Satellite communication system optimization method, apparatus and device, and readable storage medium

Abstract

The invention discloses an optimization method and device of a satellite communication system, equipment and a readable storage medium, and relates to the technical field of satellite communication. The satellite communication system comprises a main satellite used for sending a spread spectrum signal to the intelligent reflection surface; the intelligent reflection surface is used for receiving the spread spectrum signal and sending the spread spectrum signal to the user equipment; the ground base station is used for sending interference signals; the user equipment is used for receiving the spread spectrum signal; the optimization method of the satellite communication system comprises the following steps: modeling the satellite communication system to obtain a simulation model of the satellite communication system; based on different use scenes, performing problem expression on the simulation model of the satellite communication system according to the spread spectrum signal and the interference signal to obtain an expression problem of the simulation model; and solving the expression problem by taking maximization of the weighted sum rate as a target to obtain an optimal parameter of the satellite communication system. According to the scheme provided by the invention, the efficiency of satellite communication is improved on the premise that noise exists.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to an optimization method, device, equipment and readable storage medium for a satellite communication system. Background Art

[0002] Satellite Communication System (SatCom) has been widely used in many fields such as radio broadcasting due to its unique advantage of wide-area seamless coverage. However, the inherent long transmission distance of satellite communication leads to serious path loss, and satellite communication has problems such as limited onboard power and low received signal-to-noise ratio (SNR). In addition, the signal shielding effect in scenes such as urban canyons seriously restricts the performance of satellite communication.

[0003] Intelligent Reflecting Surface (IRS) is an emerging technology that has been proposed recently and can effectively help solve the above problems. IRS, also known as Reconfigurable Intelligent Surface (RIS), is a digitally controlled metasurface composed of a large number of almost passive, low-cost and low-energy reflective elements. Each reflective element can introduce a specific phase shift to the incident signal. Through a carefully designed phase shift configuration, IRS can enhance the reception of the signal at the desired receiving end while reducing interference to unintended users, thereby artificially creating favorable propagation conditions without the need for any Radio Frequency (RF) chain.

[0004] However, there is no end to the optimization of satellite communication systems. Due to the large-scale user access, satellite resources have become scarce. Under such bandwidth-limited conditions, the data transmission rate of traditional satellite communication systems is difficult to meet the demand. In actual use, there will be a large number of interference signals, which will reduce the signal-to-noise ratio of the incident signal and affect the quality of signal propagation. Therefore, satellite communication systems still need to be optimized. Summary of the invention

[0005] The purpose of the present invention is to provide an optimization method, device, equipment and readable storage medium for a satellite communication system. When stating the problem, not only the spread spectrum signal sent by the main satellite but also the interference signal sent by the ground base station are considered, and the public data flow scenario and the private data flow scenario are also considered. Therefore, the optimal parameters of the satellite communication system obtained based on the solution of the stated problem can effectively realize the optimization of the satellite communication system, so that the satellite system can increase the signal transmission quality in multiple scenarios and different signal-to-noise ratios.

[0006] In order to achieve the above object, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for optimizing a satellite communication system, the satellite communication system comprising: The primary satellite is used to send spread spectrum signals to the smart reflective surface; a smart reflective surface for receiving spread spectrum signals sent by a primary satellite and transmitting the spread spectrum signals to ground user equipment; A ground base station for sending interference signals; A user device, configured to receive a spread spectrum signal sent by the smart reflective surface; The optimization method of the satellite communication system includes: Model the satellite communication system and obtain a simulation model of the satellite communication system; Based on different usage scenarios, the simulation model of the satellite communication system is formulated according to the spread spectrum signal and the interference signal to obtain the formulation problem of the simulation model; the different usage scenarios include public data flow scenarios and private data flow scenarios; With the goal of maximizing the weighted sum rate, the formulation problem is solved to obtain the optimal parameters of the satellite communication system.

[0007] In some embodiments, the formulation problem is solved with the goal of maximizing the weighted sum rate to obtain optimal parameters of the satellite communication system, including: Transform the problem into an equivalent optimization problem for the satellite communication system. The equivalent optimization problem is solved iteratively to obtain the optimal parameters of the satellite communication system.

[0008] In some embodiments, the expression problem is transformed to obtain an equivalent optimization problem of the satellite communication system, including: Establish an equivalent conversion relationship between maximizing weighted sum rate and weighted minimum mean square error; Using Taylor approximation and based on equivalent transformation relations, the expression problem is transformed into an equivalent optimization problem of satellite communication system.

