A method and apparatus for optimizing uplink communication throughput in an air-to-ground communication system.

Abstract

This invention discloses a method and apparatus for optimizing uplink communication throughput in an air-to-ground communication system, relating to the field of wireless communication technology. The method includes: constructing an air-to-ground communication system based on ground-based communication equipment, relay station communication equipment, and satellite communication equipment; modulating the data transmitted during uplink communication from the ground-based communication equipment to the satellite communication equipment into discrete symbol form to obtain mutual information reflecting the transmission efficiency of discrete symbol information, using mutual information to characterize the throughput of the uplink communication process of the air-to-ground communication system; constructing an original optimization model with maximizing throughput as the optimization objective and the upper limit of the communication equipment's power as a constraint; and solving the original optimization model to obtain the maximum uplink communication throughput.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method and apparatus for optimizing uplink communication throughput in an air-to-ground communication system. Background Technology

[0002] A space-air-ground communication system is a communication system with relay stations between the ground and satellite. Uplink communication is the process of sending information from a ground station or lower-level network node to a satellite or higher-level network node. It is characterized by long transmission distances, complex transmission environments, and limited resources. Throughput is an indicator reflecting the information processing and transmission capabilities of a space-air-ground communication system during uplink communication; that is, the total amount of data the system can process and successfully transmit per unit of time. By adjusting parameters such as power in the space-air-ground communication system to maximize uplink throughput, communication efficiency and service quality can be improved, thereby promoting the integrated development of space-air-ground systems.

[0003] In the prior art, Chinese patent CN 116112060 A discloses a resource allocation method and apparatus for an air-to-ground communication system based on buffer relay. The method includes: dividing the transmission time into multiple time slots; establishing a resource allocation optimization problem model for the air-to-ground communication system with the goal of maximizing the average throughput of the system and combining the goal constraints; solving the resource allocation optimization problem model using an alternating iterative method to determine the channel access information and transmission power of each channel between the ground terminal and the high-altitude platform in each time slot, as well as the channel access information and transmission power of each channel between the high-altitude platform and the satellite. This method calculates the transmission rate in an air-to-ground communication system based on Gaussian input, i.e., the transmission rate is the channel capacity (i.e., the theoretically achievable maximum transmission rate).

[0004] However, due to various reasons (such as hardware limitations, modulation methods, and coding methods), it is difficult to ensure that the input signal completely follows a Gaussian distribution. The channel capacity corresponding to a Gaussian input is actually the upper limit of the transmission rate, which is often an unattainable rate. Therefore, the accuracy of the resource allocation results for air-to-ground communication systems obtained based on the above methods needs to be improved. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and apparatus for optimizing uplink communication throughput in an air-to-ground communication system to address the aforementioned technical problems.

[0006] The following technical solution is adopted in this specification:

[0007] This specification provides a method for optimizing uplink communication throughput in an air-to-ground communication system, including:

[0008] A space-air-ground communication system is constructed based on ground-based communication equipment, relay station communication equipment, and satellite communication equipment;

[0009] The data transmitted during the uplink communication process from ground-based communication equipment to satellite communication equipment is modulated into discrete symbol form to obtain mutual information, which reflects the transmission efficiency of discrete symbol information. The mutual information is used to characterize the throughput of the uplink communication process of the air-space-ground communication system.

[0010] With maximizing throughput as the optimization objective and the upper limit of the power of communication equipment as the constraint, an original optimization model is constructed; the original optimization model is solved to obtain the maximum throughput of uplink communication.

[0011] Furthermore, the construction of the space-air-ground communication system based on ground-based communication equipment, relay station communication equipment, and satellite communication equipment includes:

[0012] The relay station communication equipment receives signals transmitted by the ground-based communication equipment:

[0013]

[0014]

[0015] in, It is the signal received by the relay station communication equipment. This refers to the number of receiving antennas of the relay station's communication equipment. This indicates the channel from the ground-based communication equipment to the relay station communication equipment. Indicates the number of transmitting antennas in a ground-based communication device; Indicates the transmission signal The precoder; This indicates that the mean is zero and the variance is... Additive white Gaussian noise, ; The Rice factor in the Rice channel fading model; and These are the LoS and Rayleigh fading components in the Ricean channel fading model, respectively.

