Resource allocation method based on high-orbit satellite demand and low-orbit satellite rate

Abstract

The invention discloses a resource allocation method based on a high-orbit satellite demand and a low-orbit satellite rate, aiming at a downlink orthogonal frequency division multiplexing satellite communication system in which a high-orbit satellite and a low-orbit satellite coexist, based on the high-orbit satellite downlink communication demand, the low-orbit satellite downlink rate is maximized, and a carrier and power joint allocation model is established; converting the carrier and power joint distribution model into a continuous variable model by using a large M method and a concave-convex process method; and solving a non-convex optimization objective function and constraint conditions of the continuous variable model by using a concave-convex process method to obtain optimal values of carrier waves and power, and enabling the downlink rate of the low-orbit satellite to be maximum on the premise of ensuring the communication requirements of the high-orbit satellite. Compared with the existing methods such as a fixed access method and a branch and bound method, the method provided by the invention has the advantages that the complexity is reduced, and the downlink rate of the low-orbit satellite is improved.

Description

Technical Field

[0001] The present invention belongs to the field of satellite communication technology, and in particular relates to a resource allocation method based on high-orbit satellite requirements and low-orbit satellite rates. Background Art

[0002] With the continuous development of low-orbit (LEO) satellite systems and the growing demand for high-speed communications from ground users, the number of LEO satellites is increasing rapidly. This growth has brought unprecedented opportunities for global communication coverage, but it has also led to an extreme shortage of satellite spectrum resources. As a limited resource, spectrum is being used more and more intensively, especially with the large-scale deployment of LEO satellites. This problem is particularly prominent.

[0003] In this case, it will become an inevitable trend for satellites in multiple orbits (such as GEO and LEO) to share the same frequency band for communication. This is because sharing the same frequency band resources can not only improve spectrum utilization, but also support diversified communication needs in a wider range. However, this also brings huge challenges, requiring more advanced and intelligent resource allocation methods to effectively manage these scarce resources.

[0004] Most existing resource allocation methods focus on optimizing resources within a single satellite system, while there is little research on resource coordination between multi-orbit and multi-satellite systems. For example, in the study of joint optimization of carrier and power allocation, current technology cannot fully consider the differences in service quality of satellites in different orbits (such as GEO and LEO). These differences include different requirements of various satellites for latency, throughput, coverage, etc. If they cannot be reasonably optimized, it will directly lead to inefficient system resource allocation and make it difficult to meet the needs of an increasingly complex communication environment.

[0005] Therefore, in order to improve the resource utilization efficiency of the entire system, more sophisticated optimization algorithms must be developed. These algorithms need to be able to dynamically adapt to the diverse needs of satellite systems and perform intelligent joint optimization in multiple dimensions (such as carriers, power, time-frequency resources, etc.) to maximize the overall performance of the system and ensure service quality. Summary of the invention

[0006] Purpose of the invention: In view of the above problems, the present invention proposes a resource allocation method based on high-orbit satellite demand and low-orbit satellite rate, which maximizes the downlink rate of low-orbit satellite while ensuring the communication demand of high-orbit satellite. Compared with existing methods such as fixed access and branch-and-bound, this method reduces complexity and improves the downlink rate of low-orbit satellite.

[0007] Technical solution: To achieve the purpose of the present invention, the technical solution adopted by the present invention is:

[0008] A resource allocation method based on high-orbit satellite demand and low-orbit satellite rate includes the following steps:

[0009] Step A: According to the downlink communication requirements of the high-orbit satellite GEO and the maximization of the downlink rate of the low-orbit satellite LEO, a carrier and power joint allocation model A1 is established;

[0010] Step B: using the big M method and the concave-convex process method to transform the objective function and constraint conditions of the model A1, and transforming the model A1 into a continuous variable model A2;

[0011] Step C: using the concave-convex process method to iteratively solve the model A2 to obtain the optimal values ​​of the carrier and power;

[0012] Step D: According to the optimal value obtained in step C, the GEO satellite power allocation vector and the LEO satellite power allocation vector, the GEO satellite carrier allocation vector and the LEO satellite carrier allocation vector are obtained when the LEO satellite downlink information rate is maximum.

