Low-orbit satellite beam scheduling optimization method and system based on simulated annealing algorithm

Abstract

The invention provides a low-orbit satellite beam scheduling optimization method and system based on a simulated annealing algorithm, and belongs to the technical field of satellite communication. Firstly, modeling is carried out on a determined satellite communication system model and communication service distribution in a satellite coverage range, then a channel model between a satellite and a communication terminal is established, then constraint conditions of a problem are determined, mathematical modeling is carried out on the optimal beam scheduling problem, and the optimal beam scheduling problem is obtained. In order to simultaneously improve the coverage communication service volume and reduce the inter-beam interference, the beam scheduling problem is solved by using the simulated annealing algorithm, the optimal design of the beam pattern is realized, and the comprehensive performance of the low-orbit satellite communication system is improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a low-orbit satellite beam scheduling optimization method and system based on a simulated annealing algorithm. Background Art

[0002] The distribution of satellite communication service requirements usually presents a highly non-uniform characteristic. Therefore, in order to make full use of the limited on-board resources to meet the needs of ground communication services, modern satellite communication systems must adopt efficient resource scheduling algorithms. Due to the continuous advancement of satellite antenna technology, many studies are focusing on beam domain resource scheduling technology. Many technologies proposed by current research institutes, such as dynamic beamforming and beam domain coding, are heavily dependent on complex payload systems and require high costs, so they can only be applied to geosynchronous orbit multi-beam satellites. For low-earth orbit satellites, a more feasible and practical method is to use the flexibility of existing payloads to implement beam scheduling algorithms in an economical way. Therefore, in the face of the characteristics of diversified communication business volume and uneven temporal and spatial distribution, how to design efficient and economical beam scheduling algorithms, improve the service quality of communication terminals, and improve the overall performance of satellite communication systems is an important research topic in the current field of satellite communications.

[0003] Patent application document CN116896407A discloses a resource allocation method for a multi-beam satellite communication system based on a multi-agent A3C algorithm. By modeling beam-related variables and power allocation variables and modeling satellite channels, a satellite load balancing scheme is determined, which mainly includes modeling system throughput, system utility function, system resource allocation constraints, modeling system status, actions and rewards. In the implementation process, a multi-agent A3C network is constructed and trained, and the system beam hopping scheme and resource allocation strategy are determined based on the multi-agent A3C algorithm. Patent application document CN116527107A discloses a scheduling method and device for uplink beams of geostationary satellite phased arrays. Based on this method, the ground control center can complete efficient and unified scheduling of uplink beams of geostationary satellite phased arrays, realize rapid and real-time command injection of large-scale low-orbit spacecraft, and solve the contradiction between the small number of uplink beams and the large number of targets served at the same time. However, these methods are all based on the payload of high-orbit satellites, and the implementation cost is too high for low-orbit satellites. Summary of the invention

[0004] In view of the defects in the prior art, the object of the present invention is to provide a low-orbit satellite beam scheduling optimization method and system based on a simulated annealing algorithm.

[0005] The low-orbit satellite beam scheduling optimization method based on the simulated annealing algorithm provided by the present invention includes:

[0006] Step S1: Modeling the satellite communication system to obtain the communication terminal distribution and traffic volume distribution within the satellite range;

[0007] Step S2: Modeling the channel between the satellite and the communication terminal;

[0008] Step S3: Determine the constraints of the optimization problem;

[0009] Step S4: with the goal of simultaneously increasing the coverage communication traffic and reducing the interference between beams, mathematical modeling is performed on the optimal beam scheduling problem to determine the objective function of the optimization problem;

[0010] Step S5: Analyze the computational complexity of the problem;

[0011] Step S6: Solve the beam scheduling problem by using a simulated annealing algorithm, and use the solution of the problem as the optimal design of the beam pattern.

