CN115276756B - 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 PDFInfo
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Abstract
The invention discloses a low 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, the effectiveness and the completeness of the satellite constellation and gives definition of the service quality of the satellite constellation. Introducing error rate, signal-to-noise ratio and survivability to represent the reliability of satellite constellation; the incoming coverage represents the validity of the satellite constellation; the completeness of the user in the satellite constellation is represented by the user matching degree. On the basis, a service quality threshold value and a calculated service quality value are set, the ratio of the sum of the system capacity of a target area and the constellation construction cost is set as an objective function, the objective function value is iteratively optimized by utilizing the global searching capability of a genetic algorithm to obtain an initial constellation solution, and the optimal constellation parameter is output by utilizing the local searching capability of a tabu searching algorithm to secondarily optimize the initial solution. The invention is oriented to regional users, and the high-efficiency and economical low-orbit satellite constellation is optimally designed according to the user demands and the service quality guarantee.
Description
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: the base stations are mostly distributed in densely populated areas, equipment in mountain areas, deserts and oceans with sparse population cannot access the Internet, and meanwhile, the ground base stations are easily damaged by irresistible factors such as natural disasters, war and the like. The satellite communication can well supplement the deficiency of the ground network by virtue of the advantages of small ground limit, flexible networking, wide coverage, 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 type and function 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 realize real-time coverage of a designed target area, a satellite constellation is formed by adopting a plurality of satellites, and the coverage of satellite communication is enlarged. A satellite constellation formed by a plurality of satellites forms a satellite network by constructing inter-satellite links and ground station feeder links, so that interconnection and intercommunication in the global scope are realized. Satellite constellation design is a precondition for satellite network deployment and operation, which determines the level of operation and application of the satellite network. The traditional constellation scheme design method mainly comprises two major categories of a geometric analysis method and a simulation-based comparison analysis method. Both types of methods are optimized for satellite coverage performance only. However, if the constellation design ignores the user demand and quality of service of the target area of the design, it will result in redundancy of constellation resources, thus resulting in an increase in constellation cost and a lack of constellation service capability.
CN107329146B is an optimal design method of a navigation satellite low-orbit monitoring constellation, fully considers the prior art foundation and future technical development trend, analyzes the design requirement and constraint condition of the navigation satellite low-orbit monitoring constellation, selects a Walker-delta constellation and a solar synchronous regression orbit, and constructs an evaluation criterion comprising a monitoring station coverage factor, a performance factor and constellation orbit parameters, so that the optimally designed navigation satellite low-orbit monitoring constellation 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, and the technical scheme is scientific and optimal, and has strong realizability; the designed constellation can realize larger monitoring station coverage factors and performance factors with fewer total satellites.
The invention establishes a multi-objective optimization model for the low-latitude areas in the north-south hemisphere, 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 result in waste of satellite resources. Meanwhile, the optimal design constellation parameters are determined by adopting traditional simulation and mathematical analysis, and the optimal design constellation parameters are not solved by adopting an intelligent optimization algorithm which is the main stream of research and comparison at present. This tends to create problems that are too much labor intensive, limited by the experience of the researcher and that the last solved constellation is not the optimal constellation. The invention fully considers the multi-service quality index, not only the coverage performance of the constellation, but also the constellation destructiveness, the communication error rate, the signal-to-noise ratio and the user capacity matching degree. By using the above index as a constraint condition for the optimization model, the scope of the search space is limited. Meanwhile, the genetic algorithm and the tabu search algorithm are combined to solve the model, 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. The low orbit satellite constellation optimization design method for guaranteeing the 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 with equal areas by adopting an equal longitude and latitude method in a grid point method g A plurality of regions;
s2, determining a regression period and track height of a constellation;
s3, setting the population scale of the algorithm to 200 and the iteration number to 20 by using the global searching capability of the genetic algorithm to obtain a group of Walker constellation initial solutions;
s4, setting a threshold value of a service quality parameter of a low-orbit satellite constellation, wherein the threshold value comprises reliability, validity and constellation completeness, and the reliability comprises: bit error rate, signal-to-noise ratio, constellation survivability; the effectiveness includes: coverage of the constellation to the target area; constellation completions include the degree of matching of the constellation to the user;
s5, calculating corresponding service quality values by combining the constellation STK simulation data and corresponding formulas;
s6, judging whether the calculated service quality value meets a set threshold value, if so, calculating an objective function value according to a constellation, if not, updating an optimized constellation parameter solution vector by adopting a tabu search algorithm, and continuing to return to the S4 step;
and S7, judging whether the maximum iteration times are met, if yes, inputting an optimal optimization target value and a corresponding constellation parameter, and if not, returning to the step S4.
