Wireless resource allocation method of LEO satellite-ground fusion uplink communication system

Abstract

The invention discloses a wireless resource allocation method of an LEO satellite-ground fusion uplink communication system, which comprises a user base station selection method, a power and carrier allocation method and a power and carrier allocation method from a base station to a satellite link, and comprises the following steps of: firstly, establishing a system energy efficiency maximization model of subcarrier and power allocation; a slack variable is introduced to convert the model into a target function lower bound model; coupling the power allocation variable and the carrier allocation variable by using a sign function; decomposing the target function lower bound model into a base station access and carrier allocation optimization model and a power allocation optimization model; and a linear approximation method and a successive convex approximation method are respectively adopted for solving, so that wireless resource allocation of the uplink communication system is realized. By adopting the method provided by the invention, 0-1 integer optimization variables are removed, the calculation complexity is low, and higher uplink system energy efficiency is obtained.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a wireless resource allocation method for a LEO satellite-to-ground integrated uplink communication system. Background Art

[0002] In the existing research on the uplink transmission system of LEO satellite-ground integrated network, most of them take the uplink throughput of the system as the optimization target, lack the research on the energy efficiency of the system, and some of them also lack the consideration of the user's offload rate requirements; in addition, most of the studies use the traditional mixed integer programming scheme in the design and optimization of the whole system, and the algorithm complexity is relatively high. In most of them, the problem of base station selection for users is also handled by the strategy of nearest access. Therefore, a joint selection and allocation scheduling scheme is needed, but designing an optimization scheme for joint user base station selection, subcarrier and power allocation will introduce a large number of 0-1 integer variables, which also increases the difficulty of algorithm design. Summary of the invention

[0003] Purpose of the invention: In order to solve the problems existing in the above-mentioned prior art, the present invention provides a wireless resource allocation method for a LEO satellite-to-ground integrated uplink communication system.

[0004] Technical solution: The present invention discloses a method for allocating wireless resources of a LEO satellite-ground integrated uplink communication system, which specifically comprises the following steps: Step 1: Taking maximizing the energy efficiency of the uplink system as the objective function, constructing a system energy efficiency maximization model A1 based on the joint optimization problem of power allocation, base station selection, carrier allocation of ground network users, and base station power allocation and carrier selection of base station-to-satellite links; and constructing constraint conditions by coupling power allocation variables and carrier allocation variables using a symbolic function;

[0005] Step 2: Convert the optimization problem in step 1 into an approximate problem of optimizing the lower bound of the objective function, thereby converting model A1 into model A2;

[0006] Step 3: According to the constraints of model A2, the lower bound approximation problem in model A2 is decomposed into two sub-problems: user and base station power allocation and carrier allocation and base station selection. Slack variables are introduced to transform the non-convex constraints related to user and base station power allocation in model A2 into convex constraints. A linear iterative function is constructed to transform the non-convex constraints related to carrier allocation and base station selection in model A2 into convex constraints, thereby transforming model A2 into model A3.

[0007] Step 4: Iterate and solve model A3 to obtain the final user's carrier power allocation state p and the base station's carrier power state q; p = [p 1 ,…,p u ,…,p U ],q=[q1,…,qb ,…,q B ], where q b represents the optimal carrier power allocation of base station b, p u represents the carrier power allocation of user u; B represents the total number of base stations, U represents the total number of users, q b,m represents the transmission power allocated by base station b on the mth subcarrier of the satellite; m = 1, 2, ..., M; M represents the total number of available subcarriers between the base station and the satellite; represents the transmission power allocated to user u on the nth subcarrier of the bth base station, n = 1, 2, ..., N, N represents the total number of available subcarriers between the user and the base station.

