Communication simulation method, device and system of satellite link, computer equipment and medium

Abstract

The invention relates to the technical field of satellite communication simulation and testing, in particular to a communication simulation method, device and system of a satellite link, computer equipment and a medium. The method is applied to a test system of a satellite communication base station, and comprises the following steps: acquiring an original observation signal and executing preprocessing; based on simulation parameter configuration of a satellite link, setting a state equation, an observation equation and a to-be-estimated parameter set to construct a channel simulation model through a state space modeling framework according to the simulation parameter configuration; executing multi-fidelity channel distortion simulation through the channel simulation model to generate channel response data; and converting the channel response data into a standard simulation result. Through the mode, the technical problem of insufficient configuration and expansion flexibility caused by specialization and closure of an existing satellite link communication simulation technology is solved, and the economical efficiency, adaptability, accuracy and engineering practicability of satellite link simulation are improved.

Description

Technical Field

[0001] This invention relates to the field of satellite communication simulation and testing technology, and in particular to a satellite link communication simulation method, apparatus, system, computer equipment and medium. Background Technology

[0002] In the field of emergency communication terminal development and testing, verifying the terminal's audio and video transmission performance under satellite links is crucial, as it directly affects the optimization effect of audio and video media algorithms and the terminal's reliability in weak network environments. Traditionally, testing using real satellite links can obtain direct business data, but frequent outdoor verification is not only cumbersome and inefficient, but also incurs high communication costs, making it difficult to support iterative development needs. To reduce costs and improve testing convenience, the industry is gradually turning to satellite link communication simulation technology to complete verification in an internal environment by simulating real network conditions. However, existing simulation solutions still have significant limitations. For example, prior art document 1 (application publication number CN116319371A) discloses a real-time terrestrial network simulation system for dynamic topology. While this system improves simulation realism to some extent by dynamically adjusting link parameters between network nodes through a virtualization platform, it is primarily designed for terrestrial network environments and fails to fully consider the complex characteristics of satellite links, such as high latency, frequent jitter, and reverse power flow. More importantly, this solution relies on dedicated hardware platforms, resulting in high equipment costs, strong resource exclusivity, and cumbersome configuration processes due to the need for physical cables (such as USB connections) and dedicated drivers for management interfaces. Furthermore, it lacks the ability to flexibly inject parameters specific to certain scenarios (such as weather interference or the influence of moving objects), leading to insufficient simulation accuracy and poor adaptability. These intertwined problems make it difficult for existing simulation technologies to meet the low-cost, convenient, and customizable communication simulation requirements of satellite links in the emergency communication field, thus hindering terminal development efficiency and algorithm optimization effectiveness. Therefore, existing satellite link communication simulation technologies suffer from insufficient configuration and expansion flexibility due to their specialization and closed nature. Summary of the Invention

[0003] To address the aforementioned shortcomings or drawbacks, this invention provides a satellite link communication simulation method, apparatus, system, computer equipment, and medium, which can solve the technical problem of insufficient configuration and expansion flexibility caused by the specialization and closed nature of existing satellite link communication simulation technologies.

[0004] This invention provides a communication simulation method for satellite links, which is applied to a test system for satellite communication base stations, including: Acquire the raw observation signal and perform preprocessing on the raw observation signal.

[0005] Based on the simulation parameter configuration of the current satellite link, a channel simulation model is constructed by setting the state equation, observation equation, and set of parameters to be estimated according to the preset state space modeling framework.

[0006] The preprocessed raw observation signal is input into the channel simulation model, and multi-fidelity channel distortion simulation is performed through the channel simulation model to generate channel response data.

[0007] The channel response data is converted into standard simulation results according to the preset data format rules.

[0008] According to a second aspect, this invention provides a low-cost simulation device for implementing a communication simulation method for any satellite link in the embodiments of this invention. The low-cost simulation device includes a thin client main body, a wired network interface module, a wireless network interface module, and an operating system bridging module. The thin client main body is equipped with a Linux operating system. The wired network interface module and the wireless network interface module are connected to the thin client main body via a Universal Serial Bus interface. The operating system bridging module is configured to perform bridging functions based on the Linux operating system, bridging the onboard network interface, the wired network interface module, and the wireless network interface module of the thin client main body to generate a unified logical network data path. The wireless network interface module is configured in wireless access point mode and runs Dynamic Host Configuration Protocol (DHCP) service to provide a wireless management channel independent of the logical network data path. The wired network interface module and the onboard network interface forward data through the logical network data path to connect to the terminal device to be simulated.

[0009] According to a third aspect, this invention provides a satellite link communication simulation system, which is applied to a test system for satellite communication base stations, comprising: The raw observation signal acquisition module is used to acquire the raw observation signal and perform preprocessing on the raw observation signal.

[0010] The channel simulation model construction module is used to construct a channel simulation model based on the simulation parameter configuration of the current satellite link and through a preset state space modeling framework, by setting the state equation, observation equation and the set of parameters to be estimated according to the simulation parameter configuration.

[0011] The channel response data generation module is used to input the preprocessed raw observation signal into the channel simulation model, and perform multi-fidelity channel distortion simulation through the channel simulation model to generate channel response data.

[0012] The communication simulation result generation module is used to convert channel response data into standard simulation results according to preset data format rules.

[0013] According to a fourth aspect, the present invention provides a computer device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to execute any satellite link communication simulation method in the embodiments of the present invention.

[0014] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute a communication simulation method for any satellite link in the embodiments of the present invention.

[0015] The present invention provides a satellite link communication simulation method, which is achieved through four core steps: acquisition and preprocessing of raw observation signals, dynamic construction of a channel simulation model, high-fidelity channel distortion simulation, and standardization of simulation results. Specifically, the method involves: acquiring and preprocessing raw observation signals to collect and standardize uplink and downlink signal data of the satellite link; dynamically setting state equations, observation equations, and the set of parameters to be estimated based on the current simulation parameter configuration of the satellite link using a preset state-space modeling framework to construct a channel simulation model that can flexibly adapt to different scenarios; inputting the preprocessed signal into the model and performing high-fidelity channel distortion simulation to generate accurate channel response data; and converting the response data according to preset data format rules to output standard simulation results that can be directly applied to downstream testing processes.