[0012] Preferably, in step S2, it is assumed that the satellite-borne phased array antenna has B beams, there are N terminals within the satellite coverage area, and the directions of all beams constitute a set D, which is expressed as D = {d1, d2, ..., d B}, where d b is the direction of beam b, and the satellite antenna radiation pattern model expression is:

[0013]

[0014] Among them, G MAX represents the maximum gain of the satellite antenna pattern, δ 3dB Half-power angle of the beam, L s It represents the near sidelobe level relative to the peak gain required by the system design, and δ represents the off-axis angle of the receiving terminal to the satellite beam;

[0015] The off-axis angle δ of the ath communication terminal relative to the bth beam a,b It is expressed as:

[0016]

[0017] Among them, r a and r b They represent the distance from the satellite to terminal a and the distance from the satellite to the center of beam b respectively; r a,b represents the distance between terminal a and beam b;

[0018] By placing the satellite, the center of beam b b And the latitude-longitude-altitude coordinates of terminal a are converted to earth-centered earth-fixed coordinates (x s ,y s ,z s ), and thus calculate ra and r b , the conversion formula is:

[0019]

[0020] Among them, lat s ,lon s and h s Respectively represent the latitude, longitude and altitude of the satellite; R e is the radius of the Earth;

[0021] Channel loss is composed of rain attenuation, cloud attenuation, and free space loss parameters;

[0022] The rain attenuation expression is:

[0023] A r =f r (p,R 0.01 ,h r ,h a ,f,θ,φ a )

[0024] Among them, h a ,φ a and θ represent the aircraft’s altitude, latitude, and elevation angle relative to the satellite, respectively; R 0.01 represents the rainfall corresponding to a probability of 0.01% in the local annual average rainfall distribution, p represents the probability that the local cloud attenuation value exceeds a specific attenuation level, and h r represents the rainfall height, and f represents the carrier frequency;

[0025] Cloud attenuation refers to the attenuation effect of clouds in the troposphere on the propagation of communication signals, and the expression is:

[0026] A a =f c -1 (L(p,h a ),f,θ)

[0027] Among them, L(p,h a ) indicates that it is higher than h a The columnar liquid water content in the cloud layer is expressed as:

[0028]

[0029] The free space loss experienced by the signal propagation is L a , the expression is:

[0030] L a =-32.4-20lgf-20lgr a

[0031] The channel gain experienced by the communication signal propagating from the beam b of the low-orbit satellite to the aviation communication terminal a is expressed as:

[0032] h a,b =G tx (δ a,b )+G rx +A r +A a +L a [dB]

[0033] Among them, G rx Indicates the receiving antenna gain.

[0034] Preferably, in step S3, considering the load limitation of the phased array antenna, the angle between the line connecting the center of beam b and the satellite and the line connecting the satellite and the sub-satellite point is γ b , is always less than the maximum off-axis angle γ of the satellite antenna MAX , this constraint is expressed as: b ≤γ MAX ,

[0035] Preferably, in step S4, in order to achieve the goal of simultaneously increasing the coverage communication traffic and reducing the inter-beam interference, the objective function is:

[0036]

[0037] Among them, C b (d b ) represents the communication traffic covered by the bth beam, SIR b (D) is the average value of the ratio of the signal strength of the bth beam to the interference from other beams;

[0038] When calculating inter-beam interference, each beam aggregates the communication demands of all terminals within the coverage of the beam to form a super terminal located at the center of the beam, whose communication demand is equal to the sum of the demands of all individual terminals. At the same time, when calculating interference, the uniform distribution of power among beams is considered. The problem of optimal beam scheduling is expressed as:

[0039]

[0040] Preferably, in step S6, the simulated annealing algorithm construction process includes: after the objective function is given, the simulated annealing algorithm parameters are determined, and iterative calculations are performed until a threshold stability condition is met or a maximum number of iterations is met, and an optimal beam pattern solution is output.

[0041] The low-orbit satellite beam scheduling optimization system based on the simulated annealing algorithm provided by the present invention includes:

[0042] Module M1: Model the satellite communication system to obtain the distribution of communication terminals and traffic within the satellite range;

[0043] Module M2: Modeling the channel between the satellite and the communication terminal;

[0044] Module M3: Determine the constraints of the optimization problem;

[0045] Module M4: To simultaneously increase the coverage of communication traffic and reduce inter-beam interference, mathematical modeling of the optimal beam scheduling problem is performed to determine the objective function of the optimization problem;

[0046] Module M5: Analyze the computational complexity of the problem;

[0047] Module M6: Solve the beam scheduling problem through simulated annealing algorithm and use the solution of the problem as the optimal design of the beam pattern.

