Broadband short wave channel modeling method and system, medium and product

Abstract

The invention provides a broadband short wave channel modeling method and system, a medium and a product. In the method, a data processing unit firstly constructs a target time delay power distribution curve by using a shape parameter and a time delay spread parameter, the shape parameter can flexibly adjust an echo energy attenuation form, the time delay spread parameter can accurately reflect the duration of an echo cluster, and the combination of the shape parameter and the time delay spread parameter enables the constructed time delay power distribution to better fit an actual channel multipath structure. And the data processing unit combines a phase function for reflecting a signal frequency difference value and a random modulation function for reflecting the fading characteristic of the signal along with time, and a time-varying impulse response function obtained by multiplying the three functions comprehensively reflects the time-varying characteristics of the signal amplitude and phase on each path. And finally, the data processing unit sums all path impulse responses to obtain a total time-varying impulse response function, and actual channel characteristics such as multipath propagation and Doppler effect are integrated, so that the problem of inaccurate simulation precision of the model is effectively relieved, and the simulation accuracy of an actual physical process is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and particularly relates to a wideband shortwave channel modeling method, system, medium and product. BACKGROUND

[0002] Wideband shortwave communication uses the ionosphere as a natural reflection medium, and can realize communication beyond the horizon or even globally, and has irreplaceable strategic value in the fields of military, emergency rescue, aviation and navigation. However, the ionosphere is an extremely complex and dynamically changing time-varying dispersive medium, and when radio waves propagate in it, they will experience severe multipath propagation, Doppler shift and spread, signal fading and other effects. When the communication bandwidth increases, the multipath time delay spread of the channel will cause serious frequency selective fading, which poses a great challenge to communication quality.

[0003] At present, in the field of wideband shortwave channel modeling, a statistical channel model based on a tapped delay line (TDL) structure is widely used, and its classic representative is the Watterson model and its subsequent evolution models (such as the Vogler model, the ITS reference model, etc.).

[0004] However, in order to simplify the calculation in actual application, these traditional models usually make idealized and static assumptions on the key statistical characteristics of the channel. The most important problem is that they generally use a static and idealized function (such as a single exponential decay function) to describe the power delay profile (PDP) of the channel, which leads to the problem that the model cannot truly reflect the multipath structure of the channel under different conditions, and ultimately leads to the technical problems of insufficient simulation accuracy and poor simulation effect. SUMMARY

[0005] The present application provides a wideband shortwave channel modeling method, system, medium and product, which is used to alleviate the technical problem of inaccurate model simulation accuracy, and improves the simulation accuracy of the model to the actual physical process.

[0006] In a first aspect, the application provides a wideband shortwave channel modeling method, comprising: constructing a target time delay power distribution curve according to a shape parameter and a time delay spread parameter, and determining the power intensity of each time delay path based on the target time delay power distribution curve, wherein the shape parameter is used to adjust the attenuation form of echo energy, and the time delay spread parameter is used to represent the duration of a channel echo cluster; generating a phase function corresponding to each time delay path according to a Doppler frequency shift and a Doppler frequency shift rate, wherein the phase function is used to represent the signal frequency difference of a signal on a time delay path; generating a random modulation function corresponding to each time delay path by filtering Gaussian noise in accordance with a Doppler power spectrum, wherein the random modulation function is used to represent the fading characteristics of a signal on a time delay path over time; multiplying the power intensity, the phase function and the random modulation function for each time delay path to obtain a time-varying impulse response function of each time delay path, wherein the time-varying impulse response function is used to represent the changes of signal amplitude and phase over time on different time delay paths; summing the impulse response functions of all time delay paths to obtain a total time-varying impulse response function of the channel, and completing the modeling of the wideband shortwave channel based on the total time-varying impulse response function.

[0007] By adopting the above technical solution, the data processing unit first constructs a target time delay power distribution curve using a shape parameter and a time delay spread parameter. The shape parameter can flexibly adjust the attenuation form of echo energy, and the time delay spread parameter can accurately reflect the duration of an echo cluster. The combination of the two makes the constructed time delay power distribution more consistent with the actual channel multipath structure. The power intensity of each time delay path determined on this basis is more accurate. In combination with the phase function reflecting the signal frequency difference and the random modulation function reflecting the signal fading characteristics over time, the time-varying impulse response function obtained by multiplying the three functions comprehensively reflects the time-varying characteristics of signal amplitude and phase on each path. Finally, the data processing unit sums the impulse responses of all paths to obtain a total time-varying impulse response function. The total time-varying impulse response function comprehensively reflects various actual channel characteristics such as multipath propagation and Doppler effect, thereby effectively alleviating the problem of inaccurate model simulation accuracy and improving the simulation accuracy of the actual physical process.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the target time delay power distribution curve is constructed according to the shape parameter and the time delay spread parameter, and the power intensity of each time delay path is determined based on the target time delay power distribution curve, specifically comprising: obtaining the shape parameter and the time delay spread parameter; substituting the shape parameter and the time delay spread parameter into a gamma function to construct the target time delay power distribution curve; sampling the target time delay power distribution curve to determine the discrete time delay value and the corresponding power intensity of each time delay path.

[0009] By adopting the technical solution, the data processing unit first acquires the shape parameter and the time delay spread parameter, which respectively describe the multipath characteristics of the channel from two key dimensions of echo energy decay pattern and echo cluster duration. Then, the data processing unit substitutes the shape parameter and the time delay spread parameter into the gamma function to construct the target time delay power distribution curve. The characteristics of the gamma function enable the constructed target time delay power distribution curve to flexibly adapt to the multipath power distribution in different propagation environments, and the target time delay power distribution curve has more universality and accuracy than the traditional single exponential decay function. The data processing unit further samples the target time delay power distribution curve to determine the discrete time delay value and the corresponding power intensity of each time delay path, and provides data for subsequent construction of an accurate time-varying impulse response function, and further improves the authenticity of the model in describing the multipath structure, thereby helping to improve the simulation accuracy of the overall channel modeling.

[0010] In some embodiments, according to the Doppler shift and the Doppler shift rate, the phase function corresponding to each time delay path is generated, specifically comprising: acquiring channel environment data, the channel environment data including geographic location information and communication time information; calculating the Doppler shift and the Doppler shift rate of each time delay path according to the channel environment data; and constructing the phase function corresponding to each time delay path varying with time based on the Doppler shift and the Doppler shift rate.

[0011] By adopting the technical solution, the data processing unit first acquires the shape parameter and the time delay spread parameter, which respectively describe the multipath characteristics of the channel from two key dimensions of echo energy decay pattern and echo cluster duration. Then, the data processing unit substitutes the shape parameter and the time delay spread parameter into the gamma function to construct the target time delay power distribution curve. The characteristics of the gamma function enable the constructed target time delay power distribution curve to flexibly adapt to the multipath power distribution in different propagation environments, and the target time delay power distribution curve has more universality and accuracy than the traditional single exponential decay function. The data processing unit further samples the target time delay power distribution curve to determine the discrete time delay value and the corresponding power intensity of each time delay path, and provides data for subsequent construction of an accurate time-varying impulse response function, and further improves the authenticity of the model in describing the multipath structure, thereby helping to improve the simulation accuracy of the overall channel modeling.