[0016] In this technical solution, the present invention addresses the problems of expensive, closed, and rigid configuration of dedicated simulation equipment described in the background art. By using a pre-defined state-space modeling framework and dynamically constructing models based on external simulation parameters, it transforms the simulation core from "fixed hardware" to "configurable software," solving the deficiency of traditional dedicated simulation equipment in its inability to flexibly adapt to various satellite orbits, antennas, and environmental scenarios. Regarding the difficulty in balancing simulation accuracy and real-time performance, high-fidelity channel distortion simulation is performed within a unified state-space model framework, achieving integrated and high-precision calculations of key effects such as time-varying propagation loss, multipath delay spread, and Doppler shift. Addressing the disconnect between simulation results and existing test systems, channel response data is converted into standard simulation results through pre-defined data format rules, ensuring that the simulation output can be directly parsed and utilized by satellite communication base station test systems. Therefore, the technical solution of this invention solves the technical problems of insufficient configuration and expansion flexibility caused by the specialization and closed nature of existing satellite link communication simulation technology, improving the economy, adaptability, accuracy, and engineering practicality of satellite link simulation. Attached Figure Description

[0017] Figure 1This is a flowchart of a satellite link communication simulation method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the topology of a low-cost simulation device according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a satellite link communication simulation system according to an embodiment of the present invention; Figure 4 This is a block diagram of a computer device for implementing embodiments of the present invention. Detailed Implementation

[0018] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0019] During the development of this invention, the inventors, through extensive experiments and data analysis, revealed the intrinsic relationship between satellite orbital motion characteristics, antenna radiation patterns, complex electromagnetic environment disturbances, and terminal received signal quality: a strong coupling effect exists among the Doppler frequency shift generated by high-speed satellite motion, gain fluctuations caused by antenna pointing errors, and signal attenuation caused by atmospheric and terrain obstruction, collectively determining the final communication performance of the satellite link. Based on this discovery, the inventors innovatively proposed this technical solution, the core of which lies in using a state-space model to uniformly describe channel dynamics by mapping mixed-frequency observation data to a unified state-space framework; and by using a parameter estimation method combining Kalman filtering and the expectation-maximization algorithm, combined with real-time updated simulation configuration parameters, a high-fidelity channel simulation model capable of simultaneously simulating time-varying propagation loss, multipath delay spread, and Doppler frequency shift is constructed. This solution embodies the core inventive concept of "using dynamic state space as a unified mathematical framework and low-cost general-purpose hardware as a platform."

[0020] Specifically, the invention team conducted a comparative experiment by deploying professional channel simulation instruments (such as Spirent VR5) alongside the device of this invention. This demonstrated that traditional solutions suffer from technical drawbacks such as high equipment procurement costs (over 100,000 RMB per unit), rigid hardware resources preventing concurrent operation, and closed configuration interfaces relying on dedicated cables. In contrast, this invention utilizes software-defined channel simulation technology based on Linux TC (Traffic Control), leveraging a low-cost hardware architecture of thin clients and universal USB network cards (reducing BOM costs to the thousand-yuan level). It also provides a web-based visual configuration interface through a built-in AP (Access Point), improving device resource utilization (supporting concurrent testing of 8 terminals) and configuration flexibility (supporting real-time parameter injection and scene switching). This enables bi-weekly high-frequency satellite link simulation updates and accurate end-to-end service quality verification, resolving the long-standing industry pain point in emergency communication where the cost and efficiency of simulation equipment are difficult to balance.

[0021] Therefore, according to the first aspect, this invention provides a satellite link communication simulation method, which is applied to a test system for satellite communication base stations (hereinafter referred to as the "system"). This test system integrates a state-space modeling framework, a signal preprocessing module, a high-fidelity channel distortion simulation engine, and a standardized data interface. Specifically, the test system can drive the simulation process through software-defined parameter configuration, completing joint simulations of satellite orbits, antenna characteristics, propagation environment, and modulation and coding schemes, and performing real-time simulations of time-varying propagation loss, multipath delay spread, and Doppler shift. Furthermore, the test system can also integrate a web-based visual management interface and an automated test script engine. Specifically, the hardware equipment of the test system includes, but is not limited to: a server or thin client running a Linux operating system, software-defined wireless devices, wireless and wired network adapters with universal serial bus interfaces, and an RF front-end for receiving satellite downlink signals.

[0022] like Figure 1 As shown, the method may include: Step S110: Obtain the original observation signal and perform preprocessing on the original observation signal.

[0023] The original observation signals refer to the satellite downlink signals acquired through the radio frequency front-end and the ground terminal uplink signals acquired through the baseband processing unit. Preprocessing includes signal format unification, outlier handling, and data standardization, which aims to convert heterogeneous signal sources into standardized data formats suitable for channel simulation.

[0024] Specifically, the system can receive downlink RF sampling data from the satellite via software-defined radio equipment, and simultaneously collect uplink baseband in-phase quadrature data via a ground test terminal, and perform time slot alignment and sampling rate normalization on the two types of data.

[0025] For example, the system uses a software-defined radio device with a bandwidth of 20MHz (megahertz) to receive satellite signals with a center frequency of 3.5GHz (gigahertz) and generate radio frequency sampling data with a sampling rate of 30.72MHz; at the same time, it collects baseband in-phase orthogonal data with a sampling rate of 15.36MHz through a ground test terminal, and uses an interpolation algorithm to unify the sampling rates of the two to 61.44MHz.

[0026] Step S120: Based on the simulation parameter configuration set by the current satellite link, construct a channel simulation model by setting the state equation, observation equation and the set of parameters to be estimated according to the preset state space modeling framework.

[0027] Among them, the state-space modeling framework refers to the mathematical model architecture based on linear dynamic systems, which includes state equations describing the evolution of the internal state of the system and observation equations establishing the relationship between the state and the observed value; simulation parameter configuration includes physical layer parameters such as orbital altitude, antenna gain, and atmospheric attenuation coefficient.

[0028] Specifically, the system can extract satellite angular velocity information from orbital parameters through the parameter parsing module to construct a state transition matrix, use antenna pattern parameters to determine the observation matrix coefficients, and convert atmospheric turbulence parameters into a process noise covariance matrix.

[0029] For example, when a user sets simulation parameters through a web interface, the system's internal parameter parsing module converts these engineering parameters into coefficients required by the mathematical model. For instance, user-inputted orbital parameters such as "orbital altitude 780km, inclination 53 degrees" are calculated by the parsing module using orbital mechanics formulas to obtain the satellite's angular velocity during the simulation period, thereby determining the off-diagonal element coefficients in the state transition matrix that describe the coupling relationship between position and velocity. User-inputted antenna parameters such as "antenna gain 18dBi, beamwidth 30 degrees" are used to query or calculate the antenna pattern function, thus determining the signal gain coefficients (i.e., the corresponding elements of the measurement matrix) corresponding to different positions of the satellite within the antenna beam in the observation equation. User-inputted environmental parameters such as "clear-sky atmospheric attenuation 0.1dB / km, turbulence intensity moderate" are mapped to the diagonal element values ​​of the process noise covariance matrix describing the intensity of random disturbances in the state equation, and the observation noise covariance reflecting measurement uncertainty in the observation equation.

[0030] Furthermore, for a low-Earth orbit satellite-to-ground communication scenario, the user configures the following settings on the web interface: orbital altitude 780 km, antenna peak gain 18 dBi (isotropic), center frequency 2.1 GHz, and operating environment "urban area". The system internally calculates and constructs a model based on this information: the state equation includes location... and speed There are a total of 6 state variables, and their state transition matrix is... In the simulation, the coefficients of the off-diagonal block matrix describing the relationship between position and velocity are determined by the product of the orbital angular velocity (approximately 0.0011 radians / second) and the simulation step size (e.g., 0.1 seconds). The observation equations and measurement matrix are also relevant. In the matrix, the row vector elements corresponding to the received signal strength are determined by interpolating the instantaneous azimuth and elevation angles of the satellite relative to the ground antenna (assuming it is located at the server connected to this device) using the antenna pattern function to obtain the gain value (linear scaling). The process noise covariance matrix... The diagonal element (location perturbation variance) is set to 0.01 square meters based on the typical multipath intensity of the "urban" environment. Observation noise covariance matrix. The diagonal elements (measurement error variance) are set based on the link budget and the signal-to-noise ratio estimate under "clear sky" conditions. Ultimately, by... The complete set of parameters constitutes the channel simulation model for this simulation task.

