Beamforming and satellite-to-ground communication method, device and storage medium

Abstract

The present invention discloses a beamforming and satellite-to-ground communication method, device and storage medium, which relate to the field of satellite communications and are used to ensure communication rate and communication quality. The present invention includes: constructing a communication network architecture of a satellite network and a user terminal, wherein the satellite adopts a multi-input multi-output antenna; based on the communication network architecture, with the goal of maximizing the achievable communication rate between the satellite and the user terminal, iteratively optimizing the beamforming information of the received signal of the user terminal and the connection status information with the satellite network; and obtaining the final optimized beamforming information. The present invention improves the spectrum efficiency, and improves the communication rate and communication quality in an unreliable channel environment when the satellite network assists ground communication.

Description

Technical Field

[0001] The present invention relates to the field of satellite communications, and in particular to a beamforming method, device and storage medium, as well as a satellite-to-ground communication method, a satellite-to-ground communication device and a storage medium. Background Art

[0002] For cellular mobile communication network coverage blind spots such as remote areas, satellite networks can be used to provide communication services to user terminals in these areas. This method is usually called satellite network-assisted terrestrial communication.

[0003] The communication resources of the satellite network are limited, and the channel environment between the satellite network and the user terminal is relatively complex, which affects the communication performance between the satellite network and the user terminal.

[0004] In the existing technology, the communication power between the satellite network and the user terminal is usually improved by optimizing the signal gain in a perfect channel environment, or the co-channel interference problem is alleviated by spatial isolation. However, these methods fail to fully consider the complex and changeable channel environment between the satellite network and the user terminal, and the degree of matching with the imperfection of the actual channel environment is low, the communication robustness is poor, and it is impossible to effectively guarantee the communication rate and communication quality when the satellite network assists the ground communication. Summary of the invention

[0005] The object of the present invention is to provide a beamforming and satellite-to-ground communication method, device and storage medium to address all or part of the above-mentioned problems, so as to perform robust beamforming on satellite network-assisted ground communications and ensure communication rate and communication quality in an unreliable channel environment.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A beamforming method, comprising:

[0008] Build a communication network architecture between satellite networks and user terminals, where satellites use multiple-input multiple-output antennas;

[0009] Based on the communication network architecture, under the constraints of the maximum transmission power of the satellite and the maximum feeder link capacity, with the goal of maximizing the achievable communication rate between the satellite and the user terminal, iteratively optimizing the beamforming information of the received signal of the user terminal and the connection status information with the satellite network;

[0010] At least the finally optimized beamforming information is obtained.

[0011] The present invention also provides a beamforming device, comprising a processor and a storage medium, wherein the storage medium stores a computer program, and the processor runs the computer program to execute the above-mentioned beamforming method.

[0012] The present invention also provides a satellite-to-ground communication method, which includes: obtaining the final optimized beamforming information through the above-mentioned beamforming method, and also obtaining the final optimized connection status information, and configuring the communication network architecture with the final optimized beamforming information and the connection status information to perform satellite-to-ground communication.

[0013] The present invention also provides a satellite-to-ground communication device, comprising a processor and a storage medium, wherein the storage medium stores a computer program, and the processor runs the computer program to execute the above-mentioned satellite-to-ground communication method to configure a communication network architecture for satellite-to-ground communication.

[0014] The present invention also provides a storage medium, in which a computer program is stored. Running the computer program can execute the above-mentioned beamforming method.

[0015] The present invention also provides a storage medium, in which a computer program is stored. Running the computer program can execute the above-mentioned satellite-to-ground communication method.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0017] In the present invention, the satellite adopts a multi-input multi-output antenna, and the spectrum can be reused between satellites, thereby improving the spectrum efficiency. The present invention constructs and iteratively optimizes the optimization model by building the overall communication network architecture of the communication system, and performs beamforming design for the complex channel environment between the satellite network and the user terminal, thereby ensuring robust beamforming, realizing the association optimization of the satellite network and the user terminal, and improving the communication rate and communication quality between the satellite network and the user terminal in an unreliable channel environment. In addition, the present invention can update and solve the representation form of the designed optimization model, and the iterative convergence speed is fast. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will now be described by way of example with reference to the accompanying drawings, in which:

[0019] Figure 1 It is an architectural diagram of the communication network architecture provided in an embodiment of the present application.

