Channel unification method and system based on dynamic multipath parametric modeling

Abstract

The invention provides a channel unification method and system based on dynamic multipath parametric modeling, and relates to the technical field of signal data processing. The method comprises the following steps: firstly, determining an original input signal and channel noise according to a channel bandwidth requirement, then obtaining key parameters including a Doppler shape factor, a Rice factor and the like through a parameter configuration interface, and issuing the key parameters to a static cluster generator, a dynamic cluster generator and a Doppler spectrum synthesizer; and the Doppler spectrum synthesizer generates a corresponding frequency offset sequence according to the key parameters, determines amplitude fading distribution according to a Rice factor, activates a corresponding number of static clusters, delays the static clusters, adjusts the amplitude of an original signal, and superposes frequency offset to obtain a static multipath signal copy. And meanwhile, generating a dynamic multipath signal copy according to the dynamic cluster enable signal and the frequency offset sequence, and finally, linearly superposing the two types of multipath signal copies with channel noise to generate a channel output signal sequence. By implementing the method, the requirements of modern communication on multi-scene adaptation and low-cost deployment can be met.

Description

Technical Field

[0001] This application relates to the field of signal data processing technology, and in particular to a channel unification method and system based on dynamic multipath parameterization modeling. Background Technology

[0002] In the field of modern wireless communication, satellite communication and shortwave communication are two key communication methods widely used in scenarios such as military emergency communication, long-distance data transmission, and integrated air-space-ground networks. Channel modeling, as the core foundation for communication system design, testing, and optimization, needs to accurately simulate multipath propagation, fading characteristics, and Doppler effects under different communication scenarios. The accuracy and compatibility of its modeling directly determine the anti-interference capability, transmission reliability, and hardware deployment cost of the communication system. Therefore, developing channel models adaptable to multiple scenarios has become a key requirement for promoting the development of wireless communication technology.

[0003] Currently, for satellite and shortwave communication scenarios, the industry mainly adopts two types of independent channel models: one is the Watterson model, which is based on the Gaussian scattering assumption, uses 3-6 fixed tap delay lines to simulate multipath propagation, and is paired with a narrowband Gaussian Doppler spectrum. The signal fading follows a Rayleigh distribution, making it suitable for shortwave communication scenarios with short latency (<5ms); the other is the ITS model, which is designed specifically for satellite communication. It supports time-varying multipath clusters (such as multipath bursts caused by ionospheric disturbances), generalized Jakes Doppler spectrum, and Ricean fading distribution, and can cover long latency (>20ms) requirements. It also has the ability to dynamically capture the time-varying characteristics of the channel.

[0004] However, existing technologies exhibit fundamental differences in characteristics between the Watterson model and the ITS model, making them incompatible. The Watterson model, due to its fixed multipath structure, narrow-band Gaussian spectrum, and Rayleigh fading design, cannot characterize the long latency and dynamic multipath clustering characteristics of satellite communication. While the ITS model can meet the requirements of satellite communication, its hardware implementation is highly complex, and it is completely incompatible with the Watterson model in terms of latency range, Doppler spectrum type, and fading distribution parameters. This independent modeling situation necessitates the deployment of two independent channel simulation hardware sets for multi-scenario communication systems (such as emergency networks involving both shortwave and satellite communication). This not only wastes hardware resources such as FPGAs / ASICs (doubling resource consumption) but also increases system integration complexity and maintenance costs, making it difficult to meet the demands of modern communication for multi-scenario adaptability and low-cost deployment. Summary of the Invention

[0005] This application provides a unified channel method and system based on dynamic multipath parameterization modeling, which can achieve unified channel adaptation in multiple scenarios at a lower cost than existing technologies.

[0006] Firstly, this application provides a unified channel system based on dynamic multipath, comprising: a parameter configuration interface for receiving external scenario configuration commands via a bus, parsing and distributing key parameters to various functional modules; a static cluster generator for delaying, adjusting amplitude, and superimposing frequency offsets on the original input signal to generate multiple stable static multipath signal copies; a dynamic cluster generator for time-varying delay, activation state control, and frequency offset processing on the original input signal to generate dynamic multipath signal copies that change dynamically with the channel environment; a Doppler spectrum synthesizer for converting the frequency domain signal into a time domain signal according to the Doppler shape factor distributed by the parameter configuration interface, generating a Gaussian spectrum or a generalized Jakes spectrum, and providing the generated Doppler spectrum to the static cluster generator and the dynamic cluster generator respectively; and a signal superposition unit for linearly superimposing the fixed multipath signal output by the static cluster generator, the time-varying multipath signal output by the dynamic cluster generator, and the channel noise to generate the final channel output signal.

[0007] By adopting the above technical solution, the parameter configuration interface provides a unified control benchmark for each module, ensuring the accurate delivery of key parameters; the static cluster generator constructs a stable multipath foundation, while the dynamic cluster generator simulates time-varying characteristics, with the two complementing each other to cover different channel states; the Doppler spectrum synthesizer generates an adapted spectrum based on parameters, providing realistic frequency characteristics for multipath signals; and the signal superposition unit fuses multipath signals and noise. The modules work together to retain stable multipath characteristics while dynamically simulating environmental changes, achieving accurate reproduction of complex channel scenarios, improving the comprehensiveness and realism of channel simulation, and solving the problem that a single module cannot simultaneously handle both static and dynamic characteristics.

[0008] In some embodiments of the first aspect, the method includes: acquiring the original input signal and channel noise of a continuous time series discretized at a preset sampling rate, the preset sampling rate being determined according to channel bandwidth requirements; acquiring key parameters through a parameter configuration interface and distributing the key parameters to a static cluster generator, a dynamic cluster generator, and a Doppler spectrum synthesizer, respectively, the key parameters including at least a Doppler shape factor, a Rice factor, and a dynamic cluster enable signal; generating frequency offset sequences corresponding to the static cluster generator and the dynamic cluster generator respectively through the Doppler spectrum synthesizer based on the key parameters; and determining the amplitude fading distribution according to the Rice factor, specifically including: if the Rice factor is equal to 0, then determining the amplitude fading distribution. The signal is determined to be a Rayleigh distribution; if the Rice factor is greater than 0, the amplitude fading distribution is determined to be a Rice distribution; the number of static clusters in the key parameter is obtained, and the corresponding number of static clusters is activated based on the number of static clusters; each activated static cluster is controlled to delay the original input signal based on a preset static cluster fixed time delay, and the signal amplitude is adjusted according to the amplitude fading distribution, and superimposed based on the frequency offset sequence to obtain multiple static multipath signal copies; based on the dynamic cluster enable signal and the frequency offset sequence, the dynamic cluster is controlled to process the original input signal to obtain multiple dynamic multipath signal copies; the static multipath signal copies, the dynamic multipath signal copies, and the channel noise are linearly superimposed to generate the channel output signal sequence.