[0048] Preferably, in module M2, it is assumed that the satellite-borne phased array antenna has B beams, there are N terminals within the satellite coverage area, and the directions of all beams constitute a set D, which is expressed as D = {d1, d2, ..., d B}, where d b is the direction of beam b, and the satellite antenna radiation pattern model expression is:

[0049]

[0050] Among them, G MAX represents the maximum gain of the satellite antenna pattern, δ 3dB Half-power angle of the beam, L s It represents the near sidelobe level relative to the peak gain required by the system design, and δ represents the off-axis angle of the receiving terminal to the satellite beam;

[0051] The off-axis angle δ of the ath communication terminal relative to the bth beam a,b It is expressed as:

[0052]

[0053] Among them, r a and r b They represent the distance from the satellite to terminal a and the distance from the satellite to the center of beam b respectively; r a,b represents the distance between terminal a and beam b;

[0054] By placing the satellite, the center of beam b b And the latitude-longitude-altitude coordinates of terminal a are converted to earth-centered earth-fixed coordinates (x s ,y s ,z s ), and thus calculate ra and r b , the conversion formula is:

[0055]

[0056] Among them, lat s ,lon s and h s Respectively represent the latitude, longitude and altitude of the satellite; R e is the radius of the Earth;

[0057] Channel loss is composed of rain attenuation, cloud attenuation, and free space loss parameters;

[0058] The rain attenuation expression is:

[0059] A r =f r (p,R 0.01 ,h r ,h a ,f,θ,φ a )

[0060] Among them, h a ,φ a and θ represent the aircraft’s altitude, latitude, and elevation angle relative to the satellite, respectively; R 0.01 represents the rainfall corresponding to a probability of 0.01% in the local annual average rainfall distribution, p represents the probability that the local cloud attenuation value exceeds a specific attenuation level, and h r represents the rainfall height, and f represents the carrier frequency;

[0061] Cloud attenuation refers to the attenuation effect of clouds in the troposphere on the propagation of communication signals, and the expression is:

[0062] A a =f c -1 (L(p,h a ),f,θ)

[0063] Among them, L(p,h a ) indicates that it is higher than h a The columnar liquid water content in the cloud layer is expressed as:

[0064]

[0065] The free space loss experienced by the signal propagation is L a , the expression is:

[0066] L a =-32.4-20lgf-20lgr a

[0067] The channel gain experienced by the communication signal propagating from the beam b of the low-orbit satellite to the aviation communication terminal a is expressed as:

[0068] h a,b =G tx (δ a,b )+G rx +A r +A a +L a [dB]

[0069] Among them, G rx Indicates the receiving antenna gain.

[0070] Preferably, in module M3, considering the load limitation of the phased array antenna, the angle between the line connecting the center of beam b and the satellite and the line connecting the satellite and the sub-satellite point is γ b , is always less than the maximum off-axis angle γ of the satellite antenna MAX , this constraint is expressed as: b ≤γ MAX ,

[0071] Preferably, in module M4, in order to achieve the goal of simultaneously increasing the coverage communication traffic and reducing the inter-beam interference, the objective function is:

[0072]

[0073] Among them, C b (d b ) represents the communication traffic covered by the bth beam, SIR b (D) is the average value of the ratio of the signal strength of the bth beam to the interference from other beams;

[0074] When calculating inter-beam interference, each beam aggregates the communication demands of all terminals within the coverage of the beam to form a super terminal located at the center of the beam, whose communication demand is equal to the sum of the demands of all individual terminals. At the same time, when calculating interference, the uniform distribution of power among beams is considered. The problem of optimal beam scheduling is expressed as:

[0075]

[0076] Preferably, in module M6, the simulated annealing algorithm construction process includes: after the objective function is given, the simulated annealing algorithm parameters are determined, and iterative calculations are performed until a threshold stability condition is met or a maximum number of iterations is met, and an optimal beam pattern solution is output.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] (1) The beam scheduling of the present invention is based on the terminal-level traffic distribution, which can fully correspond to the actual traffic distribution and provide technical support for the allocation of power, channels and other resources of the satellite communication system;

[0079] (2) The constraint conditions of the present invention fully consider the limitations of existing payload technology, and can implement an efficient and economical beam scheduling algorithm to improve the service quality of communication terminals and the overall performance of satellite communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0081] Figure 1 is a schematic diagram of a satellite communication system in the present invention;

