Method and device for selecting Doppler frequency deviation estimation strategy for low-orbit satellite agile beam

Abstract

The present application provides a method and device for selecting a Doppler frequency offset estimation strategy for a low-orbit satellite agile beam, the method comprising: constructing a multi-scenario geometry and channel constraint relationship for a phased array agile beam, setting weights for different Doppler frequency offset estimation algorithm combinations, establishing a dynamic game model based on scenario constraints and multi-performance combinations, and solving Doppler frequency offset estimation strategy combinations based on recursive neural networks. The present application can realize multi-scenario dynamic Doppler frequency offset estimation under a low-orbit satellite phased array agile beam, and can effectively improve the accuracy, effectiveness, reliability and wide applicability of Doppler frequency offset estimation, thereby effectively improving the reliability of the link connection between the low-orbit satellite and the user.

Description

Technical Field

[0001] The present application relates to the technical field of Doppler frequency offset estimation, and in particular to a method and device for selecting a strategy for estimating Doppler frequency offset of a low-orbit satellite agile beam. Background Art

[0002] Low-orbit satellite Internet with large bandwidth, Ku / Ka band, phased array, and integration with 5G air interface system has become a research hotspot and important support for realizing global wide-area mobile broadband communications. The satellites of low-orbit satellite Internet move at high speed relative to the ground, and the wireless signal transmission will be affected by severe and variable Doppler frequency deviation, resulting in a sharp decrease in the reliability of the link connection between the satellite and the user. At the same time, the OFDM signal system used in 5G NR can provide high spectrum efficiency transmission for satellite Internet, but because the orthogonality of each subcarrier is easily affected by the Doppler frequency deviation of the high-speed movement of the satellite, it is difficult for the receiving end to demodulate the correct symbol or there is serious inter-symbol interference. Therefore, how to overcome the dynamic Doppler frequency deviation estimation of the satellite is an important adjustment for the high-speed transmission of low-orbit satellite Internet.

[0003] To this end, domestic and foreign research institutions and application departments have carried out a lot of research and exploration on the precise estimation and compensation of satellite Doppler frequency deviation. The main research ideas include Doppler frequency deviation estimation and compensation based on satellite ephemeris conversion and terminal Doppler estimation and compensation based on sequence correlation peak matching. Among them, the Doppler frequency deviation estimation and compensation of satellite ephemeris conversion mainly uses the satellite ephemeris that describes the satellite motion state to infer the satellite position and movement speed, and obtains the Doppler frequency deviation value according to the Doppler effect formula. When the carrier is synchronized, it is directly compensated to the carrier synchronization processing of the relevant reception. Based on this method, the terminal can predict and calculate the Doppler frequency deviation value under the ephemeris at the next moment in advance. The calculation process is simple and has strong practicality. Therefore, it has become an important support for the estimation of Doppler frequency deviation of low-orbit satellite Internet. The main problem of this method is that the deviation between the ephemeris information received by the terminal and the actual position of the satellite will increase geometrically with the increase of time. Generally, it is necessary to increase the frequency of sending ephemeris or rely on a stronger prediction algorithm, which brings severe challenges to the project. Terminal Doppler estimation and compensation based on sequence correlation peak matching is mainly based on sequences with strong autocorrelation characteristics, such as PSS (synchronization sequence), CP (pilot), DMRS (demodulation reference signal), etc. After the receiving end receives the signal containing the sequence, it performs correlation processing with the sequence stored locally at the receiving end in the form of time domain or frequency domain. After obtaining the correlation peak, the frequency deviation estimate can be obtained by calculating the angle value. This method does not require other auxiliary information, and the frequency deviation estimate can be solved by the terminal. At the same time, by configuring the sequence of the transmitting end, spectrum compensation with different accuracy and estimation range can be achieved. The main problem of this method is that it depends on the reception of the correlation sequence. When there is a large Doppler frequency deviation, it can only be estimated by blind search, which has a large delay and low efficiency.

[0004] At present, the main innovative idea of ​​Doppler frequency offset estimation for low-orbit satellite Internet is to combine the ephemeris and sequence correlation peak matching methods to realize the accurate estimation and compensation of satellite dynamic Doppler frequency offset. However, the above attempts have not yet taken into account the following two key issues: 1) In the satellite phased array agile beam scenario, each satellite has multiple agile beams that can serve different service beams within a satellite beam, and users are distributed in different beams, resulting in different Doppler effects on user signals at different service beams when the same satellite is serving. At the same time, the Doppler effects of the same user at the same location point will also be different when served by different satellites. Therefore, the traditional, single ephemeris estimation method is difficult to solve the problem of Doppler frequency offset estimation adaptation for users with different geographical distributions and different satellites under agile beams; 2) Different Doppler frequency offset estimation methods in different scenarios have large performance deviations, and the advantages and performance of each estimation algorithm are also different. The fixed Doppler frequency offset estimation strategy combination is difficult to solve the problem of optimizing the performance of multiple Doppler estimations with rapid changes in user channels under agile beams. Summary of the invention

[0005] In view of this, an embodiment of the present application provides a method and device for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy to eliminate or improve one or more defects existing in the prior art.

[0006] One aspect of the present application provides a method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy, comprising:

[0007] Based on different low-orbit satellites and user scenarios at different beam positions under the phased array agile beam, a multi-scenario geometry and channel constraint relationship of the phased array agile beam is constructed, and the relative Doppler frequency deviation estimation value of each user at the time corresponding to different elevation angles is determined respectively;

[0008] Setting weights for each preset Doppler frequency offset estimation algorithm respectively to determine the performance target value corresponding to each of the Doppler frequency offset estimation algorithms;

[0009] Generate a corresponding Doppler frequency offset estimation strategy set according to the performance target value of each Doppler frequency offset estimation algorithm, and use the Doppler frequency offset estimation strategy set as a game object of Doppler frequency offset estimation performance to establish a dynamic game model based on scenario constraints and multiple performance combinations;

[0010] Based on the dynamic game model, a Doppler frequency offset estimation strategy combination solution model based on a recursive neural network is constructed and trained, and the optimal Doppler frequency offset estimation strategy combination is obtained according to the Doppler frequency offset estimation strategy combination solution model, so as to determine the execution order and execution degree of each of the Doppler frequency offset estimation algorithms based on the optimal Doppler frequency offset estimation strategy combination.

[0011] In some embodiments of the present application, the user scenarios based on different low-orbit satellites and different wave positions under the phased array agile beam, constructing the multi-scenario geometry and channel constraint relationship of the phased array agile beam, and respectively determining the relative Doppler frequency deviation estimation value of each user at the time corresponding to different elevation angles, include:

[0012] Configure the ground position data of the phased array agile beam, and set the user position distribution data within the satellite position based on the ground position data;

[0013] Determining satellite-to-ground link geometric relationship data according to the ground wave position data and the user position distribution data;

[0014] According to the satellite-to-ground link geometric relationship data, user position distribution data, and the distance from the low-orbit satellite to the user in a communication scenario passing through a beam center at a preset sub-satellite point, a relative Doppler frequency deviation estimation value of each user at a time corresponding to different elevation angles is calculated based on the Doppler effect formula.

[0015] In some embodiments of the present application, configuring the ground position data of the phased array agile beam, and setting the user position distribution data within the satellite position based on the ground position data, includes:

[0016] Configuring multiple satellite positions under the global position planning, and setting the center point of each of the satellite positions;

[0017] Determine the number of service beams in each low-orbit satellite, divide the satellite beam into a plurality of service beams, and set the center point of each service beam; and, during a satellite service period, use an agile beam mode to serve each service beam;

[0018] Based on the preset multiple user distribution intensities, the position information of each user in the satellite beam position relative to the center point of the satellite beam position is determined respectively by applying a two-dimensional random normal distribution method to obtain the user position distribution data in the corresponding satellite beam position.