[0009] By adopting the above technical solution, discrete signals and noise with suitable bandwidth are first obtained to ensure that signal processing adapts to channel requirements; key parameters are distributed through the parameter configuration interface to provide a unified control basis for each module; a Doppler spectrum synthesizer generates a frequency offset sequence to lay the foundation for the time-frequency characteristics of multipath signals; the Elles factor dynamically determines the fading distribution to ensure that the amplitude characteristics are consistent with reality; static and dynamic clusters are activated to process the signals separately, and then superimposed to generate the output. The process achieves on-demand fusion of static and dynamic multipath signals through parameter linkage, and noise superposition conforms to the scenario, improving the flexibility of scenario adaptation and the accuracy of signal processing, and avoiding the scenario limitations of fixed processing methods.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of generating frequency offset sequences corresponding to the static cluster generator and the dynamic cluster generator respectively through a Doppler spectrum synthesizer based on the key parameter includes: obtaining the Doppler shape factor and determining the target Doppler spectrum model according to the Doppler spectrum shape factor. The specific determination method is as follows: when the Doppler spectrum shape factor is 1, the target Doppler spectrum model is determined to be a Gaussian spectrum model; when the Doppler spectrum shape factor is a value between 0.5 and 1, the target Doppler spectrum model is determined to be a generalized Jakes spectrum model; based on the target Doppler spectrum model, frequency domain discretization processing is performed within a preset Doppler frequency range to generate discrete spectral coefficients at N frequency points, the discrete spectral coefficients containing amplitude information and phase information; inverse fast Fourier transform is performed on each of the discrete spectral coefficients to generate a basic frequency offset sequence; based on the basic frequency offset sequence, corresponding frequency offset subsequences are generated for the static cluster generator and the dynamic cluster generator respectively.

[0011] By employing the above technical solutions, the Doppler spectrum synthesizer accurately selects the spectral model based on the shape factor, adapts the Gaussian spectrum to shortwave scenarios, and the generalized Jakes spectrum to satellite scenarios, thus solving the problem of single model selection. It discretizes and generates spectral coefficients containing amplitude and phase information within a preset frequency band, ensuring complete spectral details. An inverse Fourier transform converts the frequency domain to the time domain, obtaining the basic frequency offset sequence. Subsequences are then redistributed to generate subsequences. Through model adaptation and precise frequency-to-time domain conversion, the process ensures that the frequency offset sequence closely matches actual channel characteristics, providing an accurate foundation for multipath signal time-frequency simulation and improving the realism of frequency characteristic simulation.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of generating corresponding frequency offset sub-sequences for the static cluster generator and the dynamic cluster generator based on the base frequency offset sequence includes: obtaining the Markov chain transfer rate of each dynamic cluster through a parameter configuration interface; performing low-pass filtering on the base frequency offset sequence to generate a static base sequence; for each static cluster, superimposing a random frequency offset compensation value on the static base sequence to generate a corresponding first frequency offset sub-sequence, wherein the random frequency offset compensation value is a fixed value randomly generated within a preset compensation value range; performing high-pass filtering on the base frequency offset sequence to generate a dynamic base sequence; and for each dynamic cluster, performing amplitude modulation based on the dynamic base sequence according to the corresponding Markov chain transfer rate to generate a corresponding second frequency offset sub-sequence.

[0013] By employing the above technical solutions, the Markov chain transfer rate is obtained, providing a time-varying basis for dynamic cluster processing. Low-pass filtering of the base sequence generates a static base sequence, ensuring stable static offset. Superimposed random compensation values ​​introduce subtle differences into the static subsequences, closely reflecting the actual stable multipath characteristics. High-pass filtering generates a dynamic base sequence, highlighting time-varying features. Amplitude modulation of the dynamic sequence is applied according to the transfer rate, reflecting the time-varying laws of dynamic clusters. This differentiated processing ensures stable static offset and time-varying dynamic offset; the combination preserves the basic channel attributes while reflecting dynamic environmental changes, improving the simulation's sense of hierarchy and accuracy in multipath frequency characteristics.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of controlling the dynamic cluster to process the original input signal based on the dynamic cluster enable signal and the frequency offset sequence to obtain multiple dynamic multipath signal copies includes: when the dynamic cluster enable signal is 0, turning off the dynamic cluster generator; when the dynamic cluster enable signal is 1, turning on the dynamic cluster generator and constructing a two-state state machine based on the Markov chain transition rate to generate a switching function sequence for each dynamic cluster; based on the switching function sequence, controlling each dynamic cluster to perform delay processing on the original input signal, adjusting the signal amplitude according to the amplitude fading distribution, and superimposing them according to the frequency offset sequence to obtain multiple dynamic multipath signal copies.

[0015] By adopting the above technical solution, the dynamic cluster enable signal can be switched on and off as needed. When off, it adapts to scenarios without dynamic multipath, and when on, it adapts to scenarios with dynamic multipath. After the dynamic cluster is enabled, a two-state machine based on a Markov chain is used to generate switching functions to simulate the random activation and deactivation of the dynamic cluster. The switching functions then control the dynamic cluster processing signal. The mechanism simulates the intermittent characteristics of the dynamic cluster through a state machine, closely matching the burst and disappearance phenomena of multipath components in actual channels, enhancing the realism of time-varying characteristic simulation, and avoiding the scenario mismatch problem of the dynamic cluster being constantly on or constantly off.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the step of constructing a two-state state machine based on Markov chain transition rates to generate a sequence of switching functions for each dynamic cluster includes: obtaining the Markov chain transition rates for each dynamic cluster issued by a parameter configuration interface, wherein the Markov chain transition rates include the transition rates from activation to deactivation of the dynamic cluster. and the transition rate of the dynamic cluster from closed to active. Construct a state machine for the two states, namely the active state and the off state. The switching function value corresponding to the active state is 1, and the switching function value corresponding to the off state is 0. Set the initial state of the state machine for the two states, which is either the active state or the off state, corresponding to an initial switching function value of 1 or 0 respectively. In each signal sampling period... Within this process, the state transition probability is calculated based on the current state of the state machine and its corresponding transition rate. The specific calculation method is as follows: If the current state is the active state, the probability of transitioning to the off state in the next cycle is... The probability of maintaining the active state is If the current state is closed, the probability of transitioning to the active state in the next cycle is: The probability of remaining in the closed state is Generate a random number between 0 and 1, and perform the following judgment: when the current state is active, if the random number is less than or equal to... If the current state is closed, the system switches to the off state in the next cycle; otherwise, it remains in the active state. When the current state is closed, if the random number is less than or equal to... If the signal is active, it will switch to the active state in the next cycle; otherwise, it will remain in the closed state. The state of each signal sampling cycle is converted into the corresponding switching function value to obtain the switching function sequence.

[0017] By adopting the above technical solution, the Markov chain transition rate is first obtained to provide a quantitative basis for state transitions; a two-state machine is constructed to clearly define the active and offline states and their corresponding switch values, making state transitions clear; an initial state is set to ensure that the processing start point is controllable; in each sampling period, the probability is calculated based on the current state and the transition rate, and the switching is determined by combining random numbers, so that the state transition conforms to statistical laws; the transition state is a sequence of switching functions. The probability-based transition mechanism makes the activation and deactivation of dynamic clusters statistically random, accurately simulating the dynamic changes of multipath in actual channels, improving the statistical accuracy of time-varying characteristic simulation, and avoiding the unrealistic nature of fixed switching.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of linearly superimposing the static multipath signal copy, the dynamic multipath signal copy, and the channel noise, the method further includes: static clustering... The specific processing formula for the signal at that time is as follows: ;in, The static cluster amplitude coefficient is determined by the Rice factor. This represents the static cluster number. For static cluster numbering, The time delay variable is a discretized time delay value generated by the signal superposition unit based on preset parameters. It represents all possible delay moments on the time axis and is used to traverse and locate the actual delay position of the multipath signal. This is an impulse function, valid only when the parameter within the parentheses is 0. This is the Doppler phase modulation term. The static cluster frequency offset in this frequency offset sequence at the current time. The value of ; dynamic clusters in The specific processing formula for the signal at that time is as follows: ;in, This is an impulse function, valid only when the parameter within the parentheses is 0. This is the Doppler phase modulation term. The dynamic cluster frequency offset in this frequency offset sequence at the current time The value of , For the dynamic number of clusters, For dynamic cluster numbering, This is the value of the switching function.