[0020] Figure 2 It is a flow chart of a method for optimizing connection status information provided in an embodiment of the present application.

[0021] Figure 3 This is a flowchart of updating connection status information in each round of iteration provided by an embodiment of the present application.

[0022] Figure 4 This is a flow chart of a method for optimizing connection status information provided by another embodiment of the present application.

[0023] Figure 5 It is a flow chart of a method for optimizing beamforming information provided in an embodiment of the present application.

[0024] Figure 6 This is a flowchart of updating beamforming information in each round of iteration provided by an embodiment of the present application.

[0025] Figure 7 It is a flow chart of a method for optimizing beamforming information provided by another embodiment of the present application.

[0026] Figure 8 It is an overall iterative flow chart of the optimization model provided in the embodiment of the present application.

[0027] Fig. 9 It is a relationship diagram between the achievable communication rate and the maximum transmission power of the satellite under different schemes in the embodiments of the present application.

[0028] Fig.10 It is a relationship diagram between the achievable communication rate and the arrival angle jitter under different schemes in the embodiments of the present application.

[0029] Fig.11 This is a relationship diagram between the achievable communication rate and the number of satellite antennas under different schemes in the embodiments of the present application. DETAILED DESCRIPTION

[0030] All features disclosed in this specification, or steps in all methods or processes disclosed, except mutually exclusive features and / or steps, can be combined in any manner.

[0031] Any feature disclosed in this specification (including any additional claims and abstract), unless otherwise stated, may be replaced by other equivalent or alternative features having similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0032] In response to the problems of poor robustness, poor communication rate and poor communication quality in the design of satellite network-assisted ground communication methods in the prior art, the embodiments of the present application propose a beamforming and satellite-to-ground (i.e., satellite network and ground user terminal) communication method, device and storage medium, aiming to ensure robust beamforming and guarantee communication rate and communication quality.

[0033] In some embodiments, the beamforming method proposed in this application includes the following process:

[0034] S1. Build a communication network architecture between the satellite network and the user terminal, where the satellite uses a multiple-input multiple-output antenna.

[0035] As one of the important means to increase the capacity of communication systems and improve anti-interference capabilities, multiple-input multiple-output antennas play a vital role in dense wireless communication scenarios. In the scenario of satellite network-assisted ground communication, each satellite in the satellite network is designed to use multiple-input multiple-output antennas, and spectrum reuse is performed by performing large-scale beamforming on the same group of satellites to improve spectrum efficiency.

[0036] As a possible implementation, use a collection represents a set of K user terminals, any user terminal is represented by i, these K user terminals cannot access the ground cellular mobile communication network, and are represented by N s A satellite network consisting of N satellites provides communication services. s A collection of satellites denoted by s, where any satellite is denoted by s. Since the satellite uses a multiple-input multiple-output antenna, there are multiple propagation paths between the satellite s and the user terminal i. Figure 1 Shown is an example of a constructed communication network architecture diagram.

[0037] S2. Based on the communication network architecture, under the constraints of the satellite's maximum transmission power and the maximum feeder link capacity, with the goal of maximizing the achievable communication rate between the satellite and the user terminal, iteratively optimize the beamforming information of the user terminal's received signal and the connection status information with the satellite network.

[0038] There is a complex channel environment between the satellite network and the user terminal, and the channel state information is unreliable. In some embodiments, the achievable communication rate is calculated by constructing an optimization model based on the downlink channel information between the satellite and the user terminal, the beamforming information and the connection state information.

[0039] As a feasible approach, in this embodiment, it is necessary to construct a downlink channel model between the satellite and the user terminal, a signal receiving model of the user terminal with respect to the satellite, and a achievable communication rate model between the satellite and the user terminal, respectively. On this basis, combined with corresponding constraints, the optimization model is constructed.