Further, the method includes S1 dividing the target area into N equal areas by using longitude method such as grid point method g The areas specifically comprise:
1) And selecting longitude and latitude coordinates of the lower left corners of all grid points according to the target area.
2) The basic unit of grid point is selected and the target area is divided. The basic units are the transverse and longitudinal spans.
Further, the step S2 of determining the regression period and the orbit height of the constellation specifically includes:
according to the period T of earth rotation e And the required number of low orbit satellite regression turns n to determine the regression period T of the satellite SAT The formula expression is shown as the formula (1),
T e indicating the earth rotation period. Calculating the height h of the low-orbit satellite by using the obtained satellite period through the method (2);
wherein R is e Represents the radius of the earth, G represents the gravitational constant, m e Representing the mass of the earth.
Further, the step S3 initializes a set of better initial solutions by using genetic algorithms, which have crossover and mutation operations, so that the next generation phenotype generated by a larger population scale has diversity; applying this to the constellation design, a set of better initial solutions for the constellation 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 constellation orbit planes, the orbit inclination angle, the transmitting power of the satellite antenna, and the equivalent area of the satellite antenna.
Further, the setting of the qos threshold in step S4, that is, the threshold of the snr, the bit error rate, the survivability, the coverage rate, and the user matching degree, is specifically expressed as
Further, the specific calculation mode of each quality of service index in the step 5 is as follows:
(1) Threshold BER for bit error rate for a given low orbit satellite network 0 Calculating the signal-to-noise ratio of the system by the formula (3);
erfc (·) represent the complementary error functions, E, respectively b /N 0 Representing the signal to noise ratio of the system. E (E) b Represents the average bit energy, N 0 Representing the noise power spectral density.
(2) The survivability is quantified by referring to the natural connectivity of the complex network, and the survivability of the constellation is quantized and optimized by adopting the periodic dynamic natural connectivity, as shown in a formula (4);
wherein A is T (G)、T SAT 、N T 、T i P (·) respectively represents a connection probability matrix, a period dynamic natural connectivity, a satellite regression period, the number of time slices divided into satellite periods, the length of each time slice, and the natural connectivity of the corresponding matrix. A is that T (G) The element in (a) represents the probability of two nodes remaining connected during the dynamic topology period in the satellite network, a ∈>Is A T (G) In a regression period, the connection probability of any two nodes of the constellation can be obtained by obtaining STK inter-satellite link construction data.
(3) In the constellation regression period, the coverage rate CV of the constellation to the target area is a weighted statistic of the coverage condition of the satellite constellation to all grid points of the target area, and a specific calculation formula is shown in a formula (5).