[0012] In some embodiments in combination with the first aspect, in some embodiments, the random modulation function corresponding to each time delay path is generated by filtering the Gaussian noise in accordance with the Doppler power spectrum, specifically comprising: selecting a target Doppler power spectrum from a Doppler power spectrum type library according to the channel environment data, the Doppler power spectrum type library comprising Gaussian-type Doppler power spectrum and Lorentz-type Doppler power spectrum, and the channel environment data comprising geographical position information and communication time information; generating a corresponding frequency response characteristic in the frequency domain based on the target Doppler power spectrum, the square of the amplitude of the frequency response characteristic being proportional to the target Doppler power spectrum; generating a Gaussian white noise sequence and converting the Gaussian white noise sequence to the frequency domain by Fourier transform to obtain a frequency domain noise sequence; multiplying the frequency domain noise sequence and the frequency response characteristic in the frequency domain to obtain a processed frequency domain sequence; and performing inverse Fourier transform on the frequency domain sequence to obtain the random modulation function corresponding to each time delay path.

[0013] By adopting the above technical solution, the data processing unit selects a suitable target Doppler power spectrum from the Doppler power spectrum type library according to the channel environment data, so that the selected power spectrum can match the specific propagation environment. Then, the data processing unit generates a frequency response characteristic based on the target power spectrum, and multiplies the frequency response characteristic with the Gaussian white noise sequence converted to the frequency domain by Fourier transform, and then obtains the random modulation function by inverse Fourier transform. The random modulation function can truly reflect the fading change of the signal over time under a specific Doppler characteristic, enhances the simulation effect of the model on the signal fading characteristics, and improves the overall modeling accuracy.

[0014] In some embodiments in combination with the first aspect, in some embodiments, for each time delay path, the power intensity, the phase function, and the random modulation function are multiplied to obtain a time-varying impulse response function of each time delay path, specifically comprising: multiplying the power intensity, the phase function, and the random modulation function point by point in the time domain to obtain a first time-varying impulse response function; and improving the data rate of the initial time-varying impulse response function to a preset channel matching rate by interpolation processing to obtain a second time-varying impulse response function; and taking the second time-varying impulse response function as the time-varying impulse response function of each time delay path.

[0015] By employing the above technical solution, the data processing unit multiplies the power intensity, phase function, and random modulation function point-by-point in the time domain, fusing the channel characteristics represented by each parameter to obtain a first time-varying impulse response function that initially reflects the time-varying characteristics of the delay path. Then, the data processing unit uses interpolation to increase the data rate to a preset channel matching rate, resolving the simulation distortion problem that might result from data rate mismatch, thus allowing the obtained second time-varying impulse response function to better match the actual channel rate characteristics. The second time-varying impulse response function, as the final time-varying impulse response function for each path, provides data for the subsequent calculation of the total impulse response, helping to improve the accuracy of the total time-varying impulse response function and thereby enhancing the overall simulation accuracy of the model.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the impulse response functions of all time-delay paths are summed to obtain the total time-varying impulse response function of the channel. Specifically, this includes: summing the impulse response functions of all time-delay paths to obtain the initial total time-varying impulse response function of the channel; obtaining hardware impairment parameters and generating a hardware impairment response function based on the hardware impairment parameters, which include gain difference, phase difference, and parameters representing phase noise spectrum characteristics; and performing a convolution operation between the total time-varying impulse response function and the hardware impairment response function to obtain a total time-varying impulse response function that incorporates hardware impairments.

[0017] By adopting the above technical solution, the data processing unit first sums the impulse response functions of all paths to obtain the initial total time-varying impulse response function. This function reflects the basic propagation characteristics of the channel. The hardware impairment response function, generated by the hardware impairment parameters, takes into account non-ideal factors such as gain difference, phase difference, and phase noise introduced by hardware devices in the actual communication system. The data processing unit convolves the initial total time-varying impulse response function with the hardware impairment response function to obtain a total time-varying impulse response function that incorporates hardware impairments. This more comprehensively reflects the characteristics of the actual communication channel, avoids simulation deviations caused by ignoring hardware factors, and further improves the simulation accuracy of the model.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the modeling of a broadband shortwave channel is completed based on the total time-varying impulse response function (TTRF). Specifically, this includes: performing a Fourier transform on the TTRF along the time dimension to obtain a time-delay-Doppler joint scattering function; mapping the amplitude of the TTRF to a three-dimensional space with time and time delay as coordinate axes to obtain impulse response surface data; performing a short-time Fourier transform on the TTRF to obtain dynamic power spectrum slice data; and completing the modeling of the broadband shortwave channel based on the channel physical characteristics jointly represented by the time-delay-Doppler joint scattering function, the impulse response surface data, and the dynamic power spectrum slice data.

[0019] By employing the above technical solutions, the data processing unit obtains the time-delay-Doppler joint scattering function by performing a Fourier transform on the total time-varying impulse response function along the time dimension. This function clearly demonstrates the joint characteristics of the channel in the time-delay and Doppler domains. The impulse response surface data obtained by mapping the amplitude of the total time-varying impulse response function to three-dimensional space intuitively presents the variation of signal amplitude with time and time delay. The dynamic power spectrum slice data obtained by performing a short-time Fourier transform reflects the dynamic evolution of the signal power spectrum over time. These three types of data comprehensively characterize the physical characteristics of the channel from different dimensions. Modeling is completed based on the channel characteristics they collectively represent, making the model's description of the actual channel more comprehensive and in-depth, thereby effectively improving the simulation accuracy of the actual physical process.

[0020] In a second aspect, this application provides a channel modeling system, including one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, which the one or more processors call to cause the channel modeling system to perform the methods described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on a channel modeling system, cause the channel modeling system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer program product that, when run on a channel modeling system, causes the channel modeling system to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By employing techniques such as constructing target time delay power distribution curves using shape parameters and time delay spread parameters to determine the power intensity of each path, generating phase functions by combining Doppler characteristics, and generating random modulation functions through filtering, and finally fusing to obtain the total time-varying impulse response function, this approach effectively alleviates the technical problem of insufficient simulation accuracy caused by using static and idealized functions to describe time delay power distribution in existing technologies. This approach more realistically reflects the multipath structure and dynamic characteristics of the channel, thereby improving the model's accuracy in simulating actual physical processes.

[0024] 2. By employing the technique of substituting shape parameters and time delay spread parameters into the gamma function to construct the target time delay power distribution curve, and determining the discrete time delay value and corresponding power intensity of each path through sampling, the technical problem that static and idealized functions cannot truly reflect multipath structures under different conditions is effectively alleviated. This results in making the time delay power distribution more consistent with the actual propagation environment and improving the model's accuracy in describing multipath structures.