[0019] By adopting the above technical solutions, in the static cluster processing formula, the amplitude coefficient (Eisley factor) is fixed to ensure that the amplitude matches the fading distribution; the impulse function locates the multipath delay, ensuring accurate time delay; and the Doppler term reflects the frequency offset, fully presenting the static multipath characteristics. In the dynamic cluster formula, the switching function controls the start and stop of the dynamic cluster, the time-varying delay reflects the dynamic characteristics, and the impulse function and Doppler term ensure accurate time delay and frequency. Both formulas quantify the amplitude, time, and frequency characteristics of static and dynamic multipath, ensuring a strict correspondence between mathematical expressions and physical characteristics. This provides a precise mathematical foundation for subsequent superposition, improves the rigor of channel simulation theory, and avoids a disconnect between the mathematical model and actual characteristics.

[0020] In conjunction with some embodiments of the first aspect, in some embodiments, the step of linearly superimposing the static multipath signal copy, the dynamic multipath signal copy, and the channel noise to generate a channel output signal sequence includes: retaining the dynamic multipath signal copy with a switching function value of 1; and obtaining the fixed delay of the static cluster issued by the parameter configuration interface. Based on the time delay diffusion coefficient, the time-varying time delay of each dynamic cluster is calculated using a dynamic cluster generator. The calculation method is as follows: ;in Indicates the first The time-varying delay of each dynamic cluster in the next sampling period Indicates the first A dynamic cluster at the current moment Time-varying delay, This is the time delay diffusion coefficient. The signal sampling period, for The channel noise at that moment; based on the fixed delay of the static cluster. Time-varying delay of dynamic clusters Calculate the time offset between each static multipath signal replica and the dynamic multipath signal replica; based on the time offset, align the static and dynamic multipath signal replicas on the same time axis; linearly accumulate the static and dynamic multipath signal replicas to obtain the multipath composite signal. , The specific formula is as follows: The process involves: obtaining a preset channel signal-to-noise ratio (SNR) requirement parameter and calculating the amplitude adjustment coefficient of the channel noise based on this parameter; scaling the channel noise according to the amplitude adjustment coefficient and then linearly superimposing it with the multipath composite signal to obtain the initial channel output signal; discretizing the initial channel output signal according to a preset sampling period to obtain the instantaneous signal value at each sampling time; and arranging these instantaneous signal values ​​in chronological order to generate the channel output signal sequence.

[0021] By employing the above technical solution, effective dynamic multipath signals are first screened to eliminate invalid signal interference; the time-varying delay of dynamic clusters is calculated based on the time delay diffusion coefficient to match actual dynamic time delay changes; then, the time offset is calculated to align static and dynamic multipath signals in the time domain, avoiding superposition distortion caused by time delay differences; multipath composite signals are accumulated to fully represent the multipath superposition effect; the noise amplitude is adjusted according to the signal-to-noise ratio to make the noise interference conform to reality; and the output sequence is generated through discretization sampling. Through time delay calibration and noise adjustment, the process ensures that the combined effect of multipath superposition and noise interference closely matches reality, improving the overall realism of the channel output simulation and avoiding superposition distortion and noise mismatch problems.

[0022] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on a channel unification system, cause the channel unification system to perform the methods as described in the first aspect and some corresponding embodiments.

[0023] Fourthly, this application provides a computer program product that, when run on a channel unification system, causes the channel unification system to perform the method as described in any of the embodiments corresponding to the first aspect. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of a channel unification system based on dynamic multipath in an embodiment of this application; Figure 2 This is a flowchart illustrating a channel unification method based on dynamic multipath parameterization modeling in an embodiment of this application. Figure 3 This is a schematic diagram of the physical device structure of a channel unification system in the embodiments of this application. Detailed Implementation

[0025] 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 and includes any or all possible combinations of one or more of the listed items.

[0026] 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.

[0027] For ease of understanding, the structure of the channel unification system used in the method provided in this embodiment is described below. Please refer to [link / reference]. Figure 1 This is a schematic diagram of a channel unification system based on dynamic multipath in an embodiment of this application.

[0028] First, the parameter configuration interface, serving as the system's control entry point, sends out the crucial parameter, the Doppler shape factor. Next, the Doppler spectrum synthesizer receives this parameter, generates the corresponding Doppler spectrum, and provides it to both the static and dynamic cluster generators. Then, the static cluster generator uses the received Doppler spectrum to perform operations such as delaying, amplitude adjustment, and frequency offset superposition on the original input signal, generating a stable static multipath signal copy. Similarly, the dynamic cluster generator uses the Doppler spectrum to perform time-varying delay, activation state control, and frequency offset processing on the original input signal, generating a dynamic multipath signal copy that dynamically changes with the channel environment. Finally, the signal superposition unit linearly superimposes the fixed multipath signal output from the static cluster generator, the time-varying multipath signal output from the dynamic cluster generator, and the channel noise to generate the final channel output signal, thereby simulating a complex channel environment.

[0029] After considering the above structure, the method provided in this embodiment will be described in more detail below. Please refer to [link / reference]. Figure 2 This is a flowchart illustrating a channel unification method based on dynamic multipath parameterization modeling in an embodiment of this application.

[0030] S201. Obtain the original input signal and channel noise of the continuous time series discretized according to a preset sampling rate, wherein the preset sampling rate is determined according to the channel bandwidth requirements; The preset sampling rate refers to the number of samples per second set to convert a continuous-time signal into a discrete digital signal, measured in Hertz (Hz). Its value must satisfy the Nyquist sampling theorem to avoid signal distortion. The original input signal of the continuous-time series refers to the signal to be transmitted that changes continuously in time, such as voice signals and video signals. Its characteristic is that both time and amplitude are continuous values. Discretization refers to the process of converting a continuous-time signal into signal values ​​at discrete time points through sampling. Channel noise refers to unwanted signals mixed in during signal transmission, usually generated by electronic equipment noise, environmental interference, etc., such as thermal noise and electromagnetic interference.

[0031] Specifically, this step is typically performed when the channel unification system starts up and is ready to process signals. It is applicable to all scenarios requiring digitization of input signals, whether it is an analog wireless communication channel, a wired communication channel, or other types of transmission channels. First, the system needs to determine the type of signal being processed and the corresponding channel bandwidth requirements, as the sampling rate is closely related to the channel bandwidth. According to the Nyquist sampling theorem, the preset sampling rate must be at least twice the channel bandwidth to ensure that the information of the original signal is fully preserved. For example, if the channel bandwidth is 1MHz, the preset sampling rate must be at least 2MHz. Next, the system acquires the raw input signal in a continuous time series. These signals may come from the signal source generation module, such as voice signals output from a microphone or video signals output from a camera. Then, the raw input signal is discretized according to the preset sampling rate, that is, the continuous signal is sampled once every certain time interval (the sampling period, which is the reciprocal of the sampling rate) to obtain a discrete signal sample sequence. Simultaneously, the system also needs to acquire channel noise. Channel noise can be noise generated by the noise generation module that conforms to specific statistical characteristics, such as Gaussian white noise, or it can be real noise data collected from the actual environment. During the acquisition process, it is necessary to ensure that the characteristics of the noise match the current simulated channel environment. For example, when simulating a wireless channel, the noise may have a higher power spectral density and a specific frequency distribution. Through this step, the system converts the continuous raw signal into a discrete signal that can be digitally processed, and acquires the noise components that are indispensable in the simulated channel transmission process, laying the foundation for subsequent multipath signal generation and superposition.

