Method and device for constructing Gaussian scattering high-frequency ionosphere channel model

Abstract

The invention provides a Gaussian scattering high-frequency ionosphere channel model construction method and device, and relates to the technical field of communication. According to the technical scheme provided by the invention, the taps are arranged on the delay line, the relative time delay is determined, the Gaussian channel gain sequence is generated by combining the configured Gaussian filter coefficient and the white noise sequence, and then the Gaussian channel gain sequence and the relative time delay are combined to construct the Gaussian scattering high-frequency ionospheric channel model. And finally, the input original signal is converted into a simulation signal with Gaussian Doppler spread and multipath effect characteristics. According to the scheme, modeling of ionized layer multipath propagation is achieved through a delay line structure, feature description of Doppler frequency shift is achieved by modulating a white noise sequence through Gaussian filter coefficients, and coupling simulation of the multipath effect and the Doppler effect is achieved through combination of Gaussian channel gain sequences corresponding to taps and relative time delay. Therefore, the Gaussian scattering high-frequency ionosphere channel model can be constructed quickly and accurately.

Description

Technical Field

[0001] This application relates to the field of communication technology, specifically to a method and apparatus for constructing a Gaussian scattering high-frequency ionospheric channel model. Background Technology

[0002] High-frequency (HF) ionospheric radio communication plays an irreplaceable role in long-distance and specialized scenarios. Due to single-hop or multi-hop reflections in the E and F layers of the ionosphere, the propagation path is complex, resulting in typical characteristics at the receiver such as multipath propagation, time-varying fading, and Doppler spread. These characteristics cause multiplicative distortion and signal quality degradation. To facilitate system design and performance evaluation, it is essential to construct an HF channel model that reflects the actual characteristics of the ionospheric channel.

[0003] In existing technologies, the construction of ionospheric HF channel models mainly falls into two categories: one is based on extensive field measurements and data collection, extracting channel parameters through statistical analysis to establish a physical or statistical model that highly fits the real environment; the other uses simplified theoretical models or empirical formulas to quickly approximate channel characteristics. While the former offers higher accuracy, its model building cycle is long and computationally intensive, making it difficult to meet the demands of rapid response and large-scale applications. The latter, while able to complete model construction relatively quickly, fails to accurately characterize the ionospheric channel due to the neglect of some key physical characteristics. Therefore, existing technologies often fail to simultaneously balance speed and accuracy in model construction, resulting in a significant challenge in practical applications where efficiency and accuracy are difficult to achieve simultaneously. Summary of the Invention

[0004] This application provides a method and apparatus for constructing a Gaussian scattering high-frequency ionospheric channel model, which can quickly and accurately construct a Gaussian scattering high-frequency ionospheric channel model.

[0005] In a first aspect, this application provides a method for constructing a Gaussian scattering high-frequency ionospheric channel model, the method comprising: Multiple taps are set on the delay line to determine the relative time delay corresponding to each tap; Configure the Gaussian filter coefficients and white noise sequence corresponding to the tap, and generate the Gaussian channel gain sequence corresponding to the tap based on the Gaussian filter coefficients and the white noise sequence; The Gaussian channel gain sequence corresponding to each tap is combined with the relative time delay to obtain the Gaussian scattering high-frequency ionospheric channel model corresponding to the delay line. The original signal is input into the Gaussian scattering high-frequency ionospheric channel model, and the output is a simulated signal modulated by Gaussian Doppler expansion and multipath effect.

[0006] By employing the above technical solution, multiple taps are set on the delay line and their relative time delays are determined. A Gaussian channel gain sequence is generated by combining the configured Gaussian filter coefficients and a white noise sequence. This Gaussian channel gain sequence is then combined with the relative time delays to construct a Gaussian scattering high-frequency ionospheric channel model. Finally, the input original signal is converted into a simulated signal with Gaussian Doppler spread and multipath effect characteristics. This scheme achieves modeling of ionospheric multipath propagation through the delay line structure, modulates the white noise sequence using Gaussian filter coefficients to describe the Doppler frequency shift, and achieves coupled simulation of multipath and Doppler effects through the combination of the Gaussian channel gain sequence corresponding to each tap and the relative time delay. This enables the rapid and accurate construction of a Gaussian scattering high-frequency ionospheric channel model.

[0007] Optionally, configuring the Gaussian filter coefficients and white noise sequence corresponding to the tap, and generating the Gaussian channel gain sequence corresponding to the tap based on the Gaussian filter coefficients and the white noise sequence, includes: Based on the number of sampling points of the complex noise signal required by the tap, a real part white noise sequence and an imaginary part white noise sequence are generated using a random number generation function; The real part white noise sequence and the imaginary part white noise sequence are combined into a complex white noise sequence; Configure the Gaussian filter coefficients corresponding to the tap, and perform convolution processing on the white noise sequence corresponding to the tap and the Gaussian filter coefficients to obtain the Gaussian channel gain sequence.

[0008] By employing the above technical solution, real and imaginary white noise sequences are generated based on the number of complex noise signal sampling points required for the tap, and then synthesized into a complex white noise sequence. The Gaussian filter coefficients corresponding to the taps are then configured and convolved with the white noise sequence to obtain the Gaussian channel gain sequence. This scheme utilizes a random number generation function to generate real and imaginary white noise sequences and performs complex synthesis, ensuring the randomness and integrity of the white noise sequence. The convolution operation between the Gaussian filter coefficients and the white noise sequence achieves an accurate description of the channel gain characteristics, thereby accurately simulating the random fading characteristics in high-frequency ionospheric channels.

[0009] Optionally, the Gaussian filter coefficients include Gaussian filter parameters including sampling signal rate and Doppler spread. The step of configuring the Gaussian filter coefficients corresponding to the taps and performing convolution processing on the white noise sequence corresponding to the taps and the Gaussian filter coefficients to obtain a Gaussian channel gain sequence includes: Calculate the sampling ratio of the sampling signal rate to the Doppler spread; When the sampling ratio is greater than a preset first threshold, the sampling signal rate is reduced to an intermediate sampling rate; Configure the intermediate Gaussian filter coefficients corresponding to the tap according to the intermediate sampling rate, and perform convolution processing on the white noise sequence corresponding to the tap and the intermediate Gaussian filter coefficients to obtain the intermediate Gaussian channel gain sequence. The intermediate Gaussian channel gain sequence is interpolated and filtered to obtain the Gaussian channel gain sequence.