[0025] 3. By employing a technique that calculates the Doppler frequency shift and rate of change for each path based on channel environment data including geographical location and communication time, and then constructs a phase function that varies with time, the technical problem of being unable to accurately reflect the frequency and phase changes of the signal caused by the dynamic changes of the medium is effectively alleviated. This achieves the technical effect of making the phase function more consistent with the actual propagation scenario and improving the accuracy of the model in simulating the Doppler effect. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a channel modeling method in an embodiment of this application; Figure 2 This is another flowchart illustrating the channel modeling method in the embodiments of this application; Figure 3 This is a schematic diagram of the hardware structure of the channel modeling system in an embodiment of this application. Detailed Implementation

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0029] For ease of understanding, the method provided in this implementation is described in process below. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a channel modeling method in an embodiment of this application.

[0030] 101. Based on the shape parameter and delay spread parameter, construct the target delay power distribution curve, and determine the power intensity of each delay path based on the target delay power distribution curve. The shape parameter is used to adjust the attenuation pattern of the echo energy, and the delay spread parameter is used to represent the duration of the channel echo cluster.

[0031] The shape parameter represents the characteristic value used to describe the echo energy attenuation curve, and can be the control parameter of an exponential, Gaussian or other mathematical function; the time delay spread parameter refers to the time interval between the first echo and the last detectable echo in the channel; the target time delay power distribution curve represents the power intensity distribution of each time delay component in the channel; the time delay path represents a specific propagation path of the electromagnetic wave when it propagates in the ionosphere; the power intensity represents the energy of the signal on each propagation path.

[0032] Specifically, the data processing unit (the data processing unit in the channel modeling system) first receives shape parameters and delay spread parameters as input. Then, based on the received shape parameters, it adjusts the attenuation pattern of the echo energy. Simultaneously, it uses the delay spread parameters to determine the duration of the channel echo cluster. Through the coordination of the shape parameters and delay spread parameters, the data processing unit constructs a complete target delay power distribution curve. Based on the constructed target delay power distribution curve, the data processing unit further determines the power intensity on each delay path. In this process, the data processing unit transforms the target delay power distribution curve into specific power intensity values, which reflect the signal energy distribution on each delay path.

[0033] 102. Based on the Doppler frequency shift and the rate of change of the Doppler frequency shift, generate the phase function corresponding to each time delay path. The phase function is used to represent the signal frequency difference on the time delay path.

[0034] Doppler shift represents the signal frequency offset caused by the movement of the transmitter, receiver, or ionosphere; Doppler shift rate of change represents the rate at which the frequency offset changes over time; phase function represents the phase change pattern of the signal during propagation; signal frequency difference represents the frequency change of the received signal relative to the transmitted signal.

[0035] Specifically, the data processing unit first receives the Doppler frequency shift and the rate of change of the Doppler frequency shift as input parameters. Then, for each time delay path, the data processing unit calculates the frequency difference of the signal along that path based on the received Doppler frequency shift parameters. Simultaneously, the data processing unit incorporates the Doppler frequency shift rate parameter into the calculation process to characterize the frequency difference's variation over time. By comprehensively considering the Doppler frequency shift and the rate of change of the Doppler frequency shift, the data processing unit generates a corresponding phase function for each time delay path. These phase functions accurately describe the frequency variation characteristics of the signal along each time delay path, providing a basis for subsequent channel characteristic simulations based on phase changes.

[0036] 103. By filtering the Gaussian noise according to the Doppler power spectrum, a random modulation function corresponding to each time delay path is generated. The random modulation function is used to represent the fading characteristics of the signal on the time delay path as time changes.

[0037] Gaussian noise represents a random signal that follows a Gaussian distribution; the Doppler power spectrum represents the distribution characteristics of signal power in the frequency domain; filtering represents the operation of selectively processing a signal in the frequency domain; and the random modulation function represents a complex-valued function that describes the time-varying characteristics of a channel.

[0038] Specifically, the data processing unit first generates Gaussian noise that conforms to statistical characteristics. Then, based on the predetermined Doppler power spectrum characteristics, it filters the generated Gaussian noise. The filtering process ensures that the processed signal has the required Doppler power spectrum characteristics. The data processing unit performs filtering for each time delay path, generating a corresponding random modulation function. The random modulation function accurately reflects the fading characteristics of the signal over time on each time delay path, providing a time-varying description of the channel model.

[0039] 104. For each time delay path, multiply the power intensity, phase function, and random modulation function to obtain the time-varying impulse response function for each time delay path. The time-varying impulse response function is used to represent the changes in signal amplitude and phase over time on different time delay paths.

[0040] The time-varying impulse response function is a mathematical function used to represent the characteristics of the amplitude and phase of a signal changing with time during transmission; the time delay path refers to the propagation path of the signal with a specific time delay value during transmission. It's important to understand that the multiplication here refers to performing a complex multiplication operation on the power intensity term representing the signal amplitude, the phase function representing the phase change (usually in complex exponential form), and the random modulation function representing random fading (in complex form), thereby synthesizing the time-varying impulse response. Specifically, the data processing unit first acquires the power intensity value, phase function, and random modulation function corresponding to each time delay path. The phase function contains phase change information determined by the Doppler frequency shift and the rate of change of the Doppler frequency shift, while the random modulation function reflects the random fading characteristics of the signal. Then, the data processing unit multiplies the acquired power intensity value, phase function, and random modulation function. Through this multiplication, the data processing unit integrates the influence of the three parameters to generate the time-varying impulse response function for that time delay path. The time-varying impulse response function fully describes the amplitude and phase characteristics of the signal changing with time on that time delay path. The data processing unit repeats the above process for each time delay path, ultimately obtaining the time-varying impulse response functions for all time delay paths.

[0041] To describe this process more clearly, the superposition of the time-varying impulse response functions of each path can be represented by the following exemplary formula. The time delay power distribution P(τ) and the phase function are determined. (Its specific form is a complex exponential function containing the Doppler frequency shift term, where j is the imaginary unit, f) c For carrier frequency, The Doppler frequency shift term related to path delay, where t is a time variable, and the random modulation function ψ(t, τ) are superimposed along the path, and the expression is: Where h(t, τ) is the time-varying channel impulse response, describing the complete characteristics of the channel; ∑ n To sum over all n independent multipath components; P n To determine the discrete time delay τ of the power distribution curve P(τ) on the nth path. n The power intensity value obtained from sampling at point D; n (t,τ) is the phase function of the nth path, mainly caused by Doppler frequency shift; ψ n (t, τ) is the random modulation function of the nth path, which is a complex random process used to introduce random fading characteristics of the channel (such as Rayleigh fading or Rice fading) to ensure that different paths are statistically independent.

[0042] 105. Sum the impulse response functions of all time-delayed paths to obtain the total time-varying impulse response function of the channel, and complete the modeling of the broadband shortwave channel based on the total time-varying impulse response function.