[0032] S202. Obtain key parameters through the parameter configuration interface and send the key parameters to the static cluster generator, dynamic cluster generator and Doppler spectrum synthesizer respectively. The key parameters include at least the Doppler shape factor, Rice factor and dynamic cluster enable signal. Among them, the parameter configuration interface refers to the interface used in the system to receive external configuration commands and interact with parameters of various functional modules. It can be a hardware interface such as a bus interface or a software interface such as an API interface. Key parameters are parameters that play a decisive role in the functional implementation of each module of the system, and their values ​​directly affect the channel simulation effect. The Doppler shape factor is a parameter used to determine the type of Doppler spectrum model. Different values ​​correspond to different spectral distribution characteristics. For example, a value of 1 corresponds to a Gaussian spectrum model. The Rice factor is a parameter used to describe the ratio of direct component to scattered component in the signal. The larger the value, the stronger the direct component. The dynamic cluster enable signal is a signal used to control the dynamic cluster generator to be turned on or off. It is usually a binary signal, with 1 indicating on and 0 indicating off. The static cluster generator is a functional module used to generate stable multipath signal replicas. The dynamic cluster generator is a functional module used to generate multipath signal replicas that change dynamically with the environment. The Doppler spectrum synthesizer is a functional module used to generate a specific Doppler spectrum.

[0033] Specifically, this step is executed after the system has acquired and discretized the original signal and noise. It is suitable for scenarios where the system's operating state needs to be adjusted according to different channel requirements. For example, when simulating a channel in a high-speed moving scenario, it is necessary to adjust key parameters to reflect changes in the Doppler effect. First, the parameter configuration interface receives scenario configuration commands sent externally via a bus or other means. These commands may come from manual settings by the user, automatic configuration by the upper-level control system, or input from other external devices. The interface parses the received commands and extracts key parameters, which, in addition to the Doppler shape factor, Rice factor, and dynamic cluster enable signal, may also include parameters such as the number of static clusters, the number of dynamic clusters, and the Markov chain transition rate. Then, according to the needs of each functional module, the parameter configuration interface sends the corresponding key parameters to the static cluster generator, the dynamic cluster generator, and the Doppler spectrum synthesizer, respectively. For example, the Doppler shape factor is sent to the Doppler spectrum synthesizer to generate the corresponding Doppler spectrum; the Rice factor is sent to both the static and dynamic cluster generators to determine the amplitude fading distribution of the multipath signals generated by both; and the dynamic cluster enable signal is sent to the dynamic cluster generator to control its operation. During this sending process, it is crucial to ensure the accuracy and timeliness of parameter transmission to avoid malfunctions in any module due to parameter errors or delays. Through this step, the system achieves unified control over all functional modules, enabling them to work collaboratively according to preset channel scenario requirements, providing a parameter basis for subsequent generation of multipath signals conforming to specific scenarios.

[0034] S203. Based on this key parameter, frequency shift sequences corresponding to the static cluster generator and the dynamic cluster generator are generated respectively by the Doppler spectrum synthesizer. Among them, the Doppler spectrum synthesizer is a functional module specifically used to generate a specific Doppler spectrum based on input parameters; the frequency offset sequence refers to the sequence of frequency offset values ​​at different time points, which reflects the frequency change of the signal over time during transmission; the frequency offset sequence corresponding to the static cluster generator refers to the sequence used for frequency modulation of static multipath signals, and its change is relatively stable; the frequency offset sequence corresponding to the dynamic cluster generator refers to the sequence used for frequency modulation of dynamic multipath signals, and it has time-varying characteristics.

[0035] Specifically, this step is executed after the parameter configuration interface completes the distribution of key parameters. It is suitable for scenarios that require frequency characteristic parameters for static and dynamic cluster generators. Whether simulating a stationary or mobile channel, this step is necessary to generate the corresponding frequency offset sequence to represent the Doppler effect. First, the Doppler spectrum synthesizer receives the key parameters from the parameter configuration interface, especially the Doppler shape factor. Then, it determines the target Doppler spectrum model based on the Doppler shape factor: when the Doppler shape factor is 1, a Gaussian spectrum model is selected, which is suitable for describing the Doppler spectrum of channels with Gaussian distribution characteristics; when the Doppler shape factor is between 0.5 and 1, a generalized Jakes spectrum model is selected, which can better simulate the Doppler spread characteristics in mobile wireless channels. After determining the model, the Doppler spectrum synthesizer performs frequency domain discretization within a preset Doppler frequency range. The preset Doppler frequency range is usually determined based on the maximum Doppler frequency shift that may occur in the channel. For example, in a vehicle movement scenario, the maximum Doppler frequency shift may reach several hundred hertz. Frequency domain discretization divides a continuous frequency range into several discrete frequency points, generating discrete spectral coefficients for N frequency points. These coefficients contain amplitude and phase information for each frequency point; the amplitude information reflects the signal strength at that frequency point, and the phase information reflects the phase state of the signal at that frequency point. Next, an inverse fast Fourier transform (IFFT) is performed on each discrete spectral coefficient, converting the discrete spectrum in the frequency domain into a signal in the time domain, thereby generating a fundamental frequency offset sequence. This fundamental sequence reflects the overall trend of frequency offset variation over the entire time range. Finally, based on the fundamental frequency offset sequence, the Doppler spectrum synthesizer generates corresponding frequency offset sub-sequences for both the static and dynamic cluster generators. For the static cluster generator, considering the stability of the multipath signal it generates, the fundamental sequence may be smoothed or a relatively stable component may be selected as its frequency offset sub-sequence. For the dynamic cluster generator, due to the time-varying characteristics of the multipath signal it generates, the fundamental sequence may be time-varyingly modulated or a more drastically changing component may be selected as its frequency offset sub-sequence. This step provides frequency offset parameters that conform to specific Doppler characteristics for the generation of static and dynamic multipath signals, enabling the generated multipath signals to accurately reflect frequency changes under different channel scenarios.

