CN115276756A - Low-orbit satellite constellation optimization design method for guaranteeing service quality - Google Patents

Low-orbit satellite constellation optimization design method for guaranteeing service quality Download PDF

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CN115276756A
CN115276756A CN202210706998.1A CN202210706998A CN115276756A CN 115276756 A CN115276756 A CN 115276756A CN 202210706998 A CN202210706998 A CN 202210706998A CN 115276756 A CN115276756 A CN 115276756A
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戴翠琴
秦杰鹏
许涛
谢颖
廖明霞
唐宏
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Chongqing University of Post and Telecommunications
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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

Low-orbit satellite constellation optimization design method for guaranteeing service quality
Technical Field
The invention belongs to the technical field of wireless communication. In particular to a low orbit satellite constellation optimization design method for guaranteeing service quality.
Background
Today's terrestrial networks have certain limitations, such as: most base stations are distributed in densely populated areas, sparsely populated mountainous areas, deserts and oceans cannot access the internet, and meanwhile, ground base stations are easily damaged by irresistible factors such as natural disasters and wars. The satellite communication can well supplement the defects of the ground network by the advantages of small ground limit, flexible networking, wide coverage range, high service quality and the like.
Satellite constellation design is the basis for satellite communication design and networking. The satellite constellation design distributes a plurality of satellites with the same or similar types and functions on similar or complementary orbits, and cooperatively completes certain communication tasks under the management control of a centralized or distributed network. Because a single satellite cannot cover a designed target area in real time, a satellite constellation is formed by a plurality of satellites, and the coverage range of satellite communication is enlarged. The satellite constellation consisting of a plurality of satellites forms a satellite network by constructing an inter-satellite link and a ground station feeder link, so that the interconnection and intercommunication in the global range are realized. Satellite constellation design is a prerequisite for the deployment and operation of satellite networks, which determines the level of operation and application of the satellite networks. The traditional constellation scheme design method mainly comprises two categories of geometric analysis method and simulation-based comparative analysis method. These two types of methods are optimized only for the coverage performance of the satellite. However, if the constellation design ignores the user requirements and quality of service of the target area of the design, redundancy of constellation resources will result, resulting in an increase in constellation cost and a lack of constellation service capability.
CN107329146B, an optimized design method of a low-orbit monitoring constellation of a navigation satellite, fully considers the prior art base and the future technology development trend, analyzes the design requirements and constraint conditions of the low-orbit monitoring constellation of the navigation satellite, selects a Walker-delta constellation and a sun synchronous regression orbit, and simultaneously constructs an evaluation criterion comprising a coverage factor, a performance factor and a constellation orbit parameter of a monitoring station, so that the optimally designed low-orbit monitoring constellation of the navigation satellite has better monitoring performance; according to the optimal design method for the low-orbit monitoring constellation of the navigation satellite, the optimal design of the low-orbit monitoring constellation of the navigation satellite can be effectively realized, the technical scheme is scientific and optimized, and the realizability is strong; the designed constellation can realize larger coverage factor and performance factor of the monitoring station by using less total number of satellites.
The method is used for establishing a multi-objective optimization model for low-latitude areas in the southern and northern hemispheres, wherein the optimization objective comprises a minimum coverage factor and a monitoring station coverage performance factor. The optimization objective only considers the satellite-to-ground coverage characteristics. Too single an optimization objective tends to waste satellite resources. Meanwhile, the constellation parameters are optimally designed by adopting the traditional simulation and mathematical analysis to determine the final constellation parameters, and the current mainstream intelligent optimization algorithm is not adopted for solving. This is prone to problems of excessive workload, limitations from the experience of the researcher, and the final solved constellation not being the optimal constellation. The invention fully considers the multiple service quality indexes, is not only limited to the coverage performance of the constellation, but also considers the survivability of the constellation, the communication error rate, the signal to noise ratio and the user capacity matching degree. By using the above indices as constraints of the optimization model, the range of the search space is limited. Meanwhile, the model is solved by combining two intelligent optimization algorithms, namely a genetic algorithm and a tabu search algorithm, so that the workload is greatly reduced, and the output constellation meets the service quality requirement.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A low earth orbit satellite constellation optimization design method for guaranteeing service quality is provided. The technical scheme of the invention is as follows:
a low orbit satellite constellation optimization design method for guaranteeing service quality comprises 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 methodgAn 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, survivability of constellations; 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.