[0031] Step S130: Input the preprocessed original observation signal into the channel simulation model, and perform multi-fidelity channel distortion simulation through the channel simulation model to generate channel response data.

[0032] Among them, multi-fidelity channel distortion simulation refers to synchronously simulating channel impairment effects such as delay spread, Doppler shift, and path loss; channel response data is a frequency domain response matrix containing complex gain, delay, and phase information.

[0033] Specifically, the system can use the Kalman filter algorithm to estimate the state of the input signal, use the expectation-maximization algorithm to update the model parameters, and use particle filtering to process non-Gaussian noise scenarios, ultimately outputting a channel impulse response containing multipath components.

[0034] Furthermore, let's take a low-Earth orbit satellite's downlink to a fixed ground station as an example. When the pre-processed raw observation signal (such as a noisy satellite pilot signal) is input into the channel simulation model, the model's internal state estimator (such as a Kalman filter) tracks the satellite's position, velocity, acceleration, and other states in real time. Subsequently, the simulation engine performs the following calculations simultaneously: (1) Time delay spread simulation: Based on the estimated satellite position and the geometric relationship between the satellite and the ground station, the different propagation distances of multiple paths, such as the direct path and the ground reflection, are calculated and converted into minute time delay differences at the nanosecond level, forming the time delay spread. (2) Doppler frequency shift simulation: Based on the estimated satellite radial velocity, the carrier frequency shift is calculated in real time using the Doppler formula. This shift is time-varying, simulating the frequency "sweep" effect caused by satellite fly-through. (3) Path loss simulation: Based on the real-time propagation distance (which varies with satellite motion) and the preset atmospheric attenuation and rain attenuation models, the time-varying amplitude attenuation of the signal is calculated.

[0035] In other embodiments, for a satellite overhead scenario at an orbital altitude of 550 km, the system performs multi-fidelity distortion simulation on the input signal over a 10-second simulation period. The resulting channel response data is a complex matrix, which can be physically interpreted as follows: in each 1-millisecond time slice, the channel consists of four main propagation paths (e.g., direct path, one ground reflection path, and two building diffraction paths). For each path (tap), the channel response data records three core pieces of information: (1) Complex gain: For example, the amplitude of the direct path is (This indicates an attenuation of approximately 2 dB, with an initial phase of 0 degrees), the amplitude of the reflection path is... ; (2) Relative delay: For example, the delays of the four paths are respectively nanosecond; (3) Doppler frequency shift: For example, at the time of overpass, the Doppler frequency shift of all four paths is kilohertz; frequency shift during satellite approach and departure phases. It varies continuously within the kilohertz range.

[0036] Therefore, the channel response data in this example can be represented by the following dimension: At specific points in time, it precisely records the continuous dynamic changes in channel multipath structure, signal strength, and frequency within 10 seconds, fully achieving the goal of "multi-fidelity" simulation.

[0037] Step S140: Convert the channel response data into standard simulation results according to preset data format rules.

[0038] The data format rules follow the baseband data exchange specifications defined by the International Telecommunication Union; the standard simulation results are standardized data packets containing frame headers, timestamps, channel parameters, and checksums.

[0039] Specifically, the system can add frame header information containing simulation time and bandwidth identifier to the channel response data through the data encapsulation module, use forward error correction coding for data protection, and arrange bytes in little-endian format.

[0040] For example, the system encapsulates the channel response data into a 1024-byte data packet per frame, where the first 16 bytes are the frame header (containing a 4-byte synchronization word, an 8-byte timestamp, and a 4-byte frame length), the middle 992 bytes store the channel parameters, and the last 16 bytes are the cyclic redundancy check code.

[0041] Therefore, according to the above implementation method, the system first achieves its purpose through four core steps: raw observation signal acquisition and preprocessing, dynamic construction of channel simulation model, high-fidelity channel distortion simulation, and standardization of simulation results. Specifically, acquiring and preprocessing the raw observation signal is used to collect and standardize uplink and downlink signal data of the satellite link; based on the simulation parameter configuration set for the current satellite link, the state equation, observation equation, and set of parameters to be estimated are dynamically set through a preset state-space modeling framework to construct a channel simulation model that can flexibly adapt to different scenarios; the preprocessed signal is input into the model and high-fidelity channel distortion simulation is performed to generate accurate channel response data; the response data is converted according to preset data format rules to output standard simulation results that can be directly applied to downstream testing processes.

[0042] Throughout the process, this embodiment addresses the issues of expensive, closed, and rigid configuration of dedicated simulation equipment described in the background art. By employing a pre-defined state-space modeling framework and dynamically constructing models based on external simulation parameters, it transforms the simulation core from "fixed hardware" to "configurable software," overcoming the limitation of traditional dedicated simulation equipment's inability to flexibly adapt to various satellite orbits, antennas, and environmental scenarios. Regarding the difficulty in balancing simulation accuracy and real-time performance, it achieves integrated and high-precision calculations of key effects such as time-varying propagation loss, multipath delay spread, and Doppler shift by performing high-fidelity channel distortion simulation within a unified state-space model framework. Furthermore, addressing the disconnect between simulation results and existing test systems, it converts channel response data into standard simulation results using pre-defined data format rules, ensuring that the simulation output can be directly parsed and utilized by satellite communication base station test systems. Therefore, the technical solution of this embodiment solves the technical problems of insufficient configuration and expansion flexibility caused by the specialization and closed nature of existing satellite link communication simulation technologies, improving the economy, adaptability, accuracy, and engineering practicality of satellite link simulation.

[0043] In some embodiments, the original observation signal is acquired and preprocessed, including: The downlink radio frequency sampling data is obtained through a satellite signal receiving device, and the uplink baseband in-phase quadrature data is obtained through a ground test terminal. The radio frequency sampling data and the baseband in-phase quadrature data constitute the original observation signal. For the radio frequency (RF) sampled data in the original observation signal, the RF signal corresponding to the RF sampled data is converted into a baseband signal, and the baseband in-phase orthogonal data in the original observation signal is resampled with the sampling rate and time slot aligned.

[0044] Downconversion refers to the process of shifting a signal located at the radio frequency to a baseband frequency of zero or low intermediate frequency through mixing and filtering, so as to facilitate subsequent digital signal processing; resampling refers to changing the sampling rate of a digital signal sequence through interpolation or decimation, and aligning signal sequences from different sources on the time axis.

