CN117176232B - Satellite Internet of things capacity improving method combining user grouping and multi-beam - Google Patents

Abstract

The present invention discloses a method for improving the capacity of a satellite Internet of Things by combining user grouping and multi-beam, comprising two parts: user grouping and multi-beam joint processing; the first part is user grouping; according to whether the satellite knows the specific location information of the Internet of Things terminal user, the method can be divided into a user grouping method based on user distribution and a user grouping method based on user location; the second part is multi-beam joint processing; different user grouping methods correspond to different multi-beam joint grouping schemes, including a multi-beam joint processing scheme based on regional statistical channel state information and a multi-beam joint processing scheme based on user statistical channel state information and user location; compared with the traditional seven-color multiplexing method, the method can improve the user capacity by up to 10 times.

Description

A satellite Internet of Things capacity enhancement method combining user grouping and multi-beam

Technical Field

The invention discloses a satellite Internet of Things capacity improvement method combining user grouping and multi-beam, belonging to wireless communication technology.

Background technique

Since its birth, low-orbit satellite IoT has been widely concerned in the research field. Especially for some remote areas such as hills, oceans, deserts, etc., terrestrial wireless networks may have coverage loopholes. In contrast, using satellites for wireless network coverage would be a better solution. Therefore, the demand for satellite IoT communications is increasing. At the same time, as the demand for IoT traffic from satellite IoT continues to increase in the future, the number of IoT terminals will also increase significantly. Unlike the high throughput pursued by the Internet, satellite IoT pursues access to more IoT terminals, because the services of satellite IoT are mainly aimed at areas such as asset tracking, environmental monitoring and smart agriculture, where there may be a large number of low-speed IoT terminals.

In order to carry a huge number of IoT terminals, satellite IoT should provide sufficiently large user capacity. However, large IoT terminals accessing the satellite at the same time may cause co-channel interference to each other, resulting in a decrease in user capacity. In order to alleviate co-channel interference, these IoT terminals can access the satellite in an authorized manner. In this way, it takes at least four steps for the IoT terminal to transmit data to the satellite. Satellite IoT has the following characteristics: such as high mobility of low-orbit satellites, high propagation delay, large coverage, limited available spectrum resources, short burst communication, and high requirements for low power consumption. Combined with these, consider using space resources to increase the user capacity of satellite IoT networks while simplifying access steps.

Summary of the invention

In view of the defects in the above-mentioned background technology, the present invention provides a satellite Internet of Things capacity improvement method combining user grouping and multi-beam, which has high capacity improvement efficiency and large improvement.

To achieve the above purpose, the technical solution adopted by the present invention is as follows: a satellite Internet of Things capacity enhancement method combining user grouping and multi-beam, wherein the satellite forms a beam to assist Internet of Things user access, and performs user grouping for two situations, namely, with user location and without user location; after user grouping, multi-beam joint processing is implemented; specifically, the method includes two parts, namely, Internet of Things user grouping and multi-beam joint processing, wherein:

The Internet of Things user grouping includes: selecting a grouping method according to whether the satellite terminal obtains the accurate location of the user, if the satellite terminal obtains the user location, then grouping the users based on the user location; if the satellite terminal does not have the accurate location of the user, then grouping the users based on the user distribution;

The multi-beam joint processing includes: when grouping based on user location, according to the user statistical channel state information, combined with the user location error, establishing a multi-beam joint processing optimization problem, by optimizing the beamforming vector, maximizing the Internet of Things users and rate;

When grouping based on user distribution, a multi-beam joint processing optimization problem is established according to regional statistical channel state information, and the IoT users and rates are maximized by optimizing the beamforming vectors.

Furthermore, the user grouping based on user location specifically includes:

Given g _x and g _y , group The allocation is as follows:

in, Indicates the group number.