[0016] The relay station communication equipment uses an amplification and forwarding protocol to forward the received signals to the satellite communication equipment.

[0017]

[0018] Among them, y s It is a signal received by satellite communication equipment; This is the LoS channel from the relay station communication equipment to the satellite communication equipment. The number of transmitting antennas for the relay station communication equipment; It is used for signal integration The pre-encoder of the relay station communication equipment; The mean is zero and the variance is The additive thermal noise of satellite communication equipment follows .

[0019] Furthermore, the description of using mutual information to characterize the uplink communication process of the air-to-ground communication system specifically includes:

[0020]

[0021]

[0022] in, This represents the throughput of the uplink communication process. is the number of transmitting antennas of the ground-based communication equipment; M is the number of points in the signal constellation; dij = xi - xj, where xi and xj represent Nt possible modulation symbol vectors in the M-ary constellation diagram; En (·) represents the expectation operation.

[0023] Furthermore, the original optimization model, which aims to maximize throughput and is constrained by the power limit of the communication equipment, specifically includes:

[0024]

[0025]

[0026]

[0027] in, and These represent the actual power of the ground-based communication equipment and the relay station communication equipment, respectively, with the superscript H indicating conjugate transpose; and These are the upper limits of available power for ground-based communication equipment and relay station communication equipment, respectively.

[0028] Furthermore, solving the original optimization model specifically includes:

[0029] Use cutoff rate Replace the original optimization model This reduces the computational complexity of the original optimization model.

[0030] Will The original optimization model, replaced by the cutoff rate, is decomposed into sub-optimization model one and sub-optimization model two;

[0031] The sub-optimization model is one To optimize the objective, the power limit of ground-based communication equipment is used as a constraint, where, , express Known feasible solutions;

[0032] The sub-optimization model two is based on To optimize the objective, the power limit of the relay station communication equipment is used as a constraint; whereby... ;in, express Known feasible solutions;

[0033] Iteratively optimize sub-optimization model one and sub-optimization model two until the set iteration termination condition is reached, and output the optimization results to obtain the maximum throughput.

[0034] This specification provides a throughput optimization device for uplink communication in an air-to-ground communication system, comprising:

[0035] The air-to-ground communication system construction module is used to build air-to-ground communication systems based on ground-based communication equipment, relay station communication equipment, and satellite communication equipment.

[0036] The throughput characterization module is used to modulate the data transmitted by the ground-based communication equipment to the satellite communication equipment into discrete symbol form during the uplink communication process, and obtain mutual information to reflect the transmission efficiency of discrete symbol information. The mutual information is used to characterize the throughput of the uplink communication process of the air-space-ground communication system.

[0037] The throughput optimization module is used to construct an original optimization model with the goal of maximizing throughput and the power limit of the communication equipment as a constraint; the original optimization model is solved to obtain the maximum throughput of uplink communication.

[0038] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects:

[0039] In the actual uplink communication data modulation process, discrete symbols are the carriers that establish the mapping between binary bit streams and data, and measure the actual data transmission rate. Therefore, in the throughput optimization method of uplink communication in the air-to-ground communication system provided by this invention, for the transmission problem of discrete symbols, mutual information is used to characterize the throughput of air-to-ground uplink communication, rather than pursuing the theoretical maximum value under ideal conditions, so as to improve the robustness of the system under non-ideal conditions. Attached Figure Description

[0040] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0041] Figure 1 This is a flowchart illustrating the method for optimizing uplink communication throughput in the air-to-ground communication system provided in this specification.

[0042] Figure 2This is a schematic diagram of the air-to-ground communication system provided in this manual;

[0043] Figure 3 This is a schematic diagram of the optimization model algorithm provided in this manual;

[0044] Figure 4 This specification provides a data rate illustration for different design methods.