[0045] Specifically, the system can use a digital down-conversion module to mix the RF sampling data with the quadrature local oscillator signal generated by the local numerically controlled oscillator, and then perform low-pass filtering to obtain baseband in-phase quadrature data. Simultaneously, a polynomial interpolation algorithm is used to adjust the sampling interval of the baseband in-phase quadrature data to ensure strict alignment with the sampling time of the down-converted data. For example, the system performs digital down-conversion on RF sampling data with a center frequency of 3.5 GHz and a sampling rate of 30.72 MHz to generate baseband in-phase quadrature data; at the same time, it performs a 2x interpolation resampling on another source of original baseband in-phase quadrature data with a sampling rate of 15.36 MHz, so that the output sampling rate of both data sources is unified to 30.72 MHz, and the time deviation of the corresponding sampling points is less than 10 nanoseconds.

[0046] Outlier removal is performed on the original observation signal after downconversion and resampling, and the missing data formed after outlier removal is filled in using a linear interpolation method.

[0047] Outliers refer to invalid sampling points whose amplitudes significantly deviate from the statistical characteristics of the signal due to hardware transient faults or strong pulse interference; linear interpolation is a method of filling in missing points by calculating the estimated value of missing points using the values ​​of two adjacent valid data points in a linear relationship.

[0048] Specifically, the system can calculate the moving average and standard deviation of the signal amplitude using a sliding time window, and filter out amplitudes exceeding the moving average. Sampling points exceeding one-fold standard deviation are identified as outliers and discarded. For each discarded sampling point, a linear interpolation is performed using the values ​​of the one valid sampling point before and after it to generate a replacement value. For example, the system sets a sliding time window with a length of 256 sampling points. Within a certain time window, the average signal amplitude is 0.12 volts, and the standard deviation is 0.05 volts. When a sampling point with an amplitude of 0.35 volts (greater than 0.12 volts) is detected, the outlier value is determined. When the value of a sample point is less than 0.11 volts, it is identified as an outlier and removed. Then, using the value of the preceding sample point (0.11 volts) and the following sample point (0.13 volts), a linear interpolation formula is applied. volts, calculate the complete value.

[0049] The original observation signal after completion is normalized to adjust the signal amplitude to a preset dynamic range, thereby generating a preprocessed original observation signal.

[0050] Normalization is a scaling operation that linearly transforms data to a specified numerical range. It aims to eliminate amplitude reference differences between different signal sources or at different times, and ensure the numerical stability of subsequent processing.

[0051] Specifically, the system can use the min-max normalization method to traverse the completed signal sequence to find the maximum and minimum values, and then use the linear transformation formula... Map all sampled point values ​​to the target dynamic range Inside, among which The original value, and These are the minimum and maximum values ​​of the sequence, respectively. For example, the system detects that the maximum value of a completed signal sequence is 1.8 volts, and the minimum value is... Volts. Preset dynamic range is... The system transforms all sampled values ​​using the above formula, changing the original 1.8 volts to 1, and the original... Volt becomes The original 0 volts became approximately Ultimately, all signal amplitudes are constrained to Between 1 and 1.

[0052] Therefore, according to the above implementation method, the system can acquire the original observation signal from the heterogeneous signal source, and generate a high-quality, uniform and numerically stable preprocessed signal through a series of standardized operations such as downconversion, resampling, outlier processing, data completion and amplitude normalization, so as to provide a reliable data foundation for the accurate calculation of the subsequent channel simulation model.

[0053] In some embodiments, the simulation parameter configuration includes orbital parameters, antenna parameters, environmental interference parameters, and modulation and coding parameters; based on the simulation parameter configuration set by the current satellite link, a channel simulation model is constructed using a preset state-space modeling framework, by setting state equations, observation equations, and a set of parameters to be estimated according to the simulation parameter configuration, including: The coefficients of the state transition matrix describing the dynamic evolution of the satellite are determined based on the satellite orbital elements and motion characteristics characterized by the orbital parameters.

[0054] Among them, the state transition matrix coefficients refer to the matrix elements in the state equation used to quantify the evolution of state variables (such as satellite position and velocity) from the previous moment to the current moment; satellite orbital elements include Kepler orbital elements such as orbital semi-major axis, eccentricity, and inclination (Kepler orbital elements are six independent scalar parameters used to uniquely determine the shape, size, orientation, and instantaneous position of an elliptical orbit in space); motion characteristics include dynamic parameters such as orbital period and angular velocity.

[0055] Specifically, the system can use an orbital mechanics model to convert orbital parameters into position and velocity vectors of the satellite in an inertial frame, and calculate a linearized approximation of the state transition matrix based on time intervals. For near-circular orbits, the state transition matrix can be simplified to a rotated matrix form containing orbital angular velocity. For example, for a low Earth orbit satellite with an altitude of 780 km and an orbital period of 90 minutes, assuming the state vector contains position and velocity (6 dimensions) and a sampling interval of 1 second, the main diagonal elements of the state transition matrix are approximately 1, and the off-diagonal elements contain the product of the orbital angular velocity (approximately 0.0011 radians per second) and the sampling interval, such as the coefficient of the position-to-velocity coupling term in the matrix being 0.0011.

[0056] The measurement matrix coefficients that characterize the spatial response of the signal in the observation equation are determined based on the directional characteristics and polarization mode represented by the antenna parameters.

[0057] Among them, the measurement matrix coefficients refer to the linear transformation matrix elements in the observation equation used to map state variables (such as satellite position) to observed values ​​(such as received signal strength); the directional characteristics are described by the antenna gain pattern; the polarization mode includes linear polarization or circular polarization, etc.

[0058] Specifically, the system can convert the azimuth and elevation angles of the satellite relative to the antenna into antenna gain values ​​using the antenna pattern function, and then calculate the coefficients of the corresponding observation channels in the measurement matrix by combining them with the polarization matching factor. For example, for a gain of... (Decibel isotropic and is the logarithmic unit used to measure antenna gain). For a parabolic antenna with a half-power beamwidth of 30 degrees, when the satellite is located along the antenna's line of sight, the coefficient of the corresponding signal amplitude in the measurement matrix is ​​1 (normalized); when the satellite deviates from the line of sight by 15 degrees, the coefficient drops to 0.5. (Gain decreases).

[0059] The process noise covariance matrix reflecting random channel disturbances in the state equation is determined based on the atmospheric attenuation and multipath effect characteristics characterized by environmental interference parameters.

[0060] Among them, the process noise covariance matrix refers to the covariance statistics in the state equation used to describe unmodeled dynamic or random disturbances (such as atmospheric turbulence and equipment vibration); atmospheric attenuation includes signal power loss caused by oxygen and water vapor absorption; multipath effect refers to interference caused by the signal propagating through multiple paths.

[0061] Specifically, the system can use statistical models (such as the log-normal distribution for atmospheric attenuation and the Rayleigh distribution for multipath propagation) to calculate the perturbation variance of state variables based on environmental parameters and fill it into the diagonal elements of the process noise covariance matrix. For example, in The typical atmospheric attenuation value for the band (12~18 GHz) is... (decibels per kilometer), multipath delay spread is 100 nanoseconds; in the process noise covariance matrix, the variance of position disturbance is set to 0.01 square meters (corresponding to atmospheric jitter), and the variance of velocity disturbance is set to... (Corresponding to Doppler diffusion caused by multipath).

