阅读说明：本技术 一种大规模leo卫星部署下的星地传输方法 (Satellite-ground transmission method under large-scale LEO satellite deployment ) 是由 曹斌 孙伟 杨惠斌 于 2020-07-01 设计创作，主要内容包括：本发明提供了一种大规模LEO卫星部署下的星地传输方法,包括不定数目的信道匹配步骤和基于拉格朗日对偶的功率优化步骤,所述不定数目的信道匹配步骤：地面节点与卫星连接后,针对复用同一频带段的卫星中的信道资源进行分配；所述基于拉格朗日对偶的功率优化步骤：使用拉格朗日对偶的方法,通过分配确定不同地面节点的上行传输功率实现的上行传输速率的最大化。本发明的有益效果是：本发明主要应用在低轨道LEO卫星大规模部署场景下的星地传输资源的分配中,通过应用本发明提出的资源分配方法,对整个系统中的信道和功率资源进行分配,从而大幅提高星地网络的上行传输性能,实现高效传输。(The invention provides a satellite-to-ground transmission method under large-scale LEO satellite deployment, which comprises an indefinite number of channel matching steps and a Lagrangian dual-based power optimization step, wherein the indefinite number of channel matching steps comprise: after the ground node is connected with the satellite, the channel resources in the satellite multiplexing the same frequency band section are distributed; the power optimization step based on the Lagrangian dual: and the maximization of the uplink transmission rate is realized by distributing and determining the uplink transmission power of different ground nodes by using a Lagrange duality method. The invention has the beneficial effects that: the method is mainly applied to the distribution of satellite-ground transmission resources in a low-orbit LEO satellite large-scale deployment scene, and the channel and power resources in the whole system are distributed by applying the resource distribution method provided by the invention, so that the uplink transmission performance of a satellite-ground network is greatly improved, and high-efficiency transmission is realized.)

1. A satellite-to-ground transmission method under large-scale LEO satellite deployment is characterized by comprising an indefinite number of channel matching steps and a Lagrangian dual-based power optimization step,

The step of matching the indefinite number of channels: after the ground node is connected with the satellite, the channel resources in the satellite multiplexing the same frequency band section are distributed;

the power optimization step based on the Lagrangian dual: and the maximization of the uplink transmission rate is realized by distributing and determining the uplink transmission power of different ground nodes by using a Lagrange duality method.

2. The satellite-to-ground transmission method according to claim 1, wherein in the step of channel matching of the indefinite number, a utility function of the ground nodes is first defined, and the utility of each ground node served by a satellite j on a subchannel can be expressed as:

it means that each ground node TNi presents its utility function for subchannel k according to its equivalent data rate;

accordingly, each subchannel of satellite j also has a corresponding utility for terrestrial nodes, and the utility of channel k for terrestrial node i can be expressed as:

the form shown above as equation (10) is because each ground node has its lowest rate constraint R_minIt must be ensured that if channel k is assigned to the ground node TN_iThe minimum rate requirement must be met, so we quantize and represent the difference between the achievable rate and the minimum rate constraint as For satellite SAT at the same time_jThe sub-channel k must reduce the interference caused to other satellites as much as possible when allocating the sub-channel, so that the interference term

3. Method for satellite-to-ground transmission according to claim 2, characterized in that in said step of indefinite number of channel matching, h is used_i,j,kRepresenting ground node TN_iAnd satellite node SAT_jThe link channel gain on subchannel k, the corresponding received signal drying ratio is:

wherein p is_i,j,kIndicating TN on subchannel k_iTo the SAT_jTransmit power of p_i',j',kIndicating TN on subchannel k_i'To the SAT_j'The transmission power of the transmitter,indicating TN on subchannel k_iTo the SAT_jTotal channel gain of H_i',j,kIndicating TN on subchannel k_i'To the SAT_jTotal channel gain of L_i,jRepresenting large scale fading, i.e. free space loss, h_i,j,kFor shadow rice fading, G_i(j),jIs TN_iIs directed towards the SAT_jAntenna alignment gain in transmitting data, G_i'(j'),jIs TN_i'Is directed towards the SAT_j'When transmitting data, for SAT_jThe resulting off-axis antenna gain; mixing TN_i'To the SAT_j'Link and TN_i'To the SAT_jThe angle of the link is shown asOff-axis antenna gain G _i'(j'),jOff-axis angle of