[0019] In some embodiments of the present application, determining satellite-to-ground link geometric relationship data according to the ground wave position data and the user position distribution data includes:

[0020] Calculate the elevation angle data of the user in each service beam within a single satellite service cycle according to the center point of the satellite beam position, the center point of the service beam position and the user position distribution data at different times;

[0021] Based on the elevation angle data, the pre-acquired low-orbit satellite Internet orbit altitude and the location of the low-orbit satellite, the relative speed between the user and the low-orbit satellite is calculated and used as the satellite-to-ground link geometric relationship data.

[0022] In some embodiments of the present application, the weights are set for each preset Doppler frequency offset estimation algorithm to determine the performance target value corresponding to each of the Doppler frequency offset estimation algorithms, including:

[0023] According to different preset services and scenarios, a multi-weighted and multi-performance target joint objective function including Doppler frequency deviation estimation accuracy, range and complexity is set;

[0024] In each performance target indicator, weights are assigned to different Doppler frequency offset estimation algorithms respectively, and based on the corresponding weight assignment results and the multi-weight multi-performance target joint objective function, the performance target values ​​corresponding to each of the Doppler frequency offset estimation algorithms are determined;

[0025] According to the business priorities, the multi-weight and multi-performance joint objective function is globally constrained.

[0026] In some embodiments of the present application, the corresponding Doppler frequency offset estimation strategy set is generated according to the performance target value of each Doppler frequency offset estimation algorithm, and the Doppler frequency offset estimation strategy set is used as the game object of Doppler frequency offset estimation performance to establish a dynamic game model based on scenario constraints and multiple performance combinations, including:

[0027] Based on the preset combination coefficient and algorithm execution degree, setting the Doppler frequency offset estimation strategy corresponding to each of the Doppler frequency offset estimation algorithms, and rearranging the execution order of each of the Doppler frequency offset estimation algorithms to obtain a new sequence including the execution order and the execution degree;

[0028] The Doppler frequency deviation estimation strategy set at the current moment is used as the game object of the Doppler frequency deviation estimation efficiency, and the Doppler frequency deviation estimation strategy set at the next moment is derived based on the game object;

[0029] Based on communication scenario factors, service characteristics factors, user location factors and receiver performance factors, a game selection influencing factor set for Doppler frequency offset estimation performance is constructed;

[0030] The performance function and objective function of Doppler frequency deviation estimation performance are set to obtain a dynamic game model based on scenario constraints and multi-performance combinations.

[0031] In some embodiments of the present application, based on the dynamic game model, a Doppler frequency offset estimation strategy combination solution model based on a recursive neural network is constructed and trained, and an optimal Doppler frequency offset estimation strategy combination is obtained according to the Doppler frequency offset estimation strategy combination solution model, so as to determine the execution order and execution degree of each of the Doppler frequency offset estimation algorithms based on the optimal Doppler frequency offset estimation strategy combination, including:

[0032] Based on the pre-built phased array beam-hopping link simulation platform, under different combinations of Doppler frequency offset estimation algorithms, the corresponding Doppler frequency offset estimation performance set is obtained to form a priori set of Doppler frequency offset estimation strategy combinations.

[0033] According to the game selection influencing factor set of the Doppler frequency deviation estimation efficiency, a Doppler frequency deviation estimation strategy combination solution model based on iterative neural network is constructed;

[0034] Based on the output of the Doppler frequency offset estimation strategy combination solution model, an optimal Doppler frequency offset estimation strategy combination is determined, and based on the optimal Doppler frequency offset estimation strategy combination, an execution order and execution degree of each of the Doppler frequency offset estimation algorithms are determined.

[0035] Another aspect of the present application provides a low-orbit satellite agile beam Doppler frequency deviation estimation strategy selection device, comprising:

[0036] A model building module is used to build a multi-scenario geometry and channel constraint relationship of the phased array agile beam based on different low-orbit satellites and user scenarios at different beam positions under the phased array agile beam, and respectively determine the relative Doppler frequency deviation estimation value of each user at the time corresponding to different elevation angles;

[0037] A weight combination module, used to set weights for each preset Doppler frequency offset estimation algorithm, so as to determine the performance target value corresponding to each of the Doppler frequency offset estimation algorithms;

[0038] A dynamic game module, used to generate a corresponding Doppler frequency offset estimation strategy set according to the performance target value of each Doppler frequency offset estimation algorithm, and use the Doppler frequency offset estimation strategy set as a game object of Doppler frequency offset estimation performance, so as to establish a dynamic game model based on scenario constraints and multiple performance combinations;

[0039] A model convergence module is used to construct and train a Doppler frequency offset estimation strategy combination solution model based on a recursive neural network based on the dynamic game model, and obtain an optimal Doppler frequency offset estimation strategy combination according to the Doppler frequency offset estimation strategy combination solution model, so as to determine the execution order and execution degree of each of the Doppler frequency offset estimation algorithms based on the optimal Doppler frequency offset estimation strategy combination.

[0040] The third aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for selecting a low-orbit satellite agile beam Doppler frequency deviation estimation strategy when executing the computer program.

[0041] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy.

[0042] The present application provides a method for selecting a Doppler frequency deviation estimation strategy for a low-orbit satellite agile beam. The method constructs a multi-scenario geometry and channel constraint relationship of the phased array agile beam based on user scenarios of different low-orbit satellites and different wave positions under the phased array agile beam, and respectively determines the relative Doppler frequency deviation estimation value of each user at the time corresponding to different elevation angles; sets weights for each preset Doppler frequency deviation estimation algorithm to determine the performance target value corresponding to each of the Doppler frequency deviation estimation algorithms; generates a corresponding Doppler frequency deviation estimation strategy set according to the performance target value of each Doppler frequency deviation estimation algorithm, and uses the Doppler frequency deviation estimation strategy set as the game object of Doppler frequency deviation estimation performance, so as to A dynamic game model based on scenario constraints and multi-performance combinations is established; based on the dynamic game model, a Doppler frequency offset estimation strategy combination solution model based on a recursive neural network is constructed and trained, and the optimal Doppler frequency offset estimation strategy combination is obtained according to the Doppler frequency offset estimation strategy combination solution model, so as to determine the execution order and execution degree of each Doppler frequency offset estimation algorithm based on the optimal Doppler frequency offset estimation strategy combination, which can realize multi-scenario dynamic Doppler frequency offset estimation under the agile beam of the low-orbit satellite phased array, and can effectively improve the accuracy, effectiveness, reliability and wide applicability of the Doppler frequency offset estimation, thereby effectively improving the reliability of the link connection between the low-orbit satellite and the user.

[0043] Additional advantages, purposes, and features of the present application will be partially described in the following description, and will become partially apparent to those skilled in the art after studying the following, or may be learned from the practice of the present application. The purposes and other advantages of the present application can be achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0044] Those skilled in the art will understand that the purposes and advantages that can be achieved by the present application are not limited to the above specific description, and the above and other purposes that can be achieved by the present application will be more clearly understood based on the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present application, constitute a part of the present application, and do not constitute a limitation of the present application. The components in the drawings are not drawn to scale, but are only for the purpose of illustrating the principles of the present application. In order to facilitate the illustration and description of some parts of the present application, the corresponding parts in the drawings may be enlarged, that is, they may become larger relative to other components in the exemplary device actually manufactured according to the present application. In the drawings:

[0046] Figure 1 The first flowchart of the method for selecting a strategy for estimating Doppler frequency deviation of a low-orbit satellite agile beam in one embodiment of the present application is shown.

[0047] Figure 2A structural schematic diagram of a device for selecting a strategy for estimating Doppler frequency deviation of a low-orbit satellite agile beam in one embodiment of the present application.

[0048] Figure 3 The present invention is a schematic diagram of a specific process of selecting a method for estimating the Doppler frequency deviation of a low-orbit satellite agile beam provided in the application example of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the implementation modes and the accompanying drawings. Here, the illustrative implementation modes and descriptions of the present application are used to explain the present application, but are not intended to limit the present application.

