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

Abstract

The application discloses a resource allocation method based on high-orbit satellite demand and low-orbit satellite rate, and is suitable for a downlink orthogonal frequency division multiplexing satellite communication system in which high-orbit satellites and low-orbit satellites coexist. A carrier and power joint allocation model is established based on high-orbit satellite downlink communication demand and maximization of low-orbit satellite downlink rate. The carrier and power joint allocation model is converted into a continuous variable model by using a large M method and a concave-convex process method. A non-convex optimization objective function and constraint condition of the continuous variable model are solved by using the concave-convex process method, and the optimal values of the carrier and power are obtained. The low-orbit satellite downlink rate is maximized under the premise of guaranteeing high-orbit satellite communication demand. Compared with existing methods such as fixed access and branch and bound method, the application method can reduce complexity and improve the low-orbit satellite downlink rate.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of satellite communication, and particularly relates to a resource allocation method based on high-orbit satellite demand and low-orbit satellite rate. BACKGROUND

[0002] With the continuous development of low earth orbit (LEO) satellite systems and the sustained growth of ground users' demand for high-speed communication, the number of LEO satellites is rapidly increasing. This growth brings unprecedented opportunities for global communication coverage, but also leads to extreme scarcity of satellite spectrum resources. As a limited resource, spectrum is increasingly intensively used, especially under the large-scale deployment of low-orbit satellites, which is particularly prominent.

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

[0004] Existing resource allocation methods mostly focus on resource optimization within a single satellite system, and there is less research on resource coordination between multiple-orbit, multiple-satellite systems. For example, in the research of joint optimization of carrier and power allocation, the current technology is difficult to fully consider the quality of service differences of different orbit satellites (such as GEO and LEO). These differences include different requirements of various satellites for latency, throughput, coverage range, etc., and if they are not reasonably optimized, it will directly lead to low efficiency of system resource allocation, which is difficult to meet the increasingly complex communication environment needs.

[0005] Therefore, in order to improve the resource utilization efficiency of the whole system, more refined optimization algorithms must be developed, which need to be able to dynamically adapt to the diversity of demands between satellite systems, and intelligently optimize in multiple dimensions (such as carrier, power, time-frequency resources, etc.) to maximize the overall performance of the system and ensure the quality of service. SUMMARY

[0006] The application aims to solve the above problems, and provides a resource allocation method based on high-orbit satellite demand and low-orbit satellite rate, which maximizes the downlink rate of low-orbit satellites under the premise of ensuring the communication demand of high-orbit satellites. Compared with existing methods such as fixed access and branch and bound method, the downlink rate of low-orbit satellites is improved while the complexity is reduced.

[0007] The technical scheme adopted by the application is as follows:

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

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

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

[0011] Step C: using the concave-convex process method to iteratively solve the model A2, the optimal value of carrier and power is obtained;

[0012] Step D: according to the optimal value obtained by solving 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 when the LEO satellite downlink information rate is maximum are obtained.

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

[0014]

[0015] Wherein, p1 is the power allocation vector of high-orbit satellite GEO, p2 is the power allocation vector of low-orbit satellite LEO, i=1 represents high-orbit satellite GEO, i=2 represents 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 high-orbit satellite GEO, c2 is the carrier allocation vector of low-orbit satellite LEO, is the allocation vector of the kth subcarrier of satellite i, is the subcarrier allocation indicator variable, when the mth user connects the kth subcarrier on the nth beam of satellite i, the indicator variable Otherwise B0 is the bandwidth of subcarrier, B0=B / K, B is the total bandwidth; is the user set served by the nth beam of satellite i; The signal-to-interference-plus-noise ratio received by the mth user from the kth subcarrier of the nth beam of the corresponding satellite i.

[0016] Further, the constraint condition of the model A1 includes:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

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

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

[0027]

[0028]

[0029] wherein σ 2 = N0B0, N0 is the power spectral density of the received noise floor, and B0 is the bandwidth of the subcarrier; is the channel coefficient of the signal transmitted by the satellite i on the kth subcarrier of the nth beam to the mth user served by the satellite j, i = 1, 2; U 1,n′ is the user set served by the nth' beam of the high-orbit satellite GEO, U 2,n″ is the user set served by the nth" beam of the low-orbit satellite LEO.