[0009] In some embodiments, based on different usage scenarios, a simulation model of a satellite communication system is formulated according to spread spectrum signals and interference signals to obtain formulation problems of the simulation model, including: In a public data flow scenario, a first problem of generating a simulation model based on a spread spectrum signal and an interference signal received by a user equipment; In a private data flow scenario, a second problem of generating a simulation model based on a spread spectrum signal and an interference signal received by a user device; the private data flow scenario includes a time division multiple access scheme and a code division multiple access scheme; Integrating the first and second questions, we obtain the expression problem of the simulation model.

[0010] In some embodiments, in a private data flow scenario, based on a spread spectrum signal and an interference signal received by a user equipment, generating a second problem of a simulation model includes: A third problem of generating a simulation model based on a spread spectrum signal and an interference signal received by a single user device under a time division multiple access scheme; A fourth problem of generating a simulation model based on a spread spectrum signal and an interference signal received by at least two user equipments under a code division multiple access scheme; The third and fourth questions are integrated to obtain the second question of the simulation model.

[0011] In some embodiments, the method further comprises: Based on the optimal parameters of the satellite communication system, a simulation experiment is carried out on the satellite communication system.

[0012] In a second aspect, the present invention further provides an optimization device for a satellite communication system, the device comprising: A model building module is used to model the satellite communication system and obtain a simulation model of the satellite communication system; The problem statement module states the problem of the simulation model of the satellite communication system according to the spread spectrum signal and the interference signal based on different usage scenarios to obtain the statement problem of the simulation model; the different usage scenarios include public data flow scenarios and private data flow scenarios; The system optimization module is used to solve the expression problem with the goal of maximizing the weighted sum rate and obtain the optimal parameters of the satellite communication system.

[0013] In a third aspect, the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the satellite communication system optimization method provided in the first aspect is implemented.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the satellite communication system optimization method provided in the first aspect.

[0015] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the satellite communication system optimization method provided in the first aspect.

[0016] The beneficial effects of the present invention are: The optimization method of the satellite communication system in the present invention first models the satellite communication system to obtain a simulation model of the satellite communication system; then based on different usage scenarios, the simulation model of the satellite communication system is formulated according to spread spectrum signals and interference signals to obtain a formulation problem of the simulation model; the different usage scenarios include public data flow scenarios and private data flow scenarios; finally, with the goal of maximizing the weighted sum rate, the formulation problem is solved to obtain the optimal parameters of the satellite communication system. Since not only the spread spectrum signal sent by the main satellite but also the interference signal sent by the ground base station are considered when formulating the problem, and the public data flow scenario and the private data flow scenario are also considered, the optimal parameters of the satellite communication system obtained based on the solution of the formulation problem can effectively realize the optimization of the satellite communication system, so that the satellite system can increase the signal transmission quality in multiple scenarios and under different signal-to-noise ratios.

[0017] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the structure of a satellite communication system according to an embodiment of the present invention; Figure 2 A schematic flow chart of a satellite communication system optimization method according to an embodiment of the present invention; Figure 3 It is a comparison result diagram of WSR before and after optimization under different signal-to-noise ratio conditions shown in one embodiment of the present invention; wherein, part (a) represents a comparison result diagram of weighted sum rate after and before optimization under different signal-to-noise ratio conditions in a public data stream scenario; part (b) represents a comparison result diagram of weighted sum rate after and before optimization under different signal-to-noise ratio conditions in a time division multiple access scheme of a private data stream; part (c) represents a comparison result diagram of weighted sum rate after and before optimization under different signal-to-noise ratio conditions in a code division multiple access scheme of a private data stream; Figure 4 A comparison diagram of the relationship between a weighted sum rate and the number of reflective elements shown in an embodiment of the present invention; wherein, part (a) represents a comparison diagram of the relationship between the weighted sum rate and the number of reflective elements in a public data stream scenario; part (b) represents a comparison diagram of the relationship between the weighted sum rate and the number of reflective elements in a time division multiple access scheme for a private data stream; part (c) represents a comparison diagram of the relationship between the weighted sum rate and the number of reflective elements in a code division multiple access scheme for a private data stream; Figure 5A comparison diagram of the relationship between a weighted sum rate and a spread vector length shown in an embodiment of the present invention; wherein, part (a) represents a comparison diagram of the relationship between the weighted sum rate and the spread vector length in a public data stream scenario; part (b) represents a comparison diagram of the relationship between the weighted sum rate and the spread vector length in a time division multiple access scheme of a private data stream; part (c) represents a comparison diagram of the relationship between the weighted sum rate and the spread vector length in a code division multiple access scheme of a private data stream; Figure 6 A schematic flow chart of another method for optimizing a satellite communication system according to an embodiment of the present invention; Figure 7 A schematic diagram of the structure of an optimization device for a satellite communication system according to an embodiment of the present invention; Figure 8 A schematic diagram of the structure of another satellite communication system optimization device shown in one embodiment of the present invention; Fig. 9 A schematic structural diagram of another satellite communication system optimization device according to an embodiment of the present invention; Fig.10 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application.