[0082] Figure 2 is a flow chart of the beam scheduling optimization method in the present invention;

[0083] Figure 3 is the gain pattern of the transmitting antenna in the embodiment;

[0084] Figure 4 It is a flow chart of the simulated annealing algorithm in the embodiment;

[0085] Figure 5a and Figure 5b The performance comparison chart of optimized beam pattern and uniform beam pattern under different numbers of beams. DETAILED DESCRIPTION

[0086] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0087] Example 1

[0088] The present invention provides a low-orbit satellite beam scheduling optimization method based on a simulated annealing algorithm. The schematic diagram of the low-orbit satellite communication system is shown in FIG. Figure 1 The overall implementation flowchart is shown in Figure 2 shown.

[0089] First, the satellite communication system is modeled, such as Figure 1As shown in the figure, the overall structure of the low-orbit multi-beam satellite communication system is shown, and the communication traffic distribution within the satellite coverage is determined as the input of the subsequent scheduling algorithm. The example uses the flight position data of ADS-B Exchange, the world's largest ADS-B / Mode S / MLAT data sharing organization, as the terminal distribution data. The data set records the information of 13,857 aircraft around the world at 2:30 pm on May 1, 2023, including longitude, latitude, altitude and other location data. By screening out the terminals within the satellite coverage and allocating the traffic, the traffic distribution model within the satellite coverage can be obtained.

[0090] Then the channel between the satellite and the communication terminal is modeled accordingly. The channel model consists of the transmit antenna gain, channel loss, receive antenna gain, etc. The satellite antenna radiation pattern model adopts the model recommended by ITU-S.672-4, and its formula is:

[0091]

[0092] Among them, G MAX represents the maximum gain of the satellite antenna pattern, δ 3dB Half-power angle of the beam, L s represents the near sidelobe level relative to the peak gain required by the system design, and δ represents the off-axis angle of the receiving terminal to the satellite beam. The antenna gain pattern modeled by this method is as follows: Figure 3 As shown. For beams with different directions, assuming G MAX and δ 3dB is a constant. In addition, it is assumed that the communication terminal uses an omnidirectional receiving antenna. Consider the ath communication terminal and the bth beam within the satellite coverage area. The off-axis angle δ of terminal a relative to beam b is a,b It can be expressed as:

[0093]

[0094] Among them, r a and r b denote the distance from the satellite to terminal a and the distance from the satellite to the center of beam b. b And the latitude-longitude-altitude coordinates of terminal a are converted into earth-centered earth-fixed coordinates, and r can be calculated a and r b , taking the conversion of satellite LLA coordinates to ECEF coordinates as an example, the conversion formula is:

[0095]

[0096] Among them, lat s ,lon s and h sRepresents the latitude, longitude and altitude of the satellite respectively.

[0097] In the channel model, the channel parameters are assumed to be constant. Channel loss is composed of parameters such as rain attenuation, cloud attenuation, and free space loss. The rain attenuation model can be expressed as:

[0098] A r =f r (p,R 0.01 ,h r ,h a ,f,θ,φ a )

[0099] Among them, h a ,φ a and θ represent the aircraft’s altitude, latitude, and elevation angle relative to the satellite, respectively. 0.01 represents the rainfall corresponding to a probability of 0.01% in the local annual average rainfall distribution, p represents the probability that the local cloud attenuation value exceeds a specific attenuation level, and h r represents the rainfall height, and f represents the carrier frequency. The rainfall data can be estimated based on the values ​​provided in the rainfall chart recommended by ITU-R P.837-6. Cloud attenuation refers to the attenuation effect of clouds in the troposphere on the propagation of communication signals. Using the rainfall attenuation model proposed by ITU-RP.840-9, it can be expressed as:

[0100] A a =f c -1 (L(p,h a ),f,θ)

[0101] Among them, L(p,h a ) indicates that it is higher than h a The columnar liquid water content in the cloud layer can be expressed as:

[0102]

[0103] Among them, L a is the free space loss experienced by the signal propagation, which can be expressed as:

[0104] L a =-32.4-20lgf-20lgr a

[0105] The channel gain experienced by the communication signal propagating from the beam b of the low-orbit satellite to the aviation communication terminal a can be expressed as follows:

[0106] h a,b =G tx (δ a,b )+G rx +A r +Aa +L a

[0107] Among them, G rx Indicates the receiving antenna gain.