[0036] In some embodiments, after the Doppler spectrum synthesizer generates the fundamental frequency offset sequence, frequency offset sub-sequences conforming to their characteristics are provided to the static and dynamic cluster generators respectively, to accurately simulate the frequency variation characteristics of static and dynamic multipath. Specifically, firstly, the parameter configuration interface obtains the Markov chain transition rates of each dynamic cluster from external configuration instructions or internal system storage. These rates determine the frequency at which the dynamic cluster switches between active and off states. Next, the fundamental frequency offset sequence is low-pass filtered. The cutoff frequency of the low-pass filter is typically set according to the required frequency variation smoothness of the static cluster. Through low-pass filtering, high-frequency fluctuation components in the fundamental sequence are filtered out, retaining the low-frequency, relatively stable parts to generate a static fundamental sequence. This sequence will serve as the basis for the frequency offset of the static cluster, because the multipath signal generated by the static cluster should have relatively stable frequency characteristics. Then, for each static cluster, a random frequency offset compensation value is superimposed based on the generated static fundamental sequence. The preset compensation value range needs to be determined according to the small frequency offset range that may exist in the static multipath in the actual channel, for example, between -10Hz and 10Hz. The random frequency offset compensation value is randomly generated within this range and remains a fixed value. This ensures that the first frequency offset subsequence of each static cluster is based on a stable static base sequence while also possessing its own unique, slightly random frequency offset, better reflecting the subtle differences in stable multipath signals in reality. Next, the base frequency offset sequence undergoes high-pass filtering. The cutoff frequency of the high-pass filter is also set according to the time-varying frequency characteristics required by the dynamic cluster. High-pass filtering removes the low-frequency stable components from the base sequence, retaining the high-frequency, rapidly changing parts to generate the dynamic base sequence. This sequence is used to represent the time-varying frequency characteristics of the dynamic cluster's multipath signal. Finally, for each dynamic cluster, amplitude modulation is performed based on the dynamic base sequence according to the corresponding Markov chain transition rate. Dynamic clusters with high Markov chain transition rates exhibit more frequent amplitude modulation changes, meaning their frequency offset time-varying characteristics are more significant; dynamic clusters with low transition rates show relatively smooth amplitude modulation changes. Through amplitude modulation, the state transition characteristics of the dynamic cluster are combined with the time-varying characteristics of the frequency offset to generate the second frequency offset subsequence, ensuring that the frequency offset of the dynamic cluster has both a high-frequency time-varying basis and conforms to the statistical laws of its state transitions.

[0037] The Markov chain transition rate is a parameter describing how fast a dynamic cluster transitions between different states (such as active and off), used to characterize the statistical properties of dynamic cluster state changes. Low-pass filtering is a signal processing operation that allows low-frequency components in the fundamental frequency offset sequence to pass through while suppressing high-frequency components, resulting in a relatively stable signal. The static fundamental sequence is the basic signal sequence used for generating the static cluster frequency offset after low-pass filtering. The random frequency offset compensation value is a fixed value randomly generated within a preset range, used to add random variations to the frequency offset of the static cluster. The first frequency offset subsequence is the final frequency offset sequence used by the static cluster, obtained by superimposing the random frequency offset compensation value onto the static fundamental sequence. High-pass filtering is the opposite of low-pass filtering, allowing high-frequency components in the fundamental frequency offset sequence to pass through while suppressing low-frequency components, resulting in a signal reflecting time-varying characteristics. The dynamic fundamental sequence is the basic signal sequence used for generating the dynamic cluster frequency offset after high-pass filtering. Amplitude modulation refers to changing the amplitude of the dynamic fundamental sequence according to the Markov chain transition rate to reflect the time-varying law of the dynamic cluster. The second frequency offset subsequence is the final frequency offset sequence used by the dynamic cluster, obtained by amplitude modulation of the dynamic fundamental sequence.

[0038] Optionally, firstly, the Markov chain transfer rate of each dynamic cluster is read from the configuration file using the parameter configuration interface; then, the fundamental frequency offset sequence is processed using a Butterworth low-pass filter, with the filter order set to 4 and the cutoff frequency set to 100Hz, to obtain the static fundamental sequence; for each static cluster, a random number is generated within a preset compensation value range of -5Hz to 5Hz as a random frequency offset compensation value, and this value is added to each corresponding element of the static fundamental sequence to generate the first frequency offset subsequence; next, the fundamental frequency offset sequence is processed using a Butterworth high-pass filter, with the filter order set to 4 and the cutoff frequency set to 200Hz, to obtain the dynamic fundamental sequence; for each dynamic cluster, the amplitude modulation coefficient is calculated based on its Markov chain transfer rate, with a higher transfer rate resulting in a larger range of coefficient variation, and then each element of the dynamic fundamental sequence is multiplied by the corresponding amplitude modulation coefficient to generate the second frequency offset subsequence.

[0039] S204. Determine the amplitude fading distribution based on the Rice factor; The Rice factor is a parameter used to describe the relative intensity of the direct and scattered components in a signal, usually denoted by K, and its value is the ratio of the power of the direct component to the power of the scattered component. The Rayleigh distribution is a probability distribution applicable when there is no direct component in the signal (i.e., no Rice factor). The amplitude fading case with only a large number of scattering components has the following probability density function: ( , The Rice distribution is also a probability distribution, applicable when the signal contains a direct component (i.e., the Rice factor). (The standard deviation of the scattering component is also mentioned). The amplitude fading of the scattering component and the scattering component is given by the probability density function. ( A is the amplitude of the direct component. (For the zeroth-order modified Bessel function of the first kind).

[0040] Specifically, this step, performed after obtaining the Rice factor, is a crucial prerequisite for subsequently determining the amplitude variation patterns of multipath signals generated by static and dynamic clusters. First, the system extracts the Rice factor value from key parameters. Then, the Rice factor value is evaluated, specifically through steps S2041 to S2042: S2041. If the Rice factor is equal to 0, then the amplitude fading distribution is determined to be a Rayleigh distribution. When the Rice factor equals 0, it means that the signal has no obvious direct component during transmission, only numerous scattered components superimposed on each other. In this case, the amplitude fading of the signal follows a Rayleigh distribution. For example, in wireless communication scenarios in densely populated urban areas, due to the presence of numerous obstacles such as buildings, the signal mainly propagates through scattering, and the amplitude fading usually exhibits a Rayleigh distribution.

[0041] S2042. If the Rice factor is greater than 0, then the amplitude fading distribution is determined to be a Rice distribution. When the Rice factor is greater than 0, it indicates that there is a direct component and a scattered component in the signal. In this case, the amplitude fading of the signal follows a Rice distribution. For example, in line-of-sight communication scenarios in open areas, there is a clear direct signal path, and the amplitude fading will exhibit a Rice distribution. The larger the Rice factor, the higher the proportion of the direct component, and the smaller the fluctuation of the amplitude fading.

[0042] This step enables the system to provide accurate distribution data for amplitude adjustment when generating multipath signals for subsequent static and dynamic clusters, ensuring that the simulated channel amplitude fading characteristics match the actual scenario.

[0043] S205. Obtain the number of static clusters in the key parameter, and activate the corresponding number of static clusters based on the number of static clusters; Among them, the number of static clusters refers to the number of stable multipath signal replicas that need to be generated in the system; activation refers to switching the corresponding module in the static cluster generator from a dormant or inactive state to a working state that can perform signal processing tasks.

[0044] Specifically, this step is performed after the amplitude fading distribution is determined, and its purpose is to prepare a corresponding number of working modules for the subsequent generation of static multipath signal replicas. First, the system extracts the number of static clusters from key parameters through a parameter configuration interface. This number of static clusters is set based on the number of stable multipath components in the channel to be simulated; for example, when simulating a channel with 3 stable multipaths, the number of static clusters is set to 3. Then, based on the extracted number of static clusters, the system sends an activation command to the static cluster generator. The static cluster generator contains multiple independently working sub-modules, each corresponding to one static cluster. Upon receiving the activation command, the static cluster generator activates the corresponding number of sub-modules sequentially according to the number of static clusters, putting these sub-modules into working state, ready to perform processing such as delaying, amplitude adjustment, and frequency offset superposition on the original input signal. Through this step, the system can flexibly configure the number of static clusters according to actual needs, thereby simulating stable multipath channel scenarios of varying complexity.