_Ax represents the number of antennas on the x-axis of the array antenna, and _Ay represents the number of antennas on the y-axis of the array antenna.

a _x represents the antenna number of the x-axis in the array antenna, a _y represents the antenna number of the y-axis in the array antenna, 0≤a _x ≤A _x -1, 0≤a _y ≤A _y -1,

_gx represents the frequency resource number of the x-axis in the array antenna, _gy represents the frequency resource number of the y-axis in the array antenna, _{0≤gx≤Nf ,x- 1} , 0≤gy≤Nf _,y -1,

Nf _,x represents the maximum number of frequency resources allocated to the x-axis of the array antenna, and Nf _,y represents the maximum number of frequency resources allocated to the y-axis of the array antenna;

For IoT user U _k , k is the user serial number. Indicates the total number of users and whether the user U _k is assigned to a group The judgment criteria are

like and

in, It represents the angle between the IoT user and the x-axis of the array antenna when there is a position error. It represents the angle between the IoT user and the y-axis of the array antenna when there is a position error.

in: Δx＝2/(Nf _{, xAx} ), Δy＝2/( _{Nf, yAy} ).

Furthermore, in step (1), user grouping modeling based on user distribution is as follows:

(1) Initializing the genetic factor, mutation factor, crossover factor, group size, and number of iterations of the genetic algorithm to establish the first generation population of the genetic algorithm, where each individual is a beam coverage scheme;

(2) Calculate the fitness value of each individual in the population. The smaller the fitness function value, the better.

(3) Eliminate illegal solutions in the population according to certain constraints; the constraints considered include: the beam covers the required area, the overlap range between beams, and the upper limit of the number of beams; set a penalty function factor, and the fitness value of individuals that do not meet the constraints will become extremely large due to the influence of the penalty function factor;

(4) Execute the roulette wheel selection operator on the population to select the next generation of population;

(5) Execute crossover and mutation operators on the next generation population, where the crossover operator uses single-point crossover and the mutation operator uses uniform mutation;

(6) If the end condition is met, the grouping result based on user distribution is output, otherwise return to step (2).

Furthermore, the encoding of chromosomes in the genetic algorithm is:

CH＝[x ₁₁ ,x ₁₂ ,...,x _1N ,x ₂₁ ,...,x _2N ,...,x _MN ]＝[x ₁ ,x ₂ ,...,x _M×N ]

Wherein, M represents the number of beam backup points set in the x direction of the rectangular coordinate system;

N represents the number of beam backup points set in the y direction of the rectangular coordinate system;

x _ij is: beam backup points are evenly set in the area that needs beam coverage according to the x and y directions of the rectangular coordinate system. The value of x _ij is the size of the beam radius centered on this beam backup point.

Furthermore, the constraints are set as follows:

Beam radius constraint:

Overlap range constraints between beams:

The upper limit of the number of terminals covered by the beam is:

The beam will fully cover the required area:

Among them, _ri represents the radius of beam _Bi , _Ax represents the horizontal array size of the antenna array,

A _y represents the longitudinal array size of the antenna array,

N _B represents the number of beams,

_ni represents the minimum number of beams required to synthesize beam _Bi ;

r _min represents the minimum beam radius that the antenna array can provide;

ρ _max is the maximum value of the IoT user deployment density, η is the adjustment factor for the overlapping area, λ is the adjustment factor for the number of IoT users in the beam coverage area, represents the overlapping area of beams _Bi , _Bi ', represents the number of terminals covered by beam _Bi , represents the intersection point of beams _Bi , _Bi '.

Furthermore, the fitness function value is defined as:

Among them, σ ₀ represents the penalty function factor, cover represents whether the constraint that the beam fully covers the required area is satisfied, and overlap represents whether the overlap range constraint between beams is satisfied. Indicates whether the maximum number of beams supported by the antenna is met. If any of the constraints is not met, the penalty function variable corresponding to the constraint is set to 1.

Further, according to the user statistical channel state information and combined with the user position error, the multi-beam joint processing includes the following steps:

Establish multi-beam joint processing optimization problem:

in, represents the beamforming vector, express Number of IoT users in the group,

Indicates The rate of the i-th IoT user in the group;

h _s,i,sat represents the statistical channel state information from the ith IoT user to the satellite.

h _s,u,sat represents the statistical channel state information from the u-th IoT user to the satellite.

_Pt represents the transmission power of IoT users, ^σ2 represents the noise power, and H represents the conjugate transpose operation;

Calculate the beamforming vector for each beam and frequency resource:

in, I represents the identity matrix, MGE() represents the operation of finding the maximum generalized eigenvector;

Calculation and rate:

in, represents the subcarrier bandwidth, Indicates the number of subcarriers,

express The instantaneous channel state information of the link from the i-th user to the satellite in the group,

express The instantaneous channel state information of the u-th user to satellite link in the group;

The user capacity can be expressed as:

in, if otherwise _Rth is the transmission rate threshold.