Further, the step S1 of dividing the target area into N areas with equal area by using a longitude method in a grid point methodgEach area specifically comprises:
1) And selecting longitude and latitude coordinates of the lower left corners of all the grid points according to the target area.
2) A basic unit of the grid point is selected and the target area is divided. The basic units are the transverse and longitudinal spans.
Further, the step S2 determines a regression cycle and an orbit height of the constellation, and specifically includes:
according to the period of rotation of the earth TeAnd the required regression cycle number n of the low-orbit satellite to determine the regression cycle T of the satelliteSATThe formula expression is shown as formula (1),
Figure BDA0003705769330000031
Terepresenting the period of earth rotation. Using the derived satellite periodsCalculating the height h of the low-orbit satellite by the formula (2);
Figure BDA0003705769330000032
wherein R iseRepresenting the radius of the earth, G representing the constant of gravity, meRepresenting the mass of the earth.
Further, the step S3 initializes a set of better initial solution sets by using a genetic algorithm, which has crossover and mutation operations, so that the next generation phenotype generated by a larger population scale has 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 NSAT NP i PSAT ASAT]It respectively represents the number of single orbit satellites, the number of planes of constellation orbits, the orbit inclination, the transmitting power of the satellite antenna, and the equivalent area of the satellite antenna.
Further, the setting of the service quality threshold in step S4 is to set a threshold of signal-to-noise ratio, bit error rate, survivability, coverage, and user matching degree, and the specific symbol is represented as
Figure BDA0003705769330000041
Further, the specific calculation manner of each qos 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 by the formula (3);
Figure BDA0003705769330000042
erfc (·) denotes the complementary error function, E, respectivelyb/N0Representing the signal-to-noise ratio of the system. EbRepresenting 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);
Figure BDA0003705769330000043
wherein A isT(G)、
Figure BDA0003705769330000044
TSAT、NT、TiAnd P (-) respectively represents a connection probability matrix, the 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 the natural connectivity of a corresponding matrix. A. TheT(G) The element in (a) represents the probability of two nodes remaining connected within a dynamic topology cycle in the satellite network,
Figure BDA0003705769330000045
is AT(G) In a regression cycle, the connection probability of any two nodes of the constellation can be solved by obtaining link establishment 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).
Figure BDA0003705769330000046
Wherein N isgThe 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, yit=1, otherwise yit=0, calculated by passing in constellation parameters and obtaining coverage data of the constellation in the STK to the ground grid points.
Further, in the step S5, the user matching degree S in (4) is defined as a matching degree of the satellite resources to the capacity requirements of users in different grid point areas on the ground in the time slot 0,1, 2.,. L-1, and the value is between 0 and 1, where the greater the user matching degree is, the more the user requirements of the satellite constellation to different areas on the ground are matched, and the calculation steps are as follows;
carrying the signal-to-noise ratio into (6) to calculate the downlink rate R of a single satellite;
Figure BDA0003705769330000051
PSATrepresenting the transmission power, G, of the satelliteSATIndicating satellite antenna gain, GrAntenna gain, L, of a userfRepresents path loss, LMDenotes a link margin, T denotes a noise temperature of the system, and K denotes a boltzmann constant, and a gain of the antenna is calculated by equation (7);
Figure BDA0003705769330000052
wherein etaSATIndicates the efficiency of each antenna, ASATThe 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);
Figure BDA0003705769330000053
r is the downlink data rate, eta, of a single satelliteMAEEfficiency of multiple access modulation for satellite antennas, RuserThe data rate for the user is 1.554Mbps according to the T1 service standard set by the ITU;
therefore, the user matching degree is calculated by the formula (9);
Figure BDA0003705769330000054
wherein, NgNumber of grid points, STF, divided for groundtnFor the nth grid point in the t time slotA capacity match condition; 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 STFtn=1, otherwise, STFtn=0 as shown in formula (10);
Figure BDA0003705769330000055
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
Figure BDA0003705769330000061
And the proportion of satellite users
Figure BDA0003705769330000062
The product of (a) and (b), i.e. the number of satellite communication users at the nth grid point is
Figure BDA0003705769330000063
CtAnd (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.