[0050] It should also be noted here that in order to avoid obscuring the present application due to unnecessary details, only the structures and / or processing steps closely related to the scheme according to the present application are shown in the accompanying drawings, while other details that are not very relevant to the present application are omitted.

[0051] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0052] It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0053] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0054] In one or more embodiments of the present application, NR (New Radio) refers to new wireless; OFDM (Orthogonal Frequency Division Multiplexing) refers to orthogonal frequency division multiplexing; PSS (Primary Synchronization Signal) refers to primary synchronization sequence; CP (Cyclic Prefix) refers to cyclic prefix; DMRS (Demodulation Reference Signal) refers to reference demodulation signal; FCCH (Frequency Correction Channel) refers to frequency correction channel; PBCH (Physical Broadcast Channel) refers to physical broadcast channel.

[0055] In the existing Doppler frequency offset estimation and compensation method based on satellite ephemeris conversion, the Doppler frequency offset estimation and compensation of satellite ephemeris conversion mainly uses the satellite ephemeris that describes the satellite motion state to infer the satellite position and movement speed, and obtains the Doppler frequency offset value according to the Doppler effect formula. During carrier synchronization, it is directly compensated to the carrier synchronization processing of the relevant reception.

[0056] Some scholars have proposed a method for estimating high-order Doppler parameters of spaceborne SAR based on ephemeris data. The method relies on the ephemeris parameters of the echo signal to estimate the instantaneous orbital elements of the echo signal, and uses the ephemeris parameters of the satellite at the center of the echo signal to estimate the high-order Doppler parameters. First, the auxiliary data in the ephemeris is read, and then the satellite's spatial transformation matrix is ​​calculated in combination with the satellite's position vector. The position coordinates of the ground beam pointing point in the earth coordinate system are obtained through appropriate superposition processing, and the satellite's relative velocity vector and relative acceleration rate vector are further calculated to finally estimate the Doppler parameters of the echo signal.

[0057] Some scholars have also proposed a Doppler frequency compensation device and method for the Tiantong satellite onboard terminal. First, search for FCCH signals at the specified frequency point of the terminal, estimate the signal-to-noise ratio and find the frequency point with the best signal-to-noise ratio, estimate the signal-to-noise ratio of the FCCH channel of the resident beam and the adjacent beam in real time, and output the resident beam FCCH channel frequency information; secondly, read the ephemeris information and Tiantong orbital motion information file to obtain the ephemeris information and Tiantong orbital motion information at the current moment; then calculate the Doppler frequency deviation value and Doppler frequency deviation change rate based on the resident beam FCCH channel frequency information combined with the ephemeris information and Tiantong orbital motion information; finally, compensate the terminal's uplink and downlink signals based on the Doppler frequency deviation value and Doppler frequency deviation change rate.

[0058] Other scholars have proposed a synchronization method and system based on position and ephemeris. By periodically searching for the control carrier with the maximum signal level, the signal is captured, and the link frequency deviation is estimated and updated based on the information of the captured signal. The ephemeris Doppler frequency deviation and the first-order ephemeris Doppler frequency deviation change rate are calculated by combining the ephemeris and terminal position with the transmission frequency point. The frequency deviation value estimated by the ephemeris is subtracted from the frequency deviation value estimated by the signal to obtain the frequency deviation caused by the crystal oscillator, thereby compensating for the uplink Doppler frequency deviation.

[0059] In summary, when ephemeris calculation is used to estimate the satellite motion state and then calculate the Doppler and propagation delay values ​​in time-frequency synchronization, very high requirements are placed on the number of ephemeris broadcasts and accuracy. At the same time, the ephemeris calculation method does not take into account accidental errors, the estimation process has little correlation with the received data, and the synchronization residual cannot be quantified. This results in low estimation compensation accuracy and large cumulative estimation errors. This type of method is based on satellite observations and the reference frequency signal of the low-orbit satellite frequency source to obtain navigation satellite observation data, including pseudorange measurements and carrier phase observations. It has high requirements on the satellite clock accuracy and is not suitable for the development needs of low-orbit satellite Internet.

[0060] After obtaining the ephemeris estimation data, the transmitter pre-compensation error, RF loss, satellite accidental error, etc. will affect the residual value of the receiver. Fixed synchronization strategies cannot meet the requirements of efficient synchronization at the receiver. It is necessary to create a decision space between multiple types of strategies and dynamically manage the system synchronization module.

[0061] In the existing terminal Doppler estimation and compensation method based on sequence correlation, the phase offset of the received waveform is calculated by using different correlated synchronization sequences for correlation operations, and then the linear relationship between the time domain phase and the frequency offset is used to equivalently calculate the frequency offset of the received sequence, and the data is compensated in real time after the estimation is completed.

[0062] Some scholars have proposed a Doppler frequency deviation change estimation method based on the 5G air interface. According to the 3GPP 5GNR frame structure, the main synchronization signal in the SS / PBCH block is used in combination with the second-order difference method to realize the Doppler frequency deviation change rate estimation. The data of fixed sampling points are cross-correlated to obtain the correlation function containing the frequency deviation information. The correlation function is then autocorrelated to estimate the representation function of the Doppler frequency deviation change rate, and the value of the Doppler change rate is obtained through the angle calculation.

[0063] Some scholars have proposed an integrated waveform design and signal processing method for satellite-to-ground communication and ranging. The method is designed to generate a communication waveform and a ranging waveform for communication between the ground station and the target satellite. The target satellite superimposes the two waveforms to generate an integrated waveform S(t) and sends it. After it reaches the receiving end, it performs sampling and matched filtering, delay estimation and time synchronization. The CP sequence is then extracted from the synchronized signal header, and the Doppler frequency shift is calculated bit by bit with the locally stored CP sequence for frequency offset compensation to complete time-frequency synchronization.

[0064] Some scholars have also proposed a Doppler shift estimation and compensation method that combines pilot information and satellite ephemeris. The ground terminal obtains the latest satellite ephemeris data, the frequency and phase of the carrier, and the frequency and phase of the pseudo-code based on the pilot signal, and then calculates the Doppler shift between the ground terminal k and the low-orbit satellite. The pseudo-code frequency and carrier frequency are adjusted based on the Doppler frequency shift, and Doppler compensation is performed when transmitting the uplink signal.

[0065] However, traditional enhancements to synchronization performance are mainly achieved by increasing the correlation of sequences or increasing the number of synchronizations for compensated sequences to increase the reliability of the communication system. The above methods use different synchronization sequences, which have different effect gains in low-orbit satellite scenarios. At the same time, the estimated Doppler frequency deviation ranges are also different, and under the same terminal processing capabilities, the consumed computing delays are also different. How to quickly select the optimal strategy under the constraints of algorithm accuracy, estimation range, and estimation complexity is an urgent problem to be solved. At the same time, due to the high dynamic characteristics of low-orbit satellites, there are accidental errors between satellite-to-ground links, and Doppler changes quickly. Among the limited synchronization strategies, the synchronization strategy of the previous state is considered to efficiently select using the principle of dynamic game.