[0062] The observation noise covariance matrix, which characterizes the quality of the received signal, is determined based on the constellation diagram and coding scheme represented by the modulation and coding parameters.

[0063] The observation noise covariance matrix refers to the covariance statistics used in the observation equation to describe measurement errors (such as thermal noise and quantization errors); the constellation diagram refers to the distribution of modulation symbols in the complex plane (such as QPSK and 16QAM); the coding scheme includes the forward error correction code type and code rate. QPSK (Quadrature Phase Shift Keying) is a digital phase modulation method where each modulation symbol carries 2 bits of information and has 4 phase states. 16QAM (16-Quadrature Amplitude Modulation) is a digital modulation method where each modulation symbol carries 4 bits of information and performs amplitude and phase modulation simultaneously on orthogonal carriers.

[0064] Specifically, the system can calculate the noise variance of the observations based on modulation bit error rate theory, signal-to-noise ratio requirements, and demodulation algorithm accuracy. For coding systems, the effect of coding gain on the effective signal-to-noise ratio needs to be considered. For example, using QPSK modulation and a convolutional code with a code rate of 1 / 2, at a signal-to-noise ratio of 10... Under these conditions, the variance of the in-phase component in the observation noise covariance matrix is ​​set to 0.1 (corresponding to error power), and the variances of the quadrature components are the same.

[0065] The determined state transition matrix coefficients, measurement matrix coefficients, process noise covariance matrix, and observation noise covariance matrix are configured in the state-space modeling framework to construct a channel simulation model.

[0066] Here, configuration refers to assigning the calculated parameters to the corresponding data structure of the state-space model; the state-space modeling framework is a predefined software structure that contains the mathematical expressions of the state equations and observation equations.

[0067] Specifically, the system can use matrix assignment operations to fill the state transition matrix coefficients into the transition matrix variables of the state equation, fill the measurement matrix coefficients into the observation matrix variables of the observation equation, and assign the noise covariance matrix to the corresponding stochastic process description field. For example, in the MATLAB simulation environment, using... The function creates a state-space model object: Where A is the state transition matrix (dimension 1) Q is the process noise covariance matrix (dimensions 1-2). C is the measurement matrix (dimensions) R is the observation noise covariance matrix (dimensions 1-2). 2) All matrix elements are calculated using the steps described above. Among them, Functions are standard functions in the MATLAB Control Toolbox used to create or transform state-space model objects.

[0068] Therefore, according to the above implementation method, the system can dynamically generate all the key parameters of the state space model based on configurable physical parameters (orbit, antenna, environment, modulation), realize the rapid construction and accurate configuration of the satellite channel simulation model, and provide a mathematical model basis for subsequent high-fidelity simulation.

[0069] In some embodiments, the step of performing multi-fidelity channel distortion simulation using a channel simulation model to generate channel response data includes: Based on the input state vector estimate of the previous preset time step in the state equation, the prior prediction value of the state vector at the current time step is calculated by combining the state transition matrix and the process noise covariance matrix defined in the state equation. The input state vector estimate is determined by recursively executing the multi-fidelity channel distortion simulation step and processing the previous adjacent time step to obtain the posterior estimate value.

[0070] Among them, the prior prediction value refers to the prediction of the current state based on the state estimate of the previous time step using only the dynamic law of the system described by the state equation, and has not yet incorporated the actual observation data of the current time step; the state transition matrix is ​​the linear transformation matrix in the state equation that describes the evolution of the state variables over time; and the process noise covariance matrix is ​​a statistic that describes the uncertainty of the system model.

[0071] Specifically, the system can use a Kalman filter prediction step to multiply the posterior estimate of the state at the previous time step with the state transition matrix and add the statistical characteristics of process noise to obtain the prior prediction of the state at the current time step and its corresponding prior error covariance matrix. For example, at time point... At 10:00:00 on October 20th, the system based on The posterior state estimate (a 6-dimensional vector containing position, velocity, and acceleration) at time t (09:59:59 on October 20, 2024) is multiplied by a preset state transition matrix (6 rows and 6 columns) to calculate the prior prediction of the state at time t. This prediction reflects the best prediction of the system state before incorporating the actual observation data at the current time.

[0072] Based on the preprocessed original observation signal, the prior prediction value is corrected by the observation equation to obtain the posterior estimate of the state vector at the current time.

[0073] Among them, the posterior estimate refers to the state estimate obtained by combining the actual observation data at the current moment and correcting the prior prediction value through the Bayesian update rule. This estimate integrates the system dynamic model and actual observation information, and its accuracy is higher than that of the prior prediction.

[0074] Specifically, the system can use a Kalman filter update step to first calculate the innovation (the difference between the observed and predicted values) and the innovation covariance, then calculate the Kalman gain matrix, and finally use this gain to weight and correct the prior predictions to obtain the posterior estimate and the posterior error covariance matrix. For example, the system acquires the actual observed signal at time t (e.g., the received signal strength is...). After that, the new information is calculated as the difference between the actual value and the predicted value. (Decibel-milliwatt, a logarithmic unit for expressing absolute power values), the prior prediction is corrected by a Kalman gain of 0.7 to obtain a posterior estimate that is closer to the true state.

[0075] Based on the posterior estimate, the state components corresponding to the channel propagation characteristics are extracted by a preset state component selection matrix.

[0076] Among them, the state component selection matrix is ​​a matrix composed of 0 and 1 elements, used to filter out the components related to a specific physical quantity from the complete state vector; the channel propagation characteristics include physical phenomena that affect signal transmission, such as propagation loss, multipath delay, and Doppler shift.

[0077] Specifically, the system can extract subsets of the state vector related to channel propagation characteristics by multiplying the state component selection matrix with the posterior estimate through matrix multiplication. For example, for a state vector containing 10 state components such as position, velocity, and acceleration, by designing a 3x10 state component selection matrix with 1s in the 1st row and 4th column, the 2nd row and 7th column, and the 3rd row and 9th column, and 0s in the rest, the 4th, 7th, and 9th state components can be extracted. These three components correspond to propagation loss, multipath delay, and Doppler shift, respectively.

[0078] Channel response data is generated based on the extracted state components.

[0079] Among them, channel response data is a complete description of the channel's impact on signal transmission, and typically includes time series of parameters such as complex gain, delay, and Doppler.

[0080] Specifically, the system can convert extracted state components into channel response parameters through physical mapping relationships. For example, propagation loss state components can be converted into channel gain through exponential mapping, multipath delay state components can be converted into actual delay values ​​through linear scaling, and Doppler state components can be directly used as frequency shift parameters. For instance, the system can convert the extracted propagation loss state component (with a value of 2.5) into channel response parameters using the formula... Converting to a linear gain of 0.56, multiplying the multipath delay state component (value 0.8) by 100 nanoseconds to obtain a delay of 80 nanoseconds, and directly using the Doppler state component (value 150) as a 150 Hz Doppler frequency shift, thereby generating complete channel response data.

[0081] Therefore, according to the above implementation method, the system can achieve accurate conversion from raw observation signals to high-fidelity channel response data through a complete process of state prediction, observation update, component extraction and physical mapping, providing a reliable simulation basis for the performance evaluation of satellite communication systems.