[0008] Furthermore, the expression of the system energy efficiency maximization model A1 in the step is as follows:

[0009]

[0010] Among them, R u represents the data offloading rate that can be achieved by the u-th user, represents the data offloading rate that user u can achieve on the n subcarriers of the b-th base station, The expression is: B0 is the bandwidth of each subband, B0 = B c / N,B c is the OFDM frequency bandwidth of the terrestrial transmission system, is the transmission power allocated to user u on the nth subcarrier of the bth base station, is the channel gain coefficient of user u on the nth subcarrier of the bth base station, The expression is: α is the path loss exponent, is the small-scale Rayleigh fading, d u,b is the distance between the user and the base station; is the Gaussian white noise power of the ground transmission system; p0 is the rated transmission power of each user; sgn(.) is the sign function, is the indicator variable for user u to use the nth subcarrier of the bth base station, if The nth subcarrier of the bth base station is used for user u, otherwise D0 is the preset lower limit of user data offloading rate, q0 is the rated transmission power of each base station, sgn(q b,m )=y b,m ,y b,m The indicator variable for the bth base station to use the mth subcarrier of the satellite; Rb is the data offloading rate that can be achieved by the b-th base station, R b The expression is: C b is the total achievable backhaul link capacity from base station b to the satellite.

[0011] Furthermore, the expression of the model A2 is:

[0012]

[0013] Among them, t is the lower bound of the square of the energy efficiency of the uplink communication system, and v is the upper bound of the square of the total power of the base station.

[0014] Furthermore, in step 3, the non-convex constraint condition related to the user and base station power allocation in model A2 is converted into a convex constraint condition, specifically:

[0015] Step 3.1: Use lower bound slack variables Convert constraint C1.1 into C1.1.1 and C1.1.2:

[0016]

[0017] in, represents the signal-to-interference-noise ratio that can be achieved by user u on the nth subcarrier of the bth base station,

[0018] Step 3.2: Use lower bound slack variables Convert constraints C1.1.1 and C1.1.2 to C1.1.3 and

[0019] C1.1.4:

[0020]

[0021] Step 3.3: Convert the non-convex constraint C1.1.3 into a convex constraint C1.1.5:

[0022]

[0023] Among them, Θ (i) represents the convex upper bound, i represents the i-th iteration when iteratively solving the user and base station power allocation problem in model A3, and

[0024] Step 3.4: Introduce upper bound slack variables Transform non-convex constraint C1.1.2 into C1.1.6 and C1.1.7

[0025]

[0026] Step 3.5: Introduce upper bound slack variables Convert the non-convex constraint C1.1.6 to a convex constraint C1.1.8:

[0027]

[0028] Step 3.6: Convert the non-convex constraint C4.1 to a convex constraint C4.1.1:

[0029]

[0030] Step 3.7 Convert the non-convex constraint C7.1 into a convex constraint C7.1.1:

[0031]

[0032] Among them, g b,m represents the channel gain coefficient of the b-th base station on the m-th satellite subcarrier, G s and G r Denote the antenna gains of the base station and satellite respectively, λ m represents the wavelength of the mth subcarrier, d b represents the distance between the base station and the satellite, β b,m Indicates small-scale Rayleigh fading, B1=B ka / M,B ka is the OFDM frequency band bandwidth of the satellite transmission system; represents the Gaussian white noise power of the satellite transmission system.

[0033] Furthermore, the lower linear iterative function expression established in step 3 is as follows:

[0034]

[0035] Where τ1 and τ2 are constant regularization factors, c1 and c2 are constants, and the superscript k is the kth iteration when solving the carrier allocation and base station problem in model A3;

[0036] The non-convex constraints related to carrier allocation and base station selection in model A2 are transformed into convex constraints, specifically:

[0037] The non-convex constraint C3.1 for user base station selection is transformed into a convex constraint C3.1.1:

[0038]

[0039] The non-convex constraint C2.1 of user carrier allocation is transformed into a convex constraint C2.1.1:

[0040] The non-convex constraint C6.1 of base station carrier allocation is transformed into a convex constraint C6.1.1:

[0041] Furthermore, the expression of the model A3 is:

[0042]

[0043] Among them, δ is The matrix formed by The matrix formed by The matrix is ​​constructed, τ1 and τ2 are constant regularization factors, c1 and c2 are constants, and the superscript k is the kth iteration when solving the carrier allocation and base station problem in model A3.