[0066] Based on this, the technical problem that the present application needs to solve is the dynamic Doppler frequency deviation estimation in multiple scenarios under phased array agile beam. After the introduction of the 5G NR air interface system OFDM and phased array antennas, the physical layer communication system of low-orbit satellite Internet has become more complex. How to ensure reliable connection between satellites and terminals under relatively high-speed movement has become the core issue for improving the communication capabilities of low-orbit satellite Internet. In phased array low-orbit satellites, when different beams point to different wave positions, the Doppler frequency deviation of the user link varies greatly, which is an important challenge that neither existing satellite networks nor ground networks have encountered. Existing satellite Doppler frequency deviation estimation algorithms usually include two main ideas: Doppler frequency deviation estimation and compensation based on satellite ephemeris conversion, and terminal Doppler estimation and compensation based on sequence correlation. Both ideas do not fully consider the dynamic changes of Doppler frequency deviations at different wave positions under phased array agile beams, as well as the optimization problem of different performance requirements, resulting in the accuracy, range, complexity and other performance of low-orbit satellite Doppler frequency deviation estimation. It is difficult to dynamically compromise. The main problems are as follows:

[0067] (1) Doppler frequency deviation modeling problem under different satellites, time and position. Different from previous satellite communication systems and ground networks, phased array low-orbit satellites will use multiple agile beams to achieve dynamic coverage of different service positions under one satellite position. Therefore, the Doppler frequency deviation of the same satellite at different positions in the same satellite position will be different. At the same time, when the same satellite position is used by different satellites, the Doppler frequency deviation corresponding to different times will also be different due to the different effective sub-satellite point trajectories of the satellite. Therefore, modeling the Doppler frequency deviation of different satellites, different positions and different times, and constructing the constraint relationship between the terminal position point, the service satellite ephemeris, the service satellite sub-satellite point trajectory, the elevation angle between the terminal and the service satellite, and the Doppler frequency deviation value is the key to solving the problem of adapting the Doppler frequency deviation estimation of users with different geographical distributions and different satellites under agile beams.

[0068] (2) Dynamic compromise of different Doppler frequency offset estimation performance. Different Doppler frequency offset estimation methods in different scenarios have large performance deviations, such as different frequency offset estimation accuracy, frequency offset estimation range, frequency offset estimation complexity, etc., which are respectively suitable for frequency offset estimation designs with different terminal capabilities and different business needs in actual low-orbit satellite Internet. At the same time, considering that the application premises and conditions of different estimation algorithms in different scenarios are very different, such as when the ephemeris can be accurately obtained or cannot be accurately obtained, the Doppler frequency offset estimation accuracy based on satellite ephemeris conversion is difficult to determine, and the need for terminal Doppler estimation and compensation based on pilot and cyclic prefix will also change. Therefore, how to design and configure different combinations of Doppler frequency offset estimation methods in different scenarios is an important idea to solve the problem of optimizing the performance of multiple Doppler estimation methods with rapid changes in user channels under agile beams.

[0069] Therefore, the present application considers designing a dynamic game optimization method for the Doppler frequency deviation estimation strategy of low-orbit satellites, which is specifically embodied in a method for selecting a Doppler frequency deviation estimation strategy for low-orbit satellite agile beams. The main idea of ​​the present application is to analyze user scenarios of different satellites and different wave positions under phased array agile beams, construct the constraint relationship between the terminal position point, the service satellite ephemeris, the service satellite sub-satellite point trajectory, the elevation angle between the terminal and the service satellite, and the Doppler frequency deviation value, and calculate the elevation angle and Doppler frequency deviation value under different conditions as the initial input and compensation value for frequency deviation estimation; by analyzing the constraint relationship between the estimation accuracy, range, and complexity of different Doppler frequency deviation estimation algorithms, the Doppler frequency deviation estimation algorithm strategy combination at the current moment is used as the game entity for investigation, and weighted values ​​are set for different Doppler frequency deviation estimation algorithms. The objective function is the Doppler frequency deviation performance based on different scenarios and needs, and the input condition is the single Doppler frequency deviation estimation performance at the next moment. Finally, the optimal phased array agile beam Doppler frequency deviation estimation combination strategy is obtained through iterative solution of the neural network.

[0070] The details are described in detail through the following examples.

[0071] The present application provides a method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy that can be executed by a low-orbit satellite agile beam Doppler frequency offset estimation strategy selection device, see Figure 1 The method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy specifically includes the following contents:

[0072] Step 100: Based on user scenarios of different low-orbit satellites and different beam positions under the phased array agile beam, a multi-scenario geometry and channel constraint relationship of the phased array agile beam is constructed, and the relative Doppler frequency deviation estimation value of each user at the time corresponding to different elevation angles is determined respectively.

[0073] In step 100, by analyzing user scenarios of different satellites and different wave positions under the phased array agile beam, the constraint relationship between the terminal position point, the service satellite ephemeris, the service satellite sub-satellite point trajectory, the elevation angle between the terminal and the service satellite, and the Doppler frequency deviation value is constructed, and the elevation angle and Doppler frequency deviation value under different conditions are calculated as the initial input and compensation value for frequency deviation estimation.

[0074] Step 200: setting weights for each preset Doppler frequency offset estimation algorithm to determine the performance target value corresponding to each of the Doppler frequency offset estimation algorithms.

[0075] Step 300: Generate a corresponding Doppler frequency offset estimation strategy set according to the performance target value of each Doppler frequency offset estimation algorithm, and use the Doppler frequency offset estimation strategy set as a game object for Doppler frequency offset estimation performance to establish a dynamic game model based on scenario constraints and multiple performance combinations.

[0076] Step 400: Based on the dynamic game model, a Doppler frequency offset estimation strategy combination solution model based on a recursive neural network is constructed and trained, and an optimal Doppler frequency offset estimation strategy combination is obtained according to the Doppler frequency offset estimation strategy combination solution model, so as to determine the execution order and execution degree of each of the Doppler frequency offset estimation algorithms based on the optimal Doppler frequency offset estimation strategy combination.

[0077] In steps 200 to 400, by analyzing the constraint relationship among the estimation accuracy, range, and complexity of different Doppler frequency offset estimation algorithms, the Doppler frequency offset estimation algorithm strategy combination at the current moment is taken as the game entity to be examined, and weight values ​​are set for different Doppler frequency offset estimation algorithms. The objective function is the Doppler frequency offset performance according to different scenarios and requirements, and the input condition is the single Doppler frequency offset estimation performance at the next moment. Finally, the optimal phased array agile beam Doppler frequency offset estimation combination strategy is obtained through Laplace iteration.

[0078] From the above description, it can be seen that the method for selecting the Doppler frequency deviation estimation strategy for the low-orbit satellite agile beam provided in the embodiment of the present application takes into account the low-orbit satellite scenario based on the phased array agile beam, the geometric constraints from the satellite to the user, the real-time interference applied to the beam, and the users with random probability distribution (which can be called terminals or end users), and truly simulates the low-orbit satellite communication system. The modeling output of each module is used as a factor affecting the final decision, and the dynamic game is completed using the architecture of the neural network to achieve efficient and fast frequency synchronization strategy selection, and realize multi-scenario dynamic Doppler frequency deviation estimation under the low-orbit satellite phased array agile beam, and can effectively improve the accuracy, effectiveness, reliability and wide applicability of Doppler frequency deviation estimation, thereby effectively improving the reliability of the link connection between the low-orbit satellite and the user.

[0079] In a method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy provided in an embodiment of the present application, step 100 may further specifically include the following contents:

[0080] Step 110: configure the ground position data of the phased array agile beam, and set the user position distribution data within the satellite position based on the ground position data.

[0081] Step 120: Determine satellite-to-ground link geometric relationship data based on the ground wave position data and the user position distribution data.

[0082] Step 130: Calculate the relative Doppler frequency deviation estimation value of each user at the time corresponding to different elevation angles based on the Doppler effect formula according to the satellite-to-ground link geometric relationship data, the user location distribution data, and the distance from the low-orbit satellite to the user in the communication scenario passing through the beam center at a preset sub-satellite point.

[0083] It is understandable that step 110 may specifically include the following contents:

[0084] Step 111: configure multiple satellite beam positions under the global beam position planning, and set the center point of each satellite beam position.

[0085] Step 112: Determine the number of service beams in each low-orbit satellite, divide the satellite beam into multiple service beams, and set the center point of each service beam; and, during the satellite service period, use an agile beam method to serve each service beam.

[0086] Step 113: Based on the preset multiple user distribution strengths, the position information of each user in the satellite beam position relative to the center point of the satellite beam position is determined respectively using a two-dimensional random normal distribution method to obtain user position distribution data in the corresponding satellite beam position.