[0010] By employing the above technical solution, the sampling ratio of the sampling signal rate to the Doppler spread is calculated. When the sampling ratio exceeds a preset first threshold, the sampling signal rate is reduced to an intermediate sampling rate. Intermediate Gaussian filter coefficients corresponding to the taps are configured based on this intermediate sampling rate. After convolution processing, an intermediate Gaussian channel gain sequence is obtained. Finally, interpolation filtering is applied to this intermediate Gaussian channel gain sequence to obtain the Gaussian channel gain sequence. This scheme reduces computational complexity through an adaptive adjustment mechanism of the sampling ratio, while ensuring the accuracy of the channel gain sequence through interpolation filtering. By reducing the sampling signal rate and introducing an intermediate sampling rate, information loss is avoided, thus achieving a good balance between computational efficiency and model accuracy.

[0011] Optionally, when the sampling ratio is greater than a preset first threshold, reducing the sampling signal rate to an intermediate sampling rate includes: When the sampling ratio is greater than a preset first threshold, the downsampling factor corresponding to the first threshold is obtained, and the downsampling factor is a power of 2; Divide the sampling signal rate by the downsampling factor to obtain the intermediate sampling rate.

[0012] By employing the above technical solution, when the sampling ratio exceeds a preset first threshold, a downsampling factor corresponding to the first threshold is obtained. The sampling signal rate is then divided by this downsampling factor to obtain the intermediate sampling rate, where the downsampling factor is set to a power of 2. This solution simplifies the sampling rate conversion process by selecting a power of 2 as the downsampling factor, while ensuring the regularity of the intermediate sampling rate, thereby achieving efficient reduction of the sampling signal rate and reducing resource consumption during model construction.

[0013] Optionally, the step of performing interpolation filtering on the intermediate Gaussian channel gain sequence to obtain the Gaussian channel gain sequence includes: The intermediate Gaussian channel gain sequence is interpolated by a large factor using a CIC interpolation filter to obtain the first Gaussian channel gain sequence. The first Gaussian channel gain sequence is subjected to double interpolation and filtering using the HB interpolation filter to obtain the Gaussian channel gain sequence.

[0014] By employing the above technical solution, a CIC interpolation filter is used to perform large-fold interpolation on the intermediate Gaussian channel gain sequence to obtain the first Gaussian channel gain sequence. Then, an HB interpolation filter is used to perform double interpolation and filtering on the first Gaussian channel gain sequence to obtain the Gaussian channel gain sequence. This scheme uses a cascaded structure of CIC and HB interpolation filters. The CIC interpolation filter achieves efficient large-fold interpolation, while the double interpolation and filtering of the HB interpolation filter provides fine frequency response correction, thereby reducing computational complexity while ensuring the spectral characteristics of the Gaussian channel gain sequence.

[0015] Optionally, configuring the Gaussian filter coefficients corresponding to the tap includes: Obtain the attenuation information, frequency offset information, and frequency spread information from the tap gain function of the tap; Based on the attenuation information, frequency offset information, and frequency spread information, configure the Gaussian filter coefficients corresponding to the tap.

[0016] By employing the above technical solution, attenuation information, frequency offset information, and frequency spread information are obtained from the tap gain function, and the Gaussian filter coefficients corresponding to the taps are configured based on this information. This solution achieves precise configuration of the Gaussian filter coefficients by comprehensively considering the multidimensional information in the tap gain function, enabling the filter coefficients to accurately reflect the channel's attenuation characteristics, frequency offset effects, and frequency spread features.

[0017] Optionally, after inputting the original signal into the Gaussian scattering high-frequency ionospheric channel model and outputting the simulated signal modulated by Gaussian Doppler spread and multipath effect, the method further includes: The ideal Gaussian channel information is calculated using a Gaussian channel gain sequence, and the ideal channel information is used to construct an ideal channel model; the original signal is input into the ideal channel model, and the ideal signal is output. The simulated signal is compared with the ideal signal to obtain the comparison results of the Gaussian scattering high-frequency ionospheric channel model.

[0018] By employing the above technical solution, an ideal Gaussian channel information is calculated using a Gaussian channel gain sequence, and an ideal channel model is constructed. The original signal is input into the ideal channel model to obtain the ideal signal. Then, the simulated signal is compared with the ideal signal to obtain the comparison results of the Gaussian scattering high-frequency ionospheric channel model. This solution establishes a performance evaluation benchmark through the construction of the ideal channel model, and reveals the relationship between the constructed channel model and the ideal characteristics through comparative analysis of the simulated signal and the ideal signal, thereby achieving a quantitative evaluation of the performance of the Gaussian scattering high-frequency ionospheric channel model.

[0019] Secondly, this application provides a Gaussian scattering high-frequency ionospheric channel model construction apparatus, the apparatus comprising: An initialization module is used to set multiple taps of the channel on the delay line and determine the relative delay corresponding to the taps; A configuration module is used to configure the Gaussian filter coefficients and white noise sequence corresponding to the tap, and generate the Gaussian channel gain sequence corresponding to the tap based on the Gaussian filter coefficients and the white noise sequence. The model building module is used to combine the Gaussian channel gain sequence corresponding to each tap with the relative time delay to obtain the Gaussian scattering high-frequency ionospheric channel model corresponding to the delay line. The simulation module is used to input the original signal into the Gaussian scattering high-frequency ionospheric channel model and output a simulated signal modulated by Gaussian Doppler expansion and multipath effect.

[0020] Thirdly, this application provides a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing any of the methods described above.

[0021] Fourthly, this application provides an electronic device including a processor, a memory, and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform any of the methods described above.