[0044] Furthermore, the step 4 is specifically as follows:

[0045] Step 4.1: Initialize the number of iterations, accuracy, user power and base station power; initialize the user power to p0 / N, the base station power to q0 / M, and the initialized user power and base station power meet the constraints C1, C2.1.1, C3.1.1, C5.1, C6.1.1;

[0046] Step 4.2: Use the successive convex approximation method to solve the transformation model A3 and obtain the i-th iteration result t (i) ,v (i) ,δ (i) , Γ (i) ;

[0047] Step 4.3: Set t (i) ,v (i) ,δ (i) , Γ (i) Substitute it into model A3 and perform the next iteration until the objective function value of model A3 no longer changes. The iteration is terminated. * ,v * ,δ * , Γ * Get the initial values ​​of p and q, and go to step 4.4:

[0048] Step 4.4: Use the linear approximation method to iteratively find the model A3; get the kth iteration result p (k) and q (k) ;

[0049] Step 4.5: Put p (k) and q (k) Substitute it into model A3 and perform the next iteration. When the result of the iteration does not change, terminate the iteration and output the optimal values ​​of p and q.

[0050] Furthermore, the step 4 is specifically as follows: when the step 4.1 is initialized, t (0) The initial value of is:

[0051]

[0052] The superscript 0 indicates the initial value.

[0053] Beneficial effects: The present invention removes 0-1 integer optimization variables, has low computational complexity, and achieves higher uplink system energy efficiency. DETAILED DESCRIPTION

[0054] The exemplary embodiments of the present invention and their description are used to explain the present invention and do not constitute improper limitations of the present invention.

[0055] The present invention provides a wireless resource allocation method for a LEO satellite-ground integrated uplink communication system. The method is based on a single LEO satellite multi-base station uplink OFDM communication system, and takes maximizing the energy efficiency of the uplink system as the objective function. It studies the power allocation, base station selection, carrier allocation of ground network users, and the joint optimization problem of base station power allocation and carrier selection of base station-to-satellite links. First, the mathematical optimization model corresponding to the problem is derived, and the mathematical optimization problem is converted into an approximate problem of the lower bound of the optimization objective function by quoting relaxation variables. The relationship between the user and base station power allocation variables and the carrier allocation variables is established by using symbolic functions; according to the constraints of the problem, the lower bound approximation problem is decomposed into two sub-problems of user and base station power allocation and carrier allocation and base station access, which are solved iteratively; the base station and user power allocation sub-problem is solved iteratively using a successive convex approximation algorithm; the carrier allocation and base station access sub-problem is solved iteratively using a low-complexity linear approximation method.

[0056] In order to better illustrate the method of the present invention, it is described below in conjunction with more detailed embodiments:

[0057] Consider a single LEO satellite uplink OFDM transmission system with U users and B ground base stations. The uplink communication system consists of two parts: the ground transmission system and the base station-to-satellite transmission system. Each base station and user is equipped with an isotropic omnidirectional transmitting antenna. The rated transmission power of each base station is q0, and the rated transmission power of each user is p0. U users are randomly distributed in the area covered by B base stations. Each user can only access one base station and allocate one subcarrier for data unloading. The base station sends the user data unloaded to the local area to the LEO satellite through the backhaul link between the satellite and the satellite. One subcarrier can only be allocated to one base station. The transmission link from the user to the base station uses the C band, and B c is the OFDM frequency band bandwidth of the ground transmission system, N is the number of available subcarriers between the user and the base station; the transmission link from the base station to the satellite adopts the Ka band, Bka is the OFDM frequency band bandwidth of the satellite transmission system, and M is the number of available subcarriers between the base station and the satellite.

[0058] The first step is to use (b,n) to represent the nth subcarrier of the bth base station in the ground transmission system, and Indicator variable indicating that user u uses the nth subcarrier of the bth base station. If Indicates use, otherwise Use x=[x 1 ,…,x u ,…,x U ] represents the carrier allocation state vector of all users, and the base station carrier allocation state vector x of user u u There are the following expressions:

[0059]

[0060] Use p = [p 1 ,…,p u ,…,p U ] represents the carrier power allocation state vector of the user, and the carrier power allocation state vector p of user u u There are the following expressions:

[0061]

[0062] in, It represents the transmission power allocated to user u on the nth subcarrier of the bth base station. is the channel gain coefficient of user u on the nth subcarrier of the bth base station, is a complex number, which has the following expression:

[0063]

[0064] Where α represents the path loss exponent, is a complex Gaussian variable with zero mean, independent real and imaginary parts and variance 1, representing small-scale Rayleigh fading, d u,b Indicates the distance between the user and the base station.