[0045] Figure 5 This is a graph showing the relationship between signal-to-noise ratio and data rate at different modulation orders, as provided in this manual. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0047] Due to factors such as channel fading and multipath effects, the low uplink transmission rate of integrated air-space-ground networks severely limits the throughput of integrated uplink communication. Therefore, effectively improving the signal transmission rate has become an unavoidable and urgent task for integrated air-space-ground uplink transmission. Precoding, as a technique that can effectively improve the signal transmission quality in communication systems, has the ability to enhance the transmission reliability of integrated air-space-ground multi-hop networks. Therefore, how to implement efficient precoding schemes for ground users and UAV nodes is of significant research importance. Traditional optimization methods usually only focus on precoding design for ground users, neglecting the bridging role of UAVs as relays in integrated air-space-ground networks. Furthermore, there may be various interferences and changes in channel characteristics between ground users and air nodes; therefore, precoding design only for ground users may not effectively achieve anti-interference effects, leading to reduced robustness of the integrated air-space-ground network. Joint precoding for ground users and UAVs can not only optimize air resource utilization but also improve transmission reliability. Therefore, joint precoding design for ground users and UAVs further enhances the throughput of integrated air-space-ground communication. From a current technological perspective, algorithms for optimizing uplink throughput in air-space-ground communication typically study the transmission rate of communication systems under a Gaussian input model. While theoretically, wireless communication systems with Gaussian input can achieve channel capacity—the theoretical upper limit described by Shannon's theorem—this is not applicable to practical modulation schemes based on constellation points, such as quadrature amplitude modulation (QAM). An integrated air-space-ground network is a resource-constrained and computationally limited transmission system, constrained by factors including bandwidth, power limitations, and noise interference. Therefore, constructing an effective transmission scheme is crucial for the construction of an integrated air-space-ground network and facilitates its rapid implementation. Specifically, for practical discrete symbols, establishing a rate function between the binary bit stream and data, and constructing a low-complexity coding optimization scheme, significantly improves resource allocation and system reliability.

[0048] Against this backdrop, this application proposes a joint precoding design method based on throughput optimization for air-to-ground uplink communication in amplified relay relay system. Addressing the transmission problem of discrete symbols, this application utilizes mutual information to characterize the actual data rate of the air-to-ground uplink. Since the functional expression of mutual information lacks closure, a cutoff rate is proposed to replace the non-closed mutual information expression, thereby constructing a concave maximization problem with standard form, reducing computational complexity caused by expectation operations. However, due to the simultaneous precoding design for ground users and UAVs, the two variables to be processed are highly coupled in the expression. Therefore, under the model of ground users, UAVs, and satellite communication systems, for integrated air-to-ground uplink communication with finite character input, this application proposes a low-complexity solution based on alternating optimization: First, the relationship between satellite received signals and ground transmitted signals is used to obtain the mutual information of the discrete constellation to characterize the actual data rate, and an optimization problem is constructed based on power constraints; then, a closed cutoff rate is given to replace the previously non-closed mutual information objective function, and the original optimization problem is decomposed into two sub-problems using a decoupling method; the two sub-problems are solved using alternating optimization, thereby obtaining the optimal precoder and determining the maximum transmission rate. This application is applicable to the application scenario of optimizing the uplink communication throughput of air-to-ground communication systems, and can optimize the uplink communication throughput of air-to-ground communication.

[0049] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0050] Figure 1 The flowchart illustrates a method for optimizing uplink communication throughput in an air-to-ground communication system according to an embodiment of this disclosure, specifically including the following steps:

[0051] S1: Construct an air-space-ground communication system based on ground-based communication equipment, relay station communication equipment, and satellite communication equipment.