Further, according to the regional statistical channel state information, establishing the multi-beam joint processing optimization problem includes the following steps:

Establish multi-beam joint processing optimization problem:

Where, w _ij represents the beamforming vector, _NB represents the number of beams,

represents the sum rate of IoT users under the jth frequency resource in the i-th beam;

h _rs,ij represents the statistical channel state information of the beam coverage area under the jth frequency resource in the i-th beam;

_hrs,uj represents the statistical channel state information of the beam coverage area under the jth frequency resource in the uth beam, ^σ2 represents the noise power;

Calculate the beamforming vector for each beam and frequency:

w _ij =MGE(A _ij ,B _ij )

in, MGE() represents the operation of finding the maximum generalized eigenvector;

Calculation and rate:

in, represents the rate of user k in the i-th beam and j-th frequency resource, represents the number of IoT users of the i-th beam and the j-th frequency resource, represents the number of IoT users of the u-th beam and the j-th frequency resource, represents the subcarrier bandwidth, represents the number of subcarriers, h _ijk,sat represents the instantaneous channel state information of the link from the kth IoT user to the satellite under the ith beam and the jth frequency resource, h _ijl,sat represents the instantaneous channel state information of the link from the lth IoT user to the satellite under the ith beam and the jth frequency resource, h _ujz,sat represents the instantaneous channel state information of the link from the zth IoT user to the satellite under the uth beam and the jth frequency resource;

Calculate user capacity:

Where _Rth is the transmission rate threshold, Ι(·) is the indicator function, defined as

Beneficial effects: The present invention is a method for improving the capacity of satellite Internet of Things by combining user grouping and multi-beam. Compared with the traditional seven-color multiplexing method, the method can increase the user capacity by up to 10 times. The present invention utilizes space resources to increase the user capacity of the satellite Internet of Things network, reduce the probability of collision of data packets, and simplify the access steps.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a flow chart of the implementation of the present invention;

FIG2 is a diagram showing the coverage effect of user grouping beams based on user distribution according to the present invention;

FIG. 3 is a curve showing the relationship between the user capacity and the signal-to-noise ratio of the two schemes of the present invention and the traditional seven-color multiplexing scheme.

Detailed ways

The implementation of the technical solution is further described in detail below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

As shown in Figure 1: A satellite IoT capacity enhancement method that combines user grouping and multi-beam, the satellite forms a beam to assist IoT user access, and performs user grouping for the two situations of user location and no user location respectively; after user grouping, multi-beam joint processing is implemented; specifically, it includes two parts: IoT user grouping and multi-beam joint processing.

Part 1: User Grouping

Grouping method based on user distribution:

Initialize various parameters of the genetic algorithm and establish the first generation population of the genetic algorithm, in which each individual is a beam coverage scheme.

Calculate the fitness value of each individual in the population. The smaller the fitness function value, the better.

The illegal solutions in the population are repaired according to certain constraints. The constraints considered include: the beam covers the required area, the overlap range between beams, and the upper limit of the number of beams. The penalty function factor is set, and the fitness value of individuals that do not meet the constraints will become extremely large due to the influence of the penalty function factor.

Execute the roulette wheel selection operator on the population to select the next generation of population.

The crossover and mutation operators are performed on the next generation population, where the crossover operator adopts single-point crossover and the mutation operator adopts uniform mutation.

If the end condition is met, the user grouping result is output, otherwise it returns to the fitness value calculation step.

The chromosome code is:

CH＝[x ₁₁ ,x ₁₂ ,...,x _1N ,x ₂₁ ,...,x _2N ,...,x _MN ]＝[x ₁ ,x ₂ ,...,x _M×N ]

Wherein, M and N represent the number of beam backup points set in the x and y directions of the rectangular coordinate system, respectively.

The specific meaning of x _ij is: M×N beam backup points are evenly set according to the x and y directions of the rectangular coordinate system in the area that needs beam coverage, and the value of x _ij is the size of the beam radius centered on this beam backup point.