Further, the objective function value in 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 slott＝{m|θnm≥θmin}，θnmIs the elevation angle, theta, of the grid point n to the satellite mminIs the lowest elevation angle for achieving good communication conditions, C in equation (10)t(n) is represented by formula (11);
Figure BDA0003705769330000064
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;
Figure BDA0003705769330000065
the cost-to-efficiency ratio of the objective function value to the network is expressed as the ratio of the sum of the capacities to the overhead of constructing the network, i.e. as shown in formula (13);
Figure BDA0003705769330000066
further, the step S6 of optimizing the constellation parameters by using a tabu search algorithm means:
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 satisfied and kept 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 optimal state corresponding to the non-taboo object in the candidate solution set as the current new solution, and simultaneously replacing the taboo object which enters 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 yes, ending the algorithm and outputting optimized constellation parameters [ N ]SAT NP i PSAT ASAT]And the maximum objective function value Ψ otherwise goes to step 3).
The invention has the following advantages and beneficial effects:
the invention provides a low-orbit satellite constellation optimization design method for guaranteeing service quality, and service quality indexes of low-orbit satellite constellation design are defined as reliability, effectiveness and completeness. The reliability indicates that the constellation provides users with less errors and high-quality communication performance, and specific quantization indexes comprise bit error rate, signal-to-noise ratio and constellation survivability. The effectiveness refers to the capability of a network formed by low-orbit satellite constellations for providing services for all users in a target area, and the specific quantitative index is the coverage rate of the constellations. The completeness refers to the matching condition of the network for different regional user requirements, and the specific index is the user matching degree. In the service quality constraint, in a satellite regression period, the user matching degree compares the actual satellite communication population number of a grid point in a target area with the number of users which can be accommodated by a visible satellite of the grid point, and a calculation formula is defined through the relationship between the user matching degree and the grid point and the capacity of a single satellite, so that the method belongs to a unique innovation of the invention. The main innovation of the invention is that satellite simulation software STK is utilized, service quality constraint is set according to actual requirements, the cost-to-efficiency ratio of the constellation is optimized by combining an algorithm, and the low-cost constellation with high resource utilization rate is designed. In the existing research, the optimization design of the constellation considers the area-oriented coverage performance, and the optimization design is rarely carried out by combining the satellite-to-ground user link and the inter-satellite link to establish the optimization constraint index. The constellation optimization design of the invention simultaneously considers the inter-satellite and inter-satellite-ground characteristics of the low orbit satellite constellation and designs the service quality index according to the inter-satellite and inter-satellite-ground characteristics. The quality of service constraint design architecture proposed by the present invention is therefore not readily imaginable to those skilled in the art. In the invention, in a satellite regression period, by considering the establishment of inter-satellite links, the natural connectivity of survivability indexes in a complex network is introduced. In order to adapt to the dynamic property of a satellite network, a periodic dynamic natural connectivity index for representing a satellite constellation is designed. Meanwhile, by considering the satellite-ground user link, the bit error rate and the signal-to-noise ratio are introduced, the user matching degree is designed, and the relation of the constellation to the ground user requirement is established by utilizing the indexes. Therefore, the invention fully considers the inherent survivability of the constellation and the resource matching between the satellite and the ground users, and the designed constellation has higher cost performance. In the aspect of algorithm, in order to avoid the limitation of a single algorithm in the traditional constellation optimization design, the method combines the global search capability of a genetic algorithm and the local search capability of a tabu search algorithm to achieve the purposes of enabling a target not to fall into a local solution and fully searching a constellation parameter solution space. Therefore, the optimal low-orbit satellite constellation meeting the service quality index is designed.
Drawings
FIG. 1 is an overall flow chart of the preferred embodiment of the present invention;
FIG. 2 is a flow chart of a genetic algorithm initialization constellation;
fig. 3 is a schematic diagram of the tabu search algorithm quadratic optimization.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly in the following with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
a low-orbit satellite constellation optimization design scheme for guaranteeing service quality obtains an optimal satellite constellation by setting service quality constraint and combining a genetic algorithm and a tabu search algorithm. Wherein the quality of service indicators of the constellation include reliability, validity, and completeness. The specific quantization indexes are bit error rate, signal-to-noise ratio, survivability, coverage rate and user matching degree. The indexes are closely related to the constellation performance, so that certain constraint is made on the solution space of the constellation parameters by setting the constraint of the indexes. By designing the constraint conditions of the indexes in an algorithm program and combining the optimization target, namely the maximum cost-to-efficiency ratio of the constellation, iterative solution is carried out, and finally satellite constellation parameters meeting the maximum cost-to-efficiency ratio of the service quality constraint are output.