[0052] A type of air-space-ground communication system is constructed, such as Figure 2 As shown, ground users are selected as ground-based communication equipment, UAVs as relay station communication equipment, and satellite communication equipment as the entity research objects of the communication system model, constructing an integrated air-space-ground uplink communication system model. This air-space-ground communication system specifically includes the following:

[0053] In the uplink transmission of the integrated air-space-ground network, UAVs act as relays bridging communication between ground users and satellites. Under certain circumstances, due to limited access frequencies, ground users cannot communicate directly with satellites. In the integrated air-space-ground network, in the first phase, ground users first transmit modulation symbols. At that time, the signal received by the drone It can be represented as:

[0054] (1)

[0055] in, This represents the channel from the ground user to the drone. and These represent the number of receiving antennas and the number of transmitting antennas for the drone and the ground node, respectively. Furthermore, Indicates the transmission signal The precoder. Among them, This indicates that the mean is zero and the variance is zero on the drone. Additive white Gaussian noise, i.e. Considering the electromagnetic propagation characteristics affected by complex ground environments, It follows the Ricean channel fading model. That is:

[0056] (2)

[0057] In the formula, Rice factor, and These are the Loss and Rayleigh fading components, respectively.

[0058] In the second phase, the drone uses an amplified relay protocol to transmit the received data. The signal is relayed to the satellite. The signal received by the satellite is:

[0059]

[0060] (3)

[0061] in For the Loss of Satellite (LoS) channel from UAV to satellite, The number of transmitting antennas for the drone. .in addition, It is used for signal integration The drone pre-coder. The satellite's mean is zero and its variance is... Additive thermal noise, following .

[0062] S2: Modulate the data transmitted during the uplink communication process from the ground-based communication equipment to the satellite communication equipment into discrete symbol form to obtain mutual information that reflects the transmission efficiency of discrete symbol information. The mutual information is used to characterize the throughput of the uplink communication process of the space-air-ground communication system.

[0063] The transmission rate is characterized using the mutual information of discrete inputs; an instantaneous mutual information expression is constructed based on the instantaneous signal-to-noise ratio. Specifically, the mutual information based on finite character inputs is calculated, and the actual data rate is characterized using the mutual information of discrete inputs.

[0064] (4)

[0065] in, Since mutual information is used to characterize the actual data rate, and the actual data rate can also characterize the throughput during communication, maximizing... I It's about maximizing throughput. M represents the number of transmitting antennas for the ground-based communication equipment, and M represents the number of points in the signal constellation. ,in and Representing an M-ary constellation diagram There are n possible modulation symbol vectors, totaling [number] The number of possible combinations. Due to the completion... Transmission from ground users to satellites via drones requires two orthogonal time slots, hence the factor 1 / 2 in the above formula; This indicates the expected operation.

[0066] S3: Construct the original optimization model with the goal of maximizing throughput and the power limit of the communication equipment as the constraint; solve the original optimization model to obtain the maximum throughput of uplink communication.

[0067] Considering the constraints of the finite character input in the two communications, an optimization problem is constructed to maximize the reachable average mutual information; with the objective function of (4) as the objective, the power-constrained optimization problem can be expressed as:

[0068] (5a)

[0069] (5b)

[0070] (5c)

[0071] (5b) and (5c) are the power constraints for ground users and UAVs, respectively. and The actual power for ground users and drones, respectively. and These are the maximum available power limits for ground users and drones, respectively.

[0072] Given the complexity of the mutual information expression and the high coupling of the variables to be processed within the non-closed noise sampling expression, it is difficult to handle. To facilitate analysis and optimization of the pre-encoder design to improve mutual information performance, the variables to be processed will be alternately optimized to establish a practical solution with low computational complexity. The first step is to decompose the original optimization problem into two sub-problems; specifically, the following:

[0073] First, a closed form is given using the cutoff rate to replace it. It is actually The lower bound is thus reduced, thereby alleviating the computational complexity caused by the expectation operation. The cutoff rate associated with the integrated air-space-ground network based on amplified relay can be expressed as:

[0074] (6)

[0075] The substitution in (6) optimizes efficiency by removing a large number of noisy samples. However, it can be noted that the current difficulty lies in how to design an effective method to optimize variable pairs ( , Finally, an iterative method was chosen to perform alternating optimizations. and .