The constraints are set as follows:

Beam radius constraint:

Overlap range constraints between beams:

Terminal quantity constraints:

The beam will fully cover the required area:

Where _ri represents the radius of beam _Bi , _Ax , _Ay represent the horizontal and vertical array scales of the antenna array, _NB represents the number of beams, _ρmax is the maximum value of the IoT user deployment density, η is the adjustment factor of the overlapping area, and λ is the adjustment factor of the number of IoT users in the beam coverage area. represents the overlapping area of beams _Bi , _Bi ', represents the number of terminals covered by beam _Bi , represents the intersection point of beams _Bi , _Bi '.

The fitness function value is defined as:

Where _NB represents the number of beams, _σ0 represents the penalty function factor, cover represents whether the constraint that the beam covers the required area is met, and overlap represents whether the overlap range constraint between beams is met. Indicates whether the maximum number of beams supported by the antenna is met. If any of the constraints are not met, the penalty function variable corresponding to the constraint is set to 1.

According to the regional statistical channel state information, the multi-beam joint processing optimization problem is established:

Establish multi-beam joint processing optimization problem:

Where, w _ij represents the beamforming vector, _NB represents the number of beams,

represents the sum rate of IoT users under the jth frequency resource in the i-th beam;

h _rs,ij represents the statistical channel state information of the beam coverage area under the jth frequency resource in the i-th beam;

_hrs,uj represents the statistical channel state information of the beam coverage area under the jth frequency resource in the uth beam, ^σ2 represents the noise power;

Calculate the beamforming vector for each beam and frequency:

w _ij =MGE(A _ij ,B _ij )

in, MGE() represents the operation of finding the maximum generalized eigenvector;

Calculation and rate:

in, represents the rate of user k in the i-th beam and j-th frequency resource, represents the number of IoT users of the i-th beam and the j-th frequency resource, represents the number of IoT users of the u-th beam and the j-th frequency resource, represents the subcarrier bandwidth, represents the number of subcarriers, h _ijk,sat represents the instantaneous channel state information of the link from the kth IoT user to the satellite under the ith beam and the jth frequency resource, h _ijl,sat represents the instantaneous channel state information of the link from the lth IoT user to the satellite under the ith beam and the jth frequency resource, h _ujz,sat represents the instantaneous channel state information of the link from the zth IoT user to the satellite under the uth beam and the jth frequency resource;

Calculate user capacity:

Where _Rth is the transmission rate threshold, Ι(·) is the indicator function, defined as

Grouping method based on user location:

The user grouping based on user location specifically includes:

Given g _x and g _y , group The allocation is as follows:

in, Indicates the group number.

_Ax represents the number of antennas on the x-axis of the array antenna, and _Ay represents the number of antennas on the y-axis of the array antenna.

a _x represents the antenna number of the x-axis in the array antenna, a _y represents the antenna number of the y-axis in the array antenna, 0≤a _x ≤A _x -1, 0≤a _y ≤A _y -1,

_gx represents the frequency resource number of the x-axis in the array antenna, _gy represents the frequency resource number of the y-axis in the array antenna, _{0≤gx≤Nf ,x- 1} , 0≤gy≤Nf _,y -1,

Nf _,x represents the maximum number of frequency resources allocated to the x-axis of the array antenna, and Nf _,y represents the maximum number of frequency resources allocated to the y-axis of the array antenna;

For IoT user U _k , k is the user serial number. Indicates the total number of users and whether the user U _k is assigned to a group The judgment criteria are

like and in, It represents the angle between the IoT user and the x-axis of the array antenna when there is a position error. It represents the angle between the IoT user and the y-axis of the array antenna when there is a position error.

in: Δx＝2/(Nf _{, xAx} ), Δy＝2/( _{Nf, yAy} ).

According to the user's statistical channel state information and combined with the user's position error, the multi-beam joint processing includes the following steps:

Establish multi-beam joint processing optimization problem:

in, represents the beamforming vector, express Number of IoT users in the group,

Indicates The rate of the i-th IoT user in the group;

h _s,i,sat represents the statistical channel state information from the ith IoT user to the satellite.

h _s,u,sat represents the statistical channel state information from the u-th IoT user to the satellite.