[0087] And, step 120 may specifically include the following contents:

[0088] Step 121: Calculate the elevation angle data of the user in each service beam within a single satellite service cycle based on the center point of the satellite beam position, the center point of the service beam position and the user position distribution data at different times.

[0089] Step 122: Based on the elevation angle data, the pre-acquired low-orbit satellite Internet orbit altitude and the location of the low-orbit satellite, the relative speed between the user and the low-orbit satellite is calculated and used as the satellite-to-ground link geometric relationship data.

[0090] In a method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy provided in an embodiment of the present application, step 200 further specifically includes the following contents:

[0091] Step 210: For different preset services and different scenarios, a multi-weighted and multi-performance target joint objective function including Doppler frequency offset estimation accuracy, range and complexity is set.

[0092] Step 220: weights are assigned to different Doppler frequency offset estimation algorithms within each performance target indicator, and based on the corresponding weight assignment results and the multi-weight multi-performance target joint objective function, the performance target values ​​corresponding to each of the Doppler frequency offset estimation algorithms are determined.

[0093] Step 230: Globally constraining the multi-weight and multi-performance joint objective function according to business priorities.

[0094] In a method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy provided in an embodiment of the present application, step 300 further specifically includes the following contents:

[0095] Step 310: Based on the preset combination coefficient and algorithm execution degree, set the Doppler frequency offset estimation strategy corresponding to each of the Doppler frequency offset estimation algorithms, and rearrange the execution order of each of the Doppler frequency offset estimation algorithms to obtain a new sequence including the execution order and the execution degree.

[0096] Step 320: The Doppler frequency offset estimation strategy set at the current moment is used as a game object for Doppler frequency offset estimation effectiveness, and a Doppler frequency offset estimation strategy set at the next moment is gambled based on the game object.

[0097] Step 330: Based on communication scenario factors, service feature factors, user location factors and receiver performance factors, a game selection influencing factor set for Doppler frequency offset estimation performance is constructed.

[0098] Step 340: Setting the performance function and the objective function of the Doppler frequency offset estimation performance to obtain a dynamic game model based on scenario constraints and multiple performance combinations.

[0099] In a method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy provided in an embodiment of the present application, step 400 further specifically includes the following contents:

[0100] Step 410: Based on the pre-built phased array beam-hopping link simulation platform, under different combinations of Doppler frequency offset estimation algorithms, a corresponding Doppler frequency offset estimation performance set is obtained to form a priori set of Doppler frequency offset estimation strategy combinations.

[0101] Step 420: constructing a Doppler frequency offset estimation strategy combination solution model based on an iterative neural network according to a set of influencing factors selected from the game of Doppler frequency offset estimation efficiency.

[0102] Step 430: Determine an optimal Doppler frequency offset estimation strategy combination based on the output of the Doppler frequency offset estimation strategy combination solution model, and determine the execution order and execution degree of each of the Doppler frequency offset estimation algorithms based on the optimal Doppler frequency offset estimation strategy combination.

[0103] In addition, the present application also provides a low-orbit satellite agile beam Doppler frequency offset estimation strategy selection device for executing all or part of the low-orbit satellite agile beam Doppler frequency offset estimation strategy selection method, see Figure 2 The low-orbit satellite agile beam Doppler frequency deviation estimation strategy selection device specifically includes the following contents:

[0104] The model building module 10 is used to construct the multi-scenario geometry and channel constraint relationship of the phased array agile beam based on user scenarios of different low-orbit satellites and different wave positions under the phased array agile beam, and respectively determine the relative Doppler frequency deviation estimation value of each user at the time corresponding to different elevation angles.

[0105] The weight combination module 20 is used to set weights for each preset Doppler frequency offset estimation algorithm to determine the performance target value corresponding to each of the Doppler frequency offset estimation algorithms.

[0106] The dynamic game module 30 is used to generate a corresponding Doppler frequency offset estimation strategy set according to the performance target value of each Doppler frequency offset estimation algorithm, and use the Doppler frequency offset estimation strategy set as the game object of Doppler frequency offset estimation performance to establish a dynamic game model based on scenario constraints and multiple performance combinations.

[0107] The model convergence module 40 is used to construct and train a Doppler frequency offset estimation strategy combination solution model based on a recursive neural network based on the dynamic game model, and obtain the optimal Doppler frequency offset estimation strategy combination according to the Doppler frequency offset estimation strategy combination solution model, so as to determine the execution order and execution degree of each of the Doppler frequency offset estimation algorithms based on the optimal Doppler frequency offset estimation strategy combination.

[0108] The embodiment of the low-orbit satellite agile beam Doppler frequency offset estimation strategy selection device provided in the present application can be specifically used to execute the processing flow of the embodiment of the low-orbit satellite agile beam Doppler frequency offset estimation strategy selection method in the above-mentioned embodiment. Its functions will not be repeated here, and reference can be made to the detailed description of the above-mentioned low-orbit satellite agile beam Doppler frequency offset estimation strategy selection method embodiment.

[0109] The part of the low-orbit satellite agile beam Doppler frequency offset estimation strategy selection device that performs the low-orbit satellite agile beam Doppler frequency offset estimation strategy selection can be executed in the server, and in another practical application scenario, all operations can also be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application is not limited to this. If all operations are completed in the client device, the client device may also include a processor for specific processing of the low-orbit satellite agile beam Doppler frequency offset estimation strategy selection.

[0110] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0111] The server and the client device may communicate with each other using any suitable network protocol, including network protocols that have not yet been developed on the date of filing this application. The network protocols may include, for example, TCP / IP, UDP / IP, HTTP, HTTPS, etc. Of course, the network protocols may also include, for example, RPC (Remote Procedure Call Protocol) and REST (Representational State Transfer) protocols used on top of the above protocols.

[0112] From the above description, it can be seen that the low-orbit satellite agile beam Doppler frequency deviation estimation strategy selection device provided in the embodiment of the present application can realize multi-scenario dynamic Doppler frequency deviation estimation under the low-orbit satellite phased array agile beam, and can effectively improve the accuracy, effectiveness, reliability and wide applicability of Doppler frequency deviation estimation, thereby effectively improving the reliability of the link connection between the low-orbit satellite and the user.

[0113] In order to further illustrate the present scheme, the present application also provides a specific application example of a method for selecting a Doppler frequency deviation estimation strategy for a low-orbit satellite agile beam. The main idea of ​​the application example of the present application is to analyze user scenarios of different satellites and different wave positions under the phased array agile beam, construct the constraint relationship between the terminal position point, the service satellite ephemeris, the service satellite sub-satellite point trajectory, the elevation angle between the terminal and the service satellite, and the Doppler frequency deviation value, and calculate the elevation angle and Doppler frequency deviation value under different conditions as the initial input and compensation value for frequency deviation estimation; by analyzing the constraint relationship of the estimation accuracy, range, and complexity of different Doppler frequency deviation estimation algorithms, the Doppler frequency deviation estimation algorithm strategy combination at the current moment is used as the game entity for investigation, and weighted values ​​are set for different Doppler frequency deviation estimation algorithms. The objective function is the Doppler frequency deviation performance based on different scenarios and needs, and the input condition is the single Doppler frequency deviation estimation performance at the next moment. Finally, the optimal phased array agile beam Doppler frequency deviation estimation combination strategy is obtained through Laplace iteration. The main steps are as follows: constructing the multi-scenario geometry and channel constraint relationship of phased array agile beam, setting the weights of different Doppler frequency offset estimation algorithm combinations, establishing a dynamic game model based on scenario constraints and multi-performance combinations, and solving the Doppler frequency offset estimation results based on Laplace iteration. Figure 3 , as follows:

[0114] Step 1: Construct the multi-scenario geometry and channel constraint relationship of phased array agile beam

[0115] The main approach is to first plan the ground beam configuration of the agile beam, including the beam position, size, etc.; then set the user position distribution within the beam; design the geometric relationship between the satellite and ground links under the beam constraint and user distribution; and calculate the corresponding Doppler frequency deviation estimate based on the Doppler effect formula.