4. The satellite-to-ground transmission method according to claim 3, wherein the varying number of channel matching steps comprises performing an initialization step, a ground request procedure step, and a channel decision procedure step in sequence,

in the initialization step, a satellite SAT belonging structure is constructed_jServing ground node set TN ∈ SAT_jSatellite node SAT_jThe channel set CH of (2); constructing a preference list GCLIST of a ground node TN and a preference list CLIST of a channel, and initializing an unmatched TN node set UNmatch and a channel matching relation C;

in the ground request process step, the ground node TN which has not satisfied the minimum matching number limit_iPreferential update preference list GCLIST_iIf all ground nodes meet the minimum matching number limit, all ground nodes sequentially send access requests to the highest-preference channel according to the preference list;

in the channel decision process step, if CH_kAlready matched number

In the step of processing, CH_kThe TN requested in the iteration t is counted_iNumber of (2)

5. The method according to claim 3, wherein in the Lagrangian dual based power optimization step, the Lagrangian function is given by the following equation (11):

wherein, the lambda, the mu and the omega are Lagrangian multiplier vectors corresponding to constraints (4), (5) and (7) in the optimization problem, and a Lagrangian dual function is defined as

The corresponding dual problem can be expressed as:

s.t.λ,μ,ω＞0 (13)

considering the irrelevancy among different sub-channels, the dual problem is decomposed into K × J independent sub-problems according to the number of satellites and the number of sub-channels, and then the dual problem is solved by iteration in sequence, and accordingly, the lagrangian function can be rewritten as:

wherein

According to the KKT condition by referring to p_i,j,kTaking the partial derivative and making it equal to 0, TN can be obtained_iThe optimum transmission power of (a) is:

wherein [ x ]]⁺＝max(0,x)；

To derive lagrange multipliers further, a secondary gradient method is used:

where t denotes the number of iterations, τ_λ(t),τ_μ(t),τ_ω(t)Representing the iteration step at t iterations.

6. The satellite-to-ground transmission method according to claim 5, wherein LEO satellites using same band segments are represented as

Technical Field

The invention relates to the technical field of communication, in particular to a satellite-ground transmission method under large-scale LEO satellite deployment.

Background

In recent years, academic and industrial circles have started to research on Space-Sky-Terrestrial Integrated Information networks (SSTIINs), and one of the key points of SSTIINs is to implement global networking and real-time access by using a large number of LEO satellites to perform large-scale networking. Therefore, under large-scale LEO deployment, an efficient satellite-to-ground transmission method is crucial to playing the role of LEO satellites.

In the research on the relevant problems under large-scale deployment of LEO satellites, Sheng et al research the transformation and scheduling of multidimensional resources including transmission resources in a spatial information network. On this basis He et al studied the joint allocation of transmission and computation resources in spatial networks and proposed a combined allocation scheme. Jia et al propose a scheme for cooperative data downlink using inter-satellite links in LEO satellite networks to maximize downlink transmission throughput by using inter-satellite links. Huang et al studied if the data could be transferred from the ground IOT gateway via LEO satellite in an efficient and energy-saving manner, and proposed an online optimization scheme. Di et al propose a satellite-to-ground network architecture to integrate a low earth orbit satellite network and a ground network, and based thereon, a flexible exchange matching algorithm to achieve efficient data offloading.

On the study of the problems related to transmission resource allocation, Soleimani et al studied how to overcome performance loss by managing co-channel interference, establish an optimization problem and solve the optimization problem using d.c. planning. In a similar way, Khamidehi et al propose and solve the subchannel and power allocation problem in heterogeneous uplink Orthogonal Frequency Division Multiple Access (OFDMA) networks. Zhang et al have studied the problem of user association, channel allocation and power allocation in the downlink transmission of multi-cell multi-association OFDMA network, and have proposed an optimization scheme based on graph theory and Lagrangian dual theory. The application of the matching theory in resource allocation becomes more and more important, bayard et al introduces the application of the matching theory in wireless communication and provides some common matching algorithms, and solves the problems of user association and channel association by using the matching algorithms, thereby realizing the maximization of the total utility of users in the uplink OFDMA network. Zhang et al propose a distributed matching algorithm that considers the characteristics of primary and secondary users in a cognitive radio network and selects the best channel utilization to maximize the overall utility of the system by performing many-to-one matching.