[0076] use replace The original optimization problem (5) is decomposed into two subproblems, namely:

[0077] (7a)

[0078] (7b)

[0079] and

[0080] (8a)

[0081] (8b)

[0082] in

[0083] (9)

[0084] (10)

[0085] In the formula, and They represent and The known feasible solutions.

[0086] Iteratively optimize the two subproblems until the set iteration termination condition is met, and output the optimization results to obtain the maximum throughput.

[0087] Preferred optimization :exist Under fixed conditions, the lower bound of the objective function is obtained through Taylor expansion, thus deriving a convex optimization problem. In a fixed situation, the problem can be solved by using an iterative mechanism (7). The lower bound can be obtained through a first-order Taylor expansion, and it is valid at feasible points. The linear expansion is converted into a quadratic form, forming a sequence about Linear functions:

[0088] (11)

[0089] in The basis for inequality (11) to hold is:

[0090] (12)

[0091] In use lower bound After replacing it, it's easy to verify. It is a linear function and The composite, right It is convex. Therefore, given The optimal solution can be obtained by iteratively solving the following convex optimization problem. :

[0092] (13a)

[0093] (13b)

[0094] Then given optimization Solve the problem in a given situation Below and Related optimization problems. Optimize the objective function using methods such as calling auxiliary variables, and write out the related optimization problems.

[0095] In question (8) because At the same time It appears in both the numerator and denominator. To simplify this problem, we will... Represented as the ratio of two convex functions, as shown below:

[0096] (14)

[0097] in , .

[0098] Considering (14) The nonconvexity of the problem allows us to restate problem (8) by calling an auxiliary variable ζ:

[0099] (15a)

[0100] (15b)

[0101] (15c)

[0102] To facilitate the solution, given... Below and The relevant optimization problem proposes and proves Theorem 1, and then the final convex optimization problem is obtained based on Theorem 1.

[0103] Since the optimization problem in (15) is still inconvenient to solve, Theorem 1 is proposed to further improve the optimization problem.

[0104] The specific content of Theorem 1 is as follows: For any positive semi-definite matrix in the numerator of (14) ,function Compared to And ζ>0 are jointly convex.

[0105] Proof: A function is convex if and only if its superordinate graph is a convex set. We can use this to prove that the function... for And the convexity of ζ. For Its upper boundary can be represented as:

[0106] (16)

[0107] Represents the field of real numbers, where Representation function The image above. Considering its associated linear matrix inequalities:

[0108] (17)

[0109] and combined Then, according to the Schur complement of a positive semidefinite matrix, we can obtain:

[0110] (18)

[0111] This can be further expressed as:

[0112] (19)

[0113] Therefore, it can be known The superordinate graph is the intersection of a strict linear matrix inequality and a non-strict linear matrix inequality, both of which are represented as convex sets. As the intersection of two convex sets, The upper part of the image is also a convex set, therefore It is about The joint convex function of ζ and ζ.

[0114] Proof complete.

[0115] when And when ζ>0, according to Theorem 1, exist and Expanding to obtain its lower bound: (20)

[0116] It is for Both ζ and ζ are affine.

[0117] use replace The above problem of minimizing convexity is transformed into a problem of minimizing convexity. Optimization is represented as:

[0118] (21a)

[0119] (21b)

[0120] (21c)

[0121] The optimization obtained above Substituting these values ​​into the optimization problem yields the optimization result. Value, and then optimize the obtained value. Substitute the previous steps to find the optimal solution. The optimization problem is to find a new optimization. Then the new optimization Substitute and solve for the optimal Find new optimizations in the optimization problem Repeat this process until the optimal solution is found. and Thus, the optimal precoder was found.