Wherein N is g For the number of ground grid points, 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 The above calculation is calculated by entering 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 is defined as the matching degree of the satellite resource to the capacity requirements of the users in the different grid point areas on the ground on the time slots 0,1, 2..l-1, the value of the matching degree is between 0 and 1, the larger the user matching degree is, namely the more the satellite constellation matches the user requirements of the satellite constellation to the different areas on the ground, and the calculation steps are as follows;
the signal-to-noise ratio is brought into the step (6) to calculate the downlink rate R of a single satellite;
P SAT representing the transmit power of the satellite, G SAT Indicating satellite antenna gain, G r Antenna gain, L of user f Indicating path loss, L M The link margin is represented, T represents the noise temperature of the system, K represents the Boltzmann constant, and the gain of the antenna is calculated by the formula (7);
wherein eta SAT Representing the efficiency of each antenna, A SAT Representing the equivalent area of the antenna, f representing the operating frequency of the system, c representing the speed of light;
the capacity of a single satellite, i.e., the number of users that can be served, is expressed as formula (8);
r is the downlink data rate of a single satellite, eta MAE R is the efficiency of multiple access modulation of satellite antenna user For the data rate of the user, the value is 1.554Mbps according to the T1 service standard set by the ITU;
from this, the user matching degree is calculated by the formula (9);
wherein N is g Is ground surfaceDivided grid points, STF tn The capacity matching condition of the nth grid point is carried out on the nth time slot; in any time slot t (t=0, 1,2,.. The satellite constellation provides a total satellite communication population, STF, for grid point N that is equal to or greater than the grid point's capacity during any time slot t (t=0, 1,2,.. The L-1) tn =1, whereas STF tn =0, as shown in formula (10);
d (N) represents the number of satellite communication subscribers at a ground grid point, and the population N (N) at the grid point and the proportion of the communication subscribers can be usedProportion of satellite users->The product calculation of (1), i.e. the number of satellite communication subscribers at the nth grid point isC t (n) is the capacity provided by the number of satellites in view of grid point n during the t time slot, calculated from the single satellite capacity and the number of satellites in view of the ground grid point obtained from the STK.
Further, the objective function value in step S6 is calculated as follows.
Defining satellite constellation as S for serving grid point n in t time slot t ＝{m|θ nm ≥θ min }，θ nm Is the elevation angle of grid point n to satellite m, θ min Is the lowest elevation angle for good communication conditions, C in equation (10) t (n) is represented by formula (11);
whereby the satellite constellation provides the target area with a capacity that is the sum of the capacities provided in the respective time slots throughout the period;
the objective function value-network cost-effectiveness ratio is expressed as a ratio of the sum of the above capacities to the overhead of constructing the network, as shown in equation (13);
further, the step S6 of optimizing the constellation parameters by adopting a tabu search algorithm means that:
1) Obtaining an output solution of the 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 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 is satisfied or not for the candidate solution, if yes, replacing the current solution with the optimal state solution of the scofflaw criterion; and replacing the object which enters the tabu list earliest by the solution of the optimal state;
5) Judging the tabu states of the objects corresponding to the candidate solutions, selecting the optimal state corresponding to the non-tabu objects in the candidate solution set as a current new solution, and simultaneously replacing the tabu object entering the tabu table earliest by the tabu object corresponding to the new solution;
6) Judging whether the optimized target value in the algorithm changes, if so, ending the algorithm and outputting optimized constellation parameters [ N ] SAT N P i P SAT A SAT ]And the largest objective function value ψ otherwise go to step 3).
The invention has the advantages and beneficial effects as follows:
the invention provides a low-orbit satellite constellation optimization design method for guaranteeing service quality, which defines the service quality index of low-orbit satellite constellation design as reliability, effectiveness and completeness. Reliability means that this constellation gives users small errors and high quality communication performance, and specific quantization indexes include bit error rate, signal-to-noise ratio and constellation survivability. The effectiveness refers to the capability of a network formed by a low orbit satellite constellation to provide services for all users in a target area, and a specific quantization index is the coverage rate of the constellation. The completeness refers to the matching condition of the network aiming at the user demands of different regions, 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 of a target regional grid point with the number of users which can be accommodated by a grid point visible satellite, and a calculation formula is defined by the relation between the actual satellite communication population of the target regional grid point and the number of users and the capacity of a single satellite, so that the method belongs to a unique innovation of the invention. The invention mainly innovates that satellite simulation software STK is utilized, service quality constraint is set according to actual demands, and the cost-effective