Disclosure of Invention

The air-space-ground integrated information network is called as the main development direction of the next step of communication technology by virtue of the characteristics of global coverage and real-time access. In order to fully exert the advantages of the air-space-ground integrated information network, it is important to realize efficient transmission between satellites and the ground through a Low Earth Orbit (LEO) satellite. The method is mainly applied to the distribution of satellite-ground transmission resources in a low-orbit LEO satellite large-scale deployment scene, and the channel and power resources in the whole system are distributed by applying the resource distribution method provided by the invention, so that the uplink transmission performance of a satellite-ground network is greatly improved, and high-efficiency transmission is realized.

The invention provides a satellite-to-ground transmission method under large-scale LEO satellite deployment, which comprises an indefinite number of channel matching steps and a Lagrangian dual-based power optimization step,

the step of matching the indefinite number of channels: after the ground node is connected with the satellite, the channel resources in the satellite multiplexing the same frequency band section are distributed;

the power optimization step based on the Lagrangian dual: and the maximization of the uplink transmission rate is realized by distributing and determining the uplink transmission power of different ground nodes by using a Lagrange duality method.

As a further improvement of the invention, in said step of channel matching of indefinite number, the utility function of the ground nodes is first defined, and the utility of each ground node served by satellite j on a subchannel can be expressed as:

it means that each ground node TNi presents its utility function for subchannel k according to its equivalent data rate;

accordingly, satellite SAT_jEach sub-channel of (a) also has a corresponding utility for a ground node, and channel k is for ground node TN_iThe utility of (c) can be expressed as:

the form shown above as equation (10) is because each ground node has its lowest rate constraint R _minIt must be ensured that if channel k is assigned to the ground node TN_iThe minimum rate requirement must be met, so we quantize and represent the difference between the achievable rate and the minimum rate constraint asFor satellite SAT at the same time_jThe sub-channel k must reduce the interference caused to other satellites as much as possible when allocating the sub-channel, so that the interference termAs denominator of utility;is a weighting factor so that interference terms to other satellites can play a role in the utility function.

As a further development of the invention, in the step of channel matching of said indefinite number h is used_i,j,kRepresenting ground node TN_iAnd satellite node SAT_jThe link channel gain on subchannel k, the corresponding received signal drying ratio is:

wherein p is_i,j,kIndicating TN on subchannel k_iTo the SAT_jThe transmission power of the transmitter,

indicating TN on subchannel k_iTo the SAT_jTotal channel gain of L_i,jRepresenting large scale fading, i.e. free space loss, h_i,j,kFor shadow rice fading, G_i(j),jIs TN_iIs directed towards the SAT_jAntenna alignment gain in transmitting data, G_i'(j'),jIs TN_i'Is directed towards the SAT_j'When transmitting data, for SAT_jThe resulting off-axis antenna gain; mixing TN_i'To the SAT_j'Link and TN_i'To the SAT _jThe angle of the link is shown asOff-axis antenna gain G_i'(j'),jOff-axis angle ofFunction of, N₀Is the value of additive white Gaussian noise, y_i,j,kRepresenting the matrix elements of the binary matrix Y.

As a further development of the invention, said step of indefinite number of channel matching comprises the steps of performing in sequence an initialization step, a ground request procedure step and a channel decision procedure step,

in the initialization step, belong to SAT_jThe ground node set TN ∈ SAT_jSatellite node SAT_jThe channel set CH of (2); constructing a preference list GCLIST of a ground node TN and a preference list CLIST of a channel, and initializing an unmatched TN node set UNmatch and a channel matching relation C;

in the ground request process step, the ground node TN which has not satisfied the minimum matching number limit_iPreferential update preference list GCLIST_iIf all ground nodes meet the minimum matching number limit, all ground nodes sequentially send access requests to the highest-preference channel according to the preference list;

in the channel decision process step, if CH_kAlready matched numberThen, executing the processing step, otherwise rejecting all requests;

in the step of processing, CH_kCounting the number of ground nodes making requests in the iteration t CH_kCLIST according to preference list_kAfter the node, the TN with the highest preference degree is compared with CH_kRemove from unmatched list UNmatch, establish match C_i,jThe remaining ground nodes are rejected as 1.