[0019] Figure numerals: 1. main satellite; 2. smart reflective surface; 3. user equipment; 4. ground base station. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be noted that references to "one embodiment", "embodiment", "example embodiment", etc. in this specification refer to the embodiment being described which may include specific features, structures or characteristics, but not every embodiment must include these specific features, structures or characteristics. In addition, such expressions do not refer to the same embodiment. Furthermore, when describing specific features, structures or characteristics in conjunction with an embodiment, whether or not there is an explicit description, it is indicated that incorporating such features, structures or characteristics into other embodiments is within the knowledge of those skilled in the art.

[0022] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0023] In some embodiments, Figure 1As shown, a schematic diagram of the structure of a satellite communication system is provided, and the satellite communication system includes: The main satellite 1 is used to send a spread spectrum signal to the smart reflective surface 2; the smart reflective surface 2 is used to receive the spread spectrum signal sent by the main satellite 1 and send the spread spectrum signal to the ground user equipment 3; the ground base station 4 is used to send an interference signal; the user equipment 3 is used to receive the spread spectrum signal sent by the smart reflective surface 2.

[0024] In some embodiments, Figure 2 As shown, a schematic flow chart of a method for optimizing a satellite communication system is provided, the method comprising: S101, modeling the satellite communication system to obtain a simulation model of the satellite communication system.

[0025] Specifically, the following basic assumptions and channel models are established: First, it is assumed that the channel state information (CSI) of all links is completely known and quasi-static; specifically, the user equipment k For example, the direct channel from the main satellite to the intelligent reflecting surface (IRS) is modeled as , and IRS to users k The reflection channel is characterized as ,in N Indicates the total number of reflective units of the IRS, L represents the length of the spread spectrum vector; according to the physical characteristics of IRS, it is further assumed that it satisfies the ideal passive reflection condition; specifically, each reflection unit only introduces a controllable phase shift without causing amplitude attenuation, that is, the reflection coefficient modulus strictly satisfies , the corresponding phase shift matrix can be expressed as a diagonal matrix: ,in , is the imaginary unit, the phase offset In the continuous phase space, ;make s represents the message sent by the main satellite and expected to be received by the designated ground receiving user equipment. It is assumed that the transmitted data stream is an independent and identically distributed (iid) cyclic symmetric complex Gaussian (CSCG) random variable with zero mean and unit variance, that is, ; At the sending end, the length is L The spreading vector The original signal is spread spectrum, and the spread spectrum signal sent by the satellite system is defined as: ; Assume that the maximum transmission power of the satellite system is ,Right now , and then the equivalent constraint condition is ;in, represents the expected value operation, Represents the modulus of a complex number.

[0026] During downlink transmission, the primary satellite transmits a signal It undergoes a multi-stage channel transmission process: first, it passes through the forward channel from the main satellite to the IRS. , and then through the IRS phase shift matrix Perform controllable reflection and finally reach the user through IRS k Reflection channel Arrive at the target receiving end; establish user k Receive signal model: ; Among them, the elements in the noise vector , which means the power spectrum density is Additive White Gaussian Noise (AWGN); the noise vector The power is expressed as ,bandwidth ,in is the original signal bandwidth before spectrum spreading; interference component Modeled as co-channel interference from ground-based base stations.

[0027] S102, based on different usage scenarios, a problem statement is performed on a simulation model of a satellite communication system according to spread spectrum signals and interference signals to obtain a statement problem of the simulation model.

[0028] Among them, different usage scenarios include public data flow scenarios and private data flow scenarios; private data flow scenarios include time division multiple access (TMDA) schemes and code division multiple access (CDMA) schemes.

