3 initializes a set of better initial solution sets by using genetic algorithm, the genetic algorithm has crossover and mutation operations, which makes the next generation phenotype generated by larger population scale have diversity; applying this to constellation design, a better set of constellation initial solutions can be generated; the solution vector formed by the parameters of the Walker constellation includes N_SAT N_P i P_SAT A_SAT]Which respectively represent the number of single orbit satellites, the number of planes of constellation orbits, the orbital inclination, the transmitting power of the satellite antenna, and the equivalent area of the satellite antenna.

5. The method according to claim 1, wherein the setting of the qos threshold in step S4 is to set snr, ber, and rssiThe specific symbols are expressed as

6. The method according to claim 5, wherein the specific calculation manner of each service quality indicator in step 5 is as follows:

(1) Threshold BER given bit error rate of low earth orbit satellite network₀Calculating the signal-to-noise ratio of the system through the formula (3);

erfc (·) denotes the complementary error function, E, respectively_b/N₀Representing the signal-to-noise ratio of the system. E_bRepresenting the average bit energy, N₀Representing the noise power spectral density.

(2) The survivability is quantified by referring to the natural connectivity of a complex network, and the survivability of the constellation is quantified and optimized by adopting the periodic dynamic natural connectivity, as shown in a formula (4);

wherein A is_T(G)、

T_SAT、_NT、T_iP (-) respectively represents a connection probability matrix, a natural connectivity of period dynamics, a satellite regression period, the number of time slices divided into the satellite period, the length of each time slice, and a natural connectivity_T(G) The element in (a) represents the probability of two nodes remaining connected within a dynamic topology cycle in the satellite network,

is A_T(G) In a regression period, the connection probability of any two nodes of the constellation can be obtained by obtaining link establishing data of the STK inter-satellite link.

(3) In the constellation regression cycle, the coverage rate CV of the constellation to the target area is weighted statistics of the coverage condition of the satellite constellation to all grid points of the target area, and a specific calculation formula is shown in formula (5).

Wherein N is_gThe number of the ground grid points is L, the number of the divided time slots is L, if the constellation covers the grid point i at the moment t, y_it=1, otherwise y_it=0, calculated by passing in constellation parameters and obtaining coverage data of the constellation in the STK to the ground grid points.

7. The method according to claim 5, wherein in step S5, (4) the user matching degree S is defined as a degree of matching of satellite resources to capacity requirements of users in different grid point regions on the ground at time slots 0,1, 2.., L-1, and the value is between 0 and 1, and the greater the user matching degree, i.e., the more matched the user requirements of the satellite constellation to different regions on the ground, the more matched the calculation steps are as follows;

carrying the signal-to-noise ratio into (6) to calculate the downlink rate R of a single satellite;

P_SATrepresenting the transmission power, G, of the satellite_SATIndicating satellite antenna gain, G_rAntenna gain, L, of a user_fRepresents path loss, L_MIndicating link margin, T indicating noise temperature of system and K indicating boltzmannConstant, the gain of the antenna is calculated by equation (7);

wherein eta_SATIndicates the efficiency of each antenna, A_SATThe equivalent area of the antenna is shown, f is the working frequency of the system, and c is the speed of light;

the capacity of a single satellite, i.e., the number of users that can be served, is expressed by equation (8);

r is the downlink data rate, eta, of a single satellite_MAEEfficiency of multiple access modulation for satellite antennas, R_userFor the data rate of the user, the value is 1.554Mbps according to the T1 service standard set by ITU;

therefore, the user matching degree is calculated by the formula (9);

wherein, N_gNumber of grid points, STF, divided for ground_tnCapacity matching condition of the nth grid point in the tth time slot; within any time slot t (t =0,1, 2...., L-1), if the satellite constellation provides a capacity for a mesh point N equal to or greater than the total satellite communication population for that mesh point, the STF_tn=1, otherwise, STF_tn=0 as shown in formula (10);

d (N) represents the number of satellite communication users at the ground grid point, the number of population N (N) passing through the grid point and the proportion of the communication users

And the proportion of satellite users

The product of (a), i.e. the number of satellite communication users at the nth grid point is

C_tAnd (n) the capacity provided by the number of visible satellites in the grid point n in the time slot t is calculated according to the capacity of a single satellite and the number of visible satellites in the ground grid point obtained from the STK.

8. The method for optimally designing a low-earth-orbit satellite constellation for ensuring the service quality as recited in claim 1, wherein the objective function value in the step S6 is calculated as follows.

Defining the satellite set S of the satellite constellation providing service to the grid point n in the t time slot_t＝{m|θ_nm≥θ_min}，θ_nmIs the elevation angle, theta, of the grid point n to the satellite m_minIs the lowest elevation angle for achieving good communication conditions, C in equation (10)_t(n) is represented by formula (11);

thus, in the whole period, the capacity provided by the satellite constellation for the target area is the sum of the capacities provided in the time slots;

the cost-to-efficiency ratio of the objective function value-network is expressed as the ratio of the sum of the capacities to the overhead of constructing the network, i.e. as shown in equation (13);

9. the method for optimizing and designing a low earth orbit satellite constellation with guaranteed service quality as claimed in claim 8, wherein the step S6 of optimizing constellation parameters by using a tabu search algorithm is characterized by comprising the following steps:

1) Acquiring an output solution of a genetic algorithm as a current solution of a tabu search algorithm and setting a service quality threshold;

2) Judging whether the optimization target is met and keeping unchanged, if so, outputting a result; if not, entering the next step;

3) Performing neighborhood operation on the current solution to generate a neighborhood solution, and determining a candidate solution from the neighborhood according to the service quality constraint and the objective function value psi;

4) Judging whether the scofflaw principle is satisfied or not for the candidate solution, if so, replacing the current solution with the optimal state solution of the scofflaw principle criterion; replacing the object which enters the tabu table earliest with the solution in the best state;

5) Judging the taboo state of each object corresponding to the candidate solution, selecting the best state corresponding to the non-taboo object in the candidate solution set as the current new solution, and simultaneously replacing the taboo object entering the taboo table earliest by the taboo object corresponding to the current new solution;

6) Judging whether the optimized target value in the algorithm changes, if so, ending the algorithm and outputting an optimized constellation parameter [ N ]_SAT N_Pi P_SAT A_SAT]And the maximum objective function value Ψ, otherwise go to step 3).

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