[0043] The total time-varying impulse response function is a mathematical function that represents the transmission characteristics of the entire channel; broadband shortwave channel modeling refers to constructing a mathematical model that can describe the transmission characteristics of broadband shortwave signals; summation operation refers to the mathematical operation process of superimposing multiple time-varying impulse response functions.

[0044] Specifically, the data processing unit first obtains the time-varying impulse response functions for all time-delay paths, then sums these time-varying impulse response functions to synthesize the signal transmission characteristics of all time-delay paths. Through summation, the data processing unit obtains a total time-varying impulse response function that can completely describe the transmission characteristics of the entire channel. Then, based on the total time-varying impulse response function, the data processing unit constructs a broadband shortwave channel model that includes features such as multipath propagation, time-varying characteristics, and frequency selectivity. This broadband shortwave channel model can accurately reflect the various physical phenomena and changing characteristics experienced by the signal during transmission.

[0045] Using the channel modeling method in this embodiment, the data processing unit first constructs a target time-delay power distribution curve using shape parameters and time-delay spread parameters. The shape parameters can flexibly adjust the echo energy attenuation pattern, while the time-delay spread parameters accurately reflect the duration of the echo cluster. The combination of the two makes the constructed time-delay power distribution more closely resemble the actual multipath structure of the channel. Based on this, the power intensity of each time-delay path is determined more accurately. Then, combined with the phase function reflecting the signal frequency difference and the random modulation function reflecting the signal fading characteristics over time, the time-varying impulse response function obtained by multiplying the three comprehensively reflects the time-varying characteristics of signal amplitude and phase on each path. Finally, the data processing unit obtains the total time-varying impulse response function by summing the impulse responses of all paths. This total time-varying impulse response function integrates various actual channel characteristics such as multipath propagation and Doppler effect, thereby effectively alleviating the problem of inaccurate model simulation accuracy.

[0046] Based on the above, the following is a more detailed description of the process provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the channel modeling method in this application.

[0047] 201. Obtain shape parameters and time delay spread parameters.

[0048] Shape parameters are key parameters used to adjust the attenuation pattern of echo energy. They determine the attenuation trend of the time delay power distribution curve (such as the attenuation rate and the shape of the curve). For example, when the shape parameter is large, the echo energy attenuation may be smoother, while when the value is small, the attenuation is steeper. Delay spread parameters are parameters used to represent the duration of the echo cluster in the channel. They reflect the time interval between the earliest and latest arriving echo signals in the channel. For example, if the delay spread parameter of a broadband shortwave channel is 10 μs, it means that the duration of the echo cluster in that channel is approximately 10 μs. Specifically, the data processing unit first identifies the broadband shortwave communication scenario corresponding to the current modeling task (such as military emergency communication scenario, aviation and maritime communication scenario, etc.). Differences in ionospheric state and signal propagation path under different scenarios will lead to differences in the required shape parameters and delay spread parameters. Then, the data processing unit determines the source of parameter acquisition: if historical channel modeling data or a preset scenario parameter library exists, the data processing unit will retrieve the shape parameters and delay spread parameters matching the current scenario from these storage media. For example, it can retrieve the shape parameters (such as 2.5) and delay spread parameters (such as 8μs) corresponding to the low-latitude ionospheric environment from the military communication scenario parameter library. If no existing parameters exist, the data processing unit will start the real-time parameter acquisition process, collect the echo signal data of the current channel through the connected channel detection equipment (such as a shortwave channel detector), perform delay analysis and energy attenuation fitting on the echo signal, and calculate the shape parameters and delay spread parameters. For example, it can perform gamma function fitting on the data of the acquired echo signal energy changing over time and extract the shape parameters (such as 3.0) and delay spread parameters (such as 12μs) from the fitted curve. After acquiring the parameters, the data processing unit will also verify the validity of the parameters and determine whether the parameter values ​​are within a reasonable range (e.g., shape parameters are usually positive numbers, and delay spread parameters need to be compatible with the communication bandwidth of the current channel to avoid abnormal parameters that could lead to distortion in subsequent curve construction). After verification, the parameters will be temporarily stored in the internal storage module for use in subsequent steps.

[0049] 202. Substitute the shape parameters and time delay spread parameters into the gamma function to construct the target time delay power distribution curve.

[0050] Specifically, the data processing unit first reads the acquired shape parameters and time delay spread parameters. Then, it initializes the computational environment for the gamma function, including setting the computational precision and sampling interval. The shape parameters are then used as shape control parameters for the gamma function, and the time delay spread parameters are used as scale control parameters, which are substituted into the gamma function. Using numerical calculation methods, the data processing unit calculates the function values ​​of the gamma function at different time delay points within a preset time delay range. All calculated function values ​​are then connected to form a continuous target time delay power distribution curve. Finally, the data processing unit normalizes the generated target time delay power distribution curve to ensure that it meets the energy normalization requirements.

[0051] The time delay power distribution is calculated using a gamma function based on the shape parameter α and the time delay spread Δ. The expression for the time delay power distribution is as follows:

[0052] Where P(τ) is the time-delay power spectral density, representing the received signal power at a time delay of τ; τ is the time delay, referring to the time it takes for the signal to travel from the transmitter to the receiver; A is the amplitude scaling factor, usually related to the total received power of the path, used to normalize or adjust the overall power level; α is the shape parameter, controlling the shape of the time-delay power distribution curve; Γ(α) is the gamma function, an extension of the factorial function to non-integer and complex numbers; Δ is the time delay spread, a parameter that measures the severity of time dispersion in a multipath channel, representing the degree of time dispersion of the multipath components; τ c τ represents the arrival time of the first distinguishable multipath component, which is the truncated delay or minimum path delay; z is the normalized delay, a dimensionless treatment of the delay τ for ease of calculation.

[0053] 203. Sample the target time delay power distribution curve to determine the discrete time delay value and corresponding power intensity of each time delay path.

[0054] Sampling refers to the process of selecting discrete points from a continuous curve; discrete time delay value refers to the specific numerical value representing the time delay of a particular propagation path; power intensity refers to the signal energy on a specific time delay path; time delay path refers to the path through which the signal propagates and has a specific time delay value.

[0055] Specifically, the data processing unit first acquires the constructed target delay power distribution curve, then sets the sampling interval and the number of sampling points; these parameters determine the accuracy and complexity of the final model. The data processing unit performs uniform sampling on the target delay power distribution curve according to the set sampling interval, obtaining a series of discrete delay values ​​(i.e., discrete delay values). For each sampled discrete delay value, the data processing unit reads the curve function value corresponding to that delay point, using it as the power intensity of that delay path. It then associates each pair of discrete delay values ​​and power intensities to form parameter pairs for the delay path. The data processing unit repeats this process for all sampling points, ultimately obtaining a complete set of delay path parameters.

[0056] 204. Obtain channel environment data, which includes geographical location information and communication time information.