[0022] In summary, the beneficial effects of the technical solution of this application include: By employing the above technical solution, multiple taps are set on the delay line and their relative time delays are determined. A Gaussian channel gain sequence is generated by combining the configured Gaussian filter coefficients and a white noise sequence. This Gaussian channel gain sequence is then combined with the relative time delays to construct a Gaussian scattering high-frequency ionospheric channel model. Finally, the input original signal is converted into a simulated signal with Gaussian Doppler spread and multipath effect characteristics. This scheme achieves modeling of ionospheric multipath propagation through the delay line structure, modulates the white noise sequence using Gaussian filter coefficients to describe the Doppler frequency shift, and achieves coupled simulation of multipath and Doppler effects through the combination of the Gaussian channel gain sequence corresponding to each tap and the relative time delay. This enables the rapid and accurate construction of a Gaussian scattering high-frequency ionospheric channel model. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for constructing a high-frequency ionospheric channel model using Gaussian scattering, according to an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a channel model according to an embodiment of this application; Figure 3 is a simulation comparison diagram of two Gaussian scatterers according to an embodiment of this application; Figure 4 shows a simulation comparison under a Gaussian scatterer; Figure 5 This is a schematic diagram of a Gaussian scattering high-frequency ionospheric channel model construction device according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0024] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0026] In the description of the embodiments of this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0027] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple devices refer to two or more devices, and multiple screen terminals refer to two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0028] Please see Figure 1This is a flowchart illustrating a method for constructing a Gaussian scattering high-frequency ionospheric channel model according to an embodiment of this application. This method can be implemented using a computer program, a microcontroller, or run on a Gaussian scattering high-frequency ionospheric channel model construction device based on the von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application. The specific steps of the Gaussian scattering high-frequency ionospheric channel model construction method are described in detail below.

[0029] S101: Set multiple taps for the channel on the delay line and determine the relative time delay corresponding to each tap; A delay line is a basic structure used to simulate signal delay characteristics; essentially, it's a signal processing device that applies different time delays to the input signal. During high-frequency ionospheric channel propagation, due to the multi-layered structure of the ionosphere, the same signal will reach the receiver through different propagation paths, resulting in varying propagation delays. A delay line simulates this multipath propagation characteristic by setting multiple taps with different delays along the signal transmission path. In the embodiments of this application, a delay line can be understood as a signal processing unit with multiple output points, each corresponding to a tap and representing a propagation path.

[0030] In this context, a tap refers to a signal sampling point or signal output point set on a delay line. Essentially, it's an interface for extracting and processing signals at specific time delay positions on the delay line. Each tap represents a propagation path of a signal in a high-frequency ionospheric channel, and the delayed signal component corresponding to that path can be obtained through the tap. The physical meaning of a tap can be understood as an independent propagation path formed after the signal is reflected at different heights or from different reflecting surfaces in the ionosphere. In this embodiment, a tap can be understood as a signal processing unit on the delay line, including time delay characteristics and signal modulation functions. Each tap has a specific relative time delay value, used to characterize the time delay of the propagation path relative to the direct path.

[0031] In this embodiment, the high-frequency ionospheric channel suffers from multipath propagation due to the multi-layered structure and irregular reflective surface of the ionosphere. This means that the same signal, after traveling along different paths, generates multiple signal components with varying time delays at the receiving end. To accurately simulate this multipath propagation characteristic, this invention employs a tapped delay line structure, using multiple taps on the delay line to simulate different transmission paths.

[0032] In practice, the number of main propagation paths N to be simulated is first determined based on the actual transmission characteristics of the high-frequency ionospheric channel. This number is the number of taps to be set. After determining the number of taps, a relative delay τi (i = 1, 2, ..., N) needs to be assigned to each tap. The relative delay τi represents the time delay of the i-th transmission path relative to the direct path, where the delay τ1 of the direct path is set to 0. The relative delays of subsequent taps can be determined based on actual channel measurement data or theoretical analysis, and usually show an increasing trend, i.e., τ1 < τ2 < ... < τN.

[0033] For example, when selecting six taps to simulate the main transmission path in a high-frequency ionospheric channel, the first tap corresponds to the direct path with a relative delay τ1 = 0; the relative delays of the remaining taps can be set according to the actual ionospheric structure characteristics, such as τ2 = 0.5 ms, τ3 = 1.0 ms, τ4 = 1.5 ms, τ5 = 2.0 ms, and τ6 = 2.5 ms. This delay allocation method can better reflect the multipath delay spread characteristics in a high-frequency ionospheric channel.

[0034] It should be understood that the technical solution of this application is an improvement on the Watson model, and its basic framework is as follows: Figure 2 As shown, Figure 2 This is a schematic diagram of a channel model provided in an embodiment of this application.

[0035] Figure 2 The input signal, as shown, first enters a delay line with multiple taps numbered from 1 to n. Each tap represents a propagation path or mode in the ionosphere. At each tap, the signal is modulated by a corresponding complex gain function, which contains two magnetic ion components. The output signals from all taps are superimposed to simulate the synthesis of multipath effects. External noise is added to the output, and the effects of interference are considered, resulting in the final simulated output signal.

[0036] For the Gaussian scattering channel model, the gain function of each tap is defined as: The subscripts a and b identify the two magnetic ion components that can typically be present in each mode or pathway. This indicates that... and These are sample functions of two independent complex (binary) Gaussian ergodic random processes, each with a mean of zero, and independent real and imaginary components equal to the values ​​of the Rayleigh fading produced by the RMS (i.e., they are Gaussian scattering functions). The exponential function in equation (1) provides the required frequency (Doppler) shift for the magnetic ion component in the tapped gain spectrum.

[0037] Each tap gain function has a power spectrum, typically consisting of the sum of two magnetic ion components, each of which is a Gaussian function of a frequency, specified as follows: in, and For component attenuation, the frequency distribution on each component is typically determined by 2σ. ia and 2σ ib The decision is made. Six independent parameters specify a tap gain function and its spectrum: two attenuations. Two frequency offsets, v ia and v ib And two frequency extensions, 2σ ia and 2σ ib .