[0122] Figure 3 The flowchart of an algorithm for optimizing uplink communication throughput in an air-to-ground communication system is shown. This method first constructs a model of the integrated air-to-ground uplink communication system, then calculates the mutual information based on finite character input, next constructs and decomposes the optimization problem, and finally iterates through the decomposed sub-optimization models until a predetermined termination condition is met to obtain the optimal precoder. The throughput is then optimized based on the optimal precoder. The proposed method for optimizing uplink communication throughput in an air-to-ground communication system has the following advantages:

[0123] (1) Based on the power constraint characteristics of actual discrete symbol input, a closed-form optimization problem for maximizing the achievable data rate is constructed.

[0124] (2) The lower bound cutoff rate of mutual information is used to replace mutual information for optimization, which gives a closed expression and reduces the computational complexity caused by expectation operation;

[0125] (3) It solves the problem that the objective function is difficult to optimize due to the high coupling of multiple variables in the non-closed noise sampling expression. It decomposes the original problem into two sub-problems for alternating optimization, which reduces the optimization difficulty and makes the analysis and design of the precoder more convenient.

[0126] To demonstrate the correctness and effectiveness of the above derivation results, the advantages of the proposed throughput optimization algorithm based on joint ground user and UAV precoding are further verified through simulation. Specific simulation parameters are set as follows: number of UAV receiving antennas... Number of ground user transmitting antennas and the number of drone transmitting antennas Both are 2, the algorithm's iteration termination condition is ε = 0.001, and the noise levels at the drone and satellite are equal (i.e., The data rate is represented using the lower bound of mutual information, i.e., the cutoff rate. Simultaneously, a pair of parameters satisfying power limits and where both power levels reach their maximum values ​​is selected. and A control group was optimized using the Maximum Ratio Combining (MRC) method. The control group had the same antenna number N=2 and modulation order M=4 as the given optimization algorithm. Figure 4 The comparison between the control group method and the optimized algorithm of this invention also illustrates the advantages of this optimized algorithm. The optimized precoder not only enables the data rate to reach the maximum value faster, but also consistently outperforms the control group before reaching the maximum value.

[0127] Figure 5 This also proves the effectiveness of the algorithm. Figure 5 The change in achievable data rate was observed by varying the modulation order. The results show that the achievable data rate increases with increasing modulation order. The maximum achievable data rate performances for M = 2, M = 4, and M = 8 are 1 bits / s / Hz, 2 bits / s / Hz, and 3 bits / s / Hz, respectively, which perfectly matches the rate expression. Overall, when M is fixed, the achievable data rate does not continuously increase with the signal-to-noise ratio (SNR), but rather saturates after reaching a certain level.

[0128] When applying the throughput optimization method for uplink communication in the air-to-ground communication system provided in this manual, it is not necessary to consider... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this manual does not impose any restrictions on it.

[0129] The above describes a method for optimizing uplink communication throughput in an air-to-ground communication system, provided by one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding device for optimizing uplink communication throughput in an air-to-ground communication system, including:

[0130] The air-to-ground communication system construction module is used to build air-to-ground communication systems based on ground-based communication equipment, relay station communication equipment, and satellite communication equipment.

[0131] The throughput characterization module is used to modulate the data transmitted by the ground-based communication equipment to the satellite communication equipment into discrete symbol form during the uplink communication process, and obtain mutual information to reflect the transmission efficiency of discrete symbol information. The mutual information is used to characterize the throughput of the uplink communication process of the air-space-ground communication system.

[0132] The throughput optimization module is used to construct an original optimization model with the goal of maximizing throughput and the power limit of the communication equipment as a constraint; the original optimization model is solved to obtain the maximum throughput of uplink communication.