[0057] Channel environment data refers to a set of data describing the characteristics of a communication scenario; geographic location information refers to data indicating the spatial location of the sending and receiving parties; communication time information refers to time data indicating the moment when communication occurs.

[0058] Specifically, the data processing unit first checks if channel environment data exists. If it does, it directly reads the geographical location information and communication time information. If not, it receives the geographical location information and communication time information from an external interface. For geographical location information, the data processing unit obtains spatial location data such as latitude and longitude coordinates and altitude of both the transmitting and receiving parties. For communication time information, the data processing unit obtains the specific time of the communication, including year, month, day, hour, minute, and second. The data processing unit performs format verification and validity checks on the acquired channel environment data to ensure its integrity and correctness, and temporarily stores the verified channel environment data in its internal memory.

[0059] 205. Based on the channel environment data, calculate the Doppler frequency shift and Doppler frequency shift rate of change for each time delay path.

[0060] Doppler shift refers to the change in signal frequency caused by the relative motion between the transmitter and receiver; the rate of change of Doppler shift refers to how quickly the Doppler shift changes over time.

[0061] Specifically, the data processing unit first reads the channel environment data, and then calculates the relative positional relationship between the transmitting and receiving parties based on geographical location information. For each time delay path, the data processing unit calculates the signal propagation direction along that time delay path based on radio wave propagation theory, and then analyzes the motion state information of the transmitting and receiving parties at that moment, including velocity and acceleration, in conjunction with communication time information. Next, the data processing unit combines the propagation direction with the motion state information to calculate the Doppler frequency shift value along that time delay path. Finally, the data processing unit further analyzes the changing trend of the motion state, calculates the time derivative of the Doppler frequency shift, obtains the rate of change of the Doppler frequency shift, and repeats the above calculation process for each time delay path to finally obtain the Doppler parameters for all time delay paths.

[0062] 206. Based on Doppler frequency shift and Doppler frequency shift rate of change, construct the time-varying phase function corresponding to each time delay path.

[0063] A phase function is a mathematical function that describes how the phase of a signal changes over time.

[0064] Specifically, the data processing unit first acquires the Doppler frequency shift and Doppler frequency shift rate of change for each time delay path. Then, the data processing unit sets a time observation window to determine the calculation time range of the phase function. For each time delay path, the data processing unit uses the Doppler frequency shift as the first-order coefficient of the phase change and the Doppler frequency shift rate of change as the second-order coefficient of the phase change. Within the time observation window, it converts the frequency information into phase information through integration. Finally, the data processing unit considers the influence of the initial phase, superimposing the calculated phase change onto the initial phase, and then normalizes the calculation results to ensure that the value range of the phase function is within a reasonable range. The data processing unit then associates and stores the final phase function with the corresponding time delay path.

[0065] 207. Based on the channel environment data, select the target Doppler power spectrum from the Doppler power spectrum type library. The Doppler power spectrum type library includes Gaussian Doppler power spectrum and Lorentz Doppler power spectrum. The channel environment data includes geographical location information and communication time information.

[0066] Doppler power spectrum is a mathematical function that describes the distribution of signal power with Doppler frequency shift; Gaussian Doppler power spectrum is a power spectrum that conforms to a Gaussian distribution; Lorentz Doppler power spectrum is a power spectrum that conforms to a Lorentz distribution; Doppler power spectrum type library is a database that stores different types of power spectra.

[0067] Specifically, the data processing unit first analyzes the geographical location information and communication time information in the channel environment data. Based on the geographical location information, the data processing unit determines the type of communication scenario, such as urban environment, suburban environment, or open area. Simultaneously, based on the communication time information, it analyzes the changing characteristics of ionospheric propagation conditions. Then, based on the scenario type and propagation conditions, the data processing unit determines the most suitable Doppler power spectrum type. If the scenario is determined to be one with relatively concentrated scatterers, a Gaussian Doppler power spectrum is selected; if the scenario is determined to be one with relatively dispersed scatterers, a Lorentz Doppler power spectrum is selected. Finally, the data processing unit uses the selected power spectrum type as the target Doppler power spectrum and retrieves the corresponding mathematical expressions and parameters from the type library.

[0068] 208. Based on the target Doppler power spectrum, generate the corresponding frequency response characteristics in the frequency domain. The square of the amplitude of the frequency response characteristics is proportional to the target Doppler power spectrum.

[0069] Frequency response characteristics refer to the mathematical functions that describe the system's response to signals of different frequencies; amplitude refers to the magnitude of the frequency response; the frequency domain refers to the mathematical space with frequency as the independent variable.

[0070] Specifically, the data processing unit first acquires the target Doppler power spectrum data, which describes the power distribution of the target at different Doppler frequencies. Then, the data processing unit constructs a corresponding frequency response characteristic in the frequency domain, such that the square of the amplitude of this frequency response characteristic is proportional to the target Doppler power spectrum. This means that if the value of the target Doppler power spectrum at a certain frequency is P(f), then the amplitude of the frequency response characteristic H(f) should satisfy |H(f)|²∝P(f), i.e. The data processing unit ensures in this way that the generated frequency response characteristics accurately reflect the Doppler characteristics of the target, providing a foundation for subsequent signal processing.

[0071] 209. Generate a Gaussian white noise sequence and perform a Fourier transform on the Gaussian white noise sequence to convert it to the frequency domain to obtain a frequency domain noise sequence.

[0072] Gaussian white noise sequence represents a random sequence that conforms to a Gaussian distribution and has a power spectral density that is uniformly distributed across all frequencies; Fourier transform is a mathematical transformation method that converts a time-domain signal to the frequency domain; frequency-domain noise sequence refers to a noise signal represented in the frequency dimension after Fourier transform.

[0073] Specifically, the data processing unit first generates a Gaussian white noise sequence n(t) of appropriate length. Each sample point in this Gaussian white noise sequence is an independent and identically distributed random variable, following a Gaussian distribution with a mean of 0 and a variance of σ². The data processing unit typically uses a random number generator to generate these Gaussian-distributed random samples to ensure that the sequence has the characteristics of white noise, i.e., its autocorrelation function is approximately the impulse function, and its power spectral density is uniformly distributed across all frequencies. Then, the data processing unit applies a Fourier transform to the generated Gaussian white noise sequence n(t), transforming it from the time domain to the frequency domain to obtain a frequency-domain noise sequence N(f). This frequency-domain noise sequence N(f) is a complex sequence containing amplitude and phase information. Its statistical properties in the frequency domain are that the real and imaginary parts of each frequency component follow a Gaussian distribution, and different frequency points are independent of each other.

[0074] 210. Multiply the frequency domain noise sequence with the frequency response characteristics in the frequency domain to obtain the processed frequency domain sequence.

[0075] Frequency domain multiplication refers to point-to-point complex multiplication of two sequences in the frequency dimension; the processed frequency domain sequence represents the frequency dimension of the signal after frequency domain filtering or modulation.