[0065] The second step is to use the sign function sgn(·) to establish the relationship between the user carrier allocation variable and the carrier power allocation variable:

[0066]

[0067] The signal-to-interference-to-noise ratio that user u can achieve on the nth subcarrier of the bth base station is denoted as The expression is:

[0068]

[0069] in, represents the sum of the noise power of the co-channel interference caused to the nth subcarrier of user u, represents the Gaussian white noise power of the terrestrial transmission system.

[0070] In the third step, the data offloading rate that can be achieved by user u on the nth subcarrier of the bth base station is recorded as The expression is:

[0071]

[0072] Where B0 = B c / N represents the bandwidth of each subband; the data offloading rate achievable by the u-th user is R u , R u The expression is:

[0073]

[0074] The data offloading rate R that can be achieved by the b-th base station b The following are expressed:

[0075]

[0076] Step 4: In the base station to satellite transmission system, y is the carrier allocation state vector of the base station, y = [y1,…,y b ,…,y B ],y b Assign the state vector y to the carrier of base station b. b =[y b,1 ,…,y b,m ,…,y b,M ] -1 ;y b,m is the indicator variable for base station b to use the mth subcarrier of the satellite. If y b,m =1 means use, otherwise y b,m =0; q is the carrier power allocation state vector of the base station, q = [q1,…,q b ,…,q B ], q b is the carrier power allocation state vector of base station b, q b =[q b,1 ,…,q b,m ,…,q b,M ] -1 ; g b,m is the channel gain coefficient of the b-th base station on the m-th satellite subcarrier, g b,m is a complex number, which has the following representation:

[0077]

[0078] Among them G s and G r Denote the antenna gains of the base station and satellite respectively, λ m represents the wavelength of the mth subcarrier, β b,m represents small-scale Rayleigh fading, d b Indicates the distance between the base station and the satellite.

[0079] The fifth step is to use the sign function sgn(·) to establish the relationship between the base station carrier allocation variable and the carrier power allocation variable:

[0080] y b,m =sgn(q b,m )

[0081] The return link capacity that base station b can achieve on the mth subcarrier of the satellite is C b,m , which is expressed as follows:

[0082]

[0083] Where B1 = B ka / M indicates the bandwidth of each subband; represents the Gaussian white noise power of the satellite transmission system.

[0084] The total backhaul link capacity C that can be achieved from base station b to satellite b , which has the following expression:

[0085]

[0086] Step 6: All base stations have no buffer for uploading data, so the data unloading rate R of ground users collected by base station b is b and the total backhaul link capacity C between it and the satellite b The following constraints are satisfied:

[0087]

[0088] A system energy efficiency maximization model A1 for subcarrier and power allocation is established, and the model A1 is expressed as:

[0089]

[0090] Model A1 is simplified as follows:

[0091]

[0092] Constraint C1 indicates that the sum of the transmission power of all users is not greater than p0; Constraint C2 indicates that the subcarrier of any base station can only be allocated to one user; Constraint C3 indicates that a user can only access one base station and be allocated one subcarrier; Constraint C4 indicates that the user's data unloading rate cannot be lower than the preset lower limit; Constraint C5 indicates that the total transmission power of any base station is not greater than q0; Constraint C6 indicates that a subcarrier can only be allocated to one base station; Constraint C7 indicates that the data unloading rate R of the user collected by the bth ground base station without considering the upload data buffer of the base station b Should not be greater than the total link capacity C between it and the satellite b .

[0093] To solve model A1, the specific steps are as follows:

[0094] Step 1: Introduce the lower bound t of the square of the system energy efficiency and the upper bound v of the square of the total power of the base station, and transform the model A1 into the model A2. The expression of the model A2 is:

[0095]

[0096] Limited by:

[0097]

[0098] Simplify model A2 as follows:

[0099]

[0100]

[0101] Step 2: Power allocation between users and base stations;

[0102] Step 2-1: Use lower bounds to relax the variables Convert constraint C1.1 into C1.1.1 and C1.1.2:

[0103]

[0104] Step 2-2: Use lower bounds to relax variables Convert constraints C1.1.1 and C1.1.2 to C1.1.3 and C1.1.4:

[0105]

[0106] Among them, C1.1.4 is a convex constraint, and the expression on the right side of the inequality of constraint C1.1.3 is non-convex, so C1.1.3 is a non-convex constraint.