[0040] 1) The downlink channel model is constructed as:

[0041]

[0042] Where t represents the time of the complex baseband downlink spatial channel response between the satellite and the user terminal, f represents the frequency of the complex baseband downlink spatial channel response between the satellite and the user terminal, and S s,i represents the Doppler shift caused by satellite s, in represents the Doppler shift between satellite s and user terminal i, L s,irepresents the total number of propagation paths between satellite s and user terminal i, represents the Doppler frequency shift caused by user terminal i, represents the array steering vector associated with the lth propagation path between satellite s and user terminal i, and are the angles of the lth propagation path between satellite s and user terminal i about the z-axis and x-axis, respectively. is the channel gain of the lth propagation path between satellite s and user terminal i, expressed as:

[0043]

[0044] in, and They represent the antenna array element pattern and path loss of the lth propagation path between satellite s and user terminal i, respectively. For the communication from satellite s to user terminal i, the line-of-sight transmission component in the path loss can be expressed as:

[0045]

[0046] In the formula, (x s ,y s ,z s ) and (x s.i ,y s.i ,z s.i ) represent the coordinates of satellite s and user terminal i respectively. In addition, β is a constant, and the amplitude of the multipath component in the path loss is It is often 5 to 10 dB lower than the line-of-sight transmission component.

[0047] The array element pattern of the propagation path is expressed in dB as:

[0048]

[0049] Then the array element pattern of the antenna can be expressed as:

[0050]

[0051] In the formula, g max represents the maximum gain of the antenna, and g v (θ s,i ) are the relative patterns in the horizontal and vertical planes and SLL is the level of the side lobes in the antenna pattern. and g v (θ s,i ) can be expressed as:

[0052]

[0053] and

[0054]

[0055] in, and θ 3dB 3dB beamwidth for horizontal and vertical modes respectively.

[0056] The arrival angle of user terminal i belongs to a certain range, which reflects the uncertainty of the channel, which can be expressed as:

[0057]

[0058] In the formula, and They represent the lower and upper bounds of the vertical arrival angle, respectively. and They represent the lower and upper bounds of the horizontal arrival angle, respectively. This information is used as the line-of-sight transmission component to constrain the downlink channel model, and can be obtained at the satellite.

[0059] 2) Construction of receiving signal model.

[0060] As a possible approach, use the associated parameter a i,s Indicates the connection status information between satellite s and user terminal i. A connection is represented by a i,s =1, otherwise a i,s = 0. So we have Each user terminal can only connect to one satellite. Indicates the maximum number of user terminals that can be connected to satellite s.

[0061] The received signal model is constructed as:

[0062]

[0063] In the formula, represents the beamforming vector of the received signal of user terminal i associated with satellite s, x s,i is the transmission signal between satellite s and user terminal i, satisfying and ((s,i)≠(m,n)), n s,i is zero mean and has a variance of Additive white Gaussian noise.

[0064] 3) The achievable communication rate model is constructed as:

[0065]

[0066] 4) Build an optimization model.

[0067] After constructing the downlink channel model, the received signal model and the achievable communication rate model, we can start to construct the optimization model.

[0068] The optimization of beamforming information and connection status information is based on the satellite's maximum transmission power and maximum feeder link capacity as constraints.

[0069] The objective function of the optimization model is the achievable communication rate. Taking the above embodiment as an example, the objective function of the constructed optimization model is:

[0070]

[0071] The constraints are:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] in, represents the maximum feeder capacity of satellite s, P max,s is the maximum transmission energy of satellite s.

[0078] Based on the constructed optimization model, the beamforming information and the connection status information may be iteratively optimized.

[0079] In some embodiments, when constructing the optimization model, the angle of arrival at the user terminal is obtained from the line-of-sight transmission component in a discretized manner, that is, the angle of arrival in the horizontal and vertical directions is discretized and sampled within the corresponding value range.

[0080] Taking uniform discretization as an example, the angles selected in the vertical and horizontal directions are:

[0081]

[0082]

[0083] in, and They are The angle information at the viewing distance, that is, the vertical angle and the horizontal angle, M1≥M x and M2 ≥ M y Represents θ s,i and The number of samples, M x and M y Represent the number of antennas on the x-axis and y-axis respectively, p represents the vertical arrival angle sampling order, q represents the horizontal arrival angle sampling order, and Respectively represent the sampling intervals in the vertical direction and the horizontal direction. In some specific implementations, it can be designed as follows:

[0084] Through the above discretization of the arrival angle, h s,i can be approximated as The uncertainty of the channel is constructed as a convex hull consisting of a convex combination of finite discrete points, expressed as:

[0085]

[0086] in, yes The weight of , in some specific implementations, its value can be set to 1 / M1M2 that meets the robustness condition.