[0030] Further, step B converts the model A1 into a continuous variable model A2, and the steps are as follows:

[0031] Step B-1: Transform the objective function B1 of model Al into a convex objective function B3, the steps are as follows:

[0032] Step B-1-1: Transform the objective function B1 into an objective function B2, which is:

[0033]

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

[0035]

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

[0037] Step B-2: Transform the constraint condition C5 of model Al into convex constraint conditions C5.1, C5.2, C5.3.2, C5.3.3, C5.3.4, the steps are as follows:

[0038] Step B-2-1: Transform the constraint condition C5 using the big M method as follows:

[0039]

[0040]

[0041]

[0042] where ε is a positive number, at this time the constraints C5.1 and C5.2 are linear convex functions;

[0043] Step B-2-2: Transform the constraint condition C5.3 into the following three continuous constraint conditions:

[0044]

[0045]

[0046]

[0047] where C5.3.2 and C5.3.3 are convex functions, and C5.3.1 is a concave function, which is moved to get:

[0048]

[0049] Constraint C5.3.4 is obtained from constraint C5.3.1:

[0050] ​

[0051] wherein is the value of calculated at the tth iteration, with the constraint C5.3.4 being a convex constraint;

[0052] Step B-3: Transform the constraint condition C8 of the model A1 into a convex constraint condition C8.2;

[0053] Step B-3-1: The constraint condition C8 is equivalently transformed into:

[0054]

[0055] Substitute the expression of SINR to get:

[0056]

[0057] Step B-3-2: Get the constraint condition C8.2 from the constraint condition C8.1

[0058]

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

[0060]

[0061] Subject to:

[0062]

[0063]

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

[0065] Step C-1: Set the power allocation vector initial value (p1 (k) ) (0) , (p2 (k) ) (0) and the carrier allocation vector initial value (c1 (k) ) (0) , (c2 (k) ) (0) , respectively, wherein is the value of calculated at the 0th iteration, is the value of calculated at the 0th iteration;

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

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

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

[0069] Step C-1-1: set the power average allocation, calculate the initial value of the subcarrier variable, and the objective function is represented as

[0070]

[0071] wherein

[0072]

[0073] Step C-1-2: for each user and subcarrier k, calculate For a fixed user, if is larger, it indicates that the user prefers the subcarrier; for a fixed subcarrier, if is larger, it indicates that the subcarrier should be accessed by this user;

[0074] is larger, then is larger, and only needs to be compared;

[0075] Each user sends an access request to the subcarrier, including the value of , and the subcarrier k selects the user m corresponding to the largest , and sets and sets

[0076] If the two users' values are the same, then one user is randomly selected from the sub-carrier and the access request of the other user is rejected;

[0077] The above selection operation is performed for each sub-carrier, i.e., all sub-carriers and user access tasks are completed;

[0078] Step C-1-3: using the solution of step C-1-2

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

[0080]

[0081] Advantages: Compared with the prior art, the technical scheme of the present application has the following beneficial technical effects:

[0082] The present application provides a resource allocation method based on high-orbit satellite demand and low-orbit satellite rate. For a downlink orthogonal frequency division multiplexing satellite communication system coexisting with high-orbit and low-orbit satellites, based on guaranteeing high-orbit satellite downlink communication demand and maximizing low-orbit satellite downlink rate, the joint allocation of carriers and power is studied. The mixed optimization problem is converted into a continuous problem for solving by using the large M method and the concave-convex process algorithm; the initial value of the sub-carrier is allocated by using the allocation algorithm based on the matching theory, and the concave-convex algorithm is used to process the non-convex optimization problem and the constraint condition, which is converted into a series of continuous convex problems for solving. The experimental results show that compared with the fixed access and branch and bound algorithm, the complexity is reduced while the low-orbit satellite downlink rate is improved. BRIEF DESCRIPTION OF DRAWINGS

[0083] Figure 1 is the flow chart of the method of the present application.

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

[0085] The technical scheme of the present application will be further described below in combination with the drawings and examples.