ratio of the constellation is optimized by combining algorithm, so that the low-cost constellation with higher resource utilization rate is designed. In the existing research, the constellation optimization design considers the area coverage performance, and rarely combines the satellite-to-ground user links and inter-satellite links to establish optimization constraint indexes for optimization design. The constellation optimization design of the invention simultaneously considers the inter-satellite and satellite-ground characteristics of the low orbit satellite constellation, and thereby designs the service quality index. The quality of service constraint design architecture proposed by the present invention is therefore not easily imaginable to the skilled person. In the satellite regression period, the invention introduces the natural connectivity of the survivability index in the complex network by considering the inter-satellite link establishment. In order to adapt to the dynamic property of a satellite network, a survivability index of a periodic dynamic natural connectivity representing a satellite constellation is designed. Meanwhile, by considering satellite-to-ground user links, error rate and signal-to-noise ratio are introduced, user matching degree is designed, and the relation of the constellation to the ground user demands is established by using the indexes. Therefore, the invention fully considers the inherent survivability of the constellation and the matching property of the resources between satellite users, and the designed constellation has higher cost performance. In terms of algorithm, the method aims to avoid the limitation of a single algorithm in the traditional constellation optimization design, and combines the global searching capability of a genetic algorithm and the local searching capability of a tabu searching algorithm to achieve the purposes of enabling a target not to fall into a local solution and fully searching a constellation parameter solution space. Thus, an optimal low-orbit satellite constellation meeting the service quality index is designed.
Drawings
FIG. 1 is a flow chart of the overall process of the present invention providing a preferred embodiment;
FIG. 2 is a genetic algorithm initialization constellation flow diagram;
fig. 3 is a schematic diagram of a tabu search algorithm secondary optimization.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and specifically described below with reference to the drawings in the embodiments of the present invention. The described embodiments are only a few embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the low orbit satellite constellation optimizing design scheme for ensuring the service quality obtains the optimal satellite constellation by setting the service quality constraint and combining a genetic algorithm and a tabu search algorithm. Wherein the quality of service indicator of the constellation includes reliability, validity, and completeness. Specific quantization indexes are bit error rate, signal-to-noise ratio, survivability, coverage rate and user matching degree. The above indexes are closely related to the constellation performance, so that the constraint of the above indexes is set, namely, a certain constraint is made on the solution space of the constellation parameters. The constraint conditions of the indexes are designed in the algorithm program, the maximum cost-effectiveness ratio of the optimization target, namely the constellation is combined, the iteration solution is carried out, and finally the satellite constellation parameters meeting the maximum cost-effectiveness ratio of the service quality constraint are output.
The method comprises the following specific steps:
the first step: when the target number of the areas is set, the STK software for the China is automatically divided into n areas (the specific division number is 3 degrees according to the selected longitude and latitude interval division result). For calculating the population of each grid point based on the population distribution map.
And a second step of: determining a basic constellation configuration as a Walker constellation according to the target area, and determining each satellite constellationRegression period T of satellite S The constellation satellite orbit height h is thereby determined. After the orbit height is determined, parameters to be optimized of the Walker constellation are the number of single orbit satellites, the number of satellite orbits, 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 N P i P SAT A SAT ]。
And a third step of: a set of global optimal solutions is initialized by using a genetic algorithm, and the aim is to avoid the search target from falling into the local optimal solution.
Fourth step: setting threshold values of service quality index including signal-to-noise ratio, error rate, survivability, coverage rate and user matching degree, and symbolizing asAnd calculating corresponding service quality values by combining constellation performance data consisting of simulated optimization parameters in STK software and a service quality index calculation formula.
Fifth step: the tabu search algorithm searches the optimal solution from the field of solution vectors, so that the initial solution set is subjected to secondary optimization through the local searching capability of the tabu search algorithm. Judging whether the service quality threshold is met or not by the calculation method in the fourth step in the algorithm, and outputting the optimal constellation parameter configuration and the maximum optimization target when the optimization target ψ is unchanged by loop iteration.
Preferably, in the second step, the calculation method for determining the satellite period and orbit is as follows:
according to the period T of earth rotation e And the required number of low orbit satellite regression turns n to determine the regression period T of the satellite SAT The formula expression is shown as the formula (1).