[0076] Specifically, the data processing unit performs point-to-point complex multiplication of the frequency domain noise sequence N(f) and the frequency response characteristic H(f) in the frequency domain. That is, for each frequency point f, it calculates Y(f) = N(f) × H(f), where "×" represents complex multiplication. This multiplication operation involves the rules of complex multiplication, namely, the amplitude of the product of two complex numbers is equal to the product of the amplitudes of the two complex numbers, and the phase is equal to the sum of the phases of the two complex numbers. Through this frequency domain multiplication operation, the data processing unit achieves spectral shaping of the white noise, so that the processed frequency domain sequence Y(f) has spectral characteristics that match the frequency response characteristic H(f). This means that the power spectral density |Y(f)|² of Y(f) will be proportional to the target Doppler power spectrum, because |Y(f)|² = |N(f)|² × |H(f)|², and |H(f)|² is proportional to the target Doppler power spectrum.

[0077] 211. Perform an inverse Fourier transform on the frequency domain sequence to obtain the random modulation function corresponding to each time delay path.

[0078] The inverse Fourier transform is a mathematical transformation method that converts a frequency domain signal back to the time domain; the random modulation function represents a random process used to describe the time-varying characteristics of a channel; the time domain refers to the representation space of a signal in the time dimension.

[0079] Specifically, the data processing unit applies an inverse Fourier transform (IFFT) to the processed frequency domain sequence Y(f), transforming it back to the time domain to obtain a time domain sequence y(t). This time domain sequence y(t) is the random modulation function corresponding to each time delay path, describing the channel's variation characteristics in the time dimension. Since the power spectrum of Y(f) is proportional to the target Doppler power spectrum, the generated random modulation function y(t) has time-domain statistical characteristics that match the target Doppler characteristics. The data processing unit generates an independent random modulation function for each time delay path in this way. These functions collectively describe the time-varying characteristics of the multipath channel and can be used for subsequent channel simulation or signal processing. It should be noted that steps 207 to 211 are repeated for each independent time delay path to generate a set of statistically independent random modulation functions, each corresponding to the fading characteristics of its respective time delay path.

[0080] 212. For each time delay path, multiply the power intensity, phase function, and random modulation function to obtain the time-varying impulse response function for each time delay path. The time-varying impulse response function is used to represent the changes in signal amplitude and phase over time on different time delay paths.

[0081] Step 212 specifically includes steps 2121 to 2123, which are not shown in the figure.

[0082] 2121. Multiply the power intensity, phase function, and random modulation function point by point in the time domain to obtain the first time-varying impulse response function.

[0083] Point-by-point multiplication in the time domain refers to performing point-to-point multiplication operations on multiple functions in the time dimension; the first time-varying impulse response function represents the initially generated impulse response function describing the time-varying characteristics of the channel.

[0084] Specifically, the data processing unit first obtains the power intensity function P(t), which represents the signal energy distribution and describes the signal power level at different times; simultaneously, it obtains the phase function φ(t), which describes the phase change characteristics of the signal during propagation; and the random modulation function m(t) generated in the previous steps, which describes the random time-varying characteristics of the channel. Then, the data processing unit performs point-by-point multiplication of these three functions in the time domain, that is, for each time point t, it calculates h1(t) = P(t) × e (jφ(t)) ×m(t), where e (jφ(t)) This represents the complex exponential form of the phase function. Through this multiplication operation, the data processing unit integrates the amplitude characteristics, phase characteristics, and time-varying characteristics of the channel to form a complete first time-varying impulse response function h1(t). This first time-varying impulse response function h1(t) can comprehensively describe the changing characteristics of the channel in the time dimension, including amplitude attenuation, phase change, and Doppler effect.

[0085] 2122. By interpolation, the data rate of the first time-varying impulse response function is increased to the preset channel matching rate to obtain the second time-varying impulse response function.

[0086] Interpolation refers to the process of estimating new data points among known data points using mathematical methods; data rate refers to the number of data points per unit time; preset channel matching rate refers to the data sampling rate matched with the actual communication system; the second time-varying impulse response function represents the impulse response function with a higher sampling rate obtained after interpolation.

[0087] Specifically, the data processing unit first obtains a first time-varying impulse response function h1(t), which has an initial data rate f1. Then, it determines a preset channel matching rate f2, which is typically determined by the parameters of the actual communication system, and f2 > f1. Next, the data processing unit applies an interpolation algorithm to h1(t), inserting new data points between the existing data points, thus increasing the data point density from f1 to f2. The interpolation process needs to preserve the spectral characteristics of the signal and avoid introducing unnecessary distortion. Through this interpolation, the data processing unit obtains a second time-varying impulse response function h2(t), which has a higher data rate and can match the sampling rate of the actual communication system, thereby achieving more accurate channel simulation.

[0088] 2123. Use the second time-varying impulse response function as the time-varying impulse response function for each time-delay path.

[0089] The impulse response function refers to the system's response to a unit impulse input. The second time-varying impulse response function is the second time-varying impulse response function generated by the data processing unit for each time delay path, which serves as the final time-varying impulse response function for that time delay path.

[0090] Specifically, the data processing unit assigns the second time-varying impulse response function h2(t) to each time-delay path, ensuring that each path has the same time-varying characteristic model. In real multipath channels, different time-delay paths may have different time-varying characteristics, but in some channel modeling methods, it can be assumed that all paths share the same basic time-varying characteristic model, differing only in amplitude, phase, or time delay. Through this assignment, the data processing unit establishes a time-varying impulse response function h_p(t) = h2(t) for each time-delay path p. These functions together constitute a complete multipath time-varying channel model, which can be used for subsequent signal transmission simulation or system performance evaluation.

[0091] 213. Sum the impulse response functions of all time-delayed paths to obtain the total time-varying impulse response function of the channel, and complete the modeling of the broadband shortwave channel based on the total time-varying impulse response function.

[0092] Step 213 specifically includes steps 2131 to 2137, all of which are not shown in the figure.

[0093] 2131. Summing the impulse response functions of all time-delayed paths yields the initial total time-varying impulse response function of the channel.

[0094] The initial total time-varying impulse response function represents the channel response function that integrates the characteristics of all time-delay paths but does not yet consider the hardware effects.

[0095] Specifically, the data processing unit first obtains the time-varying impulse response function h_p(t, τ) for each time-delay path p, where t represents the observation time and τ represents the time delay. Then, the data processing unit sums the impulse response functions of all P paths, i.e., calculates... This summation process essentially adds up the contributions of multiple independent propagation paths to form a complete channel model. In this model, the signal reaches the receiver through different paths, each path having its own time delay, amplitude, and phase characteristics, which together determine the overall propagation characteristics of the channel. Through this summation operation, the data processing unit obtains the initial total time-varying impulse response function h_total(t, τ) of the channel, which comprehensively describes the channel's variation characteristics in both time and time delay dimensions.

[0096] 2132. Obtain hardware damage parameters and generate a hardware damage response function based on the hardware damage parameters. The hardware damage parameters include gain difference, phase difference, and parameters representing the phase noise spectrum characteristics.