[0086] ​The resource allocation method based on high-orbit satellite demand and low-orbit satellite rate provided by the application is for a downlink orthogonal frequency division multiplexing satellite communication system coexisting with high-orbit and low-orbit satellites, a carrier and power joint allocation model is established based on high-orbit satellite downlink communication demand and maximization of low-orbit satellite downlink rate, the carrier and power joint allocation model is converted into a continuous variable model by using a large M method and a concave-convex process method, a non-convex optimization objective function and constraint condition of the continuous variable model are solved by using the concave-convex process method, and the optimal values of the carrier and power are obtained, so that the low-orbit satellite downlink rate is maximized under the premise of guaranteeing high-orbit satellite communication demand. By using the method provided by the application, compared with existing methods such as fixed access and branch and bound method, the low-orbit satellite downlink rate is improved while the complexity is reduced.

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

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

[0089]

[0090] Limited to:

[0091]

[0092] Wherein p1 is a power allocation vector of a high-orbit satellite GEO, p2 is a power allocation vector of a low-orbit satellite LEO, GEO represents a high-orbit satellite, 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 a 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] Wherein c1 is a carrier allocation vector of a high-orbit satellite GEO, c2 is a carrier allocation vector of a low-orbit satellite LEO, is the allocation vector of the kth subcarrier of satellite i, Wherein is a subcarrier allocation indicator variable, when the mth user is connected to the kth subcarrier on the nth beam of satellite i, the indicator variable Otherwise B0 is the bandwidth of subcarrier, B0 = B / K, B is the total bandwidth; is 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 satellite i corresponding to the user.

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

[0095]

[0096] is the set of users served by the nth beam of satellite i, only when the mth user is in the set is the set of users served by the nth beam of satellite i, only when the mth user is in the set is the set of users served by the nth beam of satellite i, only when the mth user is in the set is the set of users served by the nth beam of satellite i, only when the mth user is in the set is the set of users served by the nth beam of satellite i, only when the mth user is in the set γ0 is the signal-to-interference-plus-noise ratio threshold that GEO satellite needs to meet.

[0097] The expression is:

[0098]

[0099]

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

[0101] The channel coefficient of the signal transmitted by the kth subcarrier of the nth beam of the i-th type satellite to the mth user served by the j-th type satellite on the ground is calculated by the formula:

[0102]

[0103] where G R is the user's receive gain, is the path loss of the user to the satellite it is connected to, G1(θ i,n；j,m ) is the beam gain of the nth GEO satellite beam to the mth user, G2(θi,n；j,m ) is the beam gain of the nth LEO satellite beam antenna to the mth user, θ i,n；j,m is the off-axis angle of the user, i.e., the angle between the main lobe direction of the ith satellite antenna and the line connecting the jth satellite serving the mth user on the ground and the satellite; The expression is as follows:

[0104]

[0105] where d i,m is the distance from the ith satellite to its corresponding mth user, λ k is the wavelength of the kth subcarrier; the 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 3dB power angle of the LEO satellite, and J1(·) and J3(·) are the first-order and third-order Bessel functions of the first kind, respectively.

[0108] Step B: Convert model A1 to continuous variable model A2, the specific steps are as follows:

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

[0110]

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

[0112]

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

[0114]

[0115] wherein is the value of at the tth iteration, the objective function B3 is a convex function with respect to the power allocation vector p1, p2.

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

[0117] Step B-2-1: Transform the constraint condition C5 using the big M method as follows:

[0118]

[0119] wherein ε is a very small positive number, and in this embodiment, ε = 10 -5 At this time, the constraints C5.1 and C5.2 are linear convex functions.

[0120] Step B-2-2: Transform the constraint condition C5.3 into the following three continuous constraint conditions:

[0121]

[0122] wherein C5.3.2 and C5.3.3 are convex functions, and C5.3.1 is a concave function. Move the term C5.3.1 to get:

[0123]

[0124] From the constraint C5.3.1, the constraint C5.3.4 is obtained:

[0125]

[0126] wherein is the value of calculated in the tth iteration, at this time, the constraint C5.3.4 is a convex constraint.