As a further improvement of the present invention, in the lagrangian dual-based power optimization step, a lagrangian function is represented by the following formula (11):

wherein, the lambda, the mu and the omega are Lagrangian multiplier vectors corresponding to constraints (4), (5) and (7) in the optimization problem, and a Lagrangian dual function is defined as

The corresponding dual problem can be expressed as:

s.t.λ,μ,ω＞0 (13)

considering the irrelevancy among different sub-channels, the dual problem is decomposed into K × J independent sub-problems according to the number of satellites and the number of sub-channels, and then the dual problem is solved by iteration in sequence, and accordingly, the lagrangian function can be rewritten as:

wherein

According to the KKT condition by referring to p_i,j,kTaking the partial derivative and making it equal to 0, TN can be obtained_iThe optimum transmission power of (a) is:

wherein [ x ]]⁺＝max(0,x)；

To derive lagrange multipliers further, a secondary gradient method is used:

where t denotes the number of iterations, τ_λ(t),τ_μ(t),_τω_(t)Represents tIteration step size at sub-iteration.

As a further improvement of the invention, LEO satellites using same frequency band segments are represented as

The number of ground nodes providing data access service for each satellite is

The band with bandwidth B is divided into

Orthogonal sub-channels, all of which are multiplexed by the LEO satellite; the constraint (4) is:the constraint (5) is:the constraint (7) is:

constraint (4) indicates that the maximum transmit power per TN cannot exceed P_max(ii) a Constraint (5) indicates that the minimum rate per TN cannot be less than R_min(ii) a I in constraint (7)_thRepresents the maximum tolerable limit of inter-satellite interference, R accordingly_i,j,kThe changes are in the form:

the invention has the beneficial effects that: the method is mainly applied to the distribution of satellite-ground transmission resources in a low-orbit LEO satellite large-scale deployment scene, and the channel and power resources in the whole system are distributed by applying the resource distribution method provided by the invention, so that the uplink transmission performance of a satellite-ground network is greatly improved, and high-efficiency transmission is realized.

Drawings

FIG. 1 is a star-to-ground transmission architecture diagram;

FIG. 2 is a graph of total system rate/iteration count;

fig. 3 is a graph of number of serving ground nodes/iterations;

FIG. 4 is a system total rate/service radius graph;

fig. 5 is a system total rate/number of terrestrial end users graph.

Detailed Description

The invention discloses a satellite-ground transmission method under large-scale LEO satellite deployment, which is an optimization solution for realizing efficient uplink transmission of a satellite-ground network by using a matching algorithm, convex optimization and other related knowledge.

The invention mainly comprises two parts: establishment of optimization problems and specific optimization solution.

1. Firstly, an optimization problem which meets the requirement of an actual scene and takes the maximum satellite-ground uplink rate as an optimization target is established, and various constraint conditions are comprehensively considered.

2. In the realization of the specific optimization solution of the problem, an optimization algorithm (MLCA) combining Matching and Lagrangian dual combining is provided by using a Matching algorithm and the relevant knowledge of convex optimization, and the optimization target is realized by the algorithm. The MLCA algorithm also mainly comprises two parts:

a. indefinite number of channel matching algorithms (indefinite number of channel matching steps)

b. Lagrange duality-based power optimization algorithm (Lagrange duality-based power optimization step)

First, the first section is introduced, the establishment of the optimization problem:

the invention mainly focuses on the satellite-ground resource allocation problem after the satellites and the ground nodes focus on under the large-scale LEO satellite deployment, namely, the channel and power allocation problem after each ground node determines the satellite with data uploading connection. The present invention considers an OFDMA satellite-to-ground network, in which a large number of LEO satellites provide network access and data uplink transmission services for a large number of ground nodes (TNs), as shown in fig. 1. We assume that the band reuse scheme between satellites and the connection between the satellite nodes has already been established After determining that each ground node has determined which satellite it is to be connected to, we need to allocate channel resources in the satellites multiplexing the same band segment, and determine uplink transmission power of different nodes through allocation to maximize the uplink transmission rate of the whole system. LEO satellites using same frequency band segments are represented asThe number of ground nodes providing data access service for each satellite is