[0013] Furthermore, the objective function of the model A1 is to maximize the total throughput of the system, that is, the sum of all user rates. The objective function B1 of the model A1 is expressed as:

[0014]

[0015] Among them, p1 is the power allocation vector of the high-orbit satellite GEO, p2 is the power allocation vector of the low-orbit satellite LEO, i=1 represents a high-orbit satellite GEO, i=2 represents a low-orbit satellite LEO, N1 is the number of GEO beams, N2 is the number of LEO beams, M1 is the number of GEO users, M2 is the number of LEO users, K is the number of subcarriers, is the power allocation vector of satellite i on the kth subcarrier, k∈[1,K], is the power obtained by the mth user from the nth beam of satellite i on the kth subcarrier; c1 is the carrier allocation vector of the high-orbit satellite GEO, c2 is the carrier allocation vector of the low-orbit satellite LEO, is the allocation vector of the kth subcarrier of satellite i, Assign indicator variables to subcarriers. When the mth user connects to the kth subcarrier on the nth beam of satellite i, the indicator variable otherwise B0 is the bandwidth of the subcarrier, B0=B / K, B is the total bandwidth; The set of users served by the nth beam of satellite i; is the signal to interference plus noise ratio received by the mth user from the kth subcarrier of the nth beam of its corresponding satellite i.

[0016] Furthermore, the constraints of the model A1 include:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] Among them, P i 0 is the rated transmit power of the GEO or LEO satellite beam, P i T is the total rated transmit power of the GEO or LEO satellite, γ0 is the signal-to-interference-noise ratio threshold that the GEO satellite needs to meet, and sgn(i) is the sign function.

[0026] Furthermore, the signal-to-interference-noise ratio The calculation formula is:

[0027]

[0028]

[0029] Among them, σ 2 =N0B0, N0 is the power spectrum density of the receiving noise floor, B0 is the bandwidth of the subcarrier; is the channel coefficient of the signal transmitted from satellite i on the kth subcarrier of the nth beam to the mth user served by satellite j, i = 1, 2; U 1,n′ The set of users served by the n′th beam of the high-orbit satellite GEO, U 2,n″ The set of users served by the n″th beam of the low-orbit satellite LEO.

[0030] Furthermore, step B transforms the model A1 into a continuous variable model A2, the steps are as follows:

[0031] Step B-1: Convert the objective function B1 of model A1 into a convex objective function B3, as follows:

[0032] Step B-1-1: Convert the objective function B1 into the objective function B2, wherein the objective function B2 is:

[0033]

[0034] Step B-1-2: Convert the objective function B2 into a convex objective function B3:

[0035]

[0036] in is obtained for the tth iteration The value of , at this time, the objective function B3 is a convex function about the power allocation vector p1, p2;

[0037] Step B-2: Convert the constraint C5 of model A1 into convex constraints C5.1, C5.2, C5.3.2, C5.3.3, C5.3.4. The steps are as follows:

[0038] Step B-2-1: Use the Big M method to transform constraint C5 as follows:

[0039]

[0040]

[0041]

[0042] Where ε is a positive number, and constraints C5.1 and C5.2 are both linear convex functions;

[0043] Step B-2-2: Convert constraint C5.3 into the following three consecutive constraints:

[0044]

[0045]

[0046]

[0047] Among them, C5.3.2 and C5.3.3 are convex functions, C5.3.1 is a concave function, and C5.3.1 is transposed to get:

[0048]

[0049] Constraint C5.3.1 leads to constraint C5.3.4:

[0050]

[0051] in Calculated for the tth iteration The value of , in this case, constraint C5.3.4 is a convex constraint;

[0052] Step B-3: Convert constraint C8 of model A1 into convex constraint C8.2;

[0053] Step B-3-1: Constraint C8 is equivalently transformed into:

[0054]

[0055] Substitute the signal-to-interference-noise ratio The expression of is:

[0056]

[0057] Step B-3-2: Obtain constraint C8.2 from constraint C8.1

[0058]

[0059] Step B-4: The model A2 is:

[0060]

[0061] Limited by:

[0062]

[0063]

[0064] Furthermore, step C uses the concave-convex process method to iteratively solve the model A2, the steps are as follows:

[0065] Step C-1: Set the initial value of the power allocation vector (p1 (k) ) (0) 、(p2 (k) ) (0) and the initial value of the carrier allocation vector (c1 (k) ) (0) 、(c2 (k) ) (0) ,in Calculated for the 0th iteration The value of Calculated for the 0th iteration The value of

[0066] Step C-2: Repeat the iteration until condition D1 is met: |r L (t+1) -rL (t) |≤δ, where r L (t) 、r L (t+1) are the LEO satellite downlink information rates of the tth and t+1th iterations, respectively, and δ is the threshold value;

[0067] After the iteration condition D1 converges, the GEO satellite power allocation vector (p1) is obtained when the LEO satellite downlink information rate is the maximum. (k) ) (t) and the LEO satellite power allocation vector (p2 (k) ) (t) , GEO satellite carrier allocation vector (c1 (k) ) (t) and LEO satellite carrier allocation vector (c2 (k) ) (t) .

[0068] Furthermore, in step C-1, the initial value of the carrier allocation vector (c1 (k) ) (0) ,(c1 (k) ) (0) , including the following steps:

[0069] Step C-1-1: Assume that the power is evenly distributed, calculate the initial value of the subcarrier variable, and the objective function is expressed as

[0070]

[0071] in

[0072]

[0073] Step C-1-2: For each user and subcarrier k, calculate For fixed users, if The larger the value, the more the user prefers the subcarrier. For a fixed subcarrier, if The larger the value, the more likely this subcarrier is to access this user.

[0074] The bigger the The larger the size;

[0075] Each user sends an access request to a subcarrier, including The subcarrier k is selected from the largest value The corresponding user m, let And let the subcarrier k correspond to other users

[0076] If two users If the values ​​are the same, a user is randomly selected from them to allocate a subcarrier, and the access requests of other users are rejected;

[0077] The above selection operation is performed for each subcarrier, that is, the access task of all subcarriers and users is completed;

[0078] Step C-1-3: Use step C-1-2 to solve

[0079] Furthermore, in step C-1, the initial value of the power allocation vector (p1 (k) ) (0) ,(p2 (k) ) (0) , the expression is as follows:

[0080]

[0081] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0082] The present invention provides a resource allocation method based on the high-orbit satellite demand and the low-orbit satellite rate. For the downlink orthogonal frequency division multiplexing satellite communication system where high-orbit and low-orbit satellites coexist, the joint allocation problem of carrier and power is studied based on ensuring the downlink communication demand of the high-orbit satellite and maximizing the downlink rate of the low-orbit satellite. The large M method and the concave-convex process algorithm are used to transform the hybrid optimization problem into a continuous problem for solution; the allocation algorithm based on matching theory is used to allocate the initial value of the subcarrier, and the concave-convex algorithm is used to handle the non-convex optimization problem and constraints, and it is transformed into a series of continuous convex problems for solution. Experimental results show that compared with algorithms such as fixed access and branch-and-bound, the downlink rate of the low-orbit satellite is improved while reducing the complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a flow chart of the method of the present invention.