[0038] S102: Configure the Gaussian filter coefficients and white noise sequence corresponding to the tap, and generate the Gaussian channel gain sequence corresponding to the tap based on the Gaussian filter coefficients and white noise sequence. The Gaussian filter coefficients refer to a set of weighted parameters used to construct the Gaussian filter. These parameters are determined by the oversampling factor, the bandwidth-time product, and the peak factor. The oversampling factor is the ratio of the sampling signal rate to the Doppler spread, reflecting the number of sampling points per cycle. The bandwidth-time product uses 0.3 as the default value to determine the filter's bandwidth characteristics. The peak factor is set to eight, indicating that the filter peak value is taken over two symbol cycles. These parameters together constitute the basic characteristics of the Gaussian filter. In this embodiment, the Gaussian filter coefficients can be understood as a set of discrete numerical sequences calculated using a Gaussian filter function. These coefficients are obtained by operating the Gaussian function based on the above three basic parameters, and their numerical distribution follows a Gaussian distribution.

[0039] White noise sequence refers to a random signal sequence with a Gaussian distribution in statistical properties. Its power spectral density is uniformly distributed across the entire frequency band, with equal amplitudes for each frequency component and randomly distributed phases. The real and imaginary parts of this sequence both follow a standard normal distribution and are independent of each other. White noise sequences expressed in complex form exhibit Rayleigh distribution characteristics, with amplitudes following a Rayleigh distribution and phases following a uniform distribution. In this embodiment, the white noise sequence can be understood as a complex sequence generated by a random number generator, where the real and imaginary parts are each composed of independent Gaussian random numbers. The length of this sequence is determined by the number of sampling points in the simulation device, and the statistical properties of the sequence remain stable, reflecting the basic characteristics of random noise in the channel.

[0040] The Gaussian channel gain sequence refers to the complex sequence obtained by filtering a white noise sequence using a Gaussian filter. This sequence reflects the gain variation characteristics of the channel at different times. In the embodiments of this application, the Gaussian channel gain sequence can be understood as the time-varying gain function corresponding to each tap, which is the output sequence obtained by processing the white noise sequence through a Gaussian filter with specific parameters.

[0041] In practice, the oversampling factor is first determined based on the ratio of the sampling signal rate to the Doppler spread, and parameters such as the bandwidth-time product and the peak factor are set. Gaussian filter coefficients are then calculated using a Gaussian filter function. These coefficients determine the frequency response characteristics of the filter and affect the channel's frequency selectivity and Doppler spread characteristics. Simultaneously, a complex white noise sequence conforming to a Gaussian distribution is generated, with its real and imaginary parts being independent Gaussian random variables to simulate the randomness of the channel. After obtaining the Gaussian filter coefficients and the white noise sequence, they are combined through convolution operations to generate the Gaussian channel gain sequence corresponding to each tap.

[0042] Optionally, obtain the attenuation information, frequency offset information, and frequency spread information from the tap gain function of the tap; and configure the Gaussian filter coefficients corresponding to the tap based on the attenuation information, frequency offset information, and frequency spread information.

[0043] In the design of a Gaussian scattering high-frequency ionospheric channel model, the first step is to extract attenuation, frequency offset, and frequency spread information from the tap gain function of each tap. These three parameters correspond to the physical characteristics of the multipath path represented by that tap during signal transmission, such as energy loss, center frequency offset, and spectral broadening. The fundamental reason for extracting this key information is that only by accurately reflecting the unique impact of each path on the signal can the channel model have real physical meaning and engineering simulation value. In practice, the tap gain function is first analyzed to identify the relative strength, frequency center, and spectral distribution range of each path. Then, based on the extracted attenuation, frequency offset, and frequency spread parameters, the Gaussian filter coefficients corresponding to that tap are configured. During configuration, the filter's amplitude response should be consistent with the tap's attenuation characteristics, the center frequency position should reflect the frequency offset, and the spectral width is determined by the frequency spread information.

[0044] Based on the above embodiments, as an optional implementation method, step S102 further includes S201-S203.

[0045] S201: Based on the number of sampling points of the complex noise signal required by the tap, use a random number generation function to generate a real part white noise sequence and an imaginary part white noise sequence; In real-world channels, random fading manifests as random variations in amplitude and phase. This characteristic can be simulated using noise signals in complex form, thus requiring the generation of white noise sequences with real and imaginary parts separately.

[0046] In the specific implementation process, the number of sampling points required for the tap is first determined based on the system sampling frequency and simulation time, and this number of sampling points is used as the input parameter of the random number generation function. The random number generation function is then used to generate a one-dimensional random sequence, resulting in real-part white noise sequences and imaginary-part white noise sequences that follow a standard normal distribution.

[0047] S202: Combine the real part white noise sequence and the imaginary part white noise sequence into a complex white noise sequence; In the specific implementation process, the real part white noise sequence is used as the real part of the complex number, and the imaginary part white noise sequence is used as the imaginary part of the complex number. The two sequences are combined into a complete complex white noise sequence through complex number addition. Since both the real and imaginary parts of the sequence follow a standard normal distribution and are independent of each other, the synthesized complex white noise sequence will have ideal statistical properties.

[0048] S203: Configure the Gaussian filter coefficients corresponding to the taps, and perform convolution processing on the white noise sequence corresponding to the taps and the Gaussian filter coefficients to obtain the Gaussian channel gain sequence.

[0049] In the specific implementation process, the configured Gaussian filter coefficients are convolved with the generated noise signal. The convolution operation uses the same dimension to ensure that the resulting Gaussian channel information has the same length as the input noise signal. Through this convolution operation, the spectral characteristics of the noise signal are reshaped by the Gaussian filter, transforming it into a Gaussian channel gain sequence with a Gaussian power spectrum.

[0050] Based on the above embodiments, as an optional implementation method, the processing method for the Gaussian channel gain sequence in step S203 further includes S301-S304.

[0051] S301: Calculate the sampling ratio of the sampling signal rate to the Doppler spread; In high-frequency ionospheric channel modeling, an excessively large ratio of sampling signal rate to Doppler spread can lead to excessively long filter coefficients, resulting in excessive resource consumption during channel model construction. The sampling ratio of sampling signal rate to Doppler spread represents the number of sampling points within one Doppler period, and this ratio directly affects the accuracy of subsequent channel modeling.