[0107] Step 2-3: For the right-hand side expression of the inequality constraint C1.1.3 The convex upper bound Θ of the i-th iteration on its domain t≥0,v≥0 (i) Expressed as:

[0108]

[0109] At this time, t and v represent the optimal values.

[0110] Convert the non-convex constraint C1.1.3 into a convex constraint C1.1.5:

[0111]

[0112] Step 2-4: Use upper bounds to relax variables Transform the non-convex constraint C1.1.2 into C1.1.6 and C1.1.7:

[0113]

[0114] Among them, C1.1.7 is a convex constraint and C1.1.6 is a non-convex constraint.

[0115] Step 2-5: Use upper bounds to relax variables The convex upper bound of the right-hand side expression of the non-convex constraint C1.1.6 inequality is expressed as:

[0116]

[0117] Where i is the number of iterations, and is the result of the i-th iteration; at this time Indicates the optimal value.

[0118] Step 2-6: Convert the non-convex constraint C1.1.6 to a convex constraint C1.1.8:

[0119]

[0120] Step 2-7: Convert the non-convex constraint C4.1 to a convex constraint C4.1.1:

[0121]

[0122] Step 2-8: Convert the non-convex constraint C7.1 to a convex constraint C7.1.1:

[0123]

[0124] Step 3: Use linear approximation method to access the user's base station and allocate subcarriers between the user and the base station;

[0125] Step 3-1: Establish the following linear iterative function:

[0126]

[0127] Where τ1 and τ2 are constant regularization factors, c1 and c2 are constants, k is the number of iterations, and is the optimization result of the kth iteration. When the iteration converges, the left and right sides of the function are equal.

[0128] Step 3-2: Convert the non-convex constraint condition C3.1 for selecting the user base station into a convex constraint condition C3.1.1:

[0129]

[0130] Step 3-3: Convert the non-convex constraint condition C2.1 of user carrier allocation into a convex constraint condition C2.1.1:

[0131]

[0132] Step 3-4: Convert the non-convex constraint condition C6.1 of base station carrier allocation into a convex constraint condition C6.1.1:

[0133]

[0134] Step 3-5: Use the above steps to transform the model A2 into model A3. The expression of the model A3 is:

[0135]

[0136]

[0137] δ is The matrix formed by The matrix formed by The matrix formed.

[0138] Step 4: Iteratively solve the model A3 to obtain the optimal solution of the lower bound t of the square of the system energy efficiency, and then determine the carrier power allocation state vector p of the user and the carrier power allocation state vector q of the base station in the constraint condition of the model A1 to realize the wireless resource allocation of the satellite-ground integrated uplink communication system;

[0139] Step 4-1: Initialize the number of iterations, accuracy, user and base station power, etc.

[0140] Step 4-1-1: Initialize the number of iterations k = 0, i = 0, and the initialization accuracy ζ = 10 -3 ω=10 -2 ;

[0141] Step 4-1-2: Initialize user power and base station power to satisfy constraints C1, C2.1.1, C3.1.1, C5.1, C6.1.1;

[0142] Step 4-1-3: Initialize the user power to p0 / N and the base station power to q0 / M;

[0143] Step 4-2: According to The expression for solving the initial value γ (0) γ is The matrix is ​​constructed, and the initial value δ is obtained by constraint C1.1.2 (0) , δ is The matrix formed by , the superscript 0 indicates the initial value;

[0144] Step 4-3: Solve for the initial value Γ according to constraint C1.1.7 (0) , Γ is The matrix formed;

[0145] Step 4-4: Solve for the initial value v according to constraint C1.2 (0) ;

[0146] Step 4-5: Solve for the initial value t (0) , solve it as follows:

[0147]

[0148] Substitute the result v(0) in step 4-4 into the above equation to solve for the initial value t (0) ;

[0149] Step 4-6: Iteratively solve the model A3. In the i-th iteration, the solution process is as follows:

[0150] Step 4-6-1: Use the successive convex approximation method to solve the transformed convex optimization problem model A3 and obtain the i-th iteration result t (i) ,v (i) ,δ (i) , Γ (i) , where represents the result value obtained at the i-th iteration;