[0082] In some embodiments, the data format rules include the data packet structure, telemetry channel coding, and synchronization data structure of the telemetry space data link protocol; converting channel response data into standard simulation results according to preset data format rules includes: According to the data packet structure of the telemetry space data link protocol, field mapping and data reassembly are performed on the channel response data to generate intermediate data blocks that match the data packet structure.

[0083] The data packet structure of the telemetry space data link protocol refers to the telemetry data frame format defined by the Advisory Committee on Space Data Systems (CCSDS) standard, which includes fixed fields such as synchronization header, data field, and checksum. Field mapping is the process of allocating parameters (such as gain and delay) of the channel response data to the corresponding positions in the data packet structure. Data reassembly is the operation of rearranging data elements according to the target format. Intermediate data blocks are temporary data structures generated during the conversion process that conform to the protocol specifications.

[0084] Specifically, the system can parse the dictionary structure of the channel response data, mapping the gain parameter to the first 1024 bytes of the data field of the data packet, and mapping the delay parameter to the following 512 bytes, and then reassemble the byte sequence according to big-endian format. For example, the system can reassemble channel response data containing a gain value of 0.56 and a delay value of 80 nanoseconds into an intermediate data block of 1536 bytes, where the first 1024 bytes store the gain floating-point number (4-byte single-precision format) and the last 512 bytes store the delay integer value (4-byte integer format).

[0085] According to the telemetry channel coding and synchronization data structure, the intermediate data blocks are subjected to binary serialization or text formatting.

[0086] Binary serialization is the process of converting a data structure into a byte stream for easy storage or transmission; text formatting converts data into a human-readable text format (such as XML or JSON); telemetry channel coding and synchronization data structures define data encoding rules (such as Reed-Solomon encoding) and frame synchronization modes (such as additional synchronization words). XML (eXtensible Markup Language) is an extensible markup language with strict tag nesting rules for defining documents and data structures, while JSON (JavaScript Object Notation) is a lightweight, human-readable, and machine-parseable data exchange format based on JavaScript object representation.

[0087] Specifically, the system can convert intermediate data blocks into binary streams using serialization libraries (such as Protocol Buffers, a language- and platform-independent interface description language developed by Google for efficient serialization of structured data, along with its related code generation tools and runtime libraries), or generate text data using JSON formatting tools, and add a Cyclic Redundancy Check (CRC) for error detection. For example, the system uses binary serialization to convert a 1536-byte intermediate data block into 1560 bytes of serialized data (including a 24-byte checksum), which is calculated using the CRC-32 algorithm.

[0088] For intermediate data blocks after serialization or formatting, add a predefined metadata header, which includes a simulation timestamp, a channel type identifier, and a data integrity check code.

[0089] The metadata header is a descriptive information block added before the data body; the simulation timestamp records the absolute time of data generation (such as UTC time, i.e., Coordinated Universal Time); the channel type identifier is used to distinguish channel models (such as Gaussian channel, Rayleigh channel); and the data integrity check code is used to verify the integrity of data transmission (such as hash value).

[0090] Specifically, the system can append a fixed-length header field to the data stream, where the timestamp occupies 8 bytes (storing the Unix timestamp), the channel type identifier occupies 2 bytes (storing the enumeration value), and the checksum occupies 16 bytes (storing the MD5 hash value). For example, the system generates a 20-byte metadata header: the first 8 bytes store the timestamp (e.g., 1633046400 represents October 1, 2021, 00:00:00), the next 2 bytes store the channel type identifier (e.g., 1 represents the Rayleigh channel), and the last 10 bytes store the checksum (e.g., the MD5 hash value "a1b2c3d4e5f6").

[0091] The data with added metadata headers is encapsulated into a telemetry space data link protocol data packet, and the output is a standard simulation result.

[0092] Encapsulation refers to adding control information such as frame headers and frame trailers according to protocol specifications; telemetry space data link protocol data packets are data transmission units that conform to the CCSDS 132.0-B-2 standard; standard simulation results are the final output data that can be directly parsed by third-party analysis tools.

[0093] Specifically, the system can use protocol stack software to add a 32-bit synchronization word (0x1ACFFC1D) to the data header and a 32-bit frame check sequence to the tail, and then encapsulate the data into packets of fixed length (e.g., 188 bytes). For example, the system can encapsulate 1580 bytes of data (20-byte header + 1560-byte serialization data) into a complete protocol data packet with a total length of 1632 bytes, where the synchronization word occupies 4 bytes and the frame check sequence occupies 4 bytes. This data packet can be directly input into the satellite ground station test system for decoding and analysis.

[0094] Therefore, according to the above implementation method, the system can convert the original channel response data into simulation results that conform to space communication standards through a four-step standardized process of field mapping, serialization processing, metadata addition and protocol encapsulation, ensuring the compatibility and traceability of data across different platforms.

[0095] In some embodiments, channel response data is generated based on the extracted state components, including: The first sub-component in the state component corresponding to the propagation loss is converted into a time-varying propagation loss feature using a preset time-varying function.

[0096] Among them, the time-varying function refers to a mathematical expression that includes a time variable, used to describe the law of change of physical quantities over time; the time-varying propagation loss characteristic refers to the characteristic of power attenuation of a signal over time due to factors such as changes in distance and fluctuations in atmospheric conditions during transmission.

[0097] Specifically, the system can convert the static propagation loss value into a time-varying characteristic that includes distance attenuation and atmospheric scintillation effects by combining an exponential decay function with a periodic modulation term. The functional form is as follows: ,in This is the initial loss value. The attenuation coefficient is... For modulation depth, The fluctuation frequency. For example, the extracted propagation loss state component value of 2.5 (in decibels) is taken as... ,set up (per second) , Hertz, generating a time-varying propagation loss sequence lasting 10 seconds, fluctuating around a baseline value of 2.5 dB. Decibels were used to simulate the effects of low-Earth orbit satellite distance changes and atmospheric turbulence.

[0098] The second sub-component in the state component corresponding to the multipath effect is converted into a multipath delay spread feature through a preset delay spread function.

[0099] Among them, the time delay spread function is a transformation relationship used to convert multipath intensity parameters into actual time delay distribution characteristics; multipath time delay spread characteristics refer to the time dispersion characteristics caused by the signal propagating through different paths, which are usually characterized by the root mean square time delay spread value.

[0100] Specifically, the system can map multipath intensity to a power delay distribution using an exponential decay model, calculate the relative delay and power of each multipath component, and obtain the root mean square delay spread value through integration. For example, the extracted multipath intensity state component value of 0.8 can be input into the exponential decay model. ,in In microseconds, a power delay distribution containing three multipath components is generated, with the delays of each component being as follows: microseconds, power ratio is The calculated root mean square delay spread is 0.45 microseconds.

[0101] The third sub-component corresponding to the frequency shift in the state component is converted into Doppler frequency shift distortion characteristics through a preset frequency shift function.

[0102] Among them, the frequency shift function is a mathematical relationship that describes the change in carrier frequency caused by relative motion; the Doppler frequency shift distortion characteristic refers to the carrier frequency offset and its time variation caused by the relative radial motion between the transmitter and receiver.