[0127] Step B-3: Transform the constraint condition C8 of model A1 into a convex constraint condition C8.2, the steps are as follows:

[0128] Step B-3-1: Transform the constraint condition C8 equivalently into:

[0129]

[0130] Substitute the expression of the signal-to-interference-and-noise ratio to get:

[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 model A2, the specific steps of which are as follows:

[0140] Step C-1: Set the initial values ​​of the power allocation vectors (p1) respectively. (k) ) (0) (p2) (k) ) (0) With the initial value of the carrier allocation vector (c1) (k) ) (0) (c2) (k) ) (0) ,in Calculated for the 0th iteration The value, 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) Let be the downlink information rates of the LEO satellite in the t-th and t+1-th iterations, respectively, and δ be the threshold value; in this embodiment, δ = 10. -2 .

[0142] Step D: After iterative condition D1 converges, the GEO satellite power allocation vector (p1) is obtained when the LEO satellite downlink information rate is maximized. (k) ) (t) and 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, set the initial value of carrier allocation vector (c1 (k) ) (0) ,(c2 (k) ) (0) , comprising the following steps:

[0144] Step C-1-1: assuming power average allocation, calculate the initial value of subcarrier variable, the objective function of optimization problem can be expressed as:

[0145]

[0146] Wherein

[0147]

[0148] Step C-1-2: for each determined user and subcarrier k, calculate For fixed user, if is larger, it means that the user is more biased to the subcarrier; for fixed subcarrier, if is larger, it means that the subcarrier should be accessed by this user; because the logarithmic function with base 2 is a monotonic increasing function, therefore is larger, then is larger, only need to compare size can be; each user sends access request to subcarrier, including value, subcarrier k will select the user m corresponding to the largest value, let and let the of other users corresponding to the subcarrier k If the values of two users are the same, then randomly select one user to allocate subcarrier, refuse the access request of other users; for each subcarrier, perform the above selection operation, that is, complete the access task of all subcarriers and users.

[0149] Step C-1-3: use the above step C-1-2 to solve

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

[0151]