The method comprises the following specific steps:
the first step is as follows: when the number of regional targets is set, the region in China is automatically divided into n regions by STK software (the specific division number is based on the division result of the selected longitude and latitude interval, and is usually 3 degrees). And calculating the population number of each grid point according to the population distribution diagram.
The second step: according to the target area, determining the basic constellation configuration as Walker constellation, and determining the regression period T of each satellite in the satellite constellationSFrom which the constellation satellite orbit height h is determined. TrackAfter the height is determined, the parameters of Walker constellation which need to be optimized are the number of single-orbit satellites, the number of orbits of the satellites, the orbit inclination angle, the transmitting power of a single-satellite antenna and the effective area of the antenna, and the symbol is represented as [ N ]SAT NP i PSAT ASAT]。
The third step: a group of global better solutions is initialized by using a genetic algorithm, and the aim is to avoid the search target from being trapped in a local optimal solution.
The fourth step: setting the threshold value of the service quality index, including signal-to-noise ratio, error rate, survivability, coverage rate and user matching degree, and symbolizing the threshold value as
Figure BDA0003705769330000091
And calculating corresponding service quality value by combining constellation performance data consisting of simulated optimization parameters in STK software and a service quality index calculation formula.
The fifth step: the tabu search algorithm seeks an optimal solution from the field of solution vectors, so that the initial solution set is secondarily optimized through the local search capability of the tabu search algorithm. In the algorithm, whether the service quality threshold is met or not is judged through the calculation method in the fourth step, and through loop iteration, when the optimization target psi is not changed, the optimal constellation parameter configuration and the maximum optimization target are output.
Preferably, in the second step, the calculation method for determining the satellite period and orbit is as follows:
according to the period of rotation of the earth TeAnd the required number n of regression turns of the low-orbit satellite to determine the regression period T of the satelliteSATThe formula expression is shown as formula (1).
Figure BDA0003705769330000092
The height h of the low-orbit satellite can be calculated by the formula (2) by using the obtained satellite period.
Figure BDA0003705769330000093
Preferably, in the third step, a group of global better solutions is initialized by using a genetic algorithm. Genetic algorithms have crossover and mutation operations that allow diversity in the next generation phenotypes generated on a larger population scale. 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 NSAT NP i PSATASAT]It respectively represents the number of single orbit satellites, the number of planes of constellation orbits, the orbit inclination, the transmitting power of the satellite antenna, and the equivalent area of the satellite antenna.
The specific steps of initializing Walker constellation parameters are as follows:
1) The population size was set to 200, and the number of iterations was 20.
2) And calculating the constellation optimization objective function value psi.
3) And judging a termination condition.
4) And if so, generating an initial constellation parameter solution set. If not, the selection, crossover and mutation operations are carried out, and the second step is returned. (the set crossover probability is 0.8, and the mutation probability is 0.1).
The specific flow is shown in fig. 2.
Preferably, in the fourth step, when the optimization algorithm calculates three service quality indexes of survivability, coverage rate and user matching degree, the constellation parameters in optimization need to be transmitted to the STK, and then inter-satellite link establishment data, satellite coverage data and visibility data of the ground grid to the constellation in the satellite regression cycle are respectively obtained.
Preferably, the fourth step is to define and calculate the signal-to-noise ratio, the bit error rate, the survivability, the coverage rate and the user matching degree respectively. The method comprises the following calculation formula:
1) Threshold BER when given bit error rate for low earth orbit satellite networks0The signal-to-noise ratio SNR of the system can be calculated by equation (3).
Figure BDA0003705769330000101
2) Survivability is quantified with reference to the natural connectivity of the complex network. The natural connectivity of the indicators in a complex network has a strictly monotonic characteristic. It represents the sum of the number of closed loops of each node in the network, and can be used for measuring the redundancy of the network. Natural connectivity can be used to measure the redundancy of an alternate path existing in a network. The formula is shown in formula (4).