[0103] Specifically, the system can be described by the Doppler formula. Calculate the instantaneous frequency shift and generate a time-varying frequency shift sequence by combining it with the motion trajectory model, where For Doppler frequency shift, The relative radial velocity, At the speed of light, The carrier frequency. For example, using the extracted frequency offset state component value of 150 Hz as a reference, combined with the satellite motion trajectory (relative velocity)... (km / s), generated on a 150 Hz basis. The dynamic offset of Hertz generates a frequency shift sequence that changes linearly from 50 Hz to 250 Hz over 10 seconds.

[0104] Channel response data are constructed based on time-varying propagation loss characteristics, multipath delay spread characteristics, and Doppler frequency shift distortion characteristics.

[0105] Here, "construction" refers to integrating the various characteristic parameters into a complete channel description data structure according to the standard channel model format; the channel response data is a time series matrix containing parameters such as complex gain, delay, and Doppler.

[0106] Specifically, the system can use a matrix synthesis method to combine time-varying propagation loss characteristics as amplitude terms, multipath delay spread characteristics as delay terms, and Doppler frequency shift characteristics as phase rotation terms to generate a time-varying channel impulse response containing multiple taps. For example, the system generates channel response data containing 4 taps, each tap containing: Delay microseconds, complex gain (Amplitude includes propagation loss, phase includes Doppler rotation), duration is 10 seconds, sampling interval is 1 millisecond, final data dimension is... A complex matrix.

[0107] Therefore, according to the above implementation method, the system can convert abstract state components into specific channel impairment characteristics through physical mapping relationships, and synthesize complete channel response data in a standard format, providing a high-fidelity channel model basis for subsequent communication system performance evaluation.

[0108] According to a second aspect, this invention provides a low-cost simulation device for implementing a communication simulation method for any satellite link in the embodiments of this invention. The low-cost simulation device includes a thin client main body, a wired network interface module, a wireless network interface module, and an operating system bridging module. The thin client main body is equipped with a Linux operating system. The wired network interface module and the wireless network interface module are connected to the thin client main body via a Universal Serial Bus interface. The operating system bridging module is configured to perform bridging functions based on the Linux operating system, bridging the onboard network interface, the wired network interface module, and the wireless network interface module of the thin client main body to generate a unified logical network data path. The wireless network interface module is configured in wireless access point mode and runs Dynamic Host Configuration Protocol (DHCP) service to provide a wireless management channel independent of the logical network data path. The wired network interface module and the onboard network interface forward data through the logical network data path to connect to the terminal device to be simulated.

[0109] Among them, low-cost simulation devices refer to dedicated equipment built using commercially available hardware components and open-source software stacks, with material costs controlled at the level of thousands of RMB, aiming to replace traditional high-priced professional instruments; logical network data paths refer to unified data transmission channels virtualized through software-defined networking technology, enabling seamless forwarding of business data between multiple physical interfaces.

[0110] Specifically, the thin client host serves as the core processing unit, running a customized Linux kernel and responsible for executing channel simulation algorithms and resource scheduling; the wired network interface module connects via a Universal Serial Bus 3.0 interface, providing gigabit Ethernet access capability; the wireless network interface module supports the IEEE 802.11ac protocol, creating an isolated management network in access point mode; the operating system bridging module calls the Linux kernel's bridge driver to bind the onboard network interface, wired network interface module, and wireless network interface module into a single logical interface, enabling Layer 2 packet switching.

[0111] For example, the device uses a thin client with a Rockchip RK3399 processor (costing approximately 500 RMB), paired with two USB 3.0 to Gigabit Ethernet adapters (each costing approximately 80 RMB) and one USB wireless network card (supporting 802.11ac, costing approximately 100 RMB); via Linux... The toolkit creates a bridge interface named br0, adding the onboard network card eth0, the USB wired network card eth1, and the wireless network card wlan0 to the bridge; the wireless network interface module is configured in AP mode, and the SSID is set to "". The IP address pool is 192.168.100.0 / 24, which provides DHCP service for management terminals.

[0112] Therefore, through the modular integration and hardware-software co-design described above, multi-terminal concurrent satellite link simulation can be achieved with a hardware cost of less than 1,000 RMB. At the same time, the convenience of configuration operations and the isolation of business data forwarding are ensured through an independent management channel, effectively solving the technical problems of high price and inconvenience of use of professional simulation equipment.

[0113] In another embodiment, the topology of the low-cost simulation device is as follows: Figure 2 As shown, this device, acting as the core node, constructs a heterogeneous hybrid network comprising wired simulation links and wireless management channels through its multiple network interfaces. Emergency terminals are directly connected to port 1 of this device via Ethernet cables, forming a simulation service data path. Network devices (such as switches or routers) are connected to this device via port 2, further connecting to the backend server and dispatch console, forming a link with the real network environment. Simultaneously, mobile configuration terminals such as mobile phones and laptops access the device's built-in wireless network interface via Wi-Fi, forming an independent management and configuration channel. A unified Web service runs internally within this device, serving as the sole configuration and monitoring interface for all connected devices. In this topology, simulation service data from emergency terminals is forwarded to the server for processing via port 2 through the logical network data path bridged within the device. Administrators access the device's Web service via the Wi-Fi channel connected to their mobile phones or laptops, enabling real-time configuration of simulation parameters and monitoring of link status. This management traffic is completely isolated from simulation service data at the physical interface and network layer. This design separates the simulation service flow from the management and control flow, ensuring the stability and security of the simulation link while providing flexible and convenient remote configuration capabilities, making it suitable for complex deployment scenarios such as emergency communications in the field.

[0114] The specific simulation parameters and device configurations described in this embodiment are illustrative and not intended to limit the scope of protection of this invention. Those skilled in the art can adjust the equipment installation method and the implementation method of key technical parameters according to actual application scenarios. For example, they can adjust the orbital angular velocity parameter in the state equation based on the satellite orbital altitude (e.g., 500 km to 1000 km), or adjust it according to antenna gain requirements (e.g., ...). to Modify the measurement matrix coefficients.

[0115] Figure 3This is a structural block diagram of a satellite link communication simulation system according to an embodiment of the present invention.

[0116] like Figure 3 As shown, the satellite link communication simulation system, which is applied to the test system of satellite communication base stations, includes: The raw observation signal acquisition module 210 is used to acquire the raw observation signal and perform preprocessing on the raw observation signal.

[0117] The channel simulation model construction module 220 is used to construct a channel simulation model based on the simulation parameter configuration set by the current satellite link and by setting the state equation, observation equation and the set of parameters to be estimated according to the simulation parameter configuration through a preset state space modeling framework.

[0118] The channel response data generation module 230 is used to input the preprocessed raw observation signal into the channel simulation model, and perform multi-fidelity channel distortion simulation through the channel simulation model to generate channel response data.

[0119] The communication simulation result generation module 240 is used to convert channel response data into standard simulation results according to preset data format rules.

[0120] The specific functions and examples of each module and submodule of the device in this embodiment can be found in the relevant descriptions of the corresponding steps in the above method embodiments, and will not be repeated here.