[0116] (1) Plan the ground beam footprint of the agile beam: Design a satellite beam footprint configuration Sat_Beam_Footprint under global beam footprint planning n , set the center point of each service beam position to Psat_c n,i Each satellite beam can be divided into smaller service beams Ser_Beam_Footprint n,iEach satellite has N service beams Ser_Beam. During the satellite service period, different service beam positions are served in an agile beam manner.

[0117] (2) Set the user's position distribution within the beam position: Set the center point of each satellite beam Psat_c n，i As the reference point, the user's position can be obtained according to the two-dimensional random normal distribution with different arrival strengths at different wave positions.

[0118]

[0119] Among them, (μ1, μ2) and are the mean and variance of the two-dimensional random normal distribution, respectively, and ρ is the different user distribution strengths, which are constrained according to different population distributions. That is, the relative center position of the i-th user Psat_c n，i of

[0120] (3) Design the geometric relationship of the satellite-to-ground link under the constraint of the beam position and the user distribution: Set the center position of the satellite beam position at time t as P sat_c (n, i, t), the center point of the service beam position in the satellite beam position is P ser_c (n, i, t). The elevation angle of each user in the service beam is:

[0121]

[0122] in, are the distance vector between the service beam position and the satellite beam position, and the distance vector between the user and the satellite. At this time, the visible angle of each satellite is limited to [0°, 180°]), that is, corresponding to P sat (n,i,t start ) and P sat (n,i,t end ) two position points. It can be used to examine the elevation angle change within a single satellite service cycle. At this time, the elevation angle Angle usp (n, i, t), low-orbit satellite Internet orbit height H sat And the satellite position Psat can calculate the relative speed v between the user and the satellite s→u .

[0123] When different satellites pass through the survey satellite wave position area, the trajectory of the satellite ground sub-satellite point is P sat→g (n, i, t), the center point of the satellite's sub-satellite trajectory Starting point End point This can determine a unique sub-satellite point trajectory. Sub-satellite point trajectories of different satellites correspond to different distances to the beam center. According to the symmetry of wave position and the equivalence of user position, it can be obtained from Characterizes all satellites at the same distance, thus covering the global situation of satellites at fixed altitude.

[0124] (4) Calculate the corresponding relative Doppler frequency deviation estimate: Design the satellite orbit altitude to be Sat height , the elevation angle of the satellite relative to the user is Ele sat→ue (n, i, t) communication scenario, if the sub-satellite point passes through the beam center Psat_c n，i , let the elevation angle be α, then the distance from the satellite to the user is equal to:

[0125]

[0126] Where r is the radius of the Earth. n，i，t is the angular distance between the current position of the satellite and the terminal's geocentric angle, and v is the linear velocity of the satellite orbiting the earth. Then the velocity component of the satellite relative to the observer when passing the subsatellite point is v s→u =v×cos(α+θ n，i，t ). It is known that the distance between user i and the reference point is D d2e , then the distance from the satellite to the beam center can be calculated as:

[0127]

[0128] At this time, the elevation angle of user u is Angle u2s :

[0129]

[0130] At this time, the velocity component of the satellite relative to the user is:

[0131]

[0132] Then calculate the current user u at an elevation angle of Angle u2s Doppler frequency deviation at time

[0133]

[0134] Among them, f c is the carrier frequency and c is the speed of light.

[0135] Step 2: Set the weights of different combinations of Doppler frequency offset estimation algorithms

[0136] The main approach is to first set a multi-weight and multi-performance target joint objective function including Doppler frequency deviation estimation accuracy, range, and complexity; then set the weight distribution of different Doppler estimation algorithms within each performance target indicator; and globally constrain the multi-weight and multi-performance joint objective function according to business priorities.

[0137] (1) Setting a multi-weighted and multi-performance target joint objective function: First, plan different services Ser and service scenarios Sce, and design the performance target value Perf_Ser_Sce for Doppler frequency offset estimation under different services and scenarios. Its value is different Doppler frequency offset estimation performance value. With weight value The product of is then summed, that is:

[0138]

[0139] In this design, The value of They represent the estimation accuracy, estimation range, estimation complexity, etc. of Doppler frequency offset estimation. Each performance indicator has a corresponding value range.

[0140] (2) Setting the weight distribution of different Doppler estimation algorithms: Each Doppler frequency deviation estimation method is set as The estimated performance of each method can be established through mapping, that is, Design for every performance The corresponding Doppler frequency offset estimation method The gain weight is This performance can be obtained Different Doppler frequency offset estimation methods The corresponding performance With gain weight After multiplying and summing, we can get Substituting it into the performance target value Perf_Ser_Sce, we can get the performance formula expanded to:

[0141]

[0142] In the application example of this application, The corresponding frequency offset estimation algorithm focuses on the Doppler frequency offset estimation algorithm based on ephemeris, the Doppler frequency offset estimation algorithm based on PSS, the Doppler frequency offset estimation algorithm based on CP, the Doppler frequency offset estimation algorithm based on DMRS, and other Doppler frequency offset estimation algorithms. After completing the extended performance description, the constraint relationship of each frequency offset estimation method at different performances is further established, that is, the comprehensive performance should be maintained at a constant value.

[0143] (3) Global constraints on the objective function: Considering that different scenarios and different services exist simultaneously in the agile beam low-orbit satellite scenario, the service quality of each service for the link connection is set as Ser_Qos and the priority of each service is set as Ser_level. The Doppler estimation efficiency of the designed wave position should be matched according to the efficiency required by the high-priority service, that is,

[0144]

[0145] Step 3: Establish a dynamic game model based on scenario constraints and multi-performance combinations

[0146] The main approach is to first establish the game object of Doppler frequency deviation estimation efficiency for multiple scenarios and multiple services; then construct the game selection influencing factor set of Doppler frequency deviation estimation efficiency; finally, design the efficiency function and objective function of Doppler frequency deviation estimation efficiency.

[0147] (1) Establish a game object for Doppler frequency offset estimation efficiency for multiple scenarios and multiple services: Select a Doppler frequency offset estimation strategy that meets a certain comprehensive efficiency as set up is the optimal combination coefficient for a certain scenario and business, Represents the execution degree of each frequency offset estimation algorithm in the whole frequency synchronization process, and sets the processing execution of each method as The execution order of the frequency offset estimation algorithm is rearranged, and a new sequence including the execution order and the execution degree is obtained after updating.

[0148]

[0149] The strategy corresponding to the current moment is the object of the game, which includes the strategy combination of Doppler frequency deviation estimation Agile beam low-orbit satellite communication scenario at the current moment Doppler frequency deviation estimation performance at the current moment As the agile beam low-orbit satellite communication scenario changes, the strategy set at the next moment Based on the current policy set Play games, and choose the strategies after the changes of the influencing factor set in each game to play dynamic games, and meet the Doppler frequency deviation estimation efficiency. Under the premise of , choose to keep or update the optimal combination coefficient of the Doppler frequency offset estimation strategy

[0150] (2) Constructing a set of game selection influencing factors for Doppler frequency offset estimation performance: Under the dynamic game architecture, communication scenarios, service characteristics, user locations, receiver performance, etc. are used as factors in the game selection influencing factor set. The specific design is as follows:

[0151] Communication scenario influencing factors set It mainly includes path loss, cloud and rain fading, shadow fading, multipath fading and the impact of satellite-borne nonlinear devices.

[0152] Business characteristic influencing factors set It mainly includes the QoS requirements of the business, the business service level, the business duration, the number of business users, etc.

[0153] User location influencing factors It mainly includes the satellite position of the user, the service position of the user, the user's moving speed, the user's position and the trajectory of the satellite's sub-satellite point, etc.