[0029] Specifically, in an IRS-assisted downlink satellite communication system, given Represents the weight parameter of each user device in the system, and adjusts the weight to achieve flexible resource allocation. c and the reflective elements at the IRS, maximizing the weighted sum rate (WSR) of the system, while being limited by the phase shifts at the satellite transmit and IRS transmit elements. For each user, the weight is a positive number, i.e. .

[0030] Optionally, in a public data stream scenario, a first problem of the simulation model can be generated based on the spread spectrum signal and the interference signal received by the user device; in a private data stream scenario, a second problem of the simulation model can be generated based on the spread spectrum signal and the interference signal received by the user device; the private data stream scenario includes a time division multiple access scheme and a code division multiple access scheme; the first problem and the second problem are integrated to obtain the expression problem of the simulation model.

[0031] Among them, in a private data stream scenario, a second problem of a simulation model is generated based on spread spectrum signals and interference signals received by a user device, including: under a time division multiple access scheme, a third problem of a simulation model is generated based on spread spectrum signals and interference signals received by a single user device; under a code division multiple access scheme, a fourth problem of a simulation model is generated based on spread spectrum signals and interference signals received by at least two user devices; the third problem and the fourth problem are integrated to obtain the second problem of the simulation model.

[0032] Specifically, in the public data flow scenario, in the system model, the broadcast signal sent by the main satellite is first considered, and the public data flow it carries is composed of The main satellite sends Spread spectrum data stream Public data stream , is reflected by the IRS and received by all user devices on the ground. Therefore, from formula (2), we can know that the user device k Received signal , the visual noise and the interference signal from the ground base station are uniformly classified as external interference items, giving the user equipment k The signal to interference plus noise ratio (SINR) of the signal at: ; So the user equipment k The transmission rate that the public signal can reach is expressed as: ; in Is a user k The public data rate is given by the definition of Shannon's formula. The weight parameter of each user in the satellite system is set to , then the WSR maximization problem is expressed as: ; Among them, the speed of user equipment is guaranteed Not less than the set minimum rate threshold .

[0033] In the private data flow scenario, the signal sent by the satellite to the user is a private signal. In the time division multiple access (TDMA) scheme, each time slot is independent of each other, and the main satellite sends the Public data stream after spread spectrum ; Under this mechanism, information crosstalk between user devices is effectively suppressed, so that the multi-user device system can be decoupled into multiple independent single-user device models for analysis; user device k The signal after decoding the private signal Considering the noise and ground base station interference as external interference, the user k SINR of the signal at: ; User Equipment k The achievable transmission rate of the private signal at is expressed as: ; in, Is a user device k Under the TDMA scheme, the performance of a single user device is regarded as the performance of the entire multi-user device satellite communication system, that is, , then the optimization problem is constructed as ; Among them, the user's speed is guaranteed Not less than the set minimum rate threshold ; Under the multiple access scheme of CDMA, multiple user devices achieve concurrent transmission by sharing time domain resources, that is, using non-orthogonal spread spectrum vectors. Dedicated signal for user equipment Perform frequency domain expansion to obtain a spread spectrum signal Under this mechanism, due to the non-ideal cross-correlation characteristics of the spreading code set, mutual interference components are inevitably generated between the signals of multiple user devices. Further expansion to obtain user equipment k Private signal at Considering the interference between user equipment, ground base station interference and noise as external interference, the user equipment k SINR of the decoded signal at: ; So the user equipment k The achievable transmission rate of the private signal at is expressed as: ; Under the CDMA scheme, the weight parameter of each user equipment in the satellite communication system is set to , then the WSR maximization problem of the satellite communication system is constructed as: ; The satellite transmission power is the sum of the transmission powers of different private data streams at the same time, that is, .

[0034] S103, solving the expression problem with the goal of maximizing the weighted sum rate to obtain the optimal parameters of the satellite communication system.

[0035] Optionally, the expression problem may be transformed first to obtain an equivalent optimization problem of the satellite communication system; the equivalent optimization problem may be solved iteratively to obtain optimal parameters of the satellite communication system.

[0036] Among them, the process of transforming the expression problem to obtain the equivalent optimization problem of the satellite communication system can be specifically divided into the following steps: establishing an equivalent conversion relationship between maximizing the weighted sum rate and the weighted minimum mean square error; using Taylor approximation and based on the equivalent conversion relationship, transforming the expression problem into an equivalent optimization problem of the satellite communication system.