[0121] According to a fourth aspect, the present invention provides a computer device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to execute any satellite link communication simulation method in the embodiments of the present invention.

[0122] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute a communication simulation method for any satellite link in the embodiments of the present invention.

[0123] Figure 4A schematic block diagram of an example computer device 600 that can be used to implement embodiments of the present invention is shown. The computer device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computer device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0124] like Figure 4 As shown, the computer device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the computer device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0125] Multiple components in computer device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows computer device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0126] The computing unit 601 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a satellite link communication simulation method. For example, in some embodiments, a satellite link communication simulation method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the computer device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the satellite link communication simulation method described above can be performed. Alternatively, in other embodiments, computing unit 601 may be configured by any other suitable means (e.g., by means of firmware) to perform a satellite link communication simulation method.

[0127] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT or LCD monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual, auditory, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0131] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0132] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0133] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0134] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this invention should be included within the scope of protection of this invention.

Claims

1. A communication simulation method for satellite links, characterized in that, The method is applied to a test system for satellite communication base stations, including: Acquire the raw observation signal and perform preprocessing on the raw observation signal; Based on the simulation parameter configuration of the current satellite link, a channel simulation model is constructed by setting the state equation, observation equation and the set of parameters to be estimated according to the preset state space modeling framework. The preprocessed raw observation signal is input into the channel simulation model, and multi-fidelity channel distortion simulation is performed through the channel simulation model to generate channel response data. The channel response data is converted into standard simulation results according to preset data format rules.

2. The method according to claim 1, characterized in that, Acquire the raw observation signal and perform preprocessing on the raw observation signal, including: The downlink radio frequency sampling data is obtained through a satellite signal receiving device, and the uplink baseband in-phase quadrature data is obtained through a ground test terminal. The radio frequency sampling data and the baseband in-phase quadrature data constitute the original observation signal. For the radio frequency sampling data in the original observation signal, the radio frequency signal corresponding to the radio frequency sampling data is converted into a baseband signal, and the baseband in-phase orthogonal data in the original observation signal is resampled with sampling rate and time slot alignment. Outlier removal is performed on the original observation signal after down-conversion and resampling, and the missing data formed after outlier removal is filled in using a linear interpolation method; The original observation signal after completion is normalized to adjust the signal amplitude to a preset dynamic range, thereby generating the preprocessed original observation signal.

3. The method according to claim 1, characterized in that, The simulation parameter configuration includes orbital parameters, antenna parameters, environmental interference parameters, and modulation and coding parameters. Based on the current satellite link settings, the simulation parameter configuration, through a preset state-space modeling framework, sets state equations, observation equations, and a set of parameters to be estimated according to the simulation parameter configuration to construct a channel simulation model, including: The state transition matrix coefficients describing the dynamic evolution of the satellite in the state equation are determined based on the satellite orbital elements and motion characteristics characterized by the orbital parameters. The measurement matrix coefficients characterizing the spatial response of the signal in the observation equation are determined based on the directional characteristics and polarization mode represented by the antenna parameters. The process noise covariance matrix reflecting random channel disturbances in the state equation is determined based on the atmospheric attenuation and multipath effect characteristics characterized by the environmental interference parameters. The observation noise covariance matrix representing the quality of the received signal in the observation equation is determined based on the constellation diagram and coding scheme characterized by the modulation and coding parameters. The determined state transition matrix coefficients, measurement matrix coefficients, process noise covariance matrix, and observation noise covariance matrix are configured in the state space modeling framework to construct the channel simulation model.

4. The method according to claim 1, characterized in that, The step of performing multi-fidelity channel distortion simulation using the channel simulation model to generate channel response data includes: Based on the input state vector estimate of the previous preset time in the state equation, the prior prediction value of the state vector at the current time is calculated by combining the state transition matrix and the process noise covariance matrix defined in the state equation. The input state vector estimate is determined by recursively executing the steps of the multi-fidelity channel distortion simulation and processing the previous adjacent time. Based on the preprocessed original observation signal, the prior prediction value is corrected by the observation equation to obtain the posterior estimate of the state vector at the current time. Based on the posterior estimate, state components corresponding to the channel propagation characteristics are extracted using a preset state component selection matrix. The channel response data is generated based on the extracted state components.

5. The method according to claim 1, characterized in that, The data format rules include the data packet structure, telemetry channel coding, and synchronization data structure of the telemetry space data link protocol; the conversion of the channel response data into standard simulation results according to the preset data format rules includes: According to the data packet structure of the telemetry space data link protocol, field mapping and data reassembly are performed on the channel response data to generate intermediate data blocks that match the data packet structure; According to the telemetry channel coding and synchronization data structure, perform binary serialization or text formatting on the intermediate data block; For the intermediate data blocks after serialization or formatting, add a predefined metadata header, which includes a simulation timestamp, a channel type identifier, and a data integrity check code. The data with the added metadata header is encapsulated into a telemetry space data link protocol data packet, and the output is the standard simulation result.

6. The method according to claim 4, characterized in that, The step of generating the channel response data based on the extracted state components includes: The first sub-component corresponding to the propagation loss in the state component is converted into a time-varying propagation loss feature through a preset time-varying function; The second sub-component corresponding to the multipath effect in the state component is converted into a multipath delay spread feature through a preset delay spread function; The third sub-component corresponding to the frequency shift in the state component is converted into Doppler frequency shift distortion characteristics through a preset frequency shift function; The channel response data is constructed based on the time-varying propagation loss characteristics, the multipath delay spread characteristics, and the Doppler frequency shift distortion characteristics.

7. A low-cost simulation device, characterized in that, For implementing the method as described in any one of claims 1-6, the low-cost simulation device includes a thin client main body, a wired network interface module, a wireless network interface module, and an operating system bridging module; The thin client is equipped with a Linux operating system. The wired network interface module and the wireless network interface module are connected to the thin client body via a universal serial bus interface; The operating system bridging module is configured to bridge the network based on the Linux operating system, bridging the onboard network interface of the thin client, the wired network interface module and the wireless network interface module to generate a unified logical network data path. The wireless network interface module is configured in wireless access point mode and runs Dynamic Host Configuration Protocol service to provide a wireless management channel independent of the logical network data path. The wired network interface module and the onboard network interface forward data through the logical network data path to connect to the terminal device to be simulated.

8. A satellite link communication simulation system, characterized in that, The communication simulation system is applied to the testing system of satellite communication base stations, including: The raw observation signal acquisition module is used to acquire the raw observation signal and perform preprocessing on the raw observation signal; The channel simulation model construction module is used to construct a channel simulation model based on the simulation parameter configuration set by the current satellite link and through a preset state space modeling framework, by setting the state equation, observation equation and the set of parameters to be estimated according to the simulation parameter configuration. The channel response data generation module is used to input the preprocessed raw observation signal into the channel simulation model, and perform multi-fidelity channel distortion simulation through the channel simulation model to generate channel response data. The communication simulation result generation module is used to convert the channel response data into standard simulation results according to preset data format rules.

9. A computer device, characterized in that, include: At least one processor; and a memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, Computer instructions are used to cause a computer to perform the method according to any one of claims 1-6.

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