[0084] Figure 2 This is the downlink communication system structure of the method of the present invention when GEO and LEO satellites coexist. DETAILED DESCRIPTION

[0085] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0086] The resource allocation method based on high-orbit satellite demand and low-orbit satellite rate described in the present invention is aimed at a downlink orthogonal frequency division multiplexing satellite communication system in which high-orbit and low-orbit satellites coexist. Based on the downlink communication demand of the high-orbit satellite and maximizing the downlink rate of the low-orbit satellite, a carrier and power joint allocation model is established; the carrier and power joint allocation model is converted into a continuous variable model using the large M method and the concave-convex process method; the non-convex optimization objective function and constraint conditions of the continuous variable model are solved using the concave-convex process method to obtain the optimal values ​​of the carrier and power, so as to maximize the downlink rate of the low-orbit satellite while ensuring the communication demand of the high-orbit satellite. Compared with existing methods such as fixed access and branch-and-bound methods, the method of the present invention reduces the complexity while improving the downlink rate of the low-orbit satellite.

[0087] A resource allocation method based on synchronous satellite demand and low-orbit satellite rate, the process is as follows Figure 1 As shown in Figure 2, the downlink communication system structure when GEO and LEO satellites coexist is as follows: Figure 2 As shown, the specific implementation includes the following steps:

[0088] Step A: Establish a carrier and power joint allocation model A1, the model A1 is:

[0089]

[0090] Limited by:

[0091]

[0092] Among them, p1 is the power allocation vector of the high-orbit satellite GEO, p2 is the power allocation vector of the low-orbit satellite LEO, represents a high-orbit satellite GEO, i=2 represents a low-orbit satellite LEO, N1 is the number of GEO beams, N2 is the number of LEO beams, M1 is the number of GEO users, M2 is the number of LEO users, and K is the number of subcarriers; is the power allocation vector of satellite i on the kth subcarrier, k∈[1,K], is the power obtained by the mth user from the nth beam of satellite i on the kth subcarrier.

[0093] Among them, c1 is the carrier allocation vector of the high-orbit satellite GEO, c2 is the carrier allocation vector of the low-orbit satellite LEO, is the allocation vector of the kth subcarrier of satellite i, in Assign indicator variables to subcarriers. When the mth user connects to the kth subcarrier on the nth beam of satellite i, the indicator variable otherwise B0 is the bandwidth of the subcarrier, B0=B / K, B is the total bandwidth; The set of users served by the nth beam of satellite i; is the signal to interference plus noise ratio received by the mth user from the kth subcarrier of the nth beam of its corresponding satellite i.

[0094] Among them, P i T is the total rated transmit power of the GEO or LEO satellite, P i 0 is the rated transmit power of the GEO or LEO satellite beam; since one subcarrier can only connect one user, there is

[0095]

[0096] gather is the set of users in the nth beam of satellite i. Only when the mth user is in the set In the middle of the day, Not in the collection In the middle of the day, γ0 is the signal-to-interference-noise ratio threshold that the GEO satellite needs to meet.

[0097] The expression is:

[0098]

[0099]

[0100] where σ 2 =N0B0, N0 is the power spectrum density of the receiving noise floor; is the channel coefficient of the signal transmitted from satellite i on the kth subcarrier of the nth beam to the mth user served by satellite j, i = 1, 2; U 1,n′ The set of users served by the n′th beam of the high-orbit satellite GEO, U 2,n″ The set of users served by the n″th beam of the low-orbit satellite LEO.

[0101] The channel coefficient calculation formula for the signal transmitted from the i-th satellite on the k-th subcarrier of the n-th beam to the m-th ground user served by the j-th satellite is:

[0102]

[0103] Among them G R is the user's receiving gain, is the path loss from the user to the satellite to which it is connected, G1(θ i,n；j,m ) is the beam gain from the nth GEO satellite beam to the mth user, G2(θi,n；j,m ) is the beam gain from the nth LEO satellite beam antenna to the mth user, θ i,n；j,m is the user's off-axis angle, that is, the angle between the main axis direction of the i-th satellite antenna and the line connecting the m-th user on the ground served by the j-th satellite and the satellite; The expression is as follows:

[0104]

[0105] where d i,m is the distance from the i-th satellite to its corresponding m-th user, λ k is the wavelength of the kth subcarrier; beam gain G i (θ i,n；j,m ) is expressed as:

[0106] G i (θ i,n；j,m )=G i,max (J1(s i,n；j,m ) / 2s i,n；j,m +36J3(s i,n；j,m ) / (s i,n；j,m ) 3 ) 2 ,i=1,2,

[0107] where s i,n；j,m =2.07123·sinθ i,n；j,m / sinθ i,3dB , G i,max is the maximum antenna gain of the satellite, θ 1,3dB is the 3dB power angle of the GEO satellite, θ 2,3dB is the 3 dB power angle of the LEO satellite, J1(·) and J3(·) are the first-order and third-order Bessel functions of the first kind, respectively.