[0108] Secondly, determine the constraints of the optimization problem, considering the load limit of the phased array antenna, the angle γ between the line connecting the center of beam b and the satellite and the line connecting the satellite and the sub-satellite point b , is always less than the maximum off-axis angle γ of the satellite antenna MAX , this constraint can be expressed as:

[0109]

[0110] In the modeling of the beam scheduling problem, the goal is to simultaneously increase the coverage of communication traffic and reduce the interference between beams. The optimal beam scheduling problem can be mathematically modeled. In order to achieve the goal of simultaneously increasing the coverage of communication traffic and reducing the interference between beams, the following objective function is designed:

[0111]

[0112] Among them, C b (d b ) represents the communication traffic covered by the bth beam, which is C b (d b ), SIR b (D) is the average value of the ratio of the signal strength of the bth beam to the interference from other beams (i.e., signal-to-interference ratio), which is the beam pattern SIR b (D) function, SIR b (D) is a scaling factor in bps, which describes the priority of the signal-to-interference ratio objective function relative to the total coverage objective function. When calculating inter-beam interference, the following simplified model is considered: each beam aggregates the communication needs of all terminals within the coverage range of the beam to form a "super terminal" located at the center of the beam, whose communication demand is equal to the sum of all individual terminal needs. At the same time, in order to focus on the performance of beam scheduling, the case where the power is evenly distributed among the beams is considered when calculating interference. In summary, the problem of optimal beam scheduling can be expressed as:

[0113]

[0114] Based on the following fact: if a special case of a general optimization problem is NP-hard, then the corresponding original problem is also NP-hard. In the computational complexity analysis of this embodiment, if the second term in the objective function is not considered, the beam scheduling problem degenerates into the maximum weighted k-set cover problem, which is an NP-hard problem. Therefore, the optimal beam scheduling problem is NP-hard.

[0115] Based on the computational complexity of the problem, a simulated annealing algorithm is designed to solve the beam scheduling problem, and the solution to the problem is used as the optimization design of the beam pattern. The construction process of the simulated annealing algorithm includes: after the objective function is given, the simulated annealing algorithm parameters are determined, the reference beam pattern is used as the algorithm input, and iterative calculations are performed until the threshold stability condition is met or the maximum number of iterations is met, and the optimal beam pattern solution is output. The calculation process of the algorithm is as follows Figure 4 As shown, this embodiment uses a uniform distribution around the sub-satellite point as the initial reference beam pattern.

[0116] The embodiment considers the comparison of performance indicators under different numbers of beams to highlight the generalization of the algorithm. Taking the beam pattern of 7 as a visualization example, the comparison of performance indicators under different beam patterns is shown in FIG5 .

[0117] Example 2

[0118] The present invention also provides a low-orbit satellite beam scheduling optimization system based on a simulated annealing algorithm. The low-orbit satellite beam scheduling optimization system based on a simulated annealing algorithm can be implemented by executing the process steps of the low-orbit satellite beam scheduling optimization method based on the simulated annealing algorithm, that is, those skilled in the art can understand the low-orbit satellite beam scheduling optimization method based on the simulated annealing algorithm as a preferred implementation of the low-orbit satellite beam scheduling optimization system based on the simulated annealing algorithm.

[0119] The low-orbit satellite beam scheduling optimization system based on the simulated annealing algorithm provided by the present invention includes: module M1: modeling the satellite communication system to obtain the communication terminal distribution and business volume distribution within the satellite range; module M2: modeling the channel between the satellite and the communication terminal; module M3: determining the constraint conditions of the optimization problem; module M4: with the goal of simultaneously improving the coverage of communication business volume and reducing interference between beams, mathematically modeling the optimal beam scheduling problem, and determining the objective function of the optimization problem; module M5: analyzing the computational complexity of the problem; module M6: solving the beam scheduling problem through the simulated annealing algorithm, and using the solution of the problem as the optimization design of the beam pattern.