[0097] Hardware impairment parameters represent a set of parameters describing the hardware imperfections of a communication system; gain difference refers to the amplitude imbalance between different signal paths or components; phase difference represents the phase inconsistency between different signal paths or components; phase noise spectrum characteristic parameters describe the spectral distribution characteristics of oscillator phase noise; hardware impairment response function refers to the response function that simulates the impact of hardware imperfections on the signal.

[0098] Specifically, the data processing unit first acquires parameters describing the system's hardware imperfections, including the gain difference ΔG (representing the degree of amplitude imbalance between different signal paths or components), the phase difference Δφ (representing the phase inconsistency between different signal paths or components), and a set of parameters P_PN describing the phase noise spectrum characteristics (such as the shape parameter and bandwidth parameter of the phase noise power spectral density). Then, the data processing unit constructs a hardware impairment response function h_hw(t) based on these parameters. This response function can typically be expressed as h_hw(t) = (1 + ΔG)·e (j(Δφ+φ_PN(t))) Here, φ_PN(t) is a time-varying phase noise process generated based on the phase noise spectrum characteristic parameters. In this way, the data processing unit creates a response function that can simulate the impact of actual hardware defects. This response function will be combined with the channel model in subsequent steps to achieve a more realistic system simulation.

[0099] 2133. Convolve the total time-varying impulse response function with the hardware impairment response function to obtain the total time-varying impulse response function that incorporates the hardware impairment.

[0100] Convolution is the integral operation between two functions.

[0101] Specifically, the data processing unit first acquires the generated total time-varying impulse response function and hardware impairment response function. Then, for each time step, the data processing unit performs a time-domain convolution operation on the total time-varying impulse response function and the hardware impairment response function. This process involves time-shifting one function, multiplying it by the other function, and integrating. The data processing unit repeats the convolution operation until the entire time range is covered. Through this convolution operation, the data processing unit combines the effects of channel transmission characteristics and hardware impairment characteristics to obtain a total time-varying impulse response function that more closely reflects the actual situation.

[0102] 2134. Perform a Fourier transform on the total time-varying impulse response function along the time dimension to obtain the time-delay-Doppler joint scattering function.

[0103] The time-delay-Doppler joint scattering function is a function that describes the scattering characteristics of a channel in two dimensions: time delay and Doppler frequency shift; the time dimension refers to the coordinate axis representing time variation.

[0104] Specifically, the data processing unit first acquires the total time-varying impulse response function (TRT) incorporating hardware impairments. For each fixed delay value, the data processing unit extracts a data sequence of the TRT changing over time at that fixed delay value. Then, it performs a Fourier transform on the extracted time sequence, converting the time-domain information into frequency-domain information to obtain the Doppler frequency shift characteristic corresponding to that delay value. Next, the data processing unit repeats the Fourier transform operation for all fixed delay values, ultimately obtaining the complete time-delay-Doppler joint scattering function. This time-delay-Doppler joint scattering function visually demonstrates the scattering intensity distribution of the channel under different delays and Doppler frequency shifts.

[0105] 2135. Map the amplitude of the total time-varying impulse response function to a three-dimensional space with time and time delay as coordinate axes to obtain impulse response surface data.

[0106] Three-dimensional space refers to a space consisting of three dimensions: time, delay, and amplitude; impulse response surface data refers to a three-dimensional dataset that describes the changes in channel response with time and delay.

[0107] Specifically, the data processing unit first acquires the total time-varying impulse response function (TTRF) incorporating hardware impairments. Then, it calculates the amplitude of the TTRF at each time point and delay value, i.e., the modulus of the complex function. Next, the data processing unit maps these amplitude data onto a three-dimensional space with time and delay as the horizontal and vertical axes and amplitude as the height, forming a three-dimensional surface. This surface visually displays the characteristics of channel response intensity changing with time and delay, with peaks representing strong signal paths and troughs representing weak signal regions. Through this mapping, the data processing unit obtains impulse response surface data, which can be used to intuitively analyze the time-varying characteristics and multipath structure of the channel, helping to understand the signal propagation behavior in a specific channel environment.

[0108] 2136. Perform a short-time Fourier transform on the total time-varying impulse response function to obtain dynamic power spectrum slice data.

[0109] Dynamic power spectrum slice data refers to a dataset that describes the changes in channel power spectrum over time; a time window refers to a local time range for performing Fourier transform.

[0110] Specifically, the data processing unit first obtains the total time-varying impulse response function (TRF) of the hardware impairment. Then, the data processing unit selects a suitable window function w(t) (such as a Hamming window, Gaussian window, etc.) and a window length T to window the TRF near each time point, obtaining local signals. Next, the data processing unit performs a Fourier transform on each local signal along the time dimension to obtain a short-time spectrum. By repeating this process at different time points, the data processing unit obtains a series of spectral slices. These slices collectively constitute dynamic power spectrum slice data. Dynamic power spectrum slice data describes how the power of the channel response dynamically changes with time, frequency, and delay, providing a time-frequency joint analysis perspective of channel characteristics, which helps in understanding the non-stationary characteristics of the channel and designing corresponding signal processing algorithms.

[0111] 2137. Based on the channel physical characteristics jointly represented by the time delay-Doppler joint scattering function, impulse response surface data, and dynamic power spectrum slice data, complete the modeling of the broadband shortwave channel.

[0112] Channel physical characteristics refer to the set of physical parameters that describe the transmission properties of a channel; broadband shortwave channel modeling refers to constructing a mathematical model that can describe the transmission characteristics of broadband shortwave signals; physical characteristics refer to physical parameters such as channel delay, Doppler, and power.

[0113] Specifically, the data processing unit first integrates the three data representations obtained in the previous steps: the time-delay-Doppler joint scattering function, impulse response surface data, and dynamic power spectrum slice data. These three types of data describe the physical characteristics of the channel from different perspectives: the time-delay-Doppler joint scattering function reveals the scattering characteristics and Doppler effect of the channel; the impulse response surface data shows the spatiotemporal distribution of the channel response intensity; and the dynamic power spectrum slice data provides a time-frequency joint analysis perspective of the channel characteristics. Then, based on these integrated data, the data processing unit establishes a complete broadband shortwave channel model. This broadband shortwave channel model can accurately simulate the unique characteristics of shortwave channels, such as ionospheric reflection, multipath propagation, Doppler spread, and time-varying fading. Through this comprehensive modeling method, the data processing unit obtains a mathematical model that comprehensively reflects the physical characteristics of broadband shortwave channels. This model can be used for communication system design, algorithm development, and performance evaluation, helping to improve the reliability and efficiency of shortwave communication systems in complex environments.