[0087] The arrival angle sampling is performed in a discretized manner, so that the uncertainty of the channel is constructed as a convex hull composed of convex combinations of finite discrete points. In this case, the optimization model can be updated, ignoring the An optimization model that is independent of the angle of arrival is obtained.

[0088] Based on the update of the optimization model, in some embodiments, each iteration of the optimization process is performed by alternately optimizing the beamforming information and the connection status information.

[0089] In some embodiments, Figure 2 As shown, the method for optimizing connection state information includes:

[0090] Given beamforming information;

[0091] Based on the initialized connection state information, the connection state information is iteratively updated with the goal of maximizing the achievable communication rate.

[0092] That is, the iterative solution of the optimization model contains an iterative update process of the connection state information. Based on the updated optimization model, given the beamforming information {w s,i}, the optimization model can be further updated as:

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] In the formula, I s represents the number of user terminals served by satellite s, and the h involved s,i Approximated as

[0099] For iterative update of connection status information, in some embodiments, such as Figure 3 The method for each round of iterative updating includes:

[0100] According to the current beamforming information and connection status information, the user terminal updates the receiving weight coefficient of the satellite, the mean square error of the received signal and the optimal solution of the positive auxiliary variable;

[0101] Updating the optimization model using the receiving weight coefficient, the positive auxiliary variable and the weighted received signal mean square error;

[0102] The optimization model is solved to update the current connection status information.

[0103] In some feasible ways, the received signal of user terminal i to satellite s is estimated by using a linear beamforming strategy:

[0104]

[0105] In the formula, v s,i is the receiving weight coefficient between user terminal i and satellite s, y s,i That is the received signal represented in the previous embodiment.

[0106] The mean square error of the received signal is expressed as:

[0107]

[0108] The optimal solution of the receiving weight coefficient is expressed as:

[0109]

[0110] The optimal solution of the mean square error of the received signal is expressed as:

[0111]

[0112] The optimal solution of positive auxiliary variables is expressed as:

[0113]

[0114] According to the current beamforming information and connection status information, the receiving weight coefficient, the received signal mean square error and the optimal solution of the positive auxiliary variable can be updated. Then according to the equivalent expression of the achievable communication rate:

[0115] R s,i =log(u s,i )-u s,i e s,i +1;

[0116] By bringing in the receiving weight coefficient, the positive auxiliary variable and the received signal mean square error weighted by the received weight coefficient, we can get the updated estimate of the objective function:

[0117]

[0118] It can be found that is about {a i,s Based on this feature, in some embodiments, the optimization model can be further updated as follows:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] Therefore, the optimization model is about {a i,s} is convex, in some embodiments, such as Figure 4 As shown, the interior point method can be used to solve the optimization model and obtain updated connection status information.

[0126] like Figure 4 As shown, as a feasible way, the method for optimizing the connection state information includes:

[0127] 1) Given feasible beamforming information and connection status information Initialize j = 0 and the maximum number of iterations N max , perform channel discretization (i.e., perform arrival angle sampling in a discretized manner).

[0128] 2) Given the beamforming information Based on the feasible connection status information at this iteration Update the optimal solution of the receiving weight coefficient of the user terminal receiving the satellite Optimal solution for mean square error of received signal and the optimal solution of positive auxiliary variables

[0129] 3) Given the receiving weight coefficient v s,i , positive auxiliary variable u s,i , update the objective function according to the weighted received signal mean square error

[0130] 4) Solve the connection status information through the interior point method

[0131] 5) Let j = j + 1

[0132] 6) Repeat the above steps 2)-5) until the objective function converges or j = N max

[0133] For iterative updating of connection state information, in some other embodiments, the method for each round of iterative updating includes:

[0134] Characterize the optimal solution of the receiving weight coefficient, the mean square error of the received signal and the positive auxiliary variable of the user terminal to the satellite; update the characterization form of the optimization model using the characterized receiving weight coefficient, the positive auxiliary variable and the mean square error of the received signal;

[0135] Update the optimization model according to the current beamforming information and connection status information;

[0136] Solve the optimized model to update the current connection status information.