[0120] In module M2, assume that the satellite-borne phased array antenna has B beams, there are N terminals within the satellite coverage area, and the directions of all beams constitute a set D, which is expressed as D = {d1, d2, ..., d B}, where d b is the direction of beam b, and the satellite antenna radiation pattern model expression is:

[0121]

[0122] Among them, G MAX represents the maximum gain of the satellite antenna pattern, δ3dB Half-power angle of the beam, L s It represents the near sidelobe level relative to the peak gain required by the system design, and δ represents the off-axis angle of the receiving terminal to the satellite beam;

[0123] The off-axis angle δ of the ath communication terminal relative to the bth beam a,b It is expressed as:

[0124]

[0125] Among them, r a and r b They represent the distance from the satellite to terminal a and the distance from the satellite to the center of beam b respectively; r a,b represents the distance between terminal a and beam b;

[0126] By placing the satellite, the center of beam b b And the latitude-longitude-altitude coordinates of terminal a are converted to earth-centered earth-fixed coordinates (x s ,y s ,z s ), and thus calculate r a and r b , the conversion formula is:

[0127]

[0128] Among them, lat s ,lon s and h s Respectively represent the latitude, longitude and altitude of the satellite; R e is the radius of the Earth;

[0129] Channel loss is composed of rain attenuation, cloud attenuation, and free space loss parameters;

[0130] The rain attenuation expression is:

[0131] A r =f r (p,R 0.01 ,h r ,h a ,f,θ,φ a )

[0132] Among them, h a ,φ a and θ represent the aircraft’s altitude, latitude, and elevation angle relative to the satellite, respectively; R 0.01 represents the rainfall corresponding to a probability of 0.01% in the local annual average rainfall distribution, p represents the probability that the local cloud attenuation value exceeds a specific attenuation level, and h r represents the rainfall height, and f represents the carrier frequency;

[0133] Cloud attenuation refers to the attenuation effect of clouds in the troposphere on the propagation of communication signals, and the expression is:

[0134] A a =f c -1 (L(p,h a ),f,θ)

[0135] Among them, L(p,h a ) indicates that it is higher than h a The columnar liquid water content in the cloud layer is expressed as:

[0136]

[0137] The free space loss experienced by the signal propagation is L a , the expression is:

[0138] L a =-32.4-20lgf-20lgr a

[0139] The channel gain experienced by the communication signal propagating from the beam b of the low-orbit satellite to the aviation communication terminal a is expressed as:

[0140] h a,b =G tx (δ a,b )+G rx +A r +A a +L a [dB]

[0141] Among them, G rx Indicates the receiving antenna gain.

[0142] In module M3, considering the load limitation of the phased array antenna, the angle between the line connecting the center of beam b and the satellite and the line connecting the satellite and the sub-satellite point is γ b , is always less than the maximum off-axis angle γ of the satellite antenna MAX , this constraint is expressed as: b ≤γ MAX ,

[0143] In module M4, in order to achieve the goal of simultaneously increasing the coverage communication traffic and reducing the interference between beams, the objective function is:

[0144]

[0145] Among them, C b (d b ) represents the communication traffic covered by the bth beam, SIR b(D) is the average value of the ratio of the signal strength of the bth beam to the interference from other beams;

[0146] When calculating inter-beam interference, each beam aggregates the communication demands of all terminals within the coverage of the beam to form a super terminal located at the center of the beam, whose communication demand is equal to the sum of the demands of all individual terminals. At the same time, when calculating interference, the uniform distribution of power among beams is considered. The problem of optimal beam scheduling is expressed as:

[0147]

[0148] In module M6, the simulated annealing algorithm construction process includes: after the objective function is given, the simulated annealing algorithm parameters are determined, and iterative calculations are performed until the threshold stability condition is met or the maximum number of iterations is met, and the optimal beam pattern solution is output.

[0149] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0150] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A low-orbit satellite beam scheduling optimization method based on simulated annealing algorithm, characterized in that: include: Step S1: Modeling the satellite communication system to obtain the communication terminal distribution and traffic volume distribution within the satellite range; Step S2: Modeling the channel between the satellite and the communication terminal; Step S3: Determine the constraints of the optimization problem; Step S4: with the goal of simultaneously increasing the coverage communication traffic and reducing the interference between beams, mathematical modeling is performed on the optimal beam scheduling problem to determine the objective function of the optimization problem; Step S5: Analyze the computational complexity of the problem; Step S6: Solve the beam scheduling problem by using a simulated annealing algorithm, and use the solution of the problem as the optimal design of the beam pattern.