[0151] Step 4-6-2: t (i+1) =t (i) , v (i+1) =v (i) , δ (i+1) =δ (i) , Γ (i+1) =Γ (i), i=i+1; substitute the updated value into the problem model A3, replace the parameters, and calculate the objective function value of the model A3 again. When the objective function value of the model A3 no longer changes, the iteration method terminates and the results p and q are output;

[0152] Step 4-6-3: Use the linear approximation method to iteratively solve the base station selection and carrier allocation problems in the convex optimization problem model A3; obtain the kth iteration result p (k) and q (k) ;

[0153] Step 4-6-4: p (k+1) =p (k) ,q (k+1) =q (k) , k=k+1; the updated values ​​are substituted into the model A3 again to replace the parameters. When the iterative power result no longer changes, the iterative method is terminated and the final result value is output.

[0154] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

Claims

1. A method for allocating wireless resources in a LEO satellite-to-ground integrated uplink communication system, wherein the uplink communication system comprises a ground transmission system and a satellite-to-ground transmission system, characterized in that: The method specifically comprises the following steps: Step 1: Taking maximizing the uplink system energy efficiency as the objective function, the system energy efficiency maximization model A1 is constructed based on the joint optimization problem of power allocation, base station selection, carrier allocation for ground network users and base station power allocation and carrier selection for base station-to-satellite links; and the power allocation variable and carrier allocation variable are coupled by a symbolic function to construct constraint conditions; Step 2: Convert the optimization problem in step 1 into an approximate problem of optimizing the lower bound of the objective function, thereby converting model A1 into model A2; Step 3: According to the constraints of model A2, the lower bound approximation problem in model A2 is decomposed into two sub-problems: user and base station power allocation and carrier allocation and base station selection. Slack variables are introduced to transform the non-convex constraints related to user and base station power allocation in model A2 into convex constraints. A linear iterative function is constructed to transform the non-convex constraints related to carrier allocation and base station selection in model A2 into convex constraints, thereby transforming model A2 into model A3. Step 4: Iterate and solve model A3 to obtain the final user's carrier power allocation state p and the base station's carrier power state q; p = [p 1 ,…,p u ,…,p U ],q=[q1,…,q b ,…,q B ], where q b represents the optimal carrier power allocation of base station b, p u represents the carrier power allocation of user u; B represents the total number of base stations, U represents the total number of users, q b,m represents the transmission power allocated by base station b on the mth subcarrier of the satellite; m = 1, 2, ..., M; M represents the total number of available subcarriers between the base station and the satellite; represents the transmission power allocated to user u on the nth subcarrier of the bth base station, n = 1, 2, ..., N, N represents the total number of available subcarriers between the user and the base station.

2. The wireless resource allocation method for a LEO satellite-to-ground integrated uplink communication system according to claim 1, characterized in that: The expression of the system energy efficiency maximization model A1 in the step is as follows: Among them, R u represents the data offloading rate that can be achieved by the u-th user, represents the data offloading rate that user u can achieve on the n subcarriers of the b-th base station, The expression is: B0 represents the bandwidth of each subband, B0=B c / N,B c is the OFDM frequency bandwidth of the terrestrial transmission system, represents the transmission power allocated to user u on the nth subcarrier of the bth base station, represents the channel gain coefficient of user u on the nth subcarrier of the bth base station, The expression is: α represents the path loss exponent, represents small-scale Rayleigh fading, d u,b Indicates the distance between the user and the base station; represents the Gaussian white noise power of the ground transmission system; p0 represents the rated transmission power of each user; sgn(.) is the sign function, Indicator variable indicating that user u uses the nth subcarrier of the bth base station. If Indicates that user u uses the nth subcarrier of the bth base station, otherwise D0 represents the preset lower limit of user data offloading rate, q0 is the rated transmission power of each base station, sgn(q b,m )=y b,m ,y b,m The indicator variable for the bth base station to use the mth subcarrier of the satellite; R b represents the data offloading rate that can be achieved by the b-th base station, R b The expression is: C b It represents the total backhaul link capacity achievable from base station b to the satellite.

3. The wireless resource allocation method for a LEO satellite-to-ground integrated uplink communication system according to claim 2, characterized in that: The expression of the model A2 is: Among them, t is the lower bound of the square of the energy efficiency of the uplink communication system, and v is the upper bound of the square of the total power of the base station.