[0037] Specifically, for convenience, the user device k For example, the total power of the received signal in the three scenarios of TDMA and CDMA of public data stream and private data stream is expressed as: ; ; ; Among them, external interference and noise are regarded as interference, and the interference power is expressed as: ; ; ; Then the achievable rate expression (4) can be expressed as: ; Expression (7) and Expression (10) are expressed as: .

[0038] At the receiving end, introduce , The equalizers are for public data streams and private data streams respectively. k The received signals are calculated separately to obtain an estimate of the original data stream , , while the equalizer also plays a role in despreading, the mean square error (MSE) of the decoded data stream is: ; ; Introduce the weight matrices associated with public data flows and private data flows respectively , , construct the augmented weighted mean square error (WMSEs): ; ; By solving the equation , , and get the optimal equalizer: ; ; Substituting formula (22) and formula (23) into formula (18) and formula (19) respectively, the minimum mean square error (MMSE) is obtained by simplification: , Then further substitute into formula (20) and formula (21), we have , On this basis, we solve , , the optimal weight of MMSE is: ; ; Then, by substituting formula (24) and formula (25) into formula (20) and formula (21) respectively, the linear equivalent relationship between MMSE and rate can be derived: ; ;

[0039] Based on the above theoretical analysis, an equivalent transformation relationship between the WSR maximization problem and the WMMSE optimization problem is established. In addition, for the nonlinear equation , using Taylor approximation to convert to ,in and They represent the phase of IRS in the current and previous iterations during the iterative optimization process. The equivalent optimization problems of three scenarios (public data stream, TDMA scheme for private data stream, and CDMA scheme for private data stream) are listed below.

[0040] For the public data flow scenario where the optimization problem is located, combined with formula (20) WMSE , and the transformed optimization problem is obtained: ; Similarly, in the private variable scenario, WMSE is obtained from formula (21): First, we discuss the TDMA multiple access scheme in which the optimization problem (8) is located. In TDMA, the performance of a single user reflects the overall performance, that is, , so the optimization problem after transformation is obtained: ; In the CDMA solution, the optimization problem converted from the original optimization problem formula (11) is: ; The optimization problems of the above three scenarios can be efficiently solved through the alternating iterative framework. The algorithm execution process is as follows: In each iteration, the spreading code vector is first fixed. With IRS phase shift matrix , based on the WMMSE criterion to analyze the equalizer gain g With the weight matrix w The closed-form optimal solution of . Then, fix g and w , by solving for the phase shift The convex optimization problem is to obtain Finally, based on the update of the channel state information, the spread spectrum vector is dynamically adjusted through convex optimization. , in order to achieve the joint optimization of interference suppression and spectrum efficiency. The pseudo code of the algorithm is shown in Algorithm 1, where The preset convergence tolerance is the algorithm tolerance, and its global convergence can be proved by the monotone convergence theorem.

[0041]

[0042] The optimization method of the satellite communication system in the above-mentioned embodiment first models the satellite communication system to obtain a simulation model of the satellite communication system; then, based on different usage scenarios, the simulation model of the satellite communication system is formulated according to the spread spectrum signal and the interference signal to obtain the formulation problem of the simulation model; the different usage scenarios include public data flow scenarios and private data flow scenarios; finally, with the goal of maximizing the weighted sum rate, the formulation problem is solved to obtain the optimal parameters of the satellite communication system. Since not only the spread spectrum signal sent by the main satellite but also the interference signal sent by the ground base station are considered when formulating the problem, the public data flow scenario and the private data flow scenario are also considered. Therefore, the optimal parameters of the satellite communication system obtained based on the solution of the formulation problem can effectively realize the optimization of the satellite communication system, so that the satellite system can increase the signal transmission quality in multiple scenarios and under different signal-to-noise ratios.

[0043] In another embodiment, a simulation experiment is also carried out on the optimized satellite communication system. The specific method is: based on the optimal parameters of the satellite communication system, a simulation experiment is carried out on the satellite communication system.

[0044] Specifically, based on the aforementioned Algorithm 1, the phase and spread vector of the IRS reflector are jointly optimized to maximize the WSR. The simulation results are used to systematically evaluate the performance of the proposed IRS-assisted satellite communication system. In this simulation study, 100 Monte Carlo simulations were performed, and the algorithm convergence tolerance was set to , the maximum number of iterations is 1000. The simulation parameters are set as follows: the number of user devices is 3, and it is assumed that the sum of the weights assigned to the user devices is 1, that is, In this experiment, the weight vector is set to The channel is a random channel implemented by a complex standard normal distribution, and the phase of the IRS is randomly initialized. For different scenarios, it is assumed that the lower limit of the rate of all user devices is 0, that is, .