2. The low-orbit satellite beam scheduling optimization method based on simulated annealing algorithm according to claim 1 is characterized in that: In step S2, it is assumed that the satellite-borne phased array antenna has B beams, there are N terminals within the satellite coverage area, and the directions of all beams constitute a set D, which is expressed as D = {d1, d2, ..., d B }, where d b is the direction of beam b, and the satellite antenna radiation pattern model expression is: Among them, G MAX represents the maximum gain of the satellite antenna pattern, δ 3dB Half-power angle of the beam, L s It represents the near sidelobe level relative to the peak gain required by the system design, and δ represents the off-axis angle of the receiving terminal to the satellite beam; The off-axis angle δ of the ath communication terminal relative to the bth beam a,b It is expressed as: Among them, r a and r b They represent the distance from the satellite to terminal a and the distance from the satellite to the center of beam b respectively; r a,b represents the distance between terminal a and beam b; By placing the satellite, the center of beam b b And the latitude-longitude-altitude coordinates of terminal a are converted to earth-centered earth-fixed coordinates (x s ,y s ,z s ), and thus calculate r a and r b , the conversion formula is: Among them, lat s ,lon s and h s Respectively represent the latitude, longitude and altitude of the satellite; R e is the radius of the Earth; Channel loss is composed of rain attenuation, cloud attenuation, and free space loss parameters; The rain attenuation expression is: A r =f r (p,R 0.01 ,h r ,h a ,f,θ,φ a ) Among them, h a ,φ a and θ represent the aircraft’s altitude, latitude, and elevation angle relative to the satellite, respectively; R 0.01 represents the rainfall corresponding to a probability of 0.01% in the local annual average rainfall distribution, p represents the probability that the local cloud attenuation value exceeds a specific attenuation level, and h r represents the rainfall height, and f represents the carrier frequency; Cloud attenuation refers to the attenuation effect of clouds in the troposphere on the propagation of communication signals, and the expression is: A a =f c -1 (L(p,h a ),f,θ) Among them, L(p,h a ) indicates that it is higher than h a The columnar liquid water content in the cloud layer is expressed as: The free space loss experienced by the signal propagation is L a , the expression is: L a =-32.4-20lgf-20lgr a The channel gain experienced by the communication signal propagating from the beam b of the low-orbit satellite to the aviation communication terminal a is expressed as: h a,b =G tx (δ a,b )+G rx +A r +A a +L a [dB] Among them, G rx Indicates the receive antenna gain.

3. The low-orbit satellite beam scheduling optimization method based on simulated annealing algorithm according to claim 2 is characterized in that: In step S3, considering the load limitation of the phased array antenna, the angle between the line connecting the center of beam b and the satellite and the line connecting the satellite and the sub-satellite point is γ b , is always less than the maximum off-axis angle γ of the satellite antenna MAX , this constraint is expressed as:

4. The low-orbit satellite beam scheduling optimization method based on simulated annealing algorithm according to claim 3 is characterized in that: In step S4, in order to achieve the goal of simultaneously increasing the coverage communication traffic and reducing the interference between beams, the objective function is: Among them, C b (d b ) represents the communication traffic covered by the bth beam, SIR b (D) is the average value of the ratio of the signal strength of the bth beam to the interference from other beams; When calculating inter-beam interference, each beam aggregates the communication demands of all terminals within the coverage of the beam to form a super terminal located at the center of the beam, whose communication demand is equal to the sum of the demands of all individual terminals. At the same time, when calculating interference, the uniform distribution of power among beams is considered. The problem of optimal beam scheduling is expressed as:

5. The low-orbit satellite beam scheduling optimization method based on simulated annealing algorithm according to claim 3 is characterized in that: In step S6, the simulated annealing algorithm construction process includes: after the objective function is given, the simulated annealing algorithm parameters are determined, and iterative calculations are performed until a threshold stability condition is met or a maximum number of iterations is met, and an optimal beam pattern solution is output.