[0137] The main difference between the latter embodiment and the former embodiment of updating the connection status information is that the former embodiment first updates the optimal solution of the user terminal's reception weight coefficient for the satellite, the mean square error of the received signal, and the positive auxiliary variable based on the given beamforming information and the connection status information, and then updates the optimization model (the objective function). In the latter embodiment, the expression of the objective function is first updated using the expression of the optimal solution of the user terminal's reception weight coefficient for the satellite, the mean square error of the received signal, and the positive auxiliary variable, and then the optimization model is directly updated under the given beamforming information and the connection status information, and then the optimization model is solved to update the current connection status information.

[0138] In some embodiments, Figure 5 As shown, the method for optimizing beamforming information includes:

[0139] Given connection status information;

[0140] Based on the initialized beamforming information, the beamforming information is iteratively updated with the goal of maximizing the achievable communication rate.

[0141] That is, the iterative solution of the optimization model contains an iterative update process of the beamforming information. Based on the updated optimization model, given the connection state information {a i,s}, the optimization model can be updated as:

[0142]

[0143]

[0144]

[0145] in, is the set of satellites associated with the user terminal, is the set of user terminals associated with satellite s. express The upper bound of .

[0146] In some embodiments, Figure 6 As shown, the method for iteratively updating the beamforming information in each round includes:

[0147] According to the current beamforming information and the connection status information, updating the receiving weight coefficient of the user terminal to the satellite, the mean square error of the received signal and the optimal solution of the positive auxiliary variable;

[0148] Updating the optimization model using the receiving weight coefficient, the positive auxiliary variable and the weighted received signal mean square error;

[0149] Calculate the upper bound of the achievable communication rate based on the updated optimization model;

[0150] The optimization model is solved based on the upper bound of the achievable communication rate to update the current beamforming information.

[0151] The objective function can be expressed as follows, using the calculation method of the receiving weight coefficient, the received signal mean square error and the optimal solution of the positive auxiliary variable, and the equivalent expression of the reachable communication speed record described in the previous embodiment:

[0152]

[0153] In some embodiments, an upper bound on the achievable communication rate is calculated by performing a first-order Taylor expansion of the updated objective function:

[0154] right Perform a first-order Taylor expansion to approximate. Then, define The expression of its upper bound is:

[0155]

[0156] Yes m,n The affine of The expression is:

[0157]

[0158] Therefore, the optimization model is solved with respect to the beamforming vector {w s,i} convex problem, can be solved iteratively using the interior point method.

[0159] like Figure 7 FIG. 1 is a flow chart of a method for optimizing beamforming information in a possible implementation manner. Figure 7 As shown, the process of optimizing beamforming information includes:

[0160] 1) Given feasible beamforming information and connection status information Initialize j = 0 and the maximum number of iterations N max , perform channel discretization.

[0161] 2) Given the connection status information Based on this, the feasible beamforming information for this iteration is given Update the optimal solution of the receiving weight coefficient of the user terminal receiving the satellite Optimal solution for mean square error of received signal and the optimal solution of positive auxiliary variables

[0162] 3) Given the receiving weight coefficient v s,i and positive auxiliary variable u s,i , update the objective function according to the weighted received signal mean square error

[0163] 4) Convex function problem obtained by solving Solve the optimization model to get the achievable communication rate

[0164] 5) Obtain the beamforming information for the next iteration through the interior point method

[0165] 6) Let j=j+1.

[0166] 7) Repeat the above steps 2)-6) until the objective function converges or j = N max

[0167] In addition, for each round of iterative update of the beamforming information, in some other embodiments, the following steps are included:

[0168] Characterize the optimal solution of the receiving weight coefficient, the mean square error of the received signal and the positive auxiliary variable of the user terminal to the satellite; update the characterization form of the optimization model using the characterized receiving weight coefficient, the positive auxiliary variable and the mean square error of the received signal;

[0169] Update the optimization model according to the current beamforming information and connection status information;

[0170] Calculate the upper bound of the achievable communication rate based on the updated optimization model;

[0171] The optimization model is solved based on the upper bound of the achievable communication rate to update the current beamforming information.