[0052] S302: When the sampling ratio is greater than the preset first threshold, the sampling signal rate is reduced to the intermediate sampling rate; A preset first threshold is set to determine whether the sampling rate needs to be reduced. When the sampling ratio exceeds this threshold, the sampling signal rate is reduced to an intermediate sampling rate.

[0053] In the specific implementation process, the downsampling factor is first set, which can be sixteen or other suitable values. Then, the original sampling signal rate is divided by this downsampling factor to obtain the intermediate sampling rate. For example, when the original sampling signal rate is 160 kHz and the downsampling factor is set to sixteen, the resulting intermediate sampling rate is 10 kHz, thus reducing the resources required for subsequent processing to one-sixteenth of the original.

[0054] Optionally, step S302 further includes obtaining the downsampling factor corresponding to the first threshold when the sampling ratio is greater than the preset first threshold, wherein the downsampling factor is a power of 2; and dividing the sampling signal rate by the downsampling factor to obtain the intermediate sampling rate.

[0055] To achieve efficient downsampling, a power of two is chosen as the downsampling factor. This choice simplifies subsequent digital signal processing, especially in implementing interpolation filtering, where it fully leverages the advantages of cascaded integrator-comb filters and half-band filters. In practice, the required downsampling level is first determined based on a preset first threshold, and then the closest power of two value is selected as the downsampling factor. Next, the original sampled signal rate is divided by this downsampling factor to obtain the intermediate sampling rate.

[0056] S303: Configure the intermediate Gaussian filter coefficients corresponding to the taps according to the intermediate sampling rate, and perform convolution processing on the white noise sequence corresponding to the taps and the intermediate Gaussian filter coefficients to obtain the intermediate Gaussian channel gain sequence. In high-frequency ionospheric channel modeling, to reduce system resource consumption, a channel gain sequence needs to be generated at an intermediate sampling rate. First, the original sampling rate needs to be reduced to an intermediate sampling rate by downsampling coefficients. Then, at this rate, the parameters of the Gaussian filter are configured, including the bandwidth-time product, oversampling factor, and peak factor. The intermediate Gaussian filter coefficients are obtained through the Gaussian filter function.

[0057] In the specific implementation process, the number of sampling points is first adjusted according to the downsampling coefficients to generate a complex white noise sequence of corresponding length. The real and imaginary parts of this white noise sequence both follow a Gaussian distribution and are independent of each other. Then, the generated white noise sequence is convolved with the configured intermediate Gaussian filter coefficients in the same dimension to obtain the intermediate Gaussian channel gain sequence. This processing method at an intermediate sampling rate significantly reduces computational complexity. Due to the use of a lower sampling rate, the length of both the filter coefficients and the noise sequence are correspondingly reduced, greatly reducing system resource consumption.

[0058] S304: Perform interpolation filtering on the intermediate Gaussian channel gain sequence to obtain the Gaussian channel gain sequence.

[0059] In high-frequency ionospheric channel modeling, an intermediate sampling rate was initially used to reduce system resource consumption. Now, interpolation filtering is needed to restore the sampling rate of the intermediate Gaussian channel gain sequence to its original level. This process can improve the sampling rate while ensuring that the signal is not distorted.

[0060] In practice, the intermediate Gaussian channel gain sequence is interpolated to insert new sampling points to increase the sampling rate, and filtering is used to eliminate spectral aliasing introduced during interpolation. The combination of interpolation and filtering ensures signal integrity during the sampling rate increase. This interpolation and filtering method converts the intermediate Gaussian channel gain sequence into a Gaussian channel gain sequence, resulting in a sequence that retains the original sampling rate level while preserving the signal's quality characteristics.

[0061] Optionally, the intermediate Gaussian channel gain sequence is interpolated by a large factor using a CIC interpolation filter to obtain the first Gaussian channel gain sequence; the first Gaussian channel gain sequence is then interpolated by a factor of two and filtered using an HB interpolation filter to obtain the Gaussian channel gain sequence.

[0062] The CIC interpolation filter is a cascaded integrator-comb interpolation filter. Its structure consists of an integrator stage and a comb stage. It does not require multipliers and can achieve high-magnitude interpolation using only adders and registers. This type of filter has linear phase characteristics, is simple to implement in hardware, and consumes few resources, making it particularly suitable for implementing high-magnitude sampling rate conversions. However, its frequency response is not ideal, and there is attenuation at the passband edge.

[0063] The HB interpolation filter is a half-band interpolation filter with a flat frequency response in the passband and large stopband attenuation, effectively suppressing image frequencies during interpolation. Due to the symmetry of its coefficients and the fact that half of them are zero, it achieves high efficiency. However, limited by its structural characteristics, it can only achieve double sampling rate conversion.

[0064] This application employs a combination of CIC and HB interpolation filters to achieve a significant increase in sampling rate. Using only the HB interpolation filter would require multiple cascaded stages, resulting in high hardware complexity; using only the CIC interpolation filter would lead to significant signal attenuation at the frequency band edges. By combining the two filters, the CIC filter achieves high-level interpolation, followed by fine processing with the HB filter, ensuring both processing efficiency and signal quality.

[0065] In the specific implementation process, the intermediate Gaussian channel gain sequence is first input into the CIC interpolation filter for large-fold interpolation to obtain the first Gaussian channel gain sequence. Subsequently, the first Gaussian channel gain sequence is input into the HB interpolation filter for double interpolation and filtering. The HB interpolation filter, with its good frequency characteristics and symmetrical coefficient structure, effectively eliminates aliasing frequencies during the interpolation process, and finally obtains the Gaussian channel gain sequence.