[0045] First, the WSR obtained after optimizing the spread spectrum vector using the WMMSE algorithm is compared with the WSR before optimization. The spread spectrum vector before optimization is replaced by the m-sequence, because the m-sequence has a sharp autocorrelation characteristic and strict balance performance as a clear theoretical benchmark, and the performance improvement of the optimized system in key indicators such as anti-interference and multi-user capacity is intuitively compared. Set the original signal bandwidth , the length of the spreading vector , so the bandwidth after spread spectrum The number of IRS reflective elements is set to 16.

[0046] like Figure 3 As shown, Figure 3 The comparison results of the optimized algorithm proposed in this application and the non-optimized solution in WSR under different signal-to-noise ratio (SNR) conditions are shown. It can be clearly seen from the figure that the WSR after the optimization of the algorithm proposed in this application is significantly higher than the non-optimized WSR, and as the SNR increases, the WSR shows a stable growth trend. Specifically, when the SNR is 0 dB, the improvement ratio of WSR can be analyzed to evaluate the algorithm performance. Figure 3 Part (a) shows that in the public data flow scenario, the optimized WSR reaches 12.1827 bit / s / Hz, which is a 17.66% performance improvement compared to 10.3545 bit / s / Hz before optimization. Figure 3 Part (b) shows that under the TDMA scheme for private data streams, the optimization scheme improves the WSR from 10.3822 bit / s / Hz to 12.9022 bit / s / Hz, with a relative gain of 24.27%. Figure 3Part (c) shows that in the CDMA scheme of private data stream, the interference signal contains interference from other users, which has a significant impact on the optimization process, so the optimized curve increase is more unstable than other transmission schemes. At the same time, from the performance of the unoptimized curve, the system without signal processing optimization shows significant performance degradation, and the value is much lower than the optimized WSR. This further confirms that the optimization effect of the algorithm proposed in this application is significant in the CDMA scenario. These results show that in a complex multi-user environment, the optimization algorithm proposed in this application can effectively improve system performance.

[0047] Next, if Figure 4 As shown, the relationship between the weighted sum rate and the number of reflection units is demonstrated when the SNR is kept fixed at 20dB and the spread spectrum vector length L is set to 7. Figure 4 Part (a) shows the relationship between the weighted sum rate WSR and the number of reflective elements in the common data flow scenario; Figure 4 Part (b) shows the relationship between the weighted sum rate WSR and the number of reflective elements under the TDMA scheme for private data flows; Figure 4 Part (c) shows the relationship between the weighted sum rate WSR and the number of reflective elements under the CDMA scheme for private data streams. It can be seen that with the increase in the number of reflective elements, WSR shows an increasing trend. This is because more reflective elements provide more signal reflection paths, thereby improving the total rate capability of the system under given transmission resources.

[0048] Finally, if Figure 5 As shown in FIG. 1 , the relationship between the weighted sum rate WSR and the spread spectrum vector length L is shown when the number of reflective elements M is set to 16. Figure 5 Part (a) shows the relationship between the weighted sum rate WSR and the spreading vector length L in the common data flow scenario; Figure 5 Part (b) shows the relationship between the weighted sum rate WSR and the spreading vector length L under the TDMA scheme of the private data stream; Figure 5 Part (c) shows the relationship between the weighted sum rate WSR and the spread vector length L under the CDMA scheme of the private data stream; the results show that the weighted sum rate shows a downward trend with the increase of the spread vector length. This is because as the spread vector length increases, the satellite communication system sacrifices more transmission rate in exchange for stronger anti-interference performance. Therefore, in order to find the best balance between transmission rate and anti-interference performance, it is necessary to select an appropriate spread length.

[0049] In summary, the simulation experiments in the above embodiments prove that the optimization method of the satellite communication system obtained in the present invention can effectively optimize the satellite communication system and improve the signal transmission efficiency.

[0050] In order to more comprehensively demonstrate the present solution, this embodiment provides an optional method for optimizing a satellite communication system, such as Figure 6 As shown: S201, modeling the satellite communication system to obtain a simulation model of the satellite communication system.