The altitude h of the low-orbit satellite can be calculated by the equation (2) using the obtained satellite period.
Preferably, in the third step, a set of global preferred solutions is initialized using a genetic algorithm. Genetic algorithms have crossover and mutation operations that allow the next generation phenotype generated at a larger population scale to be diverse. This is applied to the constellation design, which results in a set of better initial solutions for the constellation. 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 orbit planes, the orbit inclination angle, the transmitting power of the satellite antenna, and the equivalent area of the satellite antenna.
The specific steps of initializing the Walker constellation parameters are as follows:
1) Setting the population scale as 200 and the iteration times as 20 generations.
2) The constellation optimization objective function value ψ is calculated.
3) And judging a termination condition.
4) If yes, an initial constellation parameter solution set is generated. If not, the selection, crossing and mutation operations are carried out, and the process returns to the second step. (the cross probability was set to 0.8 and the mutation probability was set to 0.1).
The specific flow is shown in fig. 2.
Preferably, in the fourth step, when the optimization algorithm calculates three quality of service indexes including survivability, coverage rate and user matching degree, constellation parameters in optimization are required to be transmitted to the STK, and then inter-satellite link construction data, satellite coverage data and visibility data of ground grids to constellations are respectively acquired in a satellite regression period.
Preferably, in the fourth step, the signal-to-noise ratio, the bit error rate, the survivability, the coverage rate and the user matching degree are defined and calculated respectively. The method comprises the following calculation formulas:
1) Threshold BER for bit error rate for a given low orbit satellite network 0 The signal-to-noise ratio SNR of the system can be calculated by equation (3).
2) The survivability is quantified by reference to the natural connectivity of the complex network. The index natural connectivity in the complex network has a strictly monotonic characteristic. It represents the sum of the number of closed loops per node in the network, and can measure the redundancy of the network. Natural connectivity can be used to measure redundancy of alternative routes present in a network. The formula is shown as formula (4).
Wherein lambda is i Is the i-th feature root of the adjacency matrix a (G) of graph G (V, E), whereby the natural connectivity of a network is the feature spectrum of the adjacency matrix of the network and then taken as a natural log-averaged value. However, the natural connectivity of static networks is not suitable for satellite networks due to the rapid dynamic changes in low orbit satellite network topology.
And the survivability of the constellation is quantized and optimized by adopting the periodic dynamic natural connectivity. As shown in formula (5).
Wherein A is T (G) Representing a connection probability matrix. A is that T (G) The element in (c) represents the probability of two nodes remaining connected during a dynamic topology period in the satellite network. In a regression period, the connection probability of any two nodes of the constellation can be obtained by acquiring STK inter-satellite link construction data.
3) The coverage rate of the constellation to the target area in the constellation regression period is the weighted statistics of the coverage condition of the satellite constellation to all grid points of the target area. The specific calculation formula is shown in formula (6).
Wherein N is g Is a ground gridThe number of points, L, is the number of divided time slots. If at time t, constellation covers grid point i, then y it =1, otherwise y it =0. The calculation is obtained by inputting constellation parameters and obtaining coverage data of constellations in the STK to ground grid points.
4) Regression of the single star to period T S Divided into different small time slots deltat. The number of divided slots L may be determined by a single star regression period T S And the delta T ratio. In each time slot, the position of the satellite may be regarded as unchanged. The user matching degree is defined as the matching degree of satellite resources on time slots 0,1,2 and L-1 to the capacity requirements of users in different grid point areas on the ground, wherein the value of the matching degree is between 0 and 1, and the larger the user matching degree is, the more the satellite constellation is matched to the user requirements of the different grid point areas on the ground. The calculation steps are as follows:
first, the signal-to-noise ratio is brought into (7) to calculate the single satellite downlink rate R.
P SAT Representing the transmit power of the satellite, G SAT Indicating satellite antenna gain, G r Antenna gain, L of user f Representing various transmission losses, L M The link margin is represented, T represents the noise temperature of the system, and K represents the boltzmann constant. The gain of the antenna is calculated by equation (8).