Because the pre-determined orbit of each satellite, the altitude, velocity and position information of the satellite are known to all ground nodes, for convenience we use a quasi-static method to divide a time period into time slots. In each time slot, the channel conditions between the position of each satellite and the satellite nodes are considered to be constant and we consider that the LEO satellite already knows all the channel state information of the ground nodes.

We consider the band with bandwidth B divided into

Orthogonal subchannels, all of which are multiplexed by the LEO satellite. Due to the motion of LEO satellites in real scenes, we consider that all frequency bands have been doppler shift compensated. For the purpose of more convenient representation of the resource allocation of the satellite nodes in the following, we introduce a binary matrix Y, where the matrix elements Y _i,j,kWith 1 indicating that subchannel k of satellite j is assigned to the TN it serves_iWhere i (j) denotes the ground node i served by the satellite j, for simplicity, we shall consider i to denote i (j) hereinafter except where specifically noted to avoid confusion. Considering the particularity of the satellite-to-ground transmission, we consider that the conditions affecting the channel are mainly large-scale fading and shadow-ray fading, with h_i,j,kDenotes TN_iAnd SAT_jLink channel gain on subchannel k, corresponding receive-to-interference ratio (signal-to-interference)e-noise-radio, SINR) is:

wherein p is_i,j,kIndicating TN on subchannel k_iTo the SAT_jThe transmission power of the transmitter,

indicating TN on subchannel k_iTo the SAT_jTotal channel gain of L_i,jRepresenting large scale fading, i.e. free space loss, h_i,j,kFor shadow rice fading, G_i(j),jIs TN_iIs directed towards the SAT_jAntenna alignment gain in transmitting data, G_i'(j'),jIs TN_i'Is directed towards the SAT_j'When transmitting data, for SAT_jResulting in off-axis antenna gain. We will TN_i'To the SAT_j'Link and TN_i'To the SAT_jThe angle of the link is shown asOff-axis antenna gain G_i'(j'),jOff-axis angle ofThe specific value of the function of (a) is referred to ITU-R.S.1428, the gain of the off-axis antenna is related to the off-axis angle, and for the size of the off-axis gain of the antenna, a proposal is specially issued by the International telecommunication Union ITU, the off-axis gain of the ground-to-satellite transmission antenna under different off-axis angles is listed in the proposal, the name of the proposal is ITU-R.S.1428, and the off-axis gain of the ground-to-satellite transmission antenna is obtained by referring to the proposal after the off-axis angle of the ground-to-satellite transmission is determined. N is a radical of ₀Is the value of additive white Gaussian noise.

Accordingly, we have established an optimization problem with the goal of maximizing the total system rate of a multi-satellite node-multi-terrestrial node system, as follows:

wherein constraint (3) ensures that a subchannel for a satellite is allocated to at most one TN; constraint (4) indicates that the maximum transmit power per TN cannot exceed P_max(ii) a Constraint (5) indicates that the minimum rate per TN cannot be less than R_min(ii) a Constraint (6) is a binary value of the channel allocation variable.

Because the original optimization problem is a mixed integer nonlinear programming problem, and because the existence of binary variable Y and an interference term in a rate formula, the original problem is also a highly non-convex problem, the global optimal solution of the problem is difficult to obtain. Meanwhile, although factors such as off-axis antenna gain and the like influencing inter-satellite interference are considered, inter-satellite interference under large-scale LEO deployment is unavoidable, and when the interference is too large, the whole system becomes meaningless. We therefore consider introducing an upper interference bound as a new constraint to the optimization problem:

wherein I_thRepresents the maximum tolerable limit of inter-satellite interference, R accordingly_i,j,kThe changes are in the form:

Considering the complexity of the optimization problem, we choose to adopt a suboptimal method to divide the original problem into two sub-problems, firstly perform sub-channel allocation, then perform power allocation on the basis, and accordingly propose an MLCA algorithm to achieve the optimization goal.