_Pt represents the transmission power of IoT users, ^σ2 represents the noise power, and H represents the conjugate transpose operation;

Calculate the beamforming vector for each beam and frequency resource:

in, I represents the identity matrix, MGE() represents the operation of finding the maximum generalized eigenvector;

Calculation and rate:

in, represents the subcarrier bandwidth, Indicates the number of subcarriers,

express The instantaneous channel state information of the link from the i-th user to the satellite in the group,

express The instantaneous channel state information of the u-th user to satellite link in the group;

The user capacity can be expressed as:

in, if otherwise _Rth is the transmission rate threshold.

As shown in Figure 2, it can be seen that the number of terminals in each beam is uniform in the grouping scheme based on user distribution;

As shown in Figure 3, Scheme 1 represents a scheme in which user distribution and regional statistical channel state information are used to perform user grouping and multi-beam joint processing when there is no IoT user location. Scheme 2 represents a scheme in which user location and user statistical channel state information are used to perform user grouping and multi-beam joint processing when there is an IoT user location but the user location has an error. Scheme 3 represents the traditional seven-color multiplexing scheme. It can be seen that the user capacity of this scheme can be increased by up to 10 times compared with the traditional seven-color multiplexing scheme.

The present invention is a method for improving the capacity of a satellite Internet of Things system by combining user grouping and multi-beam processing. Compared with a traditional seven-color multiplexing method, the method can increase user capacity by up to 10 times.

The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims (1)