[0045] S206. Control each activated static cluster to delay the original input signal based on a preset static cluster fixed time delay, adjust the signal amplitude according to the amplitude fading distribution, and superimpose them based on the frequency offset sequence to obtain multiple static multipath signal copies. Among them, the activated static cluster refers to the functional unit activated from the system according to the number of static clusters in the key parameters, used to generate stable multipath signals; the static cluster fixed delay refers to the signal delay value preset for each activated static cluster that does not change with time. The fixed delay of different static clusters can be different, used to simulate the time difference of stable propagation paths of different lengths; the original input signal refers to the initial continuous time sequence signal after being discretized by a preset sampling rate; the delay processing refers to shifting the time axis of the original input signal backward by a corresponding fixed delay to restore the signal arrival time difference caused by the difference in path length in multipath propagation; the amplitude fading distribution refers to the random variation law of signal amplitude determined according to the Rice factor, including Rayleigh distribution (Rice factor = 0) and Rice distribution (Rice factor > 0); the frequency offset sequence refers to the sequence generated by the Doppler spectrum synthesizer to simulate the signal frequency changing with time, specifically the first frequency offset subsequence matched for the static cluster; the static multipath signal copy refers to the signal copy with fixed delay, stable amplitude fading and frequency offset characteristics after the static cluster processes the original input signal.

[0046] Specifically, this step is performed after activating the corresponding number of static clusters and before dynamic multipath signal processing. It is applicable to all channel scenarios that require simulating stable multipath components, such as fixed multipath formed by ground reflections in shortwave communication, or static multipath formed by stable forwarding links in satellite communication. First, the channel unification system distributes the discretized raw input signal in parallel to each activated static cluster through the internal data bus, ensuring that each static cluster receives the signal synchronously and avoiding time deviations from affecting the accuracy of multipath simulation.

[0047] Next, each static cluster performs delay processing based on a preset fixed delay: the system configures an independent delay parameter register for each static cluster, storing its corresponding fixed delay value (e.g., 1μs for cluster 1, 3μs for cluster 2). The delay processing module of the static cluster reads the register value and controls the internal programmable delay line to shift each sampling point of the original input signal backward by the corresponding time on the time axis. For example, if the sampling value of the original input signal at t=0 is A, and the fixed delay of cluster 1 is 1μs, then the output time of this sampling value after the delay becomes t=1μs, thus simulating the propagation delay of the signal through stable paths of different lengths.

[0048] Then, each static cluster adjusts the signal amplitude according to the amplitude fading distribution: the amplitude adjustment module of the static cluster first obtains the determined amplitude fading distribution type (Rayleigh or Rice distribution) and related parameters (such as the standard deviation σ of the Rayleigh distribution and the direct component amplitude A of the Rice distribution) from the parameter configuration interface. If it is a Rayleigh distribution, the module generates two independent Gaussian random variables x1 and x2 through a pseudo-random number generator, and then adjusts the signal amplitude according to the formula... The amplitude coefficients of the static cluster are calculated by performing point-by-point multiplication on the delayed signal to achieve amplitude fading. If it is a Ricean distribution, the module first generates the amplitude A of the direct component, and then superimposes it with the amplitude of the scattered component calculated from the Gaussian random variable to obtain the amplitude coefficients of the static cluster. Similarly, the signal amplitude is adjusted through multiplication to ensure that the amplitude change conforms to the fading characteristics of stable multipath in the actual channel.

[0049] Subsequently, the static clusters are superimposed based on the frequency offset sequence: the frequency modulation module of the static cluster reads the first frequency offset subsequence assigned to it, which reflects the Doppler frequency change of the stable multipath signal (such as a small frequency offset in a slowly moving scene). The module performs point-by-point complex multiplication (simplified to real number multiplication if it is a real signal) on the amplitude-adjusted signal and the frequency offset subsequence, that is, each sample value of the signal is multiplied by the frequency offset value at the corresponding time, realizing the frequency offset superposition and simulating the frequency change caused by the Doppler effect in multipath propagation.

[0050] Finally, after each activated static cluster completes the delay, amplitude adjustment, and frequency offset superposition, it outputs an independent copy of the static multipath signal. The system collects the outputs of all static clusters through the data aggregation module, forming multiple sets of static multipath signal copies, preparing for subsequent superposition with dynamic multipath signals and noise. Throughout the process, the system monitors the processing status of each static cluster in real time. If a static cluster experiences a delay deviation or amplitude adjustment anomaly, the system immediately reloads the parameters and restarts the cluster to ensure the accuracy of the static multipath signal.

[0051] S207. Based on the dynamic cluster enable signal and the frequency offset sequence, control the dynamic cluster to process the original input signal to obtain multiple dynamic multipath signal copies; Specifically, when the dynamic cluster enable signal is 0, the dynamic cluster generator is turned off; when the dynamic cluster enable signal is 1, the dynamic cluster generator is turned on, and a two-state state machine is constructed based on the Markov chain transition rate to generate the switching function sequence of each dynamic cluster; based on the switching function sequence, each dynamic cluster is controlled to perform delay processing on the original input signal, and the signal amplitude is adjusted according to the amplitude fading distribution, and superimposed according to the frequency offset sequence to obtain multiple dynamic multipath signal replicas.

[0052] The dynamic cluster enable signal is a binary control signal used to control the working state of the dynamic cluster generator. A 1 indicates that the dynamic cluster generator is allowed to start and execute signal processing tasks, while a 0 indicates that it is disabled. The Markov chain transition rate is a parameter describing the probability of a dynamic cluster switching between the "active" and "off" states, including the transition rate from the active state to the off state. and the transition rate from the off state to the active state A two-state state machine is a mathematical model used to simulate the state changes of a dynamic cluster. It contains only two states: "active" and "off," and can randomly switch states according to the transition rate. The switching function sequence is a discrete sequence generated by the two-state state machine to indicate whether the dynamic cluster is working in each sampling period. A value of 1 indicates that the dynamic cluster is in the active state and processes the signal, while a value of 0 indicates that it is in the off state and does not process the signal. The original input signal refers to the initial discretized signal without multipath processing. Delay processing refers to the operation of shifting the time axis of the original input signal backward according to the time-varying delay parameters of the dynamic cluster to simulate the multipath propagation delay. The amplitude fading distribution refers to the probability distribution used to describe the random variation of the signal amplitude, determined by the Rice factor, including the Rayleigh distribution (Rice factor = 0) and the Rice distribution (Rice factor > 0). The frequency offset sequence refers to the sequence generated by the Doppler spectrum synthesizer to simulate the change of signal frequency over time. Here, it specifically refers to the second frequency offset subsequence matched for the dynamic cluster.