Wherein eta SAT Representing the efficiency of each antenna, A SAT The equivalent area of the antenna is represented by f, the operating frequency of the system is represented by c, and the speed of light is represented by c.
Secondly, the downlink data rate R of a single satellite and the data rate R of a user user (the value of T1 service standard according to ITU settings is 1.554 Mbps.) and the efficiency η of multiple access modulation of satellite antennas MAE The capacity of a single satellite is calculated. The single star capacity is the use of the serviceThe number of households is expressed as formula (9).
Finally, the user matching degree is calculated by the formula (10).
Wherein N is g Grid points, STF, for ground partitioning tn Is the capacity match case for the nth mesh point at the nth slot. In any time slot t (t=0, 1,2,.. The satellite constellation provides a total satellite communication population, STF, for grid point n that is equal to or greater than the grid point's capacity tn =1, whereas STF tn =0. As shown in formula (11).
D (N) represents the number of satellite communication subscribers at a ground grid point, and the population N (N) at the grid point and the proportion of the communication subscribers can be usedProportion of satellite users->The product calculation of (1), i.e. the number of satellite communication subscribers at the nth grid point isC t (n) is the capacity provided by the number of satellites visible in grid point n during the t time slot. It can be calculated from the single satellite capacity and the number of satellites visible at ground grid points acquired from the STK.
Preferably, the fourth step uses a tabu search algorithm to perform local optimization, and 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 obtaining an output solution of the genetic algorithm as a current solution of the tabu search algorithm and setting a service quality threshold.
8) Judging whether the optimization target is met and kept unchanged, and if yes, 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 Judging whether the scofflaw is satisfied or not for the candidate solution, if yes, replacing the current solution with the optimal state solution of the scofflaw criterion. And replaces the object that entered the tabu table earliest with the best solution.
11 Judging the tabu state of each object corresponding to the candidate solution, selecting the optimal state corresponding to the non-tabu object in the candidate solution set as the current new solution, and simultaneously replacing the tabu object entering the tabu table earliest by the tabu object corresponding to the new solution.
12 Judging whether the optimized target value in the algorithm changes, if so, ending the algorithm and outputting optimized constellation parameters [ N ] SAT N P i P SAT A SAT ]And the largest objective function value ψ otherwise go to step 3)
The specific flow is shown in fig. 3.
The concepts and models to which the present disclosure relates are as follows:
1. network model
The main scenario of the present invention is constellation coverage for users willing to join satellite communications in a central region. The space segment is comprised of a low-orbit satellite broadband network comprised of low-orbit satellites capable of providing users with data rates of at least 1.554Mbps. The ground section is composed of ground users and a ground broadband network. The satellite broadband network can well make up the defect of coverage of the ground network to remote areas and extreme environment areas, and can also provide communication service for communication interruption caused by natural disasters of the base station. Because the population in China is extremely uneven, if the distribution characteristics of users are not considered in the constellation design, the satellite resource waste is caused and the design cost of the satellite constellation is increased. The model designs a low orbit satellite constellation for guaranteeing the user requirement and the service quality according to the actual low orbit satellite broadband network service quality setting and the user distribution in the ground grid points.
2. The technical scheme of the invention is as follows:
the invention provides a low 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 orbit satellite constellation optimization design method for guaranteeing the service quality comprises constellation design from three aspects of constellation reliability, effectiveness and completeness. Reliability takes into account bit error rate, signal-to-noise ratio and survivability among others. The effectiveness considers the constellation coverage. The completeness considers the user matching degree. By setting the thresholds of the above indicators and defining the way they are calculated, i.e. establishing a quality of service constraint to define an optimized solution space for the constellation. 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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The above examples should be understood as illustrative only and not limiting the scope of the invention. Various changes and modifications to the present invention may be made by one skilled in the art after reading the teachings herein, and such equivalent changes and modifications are intended to fall within the scope of the invention as defined in the appended claims.