[0066] S103: Combine the Gaussian channel gain sequence corresponding to each tap with the relative time delay to obtain the Gaussian scattering high-frequency ionospheric channel model corresponding to the delay line; When constructing a delayed linear Gaussian scattering high-frequency ionospheric channel model, it is first necessary to define the Gaussian channel gain sequence corresponding to each channel tap and its physical relative delay. For each tap, the physical relative delay is first converted into the number of sampling points based on the device's sampling rate. This allows for a precise representation of the arrival time of signals from different paths at the receiver in the digital domain. For example, if the relative delay of a tap is τ, then the corresponding number of sampling delay points is equal to τ multiplied by the sampling rate and rounded down. After completing the delay conversion, the Gaussian channel gain sequence of that tap needs to be shifted to the right by the corresponding number of sampling points on the time axis. Specifically, this involves inserting an equal number of zero-value elements at the beginning of the sequence, while the original gain sequence is shifted sequentially to the right. This ensures that the multipath response signal of that tap begins to function from the time point corresponding to its physical delay.

[0067] Since the relative delays of different taps vary, the total length of each Gaussian channel gain sequence may differ after delay processing. Therefore, it is necessary to perform a uniform length extension process on the gain sequences of all taps, i.e., padding the shorter sequences with zeros at the end to ensure that the gain sequences of all taps have the same length. At this point, the Gaussian channel gain sequences of all taps are aligned on the time axis and ready for superposition. At each sampling time, the Gaussian channel gain sequences after delay processing of all taps are summed according to the values ​​at the corresponding time, resulting in a new time series. Each element of this series represents the combined effect of all multipath components on the signal at that time, which is the total impulse response of the channel at that time. The entire time series constitutes a complete delayed linear Gaussian scattering high-frequency ionospheric channel model. Through the above steps, it can be ensured that the model reflects both the independent dynamic fading characteristics of each multipath path and accurately describes the relative delay relationships between the paths, thus realistically simulating the complex effects of the high-frequency ionospheric channel on the multipath, scattering, and time-varying fading of the transmitted signal.

[0068] S104: Input the original signal into the Gaussian scattering high-frequency ionospheric channel model and output the simulated signal modulated by Gaussian Doppler expansion and multipath effect.

[0069] In practice, inputting the original signal into the channel model enables a comprehensive simulation of the signal transmission process under actual high-frequency ionospheric propagation conditions. The fundamental purpose is to realistically and dynamically reproduce the Gaussian-Doppler spread and multipath effects caused by the channel at the baseband signal level, thereby facilitating communication device design, anti-interference algorithm verification, and performance evaluation. In the specific implementation, the obtained Gaussian scattering high-frequency ionospheric channel model—containing multiple taps, each tap corresponding to a Gaussian channel gain sequence, and the total impulse response with relative time delay—is first applied as a time-varying convolution kernel to the input original signal sequence. Operationally, convolution or sliding weighted summation is typically used: for each sampling point of the original signal, the Gaussian channel gain at the corresponding time is weighted and summed using several signal samples from before the current time, covering the delay and fading effects of all multipath taps. This process dynamically changes over time because the channel gain is time-varying. After such processing, the output signal not only exhibits the fast fading characteristics caused by Gaussian Doppler scattering in terms of amplitude and phase, but also includes complex channel effects such as inter-symbol interference and frequency spread caused by the superposition of various multipath components.

[0070] Based on the above embodiments, as an optional implementation, Gaussian channel gain sequence is used to calculate ideal Gaussian channel information, and ideal channel information is used to construct an ideal channel model; The original signal is input into the ideal channel model, and the ideal signal is output. By comparing the simulated signal with the ideal signal, the comparison results of the Gaussian scattering high-frequency ionospheric channel model are obtained.

[0071] In the comparative evaluation process of Gaussian scattering high-frequency ionospheric channel simulation, the ideal Gaussian channel information is first calculated using the Gaussian channel gain sequence, and an ideal channel model is constructed accordingly. The fundamental purpose of this is to obtain an "ideal environment" reference channel that does not contain multipath delay and Doppler spread, but only reflects gain changes, so that the simulation results can clearly reflect the impact of multipath and scattering effects of the actual ionospheric channel on the signal. In practice, the ideal Gaussian channel model typically retains only a single tap, excluding relative delay, and directly uses the Gaussian channel gain sequence as the time-varying coefficients of the ideal channel to achieve amplitude and phase modulation of the original signal. After inputting the original signal into this ideal channel model, the output ideal signal is only affected by the Gaussian channel gain sequence, exhibiting an ideal slow fading process that varies with time, without inter-symbol interference and Doppler broadening caused by multipath. Subsequently, the simulated signal obtained through the Gaussian scattering high-frequency ionospheric channel model is compared and analyzed with the ideal signal, which can intuitively reveal the impact of multipath delay, fast fading, and spectral spread effects of the actual channel on the signal waveform, energy distribution, and bit error rate performance.

[0072] For the comparison results, please refer to Figures 3 and 4. Figure 3 is a simulation comparison diagram of two Gaussian scatterers provided in the embodiment of this application, and Figure 4 is a simulation comparison diagram of one Gaussian scatterer provided in the embodiment of this application. In order to better reflect the simulation effect, Figures 3 and 4 are color images.

[0073] Figure 3 shows the frequency domain, amplitude distribution, and correlation characteristics of the Gaussian distribution channel under the influence of two Gaussian scatterers. Figure 3(a) is a simulation comparison of the Gaussian distribution power under two Gaussian scatterers, showing the simulation comparison of the Gaussian distribution power spectrum under the action of two Gaussian scatterers. It can be seen that two obvious Gaussian-shaped energy distribution peaks appear in the spectrum. This is because there are two main Doppler scattering centers in the channel, and the signal energy is significantly expanded near these two frequencies. The blue curve represents the actual channel model, and the red dashed line represents the ideal channel model. The two are basically consistent in the position and shape of the main lobe, indicating that the simulation model reproduces the Doppler characteristics of the ideal Gaussian distribution very well. However, due to noise and non-ideal channel factors, the spectrum curve of the actual model is more volatile.

[0074] Figure 3(b) shows the amplitude distribution probability function under two Gaussian scatterers, giving the probability distribution function of the signal amplitude under two Gaussian scatterers. The horizontal axis represents the signal amplitude, and the vertical axis represents the probability density function value. The blue curve is the theoretical Rayleigh distribution, and the red dots are the statistical results of the actual simulation data. It can be seen that the actual amplitude distribution is in high agreement with the theoretical Rayleigh distribution, indicating that under the influence of a large number of independent scatterers, the channel output amplitude follows the Rayleigh distribution characteristic, reflecting the accurate description of the statistical characteristics of channel fading by the Gaussian scattering model.