[0053] Specifically, this step is performed after the system completes the generation of the static multipath signal copy and before signal superposition. It is suitable for scenarios that require flexible simulation of dynamic multipath characteristics based on the channel scenario, such as satellite communication scenarios where ionospheric disturbances cause multipath components to suddenly appear or disappear, or stable shortwave communication scenarios without dynamic multipath. First, the channel unification system reads the value of the dynamic cluster enable signal from the parameter configuration interface, which serves as the control basis for the dynamic cluster generator: if the enable signal is 0, it means that the current scenario does not require simulation of dynamic multipath (such as a static indoor communication scenario). The system sends a power-off or sleep command to the dynamic cluster generator to shut down its internal signal processing circuit, clock module, and data interface to avoid unnecessary consumption of hardware resources. In this case, subsequent signal superposition only uses the static multipath signal copy and channel noise; if the enable signal is 1, it means that the current scenario requires simulation of dynamic multipath (such as a high-speed moving vehicle communication scenario). The system first sends a start command to the dynamic cluster generator to activate its internal power supply, clock, and data receiving modules, putting it into a standby state.

[0054] Next, the system obtains the Markov chain transition rate corresponding to each dynamic cluster from the parameter configuration interface. and For each dynamic cluster, an independent two-state state machine is constructed: First, an initial state is set for each state machine (which can be preset to an active or closed state according to the scenario requirements, such as setting the initial state to closed when simulating a sudden multipath scenario). Then, based on the signal sampling period... Calculate the state transition probability—If the current state is active, the probability of transitioning to the off state in the next cycle is: (Since the transition probability within the sampling period is proportional to the transition rate and time), the probability of maintaining the active state is... If the current state is closed, the probability of transitioning to the activated state in the next cycle is: The probability of remaining in the closed state is Then, the system generates random numbers in the 0-1 range within each sampling period using a pseudo-random number generator, compares these random numbers with the transition probability to determine the state: if the current state is active and the random number is ≤ If the current state is closed, the system will switch to the closed state in the next cycle; otherwise, it will remain in the active state. If the current state is closed and the random number is less than or equal to the given value, the system will switch to the closed state in the next cycle. If the state is active, the system switches to the active state in the next cycle; otherwise, it remains in the off state. By converting the state of each sampling cycle into the corresponding switching function value (active state = 1, off state = 0), the switching function sequence of each dynamic cluster can be obtained.

[0055] Finally, the system controls the dynamic clusters to process the original input signal based on the switching function sequence: For each dynamic cluster, if the switching function value of a certain sampling period is 1, the system first obtains the time-varying delay of that period from the delay calculation module of the dynamic cluster generator, and performs delay processing on the original input signal to ensure that the signal delay conforms to the propagation characteristics of dynamic multipath; then, according to the amplitude fading distribution determined in step S204, it generates the amplitude scaling factor of that period, adjusts the amplitude of the delayed signal, and simulates the signal fading in multipath propagation; subsequently, it multiplies the adjusted signal with the value of the corresponding second frequency offset subsequence in that period to achieve frequency offset superposition and simulate the frequency change caused by the Doppler effect; if the switching function value is 0, the dynamic cluster outputs an invalid signal with an amplitude of 0, which does not participate in subsequent superposition. After all dynamic clusters have completed processing, the output valid signal is a copy of multiple dynamic multipath signals, which can be used for subsequent superposition with static multipath signals and noise.

[0056] S208. Linearly superimpose the static multipath signal copy, the dynamic multipath signal copy, and the channel noise to generate a channel output signal sequence.

[0057] Throughout the process, static clusters are in The specific processing formula for the signal at that time is as follows: ;in, This is the static cluster amplitude coefficient, determined by the Rice factor. The specific calculation steps are explained in detail in S206 and will not be repeated here. This represents the static cluster number. For static cluster numbering, The time delay variable is a discretized time delay value generated by the signal superposition unit based on preset parameters. It represents all possible delay moments on the time axis and is used to traverse and locate the actual delay position of the multipath signal. This is an impulse function, valid only when the parameter within the parentheses is 0; This is the Doppler phase modulation term. The static cluster frequency offset in the frequency offset sequence at the current time The value of ; Dynamic clusters in The specific processing formula for the signal at that time is as follows: ;in, This is an impulse function, valid only when the parameter within the parentheses is 0. This is the Doppler phase modulation term. The dynamic cluster frequency offset in the frequency offset sequence at the current time The value of , For the dynamic number of clusters, For dynamic cluster numbering, This is the value of the switching function.

[0058] Next, linear superposition is performed to generate the channel output signal sequence. First, a copy of the dynamic multipath signal with a switching function value of 1 is retained; then, the fixed delay of the static cluster is obtained from the parameter configuration interface. Based on the time delay diffusion coefficient, the time-varying time delay of each dynamic cluster is calculated using a dynamic cluster generator. The calculation method is as follows: ;in Indicates the first The time-varying delay of each dynamic cluster in the next sampling period Indicates the first A dynamic cluster at the current moment Time-varying delay, The time delay diffusion coefficient is... The signal sampling period, for Channel noise at any given time; and then based on the fixed delay of the static cluster. Time-varying delay of dynamic clusters Calculate the time offset between each static multipath signal replica and the dynamic multipath signal replica; based on the time offset, align the static multipath signal replicas and the dynamic multipath signal replicas on the same time axis. The static multipath signal copy and the dynamic multipath signal copy are linearly accumulated to obtain the multipath composite signal. The specific formula is as follows: ; Obtain the preset channel signal-to-noise ratio (SNR) requirement parameters and calculate the amplitude adjustment coefficient of the channel noise based on the SNR requirement parameters; scale the channel noise according to the amplitude adjustment coefficient and then linearly superimpose it with the multipath composite signal to obtain the initial channel output signal; discretize the initial channel output signal according to the preset sampling period to obtain the instantaneous signal value at each sampling time; arrange the instantaneous signal values ​​in chronological order to generate the channel output signal sequence.

[0059] In this embodiment, the system obtains discretized signals and noise adapted to the channel bandwidth through step S201, laying a precise data foundation for subsequent processing; it achieves unified distribution of key parameters through S202, ensuring that each module responds collaboratively to scenario requirements; it adaptively generates frequency offset sequences and amplitude fading distributions through S203-S204, fitting the time-frequency and amplitude characteristics of different channels; it activates static clusters and dynamically controls dynamic clusters as needed through S205-S207, generating stable and dynamic multipath signal replicas respectively, taking into account both static and dynamic multipath scenarios; and finally, it completes the accurate superposition of multipath signals and noise through S208, fully reproducing the channel transmission process. Therefore, it can overcome the limitations of independent modeling of the traditional Watterson model and ITS model, realizing single-system processing of shortwave communication (static multipath, Gaussian spectrum, Rayleigh fading) and satellite communication (dynamic multipath, generalized Jakes fading). The system achieves compatibility and adaptation for scenarios such as spectrum and Ricean fading, effectively solving the problems of multiple hardware deployments, resource waste, and complex integration required for multi-scenario communication in existing technologies. This enables full coverage of channel simulation scenarios, configurable parameters, and low-cost hardware deployment, improving the efficiency of communication system testing and optimization, and reducing the operation and maintenance costs of multi-scenario communication networks.

[0060] The channel unification system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical device structure of a channel unification system in an embodiment of this application.

[0061] It should be noted that, Figure 3 The structure of the channel unification 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.

[0062] like Figure 3 As shown, the channel unification system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage section 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] Specifically, the channel unification system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the channel unification method based on dynamic multipath parameterization modeling provided in the above embodiment.