[0154] Receiver performance influencing factors It mainly includes the antenna gain of the receiver, the baseband processing capability of the receiver, the processing capability of the receiver for ephemeris, etc.

[0155] (3) Designing the performance function and objective function for optimizing the Doppler frequency deviation estimation strategy

[0156] First, a comprehensive average performance index is set. The performance index is mainly obtained by the average value of the difference between the performance value expected by the user and the actual performance value obtained. It is defined as follows:

[0157]

[0158] At this time, the objective function is set as follows:

[0159]

[0160]

[0161] The specific settings are as follows: Within the satellite beam position, the Doppler frequency deviation estimation efficiency of all users should be able to achieve the maximum value. The C1 condition ensures that for each user, at least the minimum Doppler frequency deviation estimation deviation lower limit TH under a certain signal-to-noise ratio is guaranteed. f-mmse ; C2 condition is that the total number of users that meet the Doppler frequency deviation estimation efficiency should be greater than the minimum number of users with reliable access TH num ; C3 condition is that after comprehensive evaluation, the Doppler frequency deviation estimation range should meet the minimum Doppler frequency deviation estimation range threshold TH of the business environment f-scale .

[0162] Step 4: Combination of Doppler frequency deviation estimation strategies based on recurrent neural networks

[0163] The main approach is to first form a priori set of Doppler frequency offset estimation strategy combinations; then construct a Doppler frequency offset estimation strategy combination solution model based on iterative neural network, and design the input layer, output layer and hidden layer respectively; finally, through the output results, complete the frequency offset estimation based on the optimal Doppler frequency offset estimation strategy combination.

[0164] (1) Prior set of Doppler frequency offset estimation strategy combinations: Based on the conditions of different scenarios and different services obtained in step 1, the phased array beam-hopping link simulation platform is built and set under different Doppler frequency offset estimation algorithm combination configurations to obtain the corresponding Doppler frequency offset estimation performance set, which constitutes the prior set of Doppler frequency offset estimation strategy combinations.

[0165] (2) Construct a Doppler frequency offset estimation strategy combination solution model based on iterative neural network: Design the iterative neural network input layer to collect the current communication scenario, service characteristics, user location, receiver performance, etc.; Design the iterative neural network input layer to include an initialization part and an iterative processing part, where the initialization part is a combination of Doppler frequency offset estimation methods at the current moment The iterative processing part is mainly based on the communication scenario influencing factor set Business characteristic influencing factors set User location influencing factors Receiver performance influencing factors Four sub-hidden layers are constructed, and the performance function of each sub-hidden layer is improved as the basic rule by limiting the variable constraints, and the optimal result after passing through the four sub-hidden layers is obtained through the memory path.

[0166] (3) Frequency offset estimation based on the optimal Doppler frequency offset estimation strategy combination: According to the output of the iterative neural network, the optimal combination coefficient of the Doppler frequency offset estimation strategy combination at the next moment is obtained. according to The recommended combination of the algorithm sets the execution order and execution degree of the Doppler frequency offset estimation algorithm based on ephemeris, the Doppler frequency offset estimation algorithm based on PSS, the Doppler frequency offset estimation algorithm based on CP, the Doppler frequency offset estimation algorithm based on DMRS, and other Doppler frequency offset estimation algorithms.

[0167] In other words, the application example of this application plans the multi-scenario geometry and channel constraint relationship of the phased array agile beam, and calculates the dynamic Doppler model of different users in combination with the user distribution law. Provide a Doppler frequency deviation modeling solution for complex situations. Set the weight combination of different Doppler frequency deviation estimation algorithms, and design corresponding performance functions for different services and scenarios. Establish a dynamic game model based on scenario constraints and multi-performance combinations, and update the frequency deviation estimation strategy based on the content of the strategy set at the previous moment and on the premise of satisfying the performance function.

[0168] The application example of this application solves the problem of ensuring reliable connection between the satellite and the terminal under relatively high-speed movement, and provides a frequency deviation synchronization process and solution in the case of large differences in the Doppler frequency deviation of the user link when different beams point to different wave positions in the phased array low-orbit satellite scenario.

[0169] The beneficial effects of the technical solution of the application example of this application mainly include two aspects:

[0170] (1) Providing a Doppler frequency deviation modeling solution for complex situations

[0171] Different from high-orbit satellites and ground mobile communication systems, the beam agility adopted by phased array low-orbit satellites achieves dynamic coverage of different service beams under one satellite wave position, and maximizes the power when the beam illuminates the ground. At the same time, when the same satellite is at different positions of the same satellite wave position, and different satellites are serving at different positions of the same satellite wave position, the corresponding Doppler frequency deviation will also be different. The application example of this application models the Doppler frequency deviation of different satellites, different positions, and different times, and constructs the constraint relationship between the terminal position point, the service satellite ephemeris, the service satellite sub-satellite point trajectory, the elevation angle between the terminal and the service satellite, and the Doppler frequency deviation value, so as to solve the Doppler problem of different geographical distributions, target satellites, and user positions of agile beams.

[0172] (2) Solving the dynamic tradeoff problem of Doppler frequency offset estimation performance

[0173] Under multi-user and multi-scenario conditions, the channel environment changes rapidly, and key factors affecting time-frequency synchronization, such as signal-to-noise ratio, communication capacity, and business needs, are adaptively adjusted. The application example of this application considers a combination of multiple methods and strategies. Different synchronization algorithms will obtain different benefits at different stages. Combining multiple methods, adaptive dynamic game selection of synchronization methods is carried out according to actual channel conditions. Construct a comprehensive performance analysis model for Doppler frequency offset estimation under agile beams for the 5G air interface system integrated with satellite Internet, analyze the constraint relationship between Doppler frequency offset estimation accuracy, range, and complexity, and propose a multi-constraint performance adaptive method for Doppler frequency offset estimation based on dynamic game and weight optimization, and derive an on-demand compromise strategy for joint weights such as ephemeris and CP.

[0174] The embodiment of the present application also provides an electronic device (i.e., an electronic device), such as a central server, which may include a processor, a memory, a receiver, and a transmitter, wherein the processor is used to execute the low-orbit satellite agile beam Doppler frequency offset estimation strategy selection method mentioned in the above embodiment, wherein the processor and the memory may be connected via a bus or other means, such as by bus connection. The receiver may be connected to the processor and the memory via a wired or wireless manner.

[0175] The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0176] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as the program instructions / modules corresponding to the method for selecting a strategy for estimating Doppler frequency offset of a low-orbit satellite agile beam in the embodiment of the present application. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory, that is, the method for selecting a strategy for estimating Doppler frequency offset of a low-orbit satellite agile beam in the above method embodiment is implemented.

[0177] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0178] The one or more modules are stored in the memory, and when executed by the processor, the method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy in the embodiment is executed.

[0179] In some embodiments of the present application, the user equipment may include a processor, a memory, and a transceiver unit, which may include a receiver and a transmitter. The processor, memory, receiver, and transmitter may be connected through a bus system. The memory is used to store computer instructions, and the processor is used to execute the computer instructions stored in the memory to control the transceiver unit to send and receive signals.

[0180] As an implementation method, the functions of the receiver and the transmitter in the present application can be considered to be implemented through a transceiver circuit or a dedicated chip for transceiver, and the processor can be considered to be implemented through a dedicated processing chip, a processing circuit or a general chip.

[0181] As another implementation method, it is possible to use a general-purpose computer to implement the server provided in the embodiment of the present application, that is, to store the program code for implementing the functions of the processor, receiver, and transmitter in a memory, and the general-purpose processor implements the functions of the processor, receiver, and transmitter by executing the code in the memory.