[0172] Compared with the previous embodiment of updating beamforming information, the biggest difference of the latter embodiment is that the former embodiment first updates the optimal solution of the user terminal's reception weight coefficient for the satellite, the mean square error of the received signal, and the positive auxiliary variable based on the given beamforming information and connection status information, and then updates the optimization model (objective function). In the latter embodiment, the expression of the objective function is first updated using the expression of the optimal solution of the user terminal's reception weight coefficient for the satellite, the mean square error of the received signal, and the positive auxiliary variable, and then directly updates the optimization model under the given beamforming information and connection status information.

[0173] In the overall iteration process of the optimization model, the beamforming information and the connection status information are optimized alternately. As a preferred embodiment, in each round of iterative optimization of the beamforming information and the connection status information:

[0174] Update the connection status information of the current round according to the beamforming information and connection status information of the current round;

[0175] The beamforming information of the current round is updated according to the beamforming information of the current round and the updated connection status information.

[0176] As a feasible implementation method, Figure 8 As shown in the figure, the overall iteration process of the optimization model includes:

[0177] 1) Given feasible beamforming information and connection relationship information Initialization tolerance ε>0, n=0 and maximum number of iterations N max , perform channel discretization.

[0178] 2) Given and Iterative update through the previous embodiment

[0179] 3) Given and Iterative update through the previous embodiment

[0180] 4) Let n=n+1.

[0181] 5) Repeat the above steps 2)-4) until the objective function converges or j = N max .

[0182] S3. At least obtain final optimized beamforming information.

[0183] The satellite network mentioned in the embodiments of the present application may be a LEO (Low Earth Orbit) satellite network or a MEO (Medium Earth Orbit) satellite network. In addition, the satellite network mentioned in the present application may be replaced by other non-terrestrial communication (NTN, Non Terrestrial Network) base stations, such as UAV (Unmanned Aerial Vehicle, Unmanned Aerial Vehicle). For the configuration of the communication network architecture, centralized control may be performed through a satellite gateway or a GEO (Geosynchronous Earth Orbit, Synchronous Earth Orbit) satellite.

[0184] After the iterative optimization in step S2, the final optimized beamforming information is obtained to perform beamforming of the antenna. Alternatively, the final optimized connection status information can also be obtained to configure the communication network architecture between the satellite network and the user terminal.

[0185] Based on the same purpose, an embodiment of the present application also proposes a beamforming device, including a processor and a storage medium, wherein the storage medium stores a computer program, and the processor runs the computer program to execute the beamforming method of the above embodiment.

[0186] The embodiment of the present application further proposes a storage medium, in which a computer program is stored. Running the computer program can execute the beamforming method in the above embodiment.

[0187] The embodiment of the present application also provides a satellite-to-ground communication method, which obtains the final optimized beamforming information through the beamforming method of the above embodiment, and when obtaining the final optimized beamforming information, also obtains the final optimized connection status information. The communication network architecture is configured with the final optimized beamforming information and connection status information to perform satellite-to-ground communication.

[0188] In order to verify the performance of the satellite-to-ground communication method proposed in this application, the following simulation test was carried out in the embodiment of this application:

[0189] In the simulation test of the embodiment of the present application, Ns =4, the satellite uses the 30 GHz frequency band to communicate with the user terminal, and the uncertainty of the arrival angle jitter is equal in the horizontal and vertical directions: For the sake of comparison, the scheme of the method of the present application in an imperfect channel environment is recorded as Proposed-RBF. To demonstrate the superiority and effectiveness of Proposed-RBF, the following comparison schemes are used in the embodiments of the present application: 1) The scheme of the method of the present application under perfect channel state information is recorded as Proposed-PCSI; 2) The scheme of evenly allocating satellite spectrum and performing frequency reuse within the coverage range of each satellite is recorded as ESSD scheme. 3) The scheme of frequency division multiplexing, which can orthogonally divide the available spectrum to each user terminal, is recorded as FDMA scheme.