Claims (8)
1. The low orbit satellite constellation optimization design method for guaranteeing the service quality is characterized by comprising the following steps of:
s1, dividing a target area to be covered by a low orbit satellite constellation into N with equal areas by adopting an equal longitude and latitude method in a grid point method g A plurality of regions;
s2, determining a regression period and track height of a constellation;
s3, setting the population scale of the algorithm to 200 and the iteration number to 20 by using the global searching capability of the genetic algorithm to obtain a group of Walker constellation initial solutions;
s4, setting a threshold value of a service quality parameter of a low-orbit satellite constellation, wherein the threshold value comprises reliability, validity and constellation completeness, and the reliability comprises: bit error rate, signal-to-noise ratio, constellation survivability; the effectiveness includes: coverage of the constellation to the target area; constellation completions include the degree of matching of the constellation to the user;
s5, calculating corresponding service quality values by combining the constellation STK simulation data and corresponding formulas;
s6, judging whether the calculated service quality value meets a set threshold value, if so, calculating an objective function value according to a constellation, if not, updating an optimized constellation parameter solution vector by adopting a tabu search algorithm, and continuing to return to the S4 step;
s7, judging whether the maximum iteration times are met, if yes, inputting an optimal optimization target value and a corresponding constellation parameter, and if not, returning to the step S4;
the specific calculation mode of each service quality index in the step S5 is as follows:
(1) Threshold BER for bit error rate for a given low orbit satellite network 0 Calculating the signal-to-noise ratio of the system by the formula (3);
erfc (·) represent the complementary error functions, E, respectively b /N 0 Representation systemSignal to noise ratio of (2); e (E) b Represents the average bit energy, N 0 Representing noise power spectral density;
(2) The survivability is quantified by referring to the natural connectivity of the complex network, and the survivability of the constellation is quantized and optimized by adopting the periodic dynamic natural connectivity, as shown in a formula (4);
wherein A is T (G)、T SAT 、N T 、T i P (·) represents the connection probability matrix, the natural connectivity of the period dynamics, the satellite regression period, the number of time slices divided into the satellite period, the length of each time slice, and the natural connectivity, A T (G) The element in (a) represents the probability of two nodes remaining connected during the dynamic topology period in the satellite network, a ∈>Is A T (G) In a regression period, the connection probability of any two nodes of the constellation can be obtained by obtaining STK inter-satellite link construction 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 condition of the satellite constellation to all grid points of the target area, and a specific calculation formula is shown in a formula (5);
wherein N is g For the number of ground grid points, 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, by the calculation of the incoming constellation parameters and the acquisition of constellation-to-ground grid points in the STKIs calculated from the coverage data of (a).
2. The method for optimizing low-orbit satellite constellation design for guaranteeing service quality according to claim 1, wherein the method for optimizing low-orbit satellite constellation design for guaranteeing service quality is characterized in that a target area is divided into n areas with equal areas by adopting an equal longitude and latitude method in a grid point method, and specifically comprises the following steps:
1) Selecting longitude and latitude coordinates of lower left corners of all grid points according to the target area;
2) The basic unit of grid point, that is, the transverse and longitudinal spans, is selected and the target area is divided.
3. The method for optimizing the design of the low-orbit satellite constellation for guaranteeing the service quality according to claim 1, wherein the step S2 of determining the regression period and the orbit height of the constellation specifically comprises:
according to the period T of earth rotation e And the required number of low orbit satellite regression turns n to determine the regression period T of the satellite SAT The formula expression is shown as the formula (1),
T e indicating the rotation period of the earth; calculating the height h of the low-orbit satellite by using the obtained satellite period through the method (2);
wherein R is e Represents the radius of the earth, G represents the gravitational constant, m e Representing the mass of the earth.
4. The method for optimizing low-orbit satellite constellation according to claim 1, wherein said step S3 uses a genetic algorithm to initialize a set of better initial solutions, the genetic algorithm having crossover and mutationDifferent operations, which allow diversity of the next generation phenotype generated by population scale; applying this to the constellation design, a set of better initial solutions for the constellation 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 constellation orbit planes, the orbit inclination angle, the transmitting power of the satellite antenna, and the equivalent area of the satellite antenna.