[0108] Step B: Convert model A1 into continuous variable model A2. The specific steps are as follows:

[0109] Step B-1: Convert the objective function B1 of model A1 into a convex objective function B3, where the objective function B1 is

[0110]

[0111] Step B-1-1: Convert the objective function B1 into the objective function B2, wherein the objective function B2 is:

[0112]

[0113] Step B-1-2: Convert the objective function B2 into a convex objective function B3:

[0114]

[0115] in is obtained for the tth iteration The value of , at this time, the objective function B3 is a convex function about the power allocation vectors p1, p2.

[0116] Step B-2: Convert the constraint C5 of model A1 into convex constraints C5.1, C5.2, C5.3.2, C5.3.3, C5.3.4. The steps are as follows:

[0117] Step B-2-1: Use the Big M method to transform constraint C5 as follows:

[0118]

[0119] Where ε is a small positive number, in this embodiment ε=10 -5 , in this case, constraints C5.1 and C5.2 are both linear convex functions.

[0120] Step B-2-2: Convert constraint C5.3 into the following three consecutive constraints:

[0121]

[0122] Among them, C5.3.2 and C5.3.3 are convex functions, and C5.3.1 is a concave function. Transposing C5.3.1, we get:

[0123]

[0124] Constraint C5.3.1 leads to constraint C5.3.4:

[0125]

[0126] in Calculated for the tth iteration The value of , in this case constraint C5.3.4 is a convex constraint.

[0127] Step B-3: Convert the constraint C8 of model A1 into the convex constraint C8.2 as follows:

[0128] Step B-3-1: Constraint C8 is equivalently transformed into:

[0129]

[0130] Substitute the signal-to-interference-noise ratio The expression of is:

[0131]

[0132] Step B-3-2: Obtain constraint C8.2 from constraint C8.1:

[0133]

[0134] Step B-4: The model A2 is:

[0135]

[0136] Limited by:

[0137]

[0138]

[0139] Step C: Iteratively solve the model A2. The specific steps are as follows:

[0140] Step C-1: Set the initial value of the power allocation vector (p1 (k) ) (0) 、(p2 (k) ) (0) and the initial value of the carrier allocation vector (c1 (k) ) (0) 、(c2 (k) ) (0) ,in Calculated for the 0th iteration The value of Calculated for the 0th iteration The value of .

[0141] Step C-2: Repeat the iteration until condition D1 is met: |r L (t+1) -r L (t) |≤δ, where r L (t) 、r L (t+1) are the LEO satellite downlink information rates of the tth and t+1th iterations, respectively, and δ is the threshold value; in this embodiment, δ=10 -2 .

[0142] Step D: After the iteration condition D1 converges, the GEO satellite power allocation vector (p1) is obtained when the LEO satellite downlink information rate is the maximum. (k) ) (t) and the LEO satellite power allocation vector (p2 (k) ) (t) , GEO satellite carrier allocation vector (c1 (k) ) (t) and LEO satellite carrier allocation vector (c2 (k) )(t) .