Figure BDA0003705769330000102
Wherein λ isiIs the ith eigenroot of the adjacency matrix a (G) of graph G (V, E), whereby the natural connectivity of a network is the eigenspectrum of the adjacency matrix of the network and then taken as the average of the natural logarithms. However, the natural connectivity of static networks is not suitable for satellite networks due to the rapid dynamic changes in the topology of low-earth satellite networks.
And quantifying and optimizing the survivability of the constellation by adopting the periodic dynamic natural connectivity. As shown in formula (5).
Figure BDA0003705769330000103
Wherein A isT(G) Representing a connection probability matrix. A. TheT(G) The element in (a) represents the probability of two nodes remaining connected within a dynamic topology cycle in the satellite network. In a regression period, the connection probability of any two nodes of the constellation can be solved by acquiring STK inter-satellite link establishment data.
3) In the constellation regression cycle, the coverage rate of the constellation to the target area is weighted statistics of the coverage of the satellite constellation to all grid points of the target area. The specific calculation formula is shown in formula (6).
Figure BDA0003705769330000111
Wherein N isgThe number of the ground grid points is L, and the number of the divided time slots is L. If the constellation covers the grid point i at the time t, then yit=1, otherwise yitAnd =0. The above calculation is calculated by transmitting constellation parameters and acquiring coverage data of the constellation in the STK to the ground grid point.
4) Returning the single star to the period TSDivided into different small time slots at. The divided time slot number L can be returned to the period T through a single starSAnd the Δ T ratio. In each time slot, the position of the satellite can be treated as invariant. 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 in time slots 0,1, 2. The calculation steps are as follows:
first, the signal-to-noise ratio is taken into (7) to calculate the single satellite downlink rate R.
Figure BDA0003705769330000112
PSATRepresenting the transmission power, G, of the satelliteSATShows satellite antenna gain, GrAntenna gain, L, of a userfIndicating various transmission losses, LMIndicating the link margin, T the noise temperature of the system and K the boltzmann constant. The gain of the antenna is calculated by equation (8).
Figure BDA0003705769330000113
Wherein etaSATIndicates the efficiency of each antenna, ASATRepresenting the equivalent area of the antenna, f the operating frequency of the system, and c the speed of light.
Secondly, the data rate R is descended by a single satellite and the data rate R of the useruser(value of 1.554Mbps according to ITU-defined T1 service standard) and efficiency η of multiple access modulation of satellite antennasMAEThe capacity of a single satellite is calculated. The single star capacity is the number of users that can be served and is expressed as equation (9).
Figure BDA0003705769330000121
Finally, the user matching degree is calculated by equation (10).
Figure BDA0003705769330000122
Wherein N isgNumber of grid points, STF, divided for groundtnThe capacity of the nth grid point is matched in the t-th 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 STFtn=1, whereas, STFtn=0. As shown in equation (11).
Figure BDA0003705769330000123
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
Figure BDA0003705769330000124
And the proportion of satellite users
Figure BDA0003705769330000125
The product of (a), i.e. the number of satellite communication users at the nth grid point is
Figure BDA0003705769330000126
CtAnd (n) is the capacity provided by the number of satellites in view in grid point n during the time slot t. It can be calculated from the single satellite capacity and the number of visible satellites of the ground grid points obtained from the STK.
Preferably, the fourth step of performing local optimization by using a tabu search algorithm is characterized in that a solution optimized by a genetic algorithm is used as an initial solution input by the tabu search algorithm, and local secondary optimization is performed by using local search capability of neighborhood operation of the tabu search algorithm. The method comprises the following specific steps:
7) And acquiring an output solution of the genetic algorithm as a current solution of the tabu search algorithm and setting a service quality threshold.
8) And judging whether the optimization target is met and keeping unchanged, and if so, outputting a result. If not, the next step is carried out.
9) And 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.
10 Whether the scofflaw principle is satisfied is judged for the candidate solution, and if so, the current solution is replaced by the optimal state solution of the scofflaw principle criterion. And replaces the object that entered the tabu table earliest with the best state solution.
11 Determines the taboo status of each object corresponding to the candidate solution, selects the best status corresponding to the non-taboo object in the candidate solution set as the current new solution, and replaces the taboo object that entered the taboo table earliest with the taboo object corresponding to the current new solution.
12 ) judging whether the optimized target value in the algorithm changes, if yes, ending the algorithm and outputting the optimized constellation parameter [ N ]SAT NP i PSAT ASAT]And maximum objective function value Ψ otherwise go to step 3)
The specific flow is shown in fig. 3.