[0190] Fig. 9 The achievable communication rate and the maximum satellite transmission power P are shown. max Such as Fig. 9 As shown in Figure 1, the achievable communication rate increases with the increase of satellite transmission power, and a higher transmission power will bring better transmission quality. Fig. 9 It can be seen that Proposed-PCSI has the highest achievable communication rate; since the arrival angle jitter reduces the channel gain, the achievable communication rate of Proposed-RBF is slightly lower than that of Proposed-PCSI; the ESSD scheme only uses frequency reuse within a single satellite, so the spectrum utilization rate is lower than that of Proposed-RBF, so the ESSD scheme has a lower achievable communication rate than Proposed-RBF; since the spectrum is evenly divided to user terminals, FDMA has the lowest achievable communication rate. In summary, the satellite-to-ground communication method proposed in this application can improve spectrum efficiency and communication rate.

[0191] Fig.10 The relationship between the achievable communication rate and the angle-of-arrival jitter is shown. Fig.10 Among them, Proposed-PCSI has the highest achievable communication rate because the arrival angle jitter in Proposed-PCSI is certain. On the other hand, the other three transmission schemes and the achievable communication rates all decrease with the increase of arrival angle jitter because the system performance in these three schemes is affected by channel uncertainty. However, although all are affected by channel uncertainty, the Proposed-RBF scheme is still better than the ESSD scheme and the FDMA scheme. The achievable communication rate of the FDMA scheme is the lowest because the satellite uses a single antenna in FDMA and the comprehensive variation of discrete channels is smaller than that of multiple antennas.

[0192] Fig.11 The relationship between the achievable communication rate and the number of antennas is shown, assuming that Δ = 2°. Fig.11It can be seen that Proposed-PCSI, Proposed-RBD and ESSD all increase with the number of antennas. Therefore, increasing the number of satellite antennas can effectively improve the channel quality and increase the communication rate. In addition, Proposed-PCSI has the highest achievable communication rate. Due to the uncertainty of the channel, the achievable communication rate of Proposed-RBF is slightly lower than that of Proposed-PCS. Because there is no frequency division multiplexing between satellites, the achievable communication rate of ESSD is the lowest.

[0193] It can be seen from the above simulation experiments that the satellite-to-ground communication method proposed in this application can improve spectrum efficiency and communication rate, is less affected by channel environment uncertainty, and has strong beamforming robustness.

[0194] Based on the same purpose, in some embodiments, the present application also proposes a satellite-to-ground communication device, which includes a processor and a storage medium, wherein a computer program is stored in the storage medium, and the processor runs the computer program to execute the satellite-to-ground communication method in the above embodiment to configure the communication network architecture for satellite-to-ground communication. That is, the above satellite-to-ground communication method is executed to configure the communication network architecture for satellite-to-ground communication with the final optimized beamforming information and connection status information.

[0195] In addition, in some embodiments, the present application also proposes a storage medium, in which a computer program is stored. Running the computer program can execute the satellite-to-ground communication method in the above embodiments.

[0196] The present invention is not limited to the above-mentioned specific embodiments, but extends to any new features or any new combination disclosed in this specification, as well as any new method or process steps or any new combination disclosed.

Claims

1. A beamforming method, characterized in that: include: Build a communication network architecture between satellite networks and user terminals, where satellites use multiple-input multiple-output antennas; Based on the communication network architecture, under the constraints of the maximum transmission power of the satellite and the maximum feeder link capacity, with the goal of maximizing the achievable communication rate between the satellite and the user terminal, iteratively optimizing the beamforming information of the received signal of the user terminal and the connection status information with the satellite network; At least the finally optimized beamforming information is obtained.

2. The beamforming method according to claim 1, characterized in that: The achievable communication rate is calculated by constructing an optimization model based on the downlink channel information between the satellite and the user terminal, the beamforming information and the connection status information.

3. The beamforming method according to claim 2, wherein: The downlink channel information is constrained by the line-of-sight transmission component of the user terminal at the satellite.

4. The beamforming method according to claim 3, characterized in that: When constructing the optimization model, the angle of arrival at the user terminal is obtained from the line-of-sight transmission component in a discretized manner.