[0133] Specific limitations regarding the throughput optimization device can be found in the limitations of the throughput optimization method described above, and will not be repeated here. Each module in the aforementioned throughput optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0134] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0135] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for optimizing uplink communication throughput in an air-to-ground communication system, characterized in that, include: A space-air-ground communication system is constructed based on ground-based communication equipment, relay station communication equipment, and satellite communication equipment; The data transmitted during the uplink communication process from ground-based communication equipment to satellite communication equipment is modulated into discrete symbol form to obtain mutual information, which reflects the transmission efficiency of discrete symbol information. The mutual information is used to characterize the throughput of the uplink communication process of the air-space-ground communication system. in, This represents the throughput of the uplink communication process. This refers to the number of transmitting antennas of the ground-based communication equipment. M It refers to the point value of the signal constellation; d ij = xi-xj, where xi and xj represent Nt possible modulation symbol vectors in the M-ary constellation diagram; En (·) represents the expectation operation; This is the LoS channel from relay station communication equipment to satellite communication equipment; Pre-encoder for relay station communication equipment; This refers to the number of receiving antennas on the relay station's communication equipment. This indicates the channel from the ground-based communication equipment to the relay station communication equipment. Indicates the number of transmitting antennas in a ground-based communication device; Indicates the transmission signal The precoder; This indicates that the mean is zero and the variance is... Additive white Gaussian noise, ; The mean is zero and the variance is Additive thermal noise, following ; With maximizing throughput as the optimization objective and the power limit of communication equipment as the constraint, an original optimization model is constructed; the original optimization model is solved to obtain the maximum uplink throughput. Solving the original optimization model specifically includes: Use cutoff rate Replace the original optimization model This reduces the computational complexity of the original optimization model. Will The original optimization model, replaced by the cutoff rate, is decomposed into sub-optimization model one and sub-optimization model two; The sub-optimization model is one To optimize the objective, the power limit of ground-based communication equipment is used as a constraint, where, , express Known feasible solutions; The sub-optimization model two is based on To optimize the objective, the power limit of the relay station communication equipment is used as a constraint; whereby... ;in, express Known feasible solutions; Given ,exist Under fixed conditions, solving sub-optimization model one yields the optimization... Value, and then based on optimization The value is used to solve sub-optimization model two, and the optimization is obtained. Value, then optimize The value optimization model is solved to obtain a new optimization. Value, based on the new optimization Substituting the values ​​into sub-optimization model two and solving for the new optimization results in... The value is continuously iterated until the set iteration termination condition is reached, thus obtaining the optimal result. and .

2. The method for optimizing uplink communication throughput in a space-air-ground communication system as described in claim 1, characterized in that, The aforementioned air-to-ground communication system, constructed based on ground-based communication equipment, relay station communication equipment, and satellite communication equipment, includes: The relay station communication equipment receives signals transmitted by the ground-based communication equipment: in, It is the signal received by the relay station communication equipment. The Rice factor in the Rice channel fading model; and These are the LoS and Rayleigh fading components in the Ricean channel fading model, respectively. The relay station communication equipment uses an amplification and forwarding protocol to forward the received signals to the satellite communication equipment. Where ys is the signal received by the satellite communication equipment; The number of transmitting antennas for the relay station communication equipment; It is used for signal integration The pre-encoder of the relay station communication equipment.

3. The method for optimizing uplink communication throughput in a space-air-ground communication system as described in claim 2, characterized in that, The original optimization model, which aims to maximize throughput and is constrained by the power limit of communication equipment, specifically includes: in, and These represent the actual power of the ground-based communication equipment and the relay station communication equipment, respectively, with the superscript H indicating conjugate transpose; and These are the upper limits of available power for ground-based communication equipment and relay station communication equipment, respectively.

4. A throughput optimization device for uplink communication in an air-to-ground communication system, characterized in that, The apparatus is used to implement the method according to any one of claims 1-3, the apparatus comprising: The air-to-ground communication system construction module is used to build air-to-ground communication systems based on ground-based communication equipment, relay station communication equipment, and satellite communication equipment. The throughput characterization module is used to modulate the data transmitted by the ground-based communication equipment to the satellite communication equipment into discrete symbol form during the uplink communication process, and obtain mutual information to reflect the transmission efficiency of discrete symbol information. The mutual information is used to characterize the throughput of the uplink communication process of the air-space-ground communication system. The throughput optimization module is used to construct an original optimization model with the goal of maximizing throughput and the power limit of the communication equipment as a constraint; the original optimization model is solved to obtain the maximum throughput of uplink communication.

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