[0182] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the aforementioned low-orbit satellite agile beam Doppler frequency offset estimation strategy selection method are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0183] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is specifically performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0184] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0185] In the present application, features described and / or illustrated for one embodiment may be used in the same manner or in a similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0186] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for selecting a Doppler frequency deviation estimation strategy for a low-orbit satellite agile beam, characterized in that: include: Based on different low-orbit satellites and user scenarios at different beam positions under the phased array agile beam, a multi-scenario geometry and channel constraint relationship of the phased array agile beam is constructed, and the relative Doppler frequency deviation estimation value of each user at the time corresponding to different elevation angles is determined respectively; Setting weights for each preset Doppler frequency offset estimation algorithm respectively to determine the performance target value corresponding to each of the Doppler frequency offset estimation algorithms; Generate a corresponding Doppler frequency offset estimation strategy set according to the performance target value of each Doppler frequency offset estimation algorithm, and use the Doppler frequency offset estimation strategy set as a game object of Doppler frequency offset estimation performance to establish a dynamic game model based on scenario constraints and multiple performance combinations; Based on the dynamic game model, a Doppler frequency offset estimation strategy combination solution model based on a recursive neural network is constructed and trained, and the optimal Doppler frequency offset estimation strategy combination is obtained according to the Doppler frequency offset estimation strategy combination solution model, so as to determine the execution order and execution degree of each of the Doppler frequency offset estimation algorithms based on the optimal Doppler frequency offset estimation strategy combination.

2. The method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy according to claim 1, characterized in that: The method constructs a multi-scenario geometry and channel constraint relationship of the phased array agile beam based on different low-orbit satellites and different wave positions of the user scenarios under the phased array agile beam, and determines the relative Doppler frequency deviation estimation value of each user at a time corresponding to different elevation angles, including: Configure the ground position data of the phased array agile beam, and set the user position distribution data within the satellite position based on the ground position data; Determining satellite-to-ground link geometric relationship data according to the ground wave position data and the user position distribution data; According to the satellite-to-ground link geometric relationship data, user position distribution data, and the distance from the low-orbit satellite to the user in a communication scenario passing through a beam center at a preset sub-satellite point, a relative Doppler frequency deviation estimation value of each user at a time corresponding to different elevation angles is calculated based on the Doppler effect formula.

3. The method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy according to claim 2, characterized in that: The configuring the ground position data of the phased array agile beam and setting the user position distribution data within the satellite position based on the ground position data includes: Configuring multiple satellite positions under the global position planning, and setting the center point of each of the satellite positions; Determine the number of service beams in each low-orbit satellite, divide the satellite beam into a plurality of service beams, and set the center point of each service beam; and, during a satellite service period, use an agile beam mode to serve each service beam; Based on the preset multiple user distribution intensities, the position information of each user in the satellite beam position relative to the center point of the satellite beam position is determined respectively by applying a two-dimensional random normal distribution method to obtain the user position distribution data in the corresponding satellite beam position.

4. The method for selecting a strategy for estimating Doppler frequency deviation of a low-orbit satellite agile beam according to claim 3, characterized in that: Determining satellite-to-ground link geometric relationship data according to the ground wave position data and the user position distribution data includes: According to the center point of the satellite beam position, the center point of the service beam position and the user position distribution data at different times, the elevation angle data of the user in each service beam within a single satellite service cycle is calculated; Based on the elevation angle data, the pre-acquired low-orbit satellite Internet orbit altitude and the location of the low-orbit satellite, the relative speed between the user and the low-orbit satellite is calculated and used as the satellite-to-ground link geometric relationship data.

5. The method for selecting a strategy for estimating Doppler frequency offset of a low-orbit satellite agile beam according to claim 2, characterized in that: The step of respectively setting weights for the preset Doppler frequency offset estimation algorithms to determine the performance target values ​​corresponding to the respective Doppler frequency offset estimation algorithms includes: According to different preset services and scenarios, a multi-weighted and multi-performance target joint objective function including Doppler frequency deviation estimation accuracy, range and complexity is set; In each performance target indicator, weights are assigned to different Doppler frequency offset estimation algorithms respectively, and based on the corresponding weight assignment results and the multi-weight multi-performance target joint objective function, the performance target values ​​corresponding to each of the Doppler frequency offset estimation algorithms are determined; According to the business priorities, the multi-weight and multi-performance joint objective function is globally constrained.

6. The method for selecting a strategy for estimating Doppler frequency offset of a low-orbit satellite agile beam according to claim 5, characterized in that: The method generates a corresponding Doppler frequency offset estimation strategy set according to the performance target value of each Doppler frequency offset estimation algorithm, and uses the Doppler frequency offset estimation strategy set as a game object for Doppler frequency offset estimation performance to establish a dynamic game model based on scenario constraints and multiple performance combinations, including: Based on the preset combination coefficient and algorithm execution degree, setting the Doppler frequency offset estimation strategy corresponding to each of the Doppler frequency offset estimation algorithms, and rearranging the execution order of each of the Doppler frequency offset estimation algorithms to obtain a new sequence including the execution order and the execution degree; The Doppler frequency deviation estimation strategy set at the current moment is used as the game object of the Doppler frequency deviation estimation efficiency, and the Doppler frequency deviation estimation strategy set at the next moment is derived based on the game object; Based on communication scenario factors, service characteristics factors, user location factors and receiver performance factors, a game selection influencing factor set for Doppler frequency offset estimation performance is constructed; The performance function and objective function of Doppler frequency deviation estimation performance are set to obtain a dynamic game model based on scenario constraints and multi-performance combinations.

7. The method for selecting a strategy for estimating Doppler frequency offset of a low-orbit satellite agile beam according to claim 6, characterized in that: Based on the dynamic game model, a Doppler frequency offset estimation strategy combination solution model based on a recursive neural network is constructed and trained, and an optimal Doppler frequency offset estimation strategy combination is obtained according to the Doppler frequency offset estimation strategy combination solution model, so as to determine the execution order and execution degree of each of the Doppler frequency offset estimation algorithms based on the optimal Doppler frequency offset estimation strategy combination, including: Based on the pre-built phased array beam-hopping link simulation platform, under different combinations of Doppler frequency offset estimation algorithms, the corresponding Doppler frequency offset estimation performance set is obtained to form a priori set of Doppler frequency offset estimation strategy combinations. According to the game selection influencing factor set of the Doppler frequency deviation estimation efficiency, a Doppler frequency deviation estimation strategy combination solution model based on iterative neural network is constructed; Based on the output of the Doppler frequency offset estimation strategy combination solution model, an optimal Doppler frequency offset estimation strategy combination is determined, and based on the optimal Doppler frequency offset estimation strategy combination, an execution order and execution degree of each of the Doppler frequency offset estimation algorithms are determined.

8. A device for selecting a strategy for estimating Doppler frequency deviation of a low-orbit satellite agile beam, characterized in that: include: A model building module is used to build a multi-scenario geometry and channel constraint relationship of the phased array agile beam based on different low-orbit satellites and user scenarios at different beam positions under the phased array agile beam, and respectively determine the relative Doppler frequency deviation estimation value of each user at the time corresponding to different elevation angles; A weight combination module, used to set weights for each preset Doppler frequency offset estimation algorithm, so as to determine the performance target value corresponding to each of the Doppler frequency offset estimation algorithms; A dynamic game module, used to generate a corresponding Doppler frequency offset estimation strategy set according to the performance target value of each Doppler frequency offset estimation algorithm, and use the Doppler frequency offset estimation strategy set as a game object of Doppler frequency offset estimation performance, so as to establish a dynamic game model based on scenario constraints and multiple performance combinations; A model convergence module is used to construct and train a Doppler frequency offset estimation strategy combination solution model based on a recursive neural network based on the dynamic game model, and obtain an optimal Doppler frequency offset estimation strategy combination according to the Doppler frequency offset estimation strategy combination solution model, so as to determine the execution order and execution degree of each of the Doppler frequency offset estimation algorithms based on the optimal Doppler frequency offset estimation strategy combination.

9. 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 method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for selecting a low-orbit satellite agile beam Doppler frequency offset estimation strategy as described in any one of claims 1 to 7 is implemented.

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