[0051] S202: In a public data flow scenario, a first problem of a simulation model is generated based on a spread spectrum signal and an interference signal received by a user equipment.

[0052] S203, in a private data flow scenario, under a time division multiple access scheme, based on a spread spectrum signal and an interference signal received by a single user equipment, generate a third problem of the simulation model.

[0053] S204, in a private data flow scenario, under a code division multiple access scheme, based on spread spectrum signals and interference signals received by at least two user equipments, generate a fourth problem of the simulation model.

[0054] S205, integrating the third problem and the fourth problem to obtain the second problem of the simulation model.

[0055] S206, integrating the first problem and the second problem to obtain a representation problem of the simulation model.

[0056] S207, establishing an equivalent conversion relationship between maximizing the weighted sum rate and weighted minimum mean square error.

[0057] S208, using Taylor approximation and based on equivalent conversion relations, the expression problem is transformed into an equivalent optimization problem of the satellite communication system.

[0058] S209, iteratively solving the equivalent optimization problem to obtain the optimal parameters of the satellite communication system.

[0059] S210, solving the expression problem with the goal of maximizing the weighted sum rate to obtain optimal parameters of the satellite communication system.

[0060] S211, conduct simulation experiments on the satellite communication system based on the optimal parameters of the satellite communication system.

[0061] The specific process of S201-S211 above can refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0062] Based on the same inventive concept, the embodiment of the present application also provides a satellite communication system optimization device for implementing the above-mentioned satellite communication system optimization method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more satellite communication system optimization devices provided below can refer to the above-mentioned limitations on the satellite communication system optimization method, and will not be repeated here.

[0063] In one embodiment, Figure 7 As shown, a satellite communication system optimization device is provided, the device comprising: A model building module 30 is used to model the satellite communication system to obtain a simulation model of the satellite communication system; A problem statement module 31 is used to express a problem of a simulation model of a satellite communication system according to spread spectrum signals and interference signals based on different usage scenarios to obtain a statement problem of the simulation model; the different usage scenarios include a public data flow scenario and a private data flow scenario; The system optimization module 32 is used to solve the expression problem with the goal of maximizing the weighted sum rate to obtain the optimal parameters of the satellite communication system.

[0064] In another embodiment, Figure 8 As shown above Figure 7 The system optimization module 32 includes: A problem conversion unit 320 is used to convert the expressed problem into an equivalent optimization problem of the satellite communication system; The iterative solution module 321 is used to iteratively solve the equivalent optimization problem to obtain the optimal parameters of the satellite communication system.

[0065] In another embodiment, the above Figure 8 The problem conversion unit 320 is specifically used to: establish an equivalent conversion relationship between maximizing the weighted sum rate and the weighted minimum mean square error; use Taylor approximation and based on the equivalent conversion relationship, convert the expression problem into an equivalent optimization problem of the satellite communication system.

[0066] In another embodiment, Fig. 9 As shown above Figure 7 The problem formulation module 31 in the embodiment includes: A first expression unit 310 is used to generate a first problem of a simulation model based on a spread spectrum signal and an interference signal received by a user equipment in a common data flow scenario; The second expression unit 311 is used to generate a second problem of the simulation model based on the spread spectrum signal and the interference signal received by the user equipment in a private data flow scenario; the private data flow scenario includes a time division multiple access scheme and a code division multiple access scheme; The problem integration unit 312 is used to integrate the first problem and the second problem to obtain a statement problem of the simulation model.

[0067] In another embodiment, the above Fig. 9 The second expression unit 311 in is specifically used to: generate the third problem of the simulation model based on the spread spectrum signal and interference signal received by a single user device under the time division multiple access scheme; generate the fourth problem of the simulation model based on the spread spectrum signal and interference signal received by at least two user devices under the code division multiple access scheme; integrate the third problem and the fourth problem to obtain the second problem of the simulation model.

[0068] In another embodiment, the above Figure 7 The satellite communication system optimization device is also used for: performing simulation experiments on the satellite communication system based on the optimal parameters of the satellite communication system.

[0069] The present application also provides an electronic device, in some implementations, referring to Fig.10 As shown, the electronic device 700 includes an input unit 710, a memory 720, a processor 730, and an output unit 740. The memory 720 stores program instructions that can be run on the processor 730, and the processor 730 calls the program instructions to execute the optimization method and / or technical solution of the satellite communication system based on the above-mentioned embodiment. The electronic device 700 can be a mobile terminal device such as a mobile phone, a computer, etc.