6. A low-orbit satellite beam scheduling optimization system based on simulated annealing algorithm, characterized in that: include: Module M1: Model the satellite communication system to obtain the distribution of communication terminals and traffic within the satellite range; Module M2: Modeling the channel between the satellite and the communication terminal; Module M3: Determine the constraints of the optimization problem; Module M4: To simultaneously increase the coverage of communication traffic and reduce inter-beam interference, mathematical modeling of the optimal beam scheduling problem is performed to determine the objective function of the optimization problem; Module M5: Analyze the computational complexity of the problem; Module M6: Solve the beam scheduling problem through simulated annealing algorithm and use the solution of the problem as the optimal design of the beam pattern.

7. The low-orbit satellite beam scheduling optimization system based on simulated annealing algorithm according to claim 6 is characterized in that: In module M2, assume that the satellite-borne phased array antenna has B beams, there are N terminals within the satellite coverage area, and the directions of all beams constitute a set D, which is expressed as D = {d1, d2, ..., d B }, where d b is the direction of beam b, and the satellite antenna radiation pattern model expression is: Among them, G MAX represents the maximum gain of the satellite antenna pattern, δ 3dB Half-power angle of the beam, L s It represents the near sidelobe level relative to the peak gain required by the system design, and δ represents the off-axis angle of the receiving terminal to the satellite beam; The off-axis angle δ of the ath communication terminal relative to the bth beam a,b It is expressed as: Among them, r a and r b They represent the distance from the satellite to terminal a and the distance from the satellite to the center of beam b respectively; r a,b represents the distance between terminal a and beam b; By placing the satellite, the center of beam b b And the latitude-longitude-altitude coordinates of terminal a are converted to earth-centered earth-fixed coordinates (x s ,y s ,z s ), and thus calculate r a and r b , the conversion formula is: Among them, lat s ,lon s and h s Respectively represent the latitude, longitude and altitude of the satellite; R e is the radius of the Earth; Channel loss is composed of rain attenuation, cloud attenuation, and free space loss parameters; The rain attenuation expression is: A r =f r (p,R 0.01 ,h r ,h a ,f,θ,φ a ) Among them, h a ,φ a and θ represent the aircraft’s altitude, latitude, and elevation angle relative to the satellite, respectively; R 0.01 represents the rainfall corresponding to a probability of 0.01% in the local annual average rainfall distribution, p represents the probability that the local cloud attenuation value exceeds a specific attenuation level, and h r represents the rainfall height, and f represents the carrier frequency; Cloud attenuation refers to the attenuation effect of clouds in the troposphere on the propagation of communication signals, and the expression is: A a =f c -1 (L(p,h a ),f,θ) Among them, L(p,h a ) indicates that it is higher than h a The columnar liquid water content in the cloud layer is expressed as: The free space loss experienced by the signal propagation is L a , the expression is: L a =-32.4-20lgf-20lgr a The channel gain experienced by the communication signal propagating from the beam b of the low-orbit satellite to the aviation communication terminal a is expressed as: h a,b =G tx (δ a,b )+G rx +A r +A a +L a [dB] Among them, G rx Indicates the receive antenna gain.

8. The low-orbit satellite beam scheduling optimization system based on simulated annealing algorithm according to claim 7, characterized in that: In module M3, considering the load limitation of the phased array antenna, the angle between the line connecting the center of beam b and the satellite and the line connecting the satellite and the sub-satellite point is γ b , is always less than the maximum off-axis angle γ of the satellite antenna MAX , this constraint is expressed as:

9. The low-orbit satellite beam scheduling optimization system based on simulated annealing algorithm according to claim 8, characterized in that: In module M4, in order to achieve the goal of simultaneously increasing the coverage communication traffic and reducing the interference between beams, the objective function is: Among them, C b (d b ) represents the communication traffic covered by the bth beam, SIR b (D) is the average value of the ratio of the signal strength of the bth beam to the interference from other beams; When calculating inter-beam interference, each beam aggregates the communication demands of all terminals within the coverage of the beam to form a super terminal located at the center of the beam, whose communication demand is equal to the sum of the demands of all individual terminals. At the same time, when calculating interference, the uniform distribution of power among beams is considered. The problem of optimal beam scheduling is expressed as:

10. The low-orbit satellite beam scheduling optimization system based on simulated annealing algorithm according to claim 8, characterized in that: In module M6, the simulated annealing algorithm construction process includes: after the objective function is given, the simulated annealing algorithm parameters are determined, and iterative calculations are performed until the threshold stability condition is met or the maximum number of iterations is met, and the optimal beam pattern solution is output.

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