[0075] Figure 3(c) shows the correlation coefficients of the two Gaussian scatterers. It illustrates the amplitude autocorrelation coefficients under two Gaussian scatterers, reflecting the correlation of the channel over time. The blue line represents the actual model, and the red line represents the ideal model. The curves show a consistent overall trend and significant oscillation attenuation characteristics, indicating that the channel exhibits a certain degree of time selectivity under Doppler scattering. Furthermore, the simulation model and the theoretical model are highly consistent in their autocorrelation characteristics, verifying the accuracy of the established Gaussian scattering channel model in terms of time-domain correlation.

[0076] Figure 4 shows the channel statistics and correlation characteristics under a single Gaussian scatterer. Figure 4(a) is a simulation comparison of the Gaussian power distribution under a single Gaussian scatterer, displaying the simulated power spectrum comparison. The horizontal axis represents frequency, and the vertical axis represents gain (dB). The blue curve represents the actual channel model, and the red dashed line represents the ideal channel model. As can be seen from the figure, the power spectrum exhibits a single-peak Gaussian shape, with energy distributed around the center frequency, perfectly consistent with the theoretical Gaussian model. This indicates that the channel frequency domain energy distribution is highly concentrated under a single Gaussian scatterer, and the actual simulation matches the theoretical model well, demonstrating the spectral spread characteristics of the Gaussian distribution.

[0077] Figure 4(b) shows the amplitude distribution probability function under a Gaussian scatterer, illustrating the probability distribution function of the signal amplitude. The horizontal axis represents amplitude, and the vertical axis represents probability density. The blue curve represents the theoretical Rayleigh distribution, and the red asterisks represent the actual simulation values. It can be seen that the actual simulation data highly coincides with the theoretical Rayleigh distribution across the entire amplitude range. This reflects that the amplitude statistical characteristics of the signal under a single Gaussian scatterer conform to the Rayleigh fading model, verifying the statistical accuracy of the channel model.

[0078] Figure 4(c) shows the correlation coefficient of a Gaussian scatterer. The relationship between the channel amplitude autocorrelation coefficient and time is given, with the horizontal axis representing time and the vertical axis representing amplitude. The blue line represents the actual channel model, and the red line represents the ideal channel model. It can be observed that the correlation coefficient decreases with time, and the curves of the actual channel model and the ideal model generally follow the same trend. However, due to the influence of randomness and noise, the actual model exhibits more fluctuations and oscillations than the ideal model.

[0079] In summary, the Gaussian scattering high-frequency ionospheric channel model established in this application has high simulation accuracy in terms of spectral spread, amplitude statistics, and time-domain correlation characteristics.

[0080] The following are embodiments of the apparatus of this application, which can be used to execute the embodiments of the method of this application. For details not disclosed in the embodiments of the apparatus of this application, please refer to the embodiments of the method of this application.

[0081] Please see Figure 5 This illustration shows a schematic diagram of a Gaussian scattering high-frequency ionospheric channel model construction apparatus provided in an exemplary embodiment of this application. This apparatus can be implemented entirely or partially through software, hardware, or a combination of both. An apparatus for constructing a Gaussian scattering high-frequency ionospheric channel model includes: The initialization module is used to set multiple taps of the channel on the delay line and determine the relative time delay corresponding to the taps; The configuration module is used to configure the Gaussian filter coefficients and white noise sequence corresponding to the tap, and generate the Gaussian channel gain sequence corresponding to the tap based on the Gaussian filter coefficients and white noise sequence. The model building module is used to combine the Gaussian channel gain sequence corresponding to each tap with the relative time delay to obtain the Gaussian scattering high-frequency ionospheric channel model corresponding to the delay line. The simulation module is used to input the original signal into the Gaussian scattering high-frequency ionospheric channel model and output the simulated signal modulated by Gaussian Doppler expansion and multipath effect.

[0082] Based on the above embodiments, as an optional embodiment, the configuration module is further configured to generate a real part white noise sequence and an imaginary part white noise sequence using a random number generation function according to the number of sampling points of the complex noise signal required by the tap; synthesize the real part white noise sequence and the imaginary part white noise sequence into a complex form white noise sequence; configure the Gaussian filter coefficients corresponding to the tap; and perform convolution processing on the white noise sequence corresponding to the tap and the Gaussian filter coefficients to obtain a Gaussian channel gain sequence.

[0083] Based on the above embodiments, as an optional embodiment, the configuration module is further configured to calculate the sampling ratio of the sampling signal rate to the Doppler spread; when the sampling ratio is greater than a preset first threshold, the sampling signal rate is reduced to an intermediate sampling rate; the intermediate Gaussian filter coefficients corresponding to the taps are configured according to the intermediate sampling rate, and the white noise sequence corresponding to the taps and the intermediate Gaussian filter coefficients are convolved to obtain an intermediate Gaussian channel gain sequence; the intermediate Gaussian channel gain sequence is interpolated and filtered to obtain a Gaussian channel gain sequence.

[0084] Based on the above embodiments, as an optional embodiment, the configuration module is further configured to obtain the downsampling factor corresponding to the first threshold when the sampling ratio is greater than the preset first threshold, wherein the downsampling factor is a power of 2; and divide the sampling signal rate by the downsampling factor to obtain the intermediate sampling rate.

[0085] Based on the above embodiments, as an optional embodiment, the configuration module is further configured to use a CIC interpolation filter to perform large-multiple interpolation processing on the intermediate Gaussian channel gain sequence to obtain a first Gaussian channel gain sequence; and use an HB interpolation filter to perform double interpolation processing and filtering processing on the first Gaussian channel gain sequence to obtain a Gaussian channel gain sequence.

[0086] Based on the above embodiments, as an optional embodiment, the configuration module is further configured to obtain the attenuation information, frequency offset information, and frequency spread information in the tap gain function of the tap; and configure the Gaussian filter coefficients corresponding to the tap according to the attenuation information, frequency offset information, and frequency spread information.