5. The beamforming method according to claim 4, characterized in that: Each round of iteration alternately optimizes the beamforming information and the connection status information.

6. The beamforming method according to claim 5, characterized in that: The method for optimizing the connection status information includes: Given the beamforming information; Based on the initialized connection state information, the connection state information is iteratively updated with the goal of maximizing the achievable communication rate.

7. The beamforming method according to claim 6, characterized in that: The method for iteratively updating the connection status information in each round includes: According to the current beamforming information and the connection status information, updating the receiving weight coefficient of the user terminal to the satellite, the mean square error of the received signal and the optimal solution of the positive auxiliary variable; Updating the optimization model using the receiving weight coefficient, the positive auxiliary variable and the weighted received signal mean square error; The optimization model is solved to update the current connection status information.

8. The beamforming method according to claim 6, wherein: The method for iteratively updating the connection status information in each round includes: Characterizing the optimal solution of the reception weight coefficient, the mean square error of the received signal and the positive auxiliary variable of the user terminal to the satellite; and updating the representation form of the optimization model using the characterized reception weight coefficient, the positive auxiliary variable and the mean square error of the received signal; updating the optimization model according to the current beamforming information and the connection status information; The optimization model is solved to update the current connection status information.

9. The beamforming method according to claim 5, characterized in that: The method for optimizing the beamforming information includes: Given the connection status information; Based on the initialized beamforming information, the beamforming information is iteratively updated with the goal of maximizing the achievable communication rate.

10. The beamforming method according to claim 9, characterized in that: The method for iteratively updating the beamforming information in each round includes: According to the current beamforming information and the connection status information, updating the receiving weight coefficient of the user terminal to the satellite, the mean square error of the received signal and the optimal solution of the positive auxiliary variable; Updating the optimization model using the receiving weight coefficient, the positive auxiliary variable and the weighted received signal mean square error; Calculating an upper bound of the achievable communication rate based on the updated optimization model; The optimization model is solved based on the upper bound of the achievable communication rate to update the current beamforming information.

11. The beamforming method according to claim 9, characterized in that: The method for iteratively updating the beamforming information in each round includes: Characterizing the optimal solution of the reception weight coefficient, the mean square error of the received signal and the positive auxiliary variable of the user terminal to the satellite; and updating the representation form of the optimization model using the characterized reception weight coefficient, the positive auxiliary variable and the mean square error of the received signal; updating the optimization model according to the current beamforming information and the connection status information; Calculating an upper bound of the achievable communication rate based on the updated optimization model; The optimization model is solved based on the upper bound of the achievable communication rate to update the current beamforming information.

12. The beamforming method according to claim 10 or 11, characterized in that: The upper bound of the achievable communication rate is calculated by performing a first-order Taylor expansion on the updated optimization model.

13. The beamforming method according to any one of claims 5 to 11, characterized in that: Alternatingly optimizing the beamforming information and the connection status information in each round of iteration includes: updating the connection status information of the current round according to the beamforming information and the connection status information of the current round; The beamforming information of the current round is updated according to the beamforming information of the current round and the updated connection status information.

14. A beamforming device, comprising a processor and a storage medium, characterized in that: The storage medium stores a computer program, and the processor runs the computer program to execute the beamforming method according to any one of claims 1 to 13.

15. A satellite-to-ground communication method, characterized in that: include: The finally optimized beamforming information is obtained by the beamforming method as described in any one of claims 1 to 13, and the finally optimized connection status information is also obtained, and the communication network architecture is configured with the finally optimized beamforming information and the connection status information to perform satellite-to-ground communication.

16. A satellite-to-ground communication device, comprising a processor and a storage medium, characterized in that: The storage medium stores a computer program, and the processor runs the computer program to execute the satellite-to-ground communication method according to claim 15 to configure a communication network architecture for satellite-to-ground communication.

17. A storage medium, characterized in that: The storage medium stores a computer program, and running the computer program can execute the beamforming method according to any one of claims 1 to 13.

18. A storage medium, characterized in that: The storage medium stores a computer program, and running the computer program can execute the satellite-to-earth communication method as claimed in claim 15.

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