[0114] Using the channel modeling method in this embodiment, the data processing unit first acquires shape parameters and delay spread parameters, substitutes them into the gamma function to construct the target delay power distribution curve, and then samples to determine the discrete delay value and power intensity of each path, providing basic data that fits the actual multipath structure for modeling. Next, combined with channel environment data including geographical location and communication time, the Doppler frequency shift and rate of change of each path are calculated to generate a time-varying phase function. Simultaneously, based on the environmental data, the target Doppler power spectrum is selected, and Gaussian noise is filtered to generate a random modulation function. Subsequently, the power intensity, phase function, and random modulation function are multiplied point-by-point in the time domain and interpolated to increase the rate of change, obtaining the time-varying impulse response function of each delay path. Then, the data processing unit sums the time-varying impulse response functions of each delay path and fuses the hardware impairment parameters to generate the final total time-varying impulse response function. Finally, the data processing unit obtains multi-dimensional channel characteristic data through Fourier transform and other methods to complete the modeling, realizing a comprehensive characterization of multipath propagation, Doppler effect, time-varying fading and hardware impairment in broadband shortwave channels. This not only effectively solves the problem of insufficient simulation accuracy caused by static idealization assumptions in traditional models, but also more realistically restores the dynamic transmission process of actual channels.

[0115] The methods provided in the above embodiments can be executed by the data processing unit of the channel modeling system. The channel modeling system in the embodiments of this invention is described below from a hardware processing perspective; please refer to [link to relevant documentation]. Figure 3 This is a schematic diagram of the physical device structure of a channel modeling system in an embodiment of this application.

[0116] It should be noted that, Figure 3 The structure of the channel modeling system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0117] like Figure 3 As shown, the channel modeling system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage portion 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0118] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0119] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0120] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0122] Specifically, the channel modeling system of this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the broadband shortwave channel modeling method provided in the above embodiment.

[0123] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the channel modeling system described in the above embodiments; or it may exist independently and not assembled into the channel modeling system. The storage medium carries one or more computer programs that, when executed by a processor of the channel modeling system, cause the channel modeling system to implement the broadband shortwave channel modeling method provided in the above embodiments.

[0124] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0125] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0126] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0127] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A broadband shortwave channel modeling method, characterized in that, include: Based on the shape parameter and the delay spread parameter, a target delay power distribution curve is constructed, and the power intensity of each delay path is determined based on the target delay power distribution curve. The shape parameter is used to adjust the attenuation pattern of the echo energy, and the delay spread parameter is used to represent the duration of the channel echo cluster. Based on the Doppler frequency shift and the rate of change of the Doppler frequency shift, a phase function is generated for each time delay path. The phase function is used to represent the signal frequency difference on the time delay path. By filtering the Gaussian noise according to the Doppler power spectrum, a random modulation function corresponding to each time delay path is generated. The random modulation function is used to represent the fading characteristics of the signal on the time delay path as time changes. For each time delay path, the power intensity, the phase function, and the random modulation function are multiplied to obtain the time-varying impulse response function for each time delay path. The time-varying impulse response function is used to represent the changes in signal amplitude and phase over time on different time delay paths. The impulse response functions of all time-delay paths are summed to obtain the total time-varying impulse response function of the channel, and the broadband shortwave channel is modeled based on the total time-varying impulse response function.

2. The method according to claim 1, characterized in that, The step of constructing a target delay power distribution curve based on shape parameters and delay spread parameters, and determining the power intensity of each delay path based on the target delay power distribution curve, specifically includes: Obtain shape parameters and delay spread parameters; Substitute the shape parameters and the time delay spread parameters into the gamma function to construct the target time delay power distribution curve; The target time delay power distribution curve is sampled to determine the discrete time delay value and corresponding power intensity of each time delay path.

3. The method according to claim 1, characterized in that, The step of generating the phase function corresponding to each time delay path based on the Doppler frequency shift and the rate of change of the Doppler frequency shift specifically includes: Acquire channel environment data, which includes geographical location information and communication time information; Based on the channel environment data, calculate the Doppler frequency shift and Doppler frequency shift rate of change for each time delay path; Based on the Doppler frequency shift and the Doppler frequency shift rate of change, a time-varying phase function corresponding to each time delay path is constructed.

4. The method according to claim 1, characterized in that, The step of generating a random modulation function corresponding to each time delay path by filtering the Gaussian noise according to the Doppler power spectrum specifically includes: Based on the channel environment data, a target Doppler power spectrum is selected from the Doppler power spectrum type library, which includes Gaussian Doppler power spectrum and Lorentz Doppler power spectrum. The channel environment data includes geographical location information and communication time information. Based on the target Doppler power spectrum, a corresponding frequency response characteristic is generated in the frequency domain, and the square of the amplitude of the frequency response characteristic is proportional to the target Doppler power spectrum. A Gaussian white noise sequence is generated, and the Gaussian white noise sequence is Fourier transformed to the frequency domain to obtain a frequency domain noise sequence; The frequency domain noise sequence is multiplied with the frequency response characteristic in the frequency domain to obtain the processed frequency domain sequence. Perform an inverse Fourier transform on the frequency domain sequence to obtain the random modulation function corresponding to each time delay path.

5. The method according to claim 1, characterized in that, For each time-delay path, the power intensity, the phase function, and the random modulation function are multiplied to obtain the time-varying impulse response function for each time-delay path, specifically including: The power intensity, the phase function, and the random modulation function are multiplied point by point in the time domain to obtain the first time-varying impulse response function; By interpolation, the data rate of the first time-varying impulse response function is increased to a preset channel matching rate to obtain the second time-varying impulse response function; The second time-varying impulse response function is used as the time-varying impulse response function for each time-delay path.

6. The method according to claim 1, characterized in that, The summation of the impulse response functions of all time-delayed paths to obtain the total time-varying impulse response function of the channel specifically includes: The initial total time-varying impulse response function of the channel is obtained by summing the impulse response functions of all time-delayed paths. Obtain hardware damage parameters and generate a hardware damage response function based on the hardware damage parameters, wherein the hardware damage parameters include gain difference, phase difference, and parameters representing phase noise spectrum characteristics; The total time-varying impulse response function is convolved with the hardware impairment response function to obtain a total time-varying impulse response function that incorporates hardware impairment.

7. The method according to claim 1, characterized in that, Based on the total time-varying impulse response function, the modeling of the broadband shortwave channel is completed, specifically including: Performing a Fourier transform along the time dimension on the total time-varying impulse response function yields the time-delay-Doppler joint scattering function; The amplitude of the total time-varying impulse response function is mapped to a three-dimensional space with time and time delay as coordinate axes to obtain impulse response surface data; A short-time Fourier transform is performed on the total time-varying impulse response function to obtain dynamic power spectrum slice data; Based on the channel physical characteristics jointly represented by the time delay-Doppler joint scattering function, the impulse response surface data, and the dynamic power spectrum slice data, the modeling of the broadband shortwave channel is completed.

8. A channel modeling system, characterized in that, Includes one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the channel modeling system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the channel modeling system, it causes the channel modeling system to perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the channel modeling system, the channel modeling system performs the method as described in any one of claims 1-7.

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