1. A satellite Internet of Things capacity enhancement method combining user grouping and multi-beam, characterized in that satellites form beams to assist Internet of Things user access, and perform user grouping for two situations, namely, with user locations and without user locations; after user grouping, multi-beam joint processing is implemented; specifically, the method comprises two parts: Internet of Things user grouping and multi-beam joint processing, wherein: The Internet of Things user grouping includes: selecting a grouping method according to whether the satellite terminal obtains the accurate location of the user, if the satellite terminal obtains the user location, then grouping the users based on the user location; if the satellite terminal does not have the accurate location of the user, then grouping the users based on the user distribution; The multi-beam joint processing includes: when grouping based on user location, according to the user statistical channel state information, combined with the user location error, establishing a multi-beam joint processing optimization problem, by optimizing the beamforming vector, maximizing the Internet of Things users and rate; When grouping based on user distribution, a multi-beam joint processing optimization problem is established according to regional statistical channel state information, and the IoT users and rates are maximized by optimizing the beamforming vector. The user grouping based on user location specifically includes: Given g _x and g _y , group The allocation is as follows: in, Indicates the group number. _Ax represents the number of antennas on the x-axis of the array antenna, and _Ay represents the number of antennas on the y-axis of the array antenna. a _x represents the antenna number of the x-axis in the array antenna, a _y represents the antenna number of the y-axis in the array antenna, 0≤a _x ≤A _x -1, 0≤a _y ≤A _y -1, _gx represents the frequency resource number of the x-axis in the array antenna, _gy represents the frequency resource number of the y-axis in the array antenna, _{0≤gx≤Nf ,x- 1} , 0≤gy≤Nf _,y -1, Nf _,x represents the maximum number of frequency resources allocated to the x-axis of the array antenna, and Nf _,y represents the maximum number of frequency resources allocated to the y-axis of the array antenna; For IoT user U _k , k is the user serial number. Indicates the total number of users and whether the user U _k is assigned to a group The judgment criteria are like and in, It represents the angle between the IoT user and the x-axis of the array antenna when there is a position error. It represents the angle between the IoT user and the y-axis of the array antenna when there is a position error, where: Δx＝2/(N _f,x A _x ), Δy＝2/(N _f,y A _y ), The user grouping model based on user distribution is: (1) Initializing the genetic factor, mutation factor, crossover factor, group size, and number of iterations of the genetic algorithm to establish the first generation population of the genetic algorithm, where each individual is a beam coverage scheme; (2) Calculate the fitness value of each individual in the population. The smaller the fitness function value, the better. (3) Eliminate illegal solutions in the population according to certain constraints; the constraints considered include: the beam covers the required area, the overlap range between beams, and the upper limit of the number of beams; set a penalty function factor, and the fitness value of individuals that do not meet the constraints will become extremely large due to the influence of the penalty function factor; (4) Execute the roulette wheel selection operator on the population to select the next generation of population; (5) Execute crossover and mutation operators on the next generation population, where the crossover operator uses single-point crossover and the mutation operator uses uniform mutation; (6) If the end condition is met, the grouping result based on user distribution is output, otherwise it returns to step (2). The encoding of chromosomes in the genetic algorithm is: CH＝[x ₁₁ ,x ₁₂ ,...,x _1N ,x ₂₁ ,...,x _2N ,...,x _MN ]＝[x ₁ ,x ₂ ,...,x _M×N ] Wherein, M represents the number of beam backup points set in the x direction of the rectangular coordinate system; N represents the number of beam backup points set in the y direction of the rectangular coordinate system; x _ij is: in the area that needs to be covered by the beam, the beam backup points are evenly set in the x and y directions of the rectangular coordinate system. The value of x _ij is the size of the beam radius centered on this beam backup point. The constraints are set as follows: Beam radius constraint: Overlap range constraints between beams: The upper limit of the number of terminals covered by the beam is: The beam will fully cover the required area: Among them, _ri represents the radius of beam _Bi , _Ax represents the horizontal array size of the antenna array, A _y represents the longitudinal array size of the antenna array, N _B represents the number of beams, _ni represents the minimum number of beams required to synthesize beam _Bi ; r _min represents the minimum beam radius that the antenna array can provide; ρ _max is the maximum value of the IoT user deployment density, η is the adjustment factor for the overlapping area, λ is the adjustment factor for the number of IoT users in the beam coverage area, represents the overlapping area of beams _Bi , _Bi ', represents the number of terminals covered by beam _Bi , represents the intersection point of beams _Bi , _Bi ', The fitness function value is defined as: Among them, σ ₀ represents the penalty function factor, cover represents whether the constraint that the beam fully covers the required area is satisfied, and overlap represents whether the overlap range constraint between beams is satisfied. Indicates whether the maximum number of beams supported by the antenna is met. If any of the constraints are not met, the penalty function variable corresponding to the constraint is set to 1. According to the user's statistical channel state information and combined with the user's position error, the multi-beam joint processing includes the following steps: Establish multi-beam joint processing optimization problem: in, represents the beamforming vector, express Number of IoT users in the group, Indicates The rate of the i-th IoT user in the group; h _s,i,sat represents the statistical channel state information from the ith IoT user to the satellite. h _s,u,sat represents the statistical channel state information from the u-th IoT user to the satellite. _Pt represents the transmission power of IoT users, ^σ2 represents the noise power, and H represents the conjugate transpose operation; Calculate the beamforming vector for each beam and frequency resource: in, I represents the identity matrix, MGE() represents the operation of finding the maximum generalized eigenvector; Calculation and rate: in, represents the subcarrier bandwidth, Indicates the number of subcarriers, express The instantaneous channel state information of the link from the i-th user to the satellite in the group, express The instantaneous channel state information of the u-th user to satellite link in the group; The user capacity can be expressed as: in, if otherwise _Rth is the transmission rate threshold, According to the regional statistical channel state information, establishing the multi-beam joint processing optimization problem includes the following steps: Establish multi-beam joint processing optimization problem: Where, w _ij represents the beamforming vector, _NB represents the number of beams, represents the sum rate of IoT users under the jth frequency resource in the i-th beam; h _rs,ij represents the statistical channel state information of the beam coverage area under the jth frequency resource in the i-th beam; _hrs,uj represents the statistical channel state information of the beam coverage area under the jth frequency resource in the uth beam, ^σ2 represents the noise power; Calculate the beamforming vector for each beam and frequency: w _ij =MGE(A _ij ,B _ij ) in, MGE() represents the operation of finding the maximum generalized eigenvector; Calculation and rate: in, represents the rate of user k in the i-th beam and j-th frequency resource, represents the number of IoT users of the i-th beam and the j-th frequency resource, represents the number of IoT users under the u-th beam and the j-th frequency resource, represents the subcarrier bandwidth, represents the number of subcarriers, h _ijk,sat represents the instantaneous channel state information of the link from the kth IoT user to the satellite under the ith beam and the jth frequency resource, h _ijl,sat represents the instantaneous channel state information of the link from the lth IoT user to the satellite under the ith beam and the jth frequency resource, h _ujz,sat represents the instantaneous channel state information of the link from the zth IoT user to the satellite under the uth beam and the jth frequency resource; Calculate user capacity: Where _Rth is the transmission rate threshold, Ι(·) is the indicator function, defined as