The concepts and models involved in the present disclosure are as follows:
1. network model
The main scenario of the invention is the constellation coverage for users willing to participate in satellite communication in the central region. The space segment is composed of a broadband network of low orbit satellites that provide the user with a data rate of at least 1.554Mbps. The ground segment is composed of ground users and a ground broadband network. The satellite broadband network can well make up the defects of coverage of a ground network to remote areas and extreme environment areas, and can provide communication service for communication interruption caused by natural disasters of the base station. Because the population of the Chinese region is extremely uneven, if the distribution characteristics of users are not considered in the design of the constellation, the waste of satellite resources is caused and the design cost of the satellite constellation is increased. The model designs a low orbit satellite constellation which guarantees user requirements and service quality according to actual low orbit satellite broadband network service quality setting and user distribution in a ground grid point.
2. The technical scheme of the invention is as follows:
the invention provides a low earth orbit satellite constellation optimization design method for guaranteeing service quality. First, a system model is built that contains the LEO satellite constellation and the terrestrial users. Then, the low earth orbit satellite constellation optimization design method for guaranteeing the service quality comprises the constellation design from the three aspects of constellation reliability, effectiveness and completeness. The reliability takes into account bit error rate, signal-to-noise ratio and survivability, among other things. The effectiveness takes into account the constellation coverage. The completeness takes the user matching degree into consideration. By setting the threshold of the above indexes and defining the calculation mode thereof, the service quality constraint is established to limit the optimal solution space of the constellation. And finally, initializing constellation parameters through a genetic algorithm, performing secondary optimization through a tabu search algorithm, and outputting optimal constellation parameters and a maximum objective function value psi.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

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 methodgAn 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 TeAnd the required regression cycle number n of the low-orbit satellite to determine the regression cycle T of the satelliteSATThe formula expression is shown as formula (1),
Figure FDA0003705769320000021
Teshowing 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);
Figure FDA0003705769320000022
wherein R iseRepresenting the radius of the earth, G representing the constant of gravity, meRepresenting 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 NSAT NP i PSAT ASAT]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
Figure FDA0003705769320000024
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);
Figure FDA0003705769320000023
erfc (·) denotes the complementary error function, E, respectivelyb/N0Representing the signal-to-noise ratio of the system. EbRepresenting 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);
Figure FDA0003705769320000031
wherein A isT(G)、
Figure FDA0003705769320000032
TSAT、NT、TiP (-) 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 connectivityT(G) The element in (a) represents the probability of two nodes remaining connected within a dynamic topology cycle in the satellite network,
Figure FDA0003705769320000035
is AT(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).
Figure FDA0003705769320000033
Wherein N isgThe 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, yit=1, otherwise yit=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;
Figure FDA0003705769320000034
PSATrepresenting the transmission power, G, of the satelliteSATIndicating satellite antenna gain, GrAntenna gain, L, of a userfRepresents path loss, LMIndicating link margin, T indicating noise temperature of system and K indicating boltzmannConstant, the gain of the antenna is calculated by equation (7);
Figure FDA0003705769320000041
wherein etaSATIndicates the efficiency of each antenna, ASATThe 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);
Figure FDA0003705769320000042
r is the downlink data rate, eta, of a single satelliteMAEEfficiency of multiple access modulation for satellite antennas, RuserFor 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);
Figure FDA0003705769320000043
wherein, NgNumber of grid points, STF, divided for groundtnCapacity 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 STFtn=1, otherwise, STFtn=0 as shown in formula (10);
Figure FDA0003705769320000044
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
Figure FDA0003705769320000045
And the proportion of satellite users
Figure FDA0003705769320000046
The product of (a), i.e. the number of satellite communication users at the nth grid point is
Figure FDA0003705769320000047
CtAnd (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 slott＝{m|θnm≥θmin}，θnmIs the elevation angle, theta, of the grid point n to the satellite mminIs the lowest elevation angle for achieving good communication conditions, C in equation (10)t(n) is represented by formula (11);
Figure FDA0003705769320000051
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;
Figure FDA0003705769320000052
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);
Figure FDA0003705769320000053
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 NPi PSAT ASAT]And the maximum objective function value Ψ, otherwise go to step 3).
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