[0070] In addition, the embodiment of the present application also provides a computer-readable storage medium for storing a computer program for executing the optimization method of the satellite communication system. For example, a computer program instruction, when executed by a computer, can call or provide the method and / or technical solution according to the present application through the operation of the computer. The program instruction for calling the method of the present application may be stored in a fixed or removable storage medium, and / or transmitted through a data stream in a broadcast or other signal-bearing medium and / or stored in a storage medium that runs according to the program instruction.

[0071] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0072] The technical features of the above embodiments may be arbitrarily integrated. To make the description concise, not all possible integrations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the integration of these technical features, they should be considered to be within the scope of this specification.

[0073] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for optimizing a satellite communication system, characterized in that: The satellite communication system comprises: The primary satellite is used to send spread spectrum signals to the smart reflective surface; an intelligent reflective surface, for receiving a spread spectrum signal sent by the primary satellite and sending the spread spectrum signal to a ground user device; A ground base station for sending interference signals; A user equipment, configured to receive a spread spectrum signal sent by the smart reflective surface; The optimization method of the satellite communication system comprises: Modeling the satellite communication system to obtain a simulation model of the satellite communication system; Based on different usage scenarios, a simulation model of the satellite communication system is formulated according to the spread spectrum signal and the interference signal to obtain a formulation problem of the simulation model; the different usage scenarios include a public data flow scenario and a private data flow scenario; With the goal of maximizing the weighted sum rate, the expression problem is solved to obtain the optimal parameters of the satellite communication system.

2. The method for optimizing a satellite communication system according to claim 1, wherein: With the goal of maximizing the weighted sum rate, the expression problem is solved to obtain the optimal parameters of the satellite communication system, including: Transforming the expression problem to obtain an equivalent optimization problem of the satellite communication system; The equivalent optimization problem is solved iteratively to obtain the optimal parameters of the satellite communication system.

3. The satellite communication system optimization method according to claim 2, characterized in that: The expression problem is transformed to obtain an equivalent optimization problem of the satellite communication system, including: Establishing an equivalent conversion relationship between the maximum weighted sum rate and the weighted minimum mean square error; By using Taylor approximation and based on the equivalent conversion relationship, the expression problem is transformed into an equivalent optimization problem of the satellite communication system.

4. The method for optimizing a satellite communication system according to claim 1, wherein: Based on different usage scenarios, a problem statement is performed on the simulation model of the satellite communication system according to the spread spectrum signal and the interference signal to obtain a statement problem of the simulation model, including: In a public data flow scenario, generating a first problem of the simulation model based on a spread spectrum signal and an interference signal received by a user equipment; In a private data flow scenario, a second problem of the simulation model is generated based on a spread spectrum signal and an interference signal received by a user equipment; the private data flow scenario includes a time division multiple access scheme and a code division multiple access scheme; The first problem and the second problem are integrated to obtain the expression problem of the simulation model.

5. The method for optimizing a satellite communication system according to claim 4, wherein: In a private data flow scenario, based on a spread spectrum signal and an interference signal received by a user equipment, generating a second problem of the simulation model includes: Under a time division multiple access scheme, generating a third problem of the simulation model based on a spread spectrum signal and an interference signal received by a single user equipment; Under a code division multiple access scheme, generating a fourth problem of the simulation model based on a spread spectrum signal and an interference signal received by at least two user equipments; The third problem and the fourth problem are integrated to obtain the second problem of the simulation model.

6. The method for optimizing a satellite communication system according to any one of claims 1 to 5, characterized in that: The method further comprises: Based on the optimal parameters of the satellite communication system, a simulation experiment is performed on the satellite communication system.

7. An optimization device for a satellite communication system, characterized in that: The device comprises: A model building module, used for modeling the satellite communication system to obtain a simulation model of the satellite communication system; A problem statement module is configured to express a problem of the simulation model of the satellite communication system according to the spread spectrum signal and the interference signal based on different usage scenarios to obtain a statement problem of the simulation model; the different usage scenarios include a public data flow scenario and a private data flow scenario; The system optimization module is used to solve the expression problem with the goal of maximizing the weighted sum rate to obtain the optimal parameters of the satellite communication system.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the satellite communication system optimization method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for optimizing a satellite communication system according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the optimization method of the satellite communication system according to any one of claims 1 to 6 is implemented.

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