[0143] In step C-1, the initial value of the carrier allocation vector (c1 (k) ) (0) ,(c2 (k) ) (0) , including the following steps:

[0144] Step C-1-1: Assuming that the power is evenly distributed, the initial value of the subcarrier variable is calculated. The objective function of the optimization problem can be expressed as:

[0145]

[0146] in

[0147]

[0148] Step C-1-2: For each identified user and subcarrier k, calculate For fixed users, if The larger the value, the more the user prefers the subcarrier. For a fixed subcarrier, if The larger the value, the more likely the subcarrier should access this user. And because the logarithmic function with base 2 is a monotonically increasing function, The bigger the The larger the Each user sends an access request to the subcarrier, including The subcarrier k will select the largest value among them. The corresponding user m, let And let the subcarrier k correspond to other users If two users If the values ​​are the same, a user is randomly selected to allocate a subcarrier, and the access requests of other users are rejected; the above selection operation is performed on each subcarrier, and the access task of all subcarriers and users is completed.

[0149] Step C-1-3: Solve using the above step C-1-2

[0150] In step C-1, the initial value of the power allocation vector (p1 (k) ) (0) ,(p2 (k) ) (0) , which is expressed as follows:

[0151]

Claims

1. A resource allocation method based on high-orbit satellite demand and low-orbit satellite rate, characterized in that: The method comprises the following steps: Step A: According to the downlink communication requirements of the high-orbit satellite GEO and the maximization of the downlink rate of the low-orbit satellite LEO, a carrier and power joint allocation model A1 is established; Step B: using the big M method and the concave-convex process method to transform the objective function and constraint conditions of the model A1, and transforming the model A1 into a continuous variable model A2; Step C: using the concave-convex process method to iteratively solve the model A2 to obtain the optimal values ​​of the carrier and power; Step D: According to the optimal value obtained in step C, the GEO satellite power allocation vector and the LEO satellite power allocation vector, the GEO satellite carrier allocation vector and the LEO satellite carrier allocation vector are obtained when the LEO satellite downlink information rate is maximum.

2. The resource allocation method based on high-orbit satellite demand and low-orbit satellite rate according to claim 1, characterized in that: The objective function of the model A1 is to maximize the total throughput of the system, that is, the sum of all user rates. The objective function B1 of the model A1 is expressed as: Among them, p1 is the power allocation vector of the high-orbit satellite GEO, p2 is the power allocation vector of the low-orbit satellite LEO, represents the high-orbit satellite GEO, i=2 represents the low-orbit satellite LEO, N1 is the number of GEO beams, N2 is the number of LEO beams, M1 is the number of GEO users, M2 is the number of LEO users, K is the number of subcarriers, is the power allocation vector of satellite i on the kth subcarrier, k∈[1,K], is the power obtained by the mth user from the nth beam of satellite i on the kth subcarrier; c1 is the carrier allocation vector of the high-orbit satellite GEO, c2 is the carrier allocation vector of the low-orbit satellite LEO, is the allocation vector of the kth subcarrier of satellite i, Assign indicator variables to subcarriers. When the mth user connects to the kth subcarrier on the nth beam of satellite i, the indicator variable otherwise B0 is the bandwidth of the subcarrier, B0=B / K, B is the total bandwidth; The set of users served by the nth beam of satellite i; is the signal to interference plus noise ratio received by the mth user from the kth subcarrier of the nth beam of its corresponding satellite i.

3. The resource allocation method based on high-orbit satellite demand and low-orbit satellite rate according to claim 2 is characterized in that: The constraints of the model A1 include: Among them, P i 0 is the rated transmit power of the GEO or LEO satellite beam, P i T is the total rated transmit power of the GEO or LEO satellite, γ0 is the signal-to-interference-noise ratio threshold that the GEO satellite needs to meet, and sgn(i) is the sign function.

4. The resource allocation method based on high-orbit satellite demand and low-orbit satellite rate according to claim 3 is characterized in that: Signal to Interference and Noise Ratio The calculation formula is: Among them, σ 2 =N0B0, N0 is the power spectrum density of the receiving noise floor, B0 is the bandwidth of the subcarrier; is the channel coefficient of the signal transmitted from satellite i on the kth subcarrier of the nth beam to the mth user served by satellite j, i = 1, 2; U 1,n′ The set of users served by the n′th beam of the high-orbit satellite GEO, U 2,n″ The set of users served by the n″th beam of the low-orbit satellite LEO.