Claims

1. A method for resource allocation 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 high orbit satellite GEO downlink communication demand, a carrier and power joint allocation model A1 is established, and a target function of the model A1 is to maximize the total throughput of the system, that is, the sum of all user rates; Step B: the target function B1 of the model A1 is converted into a convex target function B3, the constraint condition of the model A1 is converted by using a large M method, and the model A1 is converted into a continuous variable model A2; Step C: the model A2 is iteratively solved to obtain the optimal values of the carrier and the power; Step C-1: Set the power allocation vector initial value (p1 (k) ) (0) , the power allocation vector (p2 (k) ) (0) , and the carrier allocation vector initial value (c1 (k) ) (0) , the carrier allocation vector (c2 (k) ) (0) , respectively, where is the value of calculated in the 0th iteration, is the value of calculated in the 0th iteration. Step C-2: Repeat the iteration until the condition D1: |r L (t+1) -r L (t) |≤δ, where r L (t) , r L (t+1) are the LEO satellite downlink information rates at the t-th and t+1-th iteration, respectively, and δ is a threshold value; Step D: After the iteration condition D1 converges, the GEO satellite power allocation vector (p1 (k) ) (t) and the LEO satellite power allocation vector (p2 (k) ) (t) , the GEO satellite carrier allocation vector (c1 (k) ) (t) and the LEO satellite carrier allocation vector (c2 (k) ) (t) are obtained. 2.The method for allocating resources based on high-orbit satellite demand and low-orbit satellite speed according to claim 1, characterized in that, The target function B1 of the model A1 is represented as: where p1 is the power allocation vector of GEO satellite, p2 is the power allocation vector of LEO satellite, GEO represents high orbit satellite, i=2 represents 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 GEO satellite, c2 is the carrier allocation vector of LEO satellite, is the allocation vector of the kth subcarrier of satellite i, is the subcarrier allocation indicator variable, when the mth user is connected to the kth subcarrier on the nth beam of satellite i, the indicator variable otherwise B0 is the bandwidth of subcarrier, B0=B / K, B is the total bandwidth; is the user set 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 method for resource allocation based on high orbit satellite demand and low orbit satellite rate according to claim 2, characterized in that, The constraint condition of the model A1 comprises: wherein is the nominal transmit power of a GEO or LEO satellite beam, is the total nominal transmit power of a GEO or LEO satellite, γ0is a signal-to-noise ratio threshold that GEO satellites need to satisfy, and sgn(·) is the sign function. 4.The method for resource allocation based on high orbit satellite demand and low orbit satellite rate according to claim 3, characterized in that, signal-to-interference-plus-noise ratio The calculation formula is: where σ 2 = N0B0, N0 is the power spectral density of the received noise floor, and B0 is the bandwidth of the subcarrier; is the channel coefficient of the signal transmitted by satellite i on the kth subcarrier of the nth beam to the mth user served by satellite j, i = 1, 2; U 1,n′ is the set of users served by the nth beam of the high-orbit satellite GEO 2,n″ is the set of users served by the n"th beam of the low-orbit satellite LEO. 5.The method for resource allocation based on high orbit satellite demand and low orbit satellite rate according to claim 4, characterized in that, Step B converts the model A1 into the continuous variable model A2, and the steps are as follows: Step B-1: the target function B1 of the model A1 is converted into a convex target function B3, and the steps are as follows: Step B-1-1: the target function B1 is converted into a target function B2, and the target function B2 is as follows: Step B-1-2: the target function B2 is converted into a convex target function B3: wherein is the value obtained for the tth iteration of the objective function B3 is a convex function with respect to the power allocation vectors p1, p2; Step B-2: the constraint condition C5 of the model A1 is converted into convex constraint conditions C5.1, C5.2, C5.3.2, C5.3.3, C5.3.4, and the steps are as follows: Step B-2-1: the constraint condition C5 is converted by using a large M method as follows: Wherein ε is a positive number, at this time, the constraint C5.1 and C5.2 are linear convex functions; Step B-2-2: the constraint condition C5.3 is converted into the following three continuous constraint conditions: Wherein C5.3.2 and C5.3.3 are convex functions, and C5.3.1 is a concave function, and C5.3.1 is moved to obtain: The constraint C5.3.4 is obtained from the constraint C5.3.1: wherein the value of for the tth iteration, at which time the constraint C5.3.4 is a convex constraint; Step B-3: the constraint condition C8 of the model A1 is converted into a convex constraint condition C8.2; Step B-3-1: the constraint condition C8 is equivalently converted into: Substituting the expression for the signal-to-interference-and-noise ratio yields: Step B-3-2: the constraint condition C8.2 is obtained from the constraint condition C8.1 Step B-4: the model A2 is as follows: Limited to: 6.The method for resource allocation based on high orbit satellite demand and low orbit satellite rate according to claim 1, wherein, In step C-1, the carrier allocation vector is initialized (c1 (k) ) (0) ,(c1 (k) ) (0) , comprising the steps of: Step C-1-1: assuming that the power is averagely distributed, the initial value of the subcarrier variable is calculated, and the target function is represented as Wherein Step C-1-2: For each user and subcarrier k, compute For a fixed user, if is larger, it means the user prefers this subcarrier more; for a fixed subcarrier, if is larger, it means this subcarrier should be accessed by this user more. The greater the The greater, only need to compare Size; Each user sends an access request to a subcarrier, including the value of the subcarrier k, the user m corresponding to the largest value is allowed to access the subcarrier k, and let the other users of the subcarrier k be If the two users' values are the same, then one of the users is randomly selected to be assigned the subcarrier, and the other user's access request is denied. If the two users' values are the same, then one of the users is randomly selected to be assigned the subcarrier, and the other user's access request is denied The above selection operation is performed on each subcarrier, that is, the access task of all subcarriers and users is completed; Step C-1-3: Solve for Step C-1-2 using 7.The method for resource allocation based on high orbit satellite demand and low orbit satellite rate according to claim 1, characterized in that, In step C-1, set the power allocation vector initial value (p1 (k) ) (0) ,(p2 (k) ) (0) , the expression is as follows:

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