5. The method for optimizing low-orbit satellite constellation according to claim 1, wherein the threshold value of the quality of service in step S4 is a threshold value of signal-to-noise ratio, bit error rate, survivability, coverage rate and user matching degree, and the specific symbol is
6. The optimization design method of low orbit satellite constellation for guaranteeing service quality according to claim 5, wherein in the step S5, (4) the user matching degree S is defined as the matching degree of satellite resources to the capacity demands of users in different grid point areas on the ground in time slots 0,1,2, and L-1, the value of the matching degree is between 0 and 1, the greater the user matching degree is, the more the user demands of satellite constellation to different areas on the ground are matched, and the calculation steps are as follows;
the signal-to-noise ratio is brought into the step (6) to calculate the downlink rate R of a single satellite;
P SAT representing the transmit power of the satellite, G SAT Indicating satellite antenna gain, G r Antenna gain, L of user f Indicating path loss, L M The link margin is represented, T represents the noise temperature of the system, K represents the Boltzmann constant, and the gain of the antenna is calculated by the formula (7);
wherein eta SAT Representing the efficiency of each antenna, A SAT Representing the equivalent area of the antenna, f representing the operating frequency of the system, c representing the speed of light;
the capacity of a single satellite, i.e., the number of users that can be served, is expressed as formula (8);
r is the downlink data rate of a single satellite, eta MAE R is the efficiency of multiple access modulation of satellite antenna user For the data rate of the user, the value is 1.554Mbps according to the T1 service standard set by the ITU;
from this, the user matching degree is calculated by the formula (9);
wherein N is g Grid points, STF, for ground partitioning tn The capacity matching condition of the nth grid point is carried out on the nth time slot; in any time slot t (t=0, 1,2,.. The satellite constellation provides a total satellite communication population, STF, for grid point N that is equal to or greater than the grid point's capacity during any time slot t (t=0, 1,2,.. The L-1) tn =1, whereas STF tn =0, as shown in formula (10);
d (N) represents the number of satellite communication subscribers at a ground grid point, and the population N (N) at the grid point and the proportion of the communication subscribers can be usedProportion of satellite users->The product calculation of (1), i.e. the number of satellite communication subscribers at the nth grid point isC t (n) is the capacity provided by the number of satellites in view of grid point n during the t time slot, calculated from the single satellite capacity and the number of satellites in view of the ground grid point obtained from the STK.
7. The optimization design method of low-orbit satellite constellation according to claim 1, wherein the objective function value in step S6 is calculated as follows:
defining satellite constellation as S for serving grid point n in t time slot t ＝{m|θ nm ≥θ min }，θ nm Is the elevation angle of grid point n to satellite m, θ min Is the lowest elevation angle for good communication conditions, C in equation (10) t (n) is represented by formula (11);
whereby the satellite constellation provides the target area with a capacity that is the sum of the capacities provided in the respective time slots throughout the period;
the objective function value-network cost-effectiveness ratio is expressed as a ratio of the sum of the above capacities to the overhead of constructing the network, as shown in equation (13);
8. the method for optimizing constellation of low orbit satellite for guaranteeing service quality according to claim 7, wherein the step S6 of optimizing constellation parameters by adopting a tabu search algorithm means that:
1) Obtaining an output solution of the 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 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 is satisfied or not for the candidate solution, if yes, replacing the current solution with the optimal state solution of the scofflaw criterion; and replacing the object which enters the tabu list earliest by the solution of the optimal state;
5) Judging the tabu states of the objects corresponding to the candidate solutions, selecting the optimal state corresponding to the non-tabu objects in the candidate solution set as a current new solution, and simultaneously replacing the tabu object entering the tabu table earliest by the tabu object corresponding to the new solution;
6) Judging whether the optimized target value in the algorithm changes, if so, ending the algorithm and outputting optimized constellation parameters [ N ] SAT N P i P SAT A SAT ]And the largest objective function value ψ, otherwise go to step 3).
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