5. The resource allocation method based on high-orbit satellite demand and low-orbit satellite rate according to claim 4, characterized in that: Step B transforms the model A1 into a continuous variable model A2, the steps are as follows: Step B-1: Convert the objective function B1 of model A1 into a convex objective function B3, as follows: Step B-1-1: Convert the objective function B1 into the objective function B2, wherein the objective function B2 is: Step B-1-2: Convert the objective function B2 into a convex objective function B3: in is obtained for the tth iteration The value of , at this time, the objective function B3 is a convex function about the power allocation vector p1, p2; Step B-2: Convert the constraint C5 of model A1 into convex constraints C5.1, C5.2, C5.3.2, C5.3.3, C5.3.

4. The steps are as follows: Step B-2-1: Use the Big M method to transform constraint C5 as follows: Where ε is a positive number, and constraints C5.1 and C5.2 are both linear convex functions; Step B-2-2: Convert constraint C5.3 into the following three consecutive constraints: Among them, C5.3.2 and C5.3.3 are convex functions, C5.3.1 is a concave function, and C5.3.1 is transposed to get: Constraint C5.3.1 leads to constraint C5.3.4: in Calculated for the tth iteration The value of , in this case, constraint C5.3.4 is a convex constraint; Step B-3: Convert constraint C8 of model A1 into convex constraint C8.2; Step B-3-1: Constraint C8 is equivalently transformed into: Substitute the signal-to-interference-noise ratio The expression of is: Step B-3-2: Obtain constraint C8.2 from constraint C8.1 Step B-4: The model A2 is: Limited by:

6. The resource allocation method based on high-orbit satellite demand and low-orbit satellite rate according to claim 4, characterized in that: Step C uses the concave-convex process method to iteratively solve the model A2, the steps are as follows: Step C-1: Set the initial value of the power allocation vector (p1 (k) ) (0) 、(p2 (k) ) (0) and the initial value of the carrier allocation vector (c1 (k) ) (0) 、(c2 (k) ) (0) ,in Calculated for the 0th iteration The value of Calculated for the 0th iteration The value of Step C-2: Repeat the iteration until condition D1 is met: |r L (t+1) -r L (t) |≤δ, where r L (t) 、r L (t+1) are the LEO satellite downlink information rates of the tth and t+1th iterations, respectively, and δ is the threshold value; After the iteration condition D1 converges, the GEO satellite power allocation vector (p1) is obtained when the LEO satellite downlink information rate is the maximum. (k) ) (t) and the LEO satellite power allocation vector (p2 (k) ) (t) , GEO satellite carrier allocation vector (c1 (k) ) (t) and LEO satellite carrier allocation vector (c2 (k) ) (t) .

7. The resource allocation method based on high-orbit satellite demand and low-orbit satellite rate according to claim 6, characterized in that: In step C-1, the initial value of the carrier allocation vector (c1 (k) ) (0) ,(c1 (k) ) (0) , including the following steps: Step C-1-1: Assume that the power is evenly distributed, calculate the initial value of the subcarrier variable, and the objective function is expressed as in Step C-1-2: For each user and subcarrier k, calculate For fixed users, if The larger the value, the more the user prefers the subcarrier. For a fixed subcarrier, if The larger the value, the more likely this subcarrier is to access this user. The bigger the The larger the size; Each user sends an access request to a subcarrier, including The subcarrier k is selected from the largest value The corresponding user m, let And let the subcarrier k correspond to other users If two users If the values ​​are the same, a user is randomly selected from them to allocate a subcarrier, and the access requests of other users are rejected; The above selection operation is performed for each subcarrier, that is, the access task of all subcarriers and users is completed; Step C-1-3: Use step C-1-2 to solve 8. The resource allocation method based on high-orbit satellite demand and low-orbit satellite rate according to claim 6, characterized in that: In step C-1, the initial value of the power allocation vector (p1 (k) ) (0) ,(p2 (k) ) (0) , the expression is as follows:

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