In order to solve the optimization problem proposed by the invention, the invention proposes an MLCA algorithm, which mainly comprises two parts of channel matching and power allocation. The following describes a specific implementation of the MLCA algorithm.

Since the channel allocation variable of the optimization problem is a binary variable, the channel allocation variable can be modeled into a matching problem, and an infinite number of many-to-one matching algorithms are firstly proposed. It should be noted that for an uplink system, no matter which terrestrial node the satellite assigns its subchannel to, the interference received at the satellite from other satellites is fixed, so we decompose it into J independent sub-problems and solve them in turn, depending on the number of satellites J. In the following we assume that the sub-channel assignment is made to satellite j.

For the matching algorithm, first we define the utility function of the ground nodes, and the utility of each ground node served by satellite j on the subchannel can be expressed as:

It means that each terrestrial node TNi proposes its utility function for subchannel k according to its equivalent data rate, since each node wants to obtain a higher data rate.

Accordingly, each subchannel of satellite j also has a corresponding utility for terrestrial nodes, and the utility of channel k for terrestrial node i can be expressed as:

it is thus indicated asThe form of equation (10) is because each ground node has its lowest rate constraint R_minIt must be ensured that if channel k is assigned to ground node i, its minimum rate requirement must be met, so we quantify the difference between the representation of the achievable rate and the minimum rate constraint and represent it as the difference

Meanwhile, for the sub-channel k of the satellite j, the interference caused by the sub-channel to other satellites must be reduced as much as possible when the sub-channel is allocated, so that the interference term is used

As denominator of utility;is a weighting factor so that interference terms to other satellites can play a role in the utility function.

After determining the allocation of subchannels, the power allocation scheme needs to be optimized to coordinate the interference and improve the overall throughput of the uplink-to-satellite network. We solve the power allocation problem by using the lagrangian dual approach. The lagrange function is shown in the following equation (11):

Wherein, λ, μ, ω are lagrangian multiplier vectors corresponding to constraints (4), (5), (7) in the original optimization problem, and binary boundary constraint (3) has been solved in subchannel allocation. Thus the Lagrangian dual function is defined as

The corresponding dual problem can be expressed as:

s.t.λ,μ,ω＞0 (13)

considering the irrelevancy among different sub-channels, the dual problem is decomposed into K multiplied by J independent sub-problems according to the number of satellites and the number of sub-channels, and then the dual problem is sequentially solved in an iteration mode. Accordingly, the lagrange function can be rewritten as:

wherein

According to the KKT (Karush-Kuhn-Tucker) conditions, by referring to p_i,j,kBy taking the partial derivatives and making them equal to 0, we can obtain TN_iThe optimum transmission power of (a) is:

wherein [ x ]]⁺＝max(0,x)。

To derive lagrange multipliers further, we use a secondary gradient method:

where t denotes the number of iterations, τ_λ(t),τ_μ(t),τ_ω(t)Representing the iteration step at t iterations.

Therefore, the method provided by the invention realizes the efficient transmission between the satellite and the ground.

In order to show the effect of the technical means, simulation verification is carried out on the technical scheme. Consider a spectrum-shared satellite-to-terrestrial transmission system that includes J-4 LEO satellites and I-200 TNs. The altitude of the satellite is set to 1000Km, all the satellites operate in the Ka band and the total bandwidth is 400MHz, and in this scenario we set the number of subchannels to 80. Small scale fading over the Ka band is modeled as shadowing rice fading and sets the degree of fading to medium fading, while we also consider the free space loss model in the satellite-to-ground transmission. Furthermore, considering the particularity of the satellite transmission, we choose to use the off-axis antenna gain proposed above. Specific simulation parameters are shown below in table 1, except where specifically mentioned:

Table 1: simulation parameter table

In a real communication scenario, in order to fully utilize the communication window of the satellite, only the terrestrial node with better channel condition is sometimes allowed to access the satellite, and the terrestrial node with worse channel condition is denied access. Therefore, under the condition of not considering the minimum rate constraint of the ground node, the channel and Power allocation can adopt a Lagrangian dual method for Joint optimization, which is called as a Joint Power and channelization optimization Algorithm (WJPCA) Without minimum rate constraint. Fig. 2 below shows the convergence of our proposed MLCA Algorithm, we choose the WJPCA Algorithm and the CRPA (Channel Random and power average Algorithm) Algorithm as two reference algorithms. From fig. 2, it can be seen that the MLCA algorithm has a good convergence, and the MLCA algorithm has a good performance improvement compared to the CRPA algorithm. Since the MLCA algorithm includes the minimum rate constraint of the ground node, it is necessary to maximize the total rate of the system under the condition of ensuring the minimum rate constraint of the node, so the total rate of convergence is lower than that of the WJPCA algorithm, because the WJPCA will distribute as much resources as possible to users with good channel conditions, which inevitably leads to an increase in the total system rate.

Of course, fig. 2 does not mean that the MLCA algorithm proposed by the present invention has a poor performance, and although it has a relatively low system rate, it guarantees the minimum rate requirements of more terrestrial nodes, with a good performance in terms of the number of successfully accessed terrestrial nodes. This can be seen in figure 3, which is the same process as figure 2.

Changes in the regional extent of satellite service also affect overall system throughput. In fig. 4, we compare the system throughput under different radius conditions, we set the satellite number to be 4, the ground node number to be 200, and the subchannel number to be 50, and simulate the system performance under the range of radius varying from 30Km to 100 Km.

As can be seen from fig. 4, the change in the service radius affects the overall system rate, and as the radius gradually increases, the overall system rate gradually decreases. It is well understood that as the distance increases, the overall channel condition becomes worse and worse. The off-axis angle will become larger and larger, and the corresponding off-axis gain will gradually decrease, although the interference will be reduced to some extent, but the overall system velocity will tend to be downward. This is mainly due to the relatively high altitude of the satellite, which we can conclude from the pythagorean theorem that when the ground service radius is not particularly large, the distance between the LEO satellite and the ground nodes is not very large, and the off-axis angle is not very large. Because the off-axis gain is related to the off-axis angle, when the off-axis angle is in a smaller range, the change of the off-axis angle does not cause great change of the off-axis gain, and accordingly co-channel interference is not great. As the distance increases, the overall channel fading will vary more dramatically than the off-axis gain, resulting in overall performance continuing downward.

Finally, we analyzed the system performance for different ground node numbers, and we selected Particle Swarm Optimization (PSO) and CRPA as the control simulation algorithms, as shown in fig. 5.

From fig. 5, it can be seen that the total system rate of all three algorithms shows a decreasing trend as the number of ground nodes increases, because four independent satellite systems are considered in the model, and each satellite shares the same frequency band, so when the ground nodes transmit data to one satellite, the ground nodes cause co-channel interference to other satellites. Since an uplink system is considered in this case, each ground node has its maximum transmit power, and the maximum powers are equal in magnitude. Therefore, when the number of ground nodes is not particularly large, the overall rate increases with the number of ground nodes, but when the number of ground nodes increases to a certain extent, the influence of power decreases significantly, and the overall system rate is more influenced by channel variation. The more the number of ground nodes, the more ground nodes that need to guarantee the minimum rate requirement, the more and more resources will be transferred from the node with better channel condition to the node with worse channel condition, the rate of the node with better condition will be correspondingly reduced, and the rate of the node with worse condition will be increased. Its effect on the overall rate of the system is also relatively large. This ensures that all ground nodes accessing the satellite meet the minimum rate constraint, but eventually the overall rate will be reduced. From fig. 5 we can also see that the proposed MLCA algorithm has a better performance improvement over the other two algorithms in the total system rate.

The invention relates to a satellite-to-ground transmission method under large-scale LEO satellite deployment, in particular to an MLCA algorithm for realizing the maximization of the overall throughput of a system, which is established under the framework provided by the invention and is used for solving the provided optimization problem. Under the consideration of various constraints such as power, speed and interference limitation in a communication scene, by combining the two algorithms, an efficient transmission scheme between a multi-satellite node and a multi-ground node is obtained, and the advantages of the scheme provided by the invention in the aspects of throughput, access quantity and the like are obtained through simulation verification.

The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.