4. The wireless resource allocation method for a LEO satellite-to-ground integrated uplink communication system according to claim 3 is characterized in that: In step 3, the non-convex constraint conditions related to the power allocation between users and base stations in model A2 are converted into convex constraints, specifically: Step 3.1: Use lower bound slack variables Convert constraint C1.1 into C1.1.1 and C1.1.2: in, represents the signal-to-interference-noise ratio that can be achieved by user u on the nth subcarrier of the bth base station, Step 3.2: Use lower bound slack variables Convert constraints C1.1.1 and C1.1.2 to C1.1.3 and C1.1.4: Step 3.3: Convert the non-convex constraint C1.1.3 into a convex constraint C1.1.5: Among them, Θ (i) represents the convex upper bound, i represents the i-th iteration when iteratively solving the user and base station power allocation problem in model A3, and Step 3.4: Introduce upper bound slack variables Transform non-convex constraint C1.1.2 into C1.1.6 and C1.1.7 Step 3.5: Introduce upper bound slack variables Convert the non-convex constraint C1.1.6 to a convex constraint C1.1.8: Step 3.6: Convert the non-convex constraint C4.1 to a convex constraint C4.1.1: Step 3.7 Convert the non-convex constraint C7.1 into a convex constraint C7.1.1: Among them, g b,m represents the channel gain coefficient of the b-th base station on the m-th satellite subcarrier, G s and G r Denote the antenna gains of the base station and satellite respectively, λ m represents the wavelength of the mth subcarrier, d b represents the distance between the base station and the satellite, β b,m Indicates small-scale Rayleigh fading, B1=B ka / M,B ka is the OFDM frequency band bandwidth of the satellite transmission system; represents the Gaussian white noise power of the satellite transmission system.

5. The wireless resource allocation method for a LEO satellite-to-ground integrated uplink communication system according to claim 3, characterized in that: The lower linear iterative function expression established in step 3 is as follows: Among them, τ1 and τ2 are constant regularization factors, c1 and c2 are constants, and the superscript k is the kth iteration when solving the carrier allocation and base station problem in model A3; The non-convex constraints related to carrier allocation and base station selection in model A2 are transformed into convex constraints, specifically: The non-convex constraint C3.1 for user base station selection is transformed into a convex constraint C3.1.1: The non-convex constraint C2.1 of user carrier allocation is transformed into a convex constraint C2.1.1: The non-convex constraint condition C6.1 of base station carrier allocation is transformed into a convex constraint condition C6.1.1:

6. The wireless resource allocation method for a LEO satellite-to-ground integrated uplink communication system according to claim 4, characterized in that: The expression of the model A3 is: Among them, δ is The matrix formed by The matrix formed by The matrix is ​​constructed, τ1 and τ2 are constant regularization factors, c1 and c2 are constants, and the superscript k is the kth iteration when solving the carrier allocation and base station problem in model A3.

7. The wireless resource allocation method for a LEO satellite-to-ground integrated uplink communication system according to claim 6, characterized in that: The step 4 is specifically as follows: Step 4.1: Initialize the number of iterations, accuracy, user power and base station power; initialize the user power to p0 / N, the base station power to q0 / M, and the initialized user power and base station power meet the constraints C1, C2.1.1, C3.1.1, C5.1, C6.1.1; Step 4.2: Use the successive convex approximation method to solve the transformation model A3 and obtain the i-th iteration result t (i) ,v (i) ,δ (i) , Γ (i) ; Step 4.3: Set t (i) ,v (i) ,δ (i) , Γ (i) Substitute it into model A3 and perform the next iteration until the objective function value of model A3 no longer changes. The iteration is terminated. * ,v * ,δ * , Γ * Get the initial values ​​of p and q, and go to step 4.4: Step 4.4: Use the linear approximation method to iteratively find the model A3; get the kth iteration result p (k) and q (k) ; Step 4.5: Put p (k) and q (k) Substitute it into model A3 and perform the next iteration. When the result of the iteration does not change, terminate the iteration and output the optimal values ​​of p and q.

8. The wireless resource allocation method for a LEO satellite-to-ground integrated uplink communication system according to claim 7, characterized in that: The specific step 4 is as follows: When the step 4.1 is initialized, t (0) The initial value of is: The superscript 0 indicates the initial value.

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