[0087] Based on the above embodiments, as an optional embodiment, the simulation module is also used to calculate the ideal Gaussian channel information using the Gaussian channel gain sequence, and construct an ideal channel model using the ideal channel information; input the original signal into the ideal channel model and output the ideal signal; compare the simulated signal with the ideal signal to obtain the comparison results of the Gaussian scattering high-frequency ionospheric channel model.

[0088] This application also provides a computer storage medium that can store multiple instructions. The instructions are adapted to be loaded and executed by a processor as described above in the Gaussian scattering high-frequency ionospheric channel model construction method. For the specific execution process, please refer to the detailed description of the embodiments, which will not be repeated here.

[0089] Please see Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 300 may include: at least one processor 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0090] The communication bus 302 is used to enable communication between these components.

[0091] The user interface 303 may include a display screen and a camera.

[0092] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0093] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0094] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 6 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for constructing a Gaussian scattering high-frequency ionospheric channel model.

[0095] exist Figure 6In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call the application program stored in the memory 305 for constructing a Gaussian scattering high-frequency ionospheric channel model. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0096] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more methods as described in the above embodiments.

[0097] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0098] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0103] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truths. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A method for constructing a Gaussian scattering high-frequency ionospheric channel model, characterized in that, The method includes: Multiple taps are set on the delay line to determine the relative time delay corresponding to each tap; Configure the Gaussian filter coefficients and white noise sequence corresponding to the tap, and generate the Gaussian channel gain sequence corresponding to the tap based on the Gaussian filter coefficients and the white noise sequence; The Gaussian channel gain sequence corresponding to each tap is combined with the relative time delay to obtain the Gaussian scattering high-frequency ionospheric channel model corresponding to the delay line. The original signal is input into the Gaussian scattering high-frequency ionospheric channel model, and the output is a simulated signal modulated by Gaussian Doppler expansion and multipath effect.

2. The method according to claim 1, characterized in that, The step of configuring the Gaussian filter coefficients and white noise sequence corresponding to the tap, and generating the Gaussian channel gain sequence corresponding to the tap based on the Gaussian filter coefficients and white noise sequence, includes: Based on the number of sampling points of the complex noise signal required by the tap, a real part white noise sequence and an imaginary part white noise sequence are generated using a random number generation function; The real part white noise sequence and the imaginary part white noise sequence are combined into a complex white noise sequence; Configure the Gaussian filter coefficients corresponding to the tap, and perform convolution processing on the white noise sequence corresponding to the tap and the Gaussian filter coefficients to obtain the Gaussian channel gain sequence.

3. The method according to claim 2, characterized in that, The Gaussian filter coefficients include Gaussian filter parameters including sampling signal rate and Doppler spread. The Gaussian filter coefficients corresponding to the taps are configured, and the white noise sequence corresponding to the taps and the Gaussian filter coefficients are convolved to obtain a Gaussian channel gain sequence, including: Calculate the sampling ratio of the sampling signal rate to the Doppler spread; When the sampling ratio is greater than a preset first threshold, the sampling signal rate is reduced to an intermediate sampling rate; Configure the intermediate Gaussian filter coefficients corresponding to the tap according to the intermediate sampling rate, and perform convolution processing on the white noise sequence corresponding to the tap and the intermediate Gaussian filter coefficients to obtain the intermediate Gaussian channel gain sequence. The intermediate Gaussian channel gain sequence is interpolated and filtered to obtain the Gaussian channel gain sequence.

4. The method according to claim 3, characterized in that, When the sampling ratio is greater than a preset first threshold, reducing the sampling signal rate to an intermediate sampling rate includes: When the sampling ratio is greater than a preset first threshold, the downsampling factor corresponding to the first threshold is obtained, and the downsampling factor is a power of 2; Divide the sampling signal rate by the downsampling factor to obtain the intermediate sampling rate.

5. The method according to claim 3, characterized in that, The step of interpolating and filtering the intermediate Gaussian channel gain sequence to obtain the Gaussian channel gain sequence includes: The intermediate Gaussian channel gain sequence is interpolated by a large factor using a CIC interpolation filter to obtain the first Gaussian channel gain sequence. The first Gaussian channel gain sequence is subjected to double interpolation and filtering using the HB interpolation filter to obtain the Gaussian channel gain sequence.

6. The method according to claim 1, characterized in that, The configuration of the Gaussian filter coefficients corresponding to the taps includes: Obtain the attenuation information, frequency offset information, and frequency spread information from the tap gain function of the tap; Based on the attenuation information, frequency offset information, and frequency spread information, configure the Gaussian filter coefficients corresponding to the tap.

7. The method according to claim 1, characterized in that, After inputting the original signal into the Gaussian scattering high-frequency ionospheric channel model and outputting the simulated signal modulated by Gaussian Doppler spread and multipath effect, the method further includes: The ideal Gaussian channel information is calculated using the Gaussian channel gain sequence, and the ideal channel information is used to construct an ideal channel model. The original signal is input into the ideal channel model, and the ideal signal is output. The simulated signal is compared with the ideal signal to obtain the comparison results of the Gaussian scattering high-frequency ionospheric channel model.

8. A device for constructing a Gaussian scattering high-frequency ionospheric channel model, characterized in that, The device includes: An initialization module is used to set multiple taps of the channel on the delay line and determine the relative delay corresponding to the taps; A configuration module is used to configure the Gaussian filter coefficients and white noise sequence corresponding to the tap, and generate the Gaussian channel gain sequence corresponding to the tap based on the Gaussian filter coefficients and the white noise sequence. The model building module is used to combine the Gaussian channel gain sequence corresponding to each tap with the relative time delay to obtain the Gaussian scattering high-frequency ionospheric channel model corresponding to the delay line. The simulation module is used to input the original signal into the Gaussian scattering high-frequency ionospheric channel model and output a simulated signal modulated by Gaussian Doppler expansion and multipath effect.

9. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions, which are adapted to be loaded by a processor and executed as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a processor, a memory, and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

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