[0068] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the channel unification system described in the above embodiments; or it may exist independently and not assembled into the channel unification system. The storage medium carries one or more computer programs that, when executed by a processor of the channel unification system, cause the channel unification system to implement the channel unification method based on dynamic multipath parameterization modeling provided in the above embodiments.

[0069] 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.

[0070] 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)".

[0071] 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 unified channel system based on dynamic multipath, characterized in that, include: The parameter configuration interface is used to receive external scene configuration commands via the bus, parse them, and send key parameters to each functional module. A static cluster generator is used to delay, adjust the amplitude, and superimpose the frequency offset of the original input signal to generate multiple stable static multipath signal copies. The dynamic cluster generator is used to perform time-varying delay, activation state control and frequency offset processing on the original input signal to generate a dynamic multipath signal copy that changes dynamically with the channel environment. The Doppler spectrum synthesizer is used to convert the frequency domain signal into the time domain signal according to the Doppler shape factor sent by the parameter configuration interface, generate a Gaussian spectrum or a generalized Jakes spectrum, and provide the generated Doppler spectrum to the static cluster generator and the dynamic cluster generator respectively. The signal superposition unit is used to linearly superimpose the static multipath signal copy output by the static cluster generator, the dynamic multipath signal copy output by the dynamic cluster generator, and the channel noise to generate the final channel output signal.

2. A channel unification method based on dynamic multipath parameterization modeling, applied to the channel unification system, characterized in that, The method includes: The original input signal and channel noise of a continuous time series discretized at a preset sampling rate are obtained, wherein the preset sampling rate is determined according to the channel bandwidth requirements; Key parameters are obtained through the parameter configuration interface and then sent to the static cluster generator, dynamic cluster generator, and Doppler spectrum synthesizer respectively. The key parameters include at least the Doppler shape factor, Rice factor, and dynamic cluster enable signal. Based on the aforementioned key parameters, frequency shift sequences corresponding to the static cluster generator and the dynamic cluster generator are generated respectively using a Doppler spectrum synthesizer. Determining the amplitude fading distribution based on the Rice factor specifically includes: If the Rice factor is equal to 0, then the amplitude fading distribution is determined to be a Rayleigh distribution; If the Rice factor is greater than 0, then the amplitude fading distribution is determined to be a Rice distribution; Obtain the number of static clusters from the key parameters, and activate the corresponding number of static clusters based on the number of static clusters; Each activated static cluster is controlled to delay the original input signal based on a preset static cluster fixed time delay, and the signal amplitude is adjusted according to the amplitude fading distribution. The signals are then superimposed based on the frequency offset sequence to obtain multiple static multipath signal replicas. Based on the dynamic cluster enable signal and the frequency offset sequence, the dynamic cluster is controlled to process the original input signal to obtain multiple dynamic multipath signal copies. The static multipath signal copy, the dynamic multipath signal copy, and the channel noise are linearly superimposed to generate a channel output signal sequence.

3. The method according to claim 2, characterized in that, The step of generating frequency shift sequences corresponding to the static cluster generator and the dynamic cluster generator respectively using a Doppler spectrum synthesizer based on the key parameters includes: Obtain the Doppler shape factor, and determine the target Doppler spectral model based on the Doppler spectral shape factor. The specific determination method is as follows: When the shape factor of the Doppler spectrum is 1, the target Doppler spectrum model is determined to be a Gaussian spectrum model; When the shape factor of the Doppler spectrum is between 0.5 and 1, the target Doppler spectrum model is determined to be the generalized Jakes spectrum model. Based on the target Doppler spectrum model, frequency domain discretization is performed within a preset Doppler frequency range to generate discrete spectral coefficients at N frequency points. The discrete spectral coefficients contain amplitude information and phase information. Perform an inverse fast Fourier transform on each of the discrete spectral coefficients to generate a fundamental frequency offset sequence; Based on the aforementioned base frequency offset sequence, corresponding frequency offset sub-sequences are generated for the static cluster generator and the dynamic cluster generator, respectively.

4. The method according to claim 3, characterized in that, The step of generating corresponding frequency offset sub-sequences for the static cluster generator and the dynamic cluster generator based on the base frequency offset sequence includes: The Markov chain transition rate of each dynamic cluster can be obtained through the parameter configuration interface; The fundamental frequency offset sequence is low-pass filtered to generate a static fundamental sequence; For each static cluster, based on the static base sequence, a random frequency offset compensation value is superimposed to generate a corresponding first frequency offset subsequence, wherein the random frequency offset compensation value is a fixed value randomly generated within a preset compensation value range; The fundamental frequency offset sequence is subjected to high-pass filtering to generate a dynamic fundamental sequence; For each dynamic cluster, based on the dynamic base sequence, amplitude modulation is performed according to the corresponding Markov chain transfer rate to generate a corresponding second frequency offset subsequence.

5. The method according to claim 2, characterized in that, The step of controlling the dynamic cluster to process the original input signal based on the dynamic cluster enable signal and the frequency offset sequence to obtain multiple dynamic multipath signal replicas includes: When the dynamic cluster enable signal is 0, the dynamic cluster generator is turned off; When the dynamic cluster enable signal is 1, the dynamic cluster generator is turned on, and a two-state state machine is constructed based on the Markov chain transition rate to generate the switching function sequence of each dynamic cluster. Based on the switching function sequence, each of the dynamic clusters is controlled to perform delay processing on the original input signal, adjust the signal amplitude according to the amplitude fading distribution, and superimpose according to the frequency offset sequence to obtain multiple dynamic multipath signal replicas.

6. The method according to claim 5, characterized in that, The step of constructing a two-state state machine based on the Markov chain transition rate to generate the switching function sequence for each of the dynamic clusters includes: The Markov chain transition rates of each dynamic cluster are obtained from the parameter configuration interface. These Markov chain transition rates include the transition rates from activation to deactivation of the dynamic cluster. and the transition rate of the dynamic cluster from off to active ; Construct a state machine with the two states, namely an active state and a closed state, wherein the switching function value corresponding to the active state is 1 and the switching function value corresponding to the closed state is 0; Set the initial state of the state machine with the two states, wherein the initial state is either an active state or an off state, and the initial value of the switching function is 1 or 0 respectively; In each signal sampling period Within this process, the state transition probability is calculated based on the current state of the state machine in the two states and the corresponding transition rate. The specific calculation method is as follows: If the current state is active, the probability of transitioning to the off state in the next cycle is: The probability of maintaining the active state is ; If the current state is closed, the probability of transitioning to the active state in the next cycle is: The probability of remaining in the closed state is ; Generate a random number between 0 and 1, and perform the following checks: When the current state is active, if the random number is less than or equal to If it does, the system will switch to the off state in the next cycle; otherwise, it will remain in the active state. When the current state is closed, if the random number is less than or equal to If it is active, it will switch to the active state in the next cycle; otherwise, it will remain in the closed state. The state of each signal sampling period is converted into the corresponding switching function value to obtain the switching function sequence.

7. The method according to claim 2, characterized in that, Before the step of linearly superimposing the static multipath signal copy, the dynamic multipath signal copy, and the channel noise, the method further includes: Static clusters in The specific processing formula for the signal at that time is as follows: ； in, The static cluster amplitude coefficient is determined by the Rice factor. This represents the static cluster number. For static cluster numbering, The time delay variable is a discretized time delay value generated by the signal superposition unit based on preset parameters. It represents all delay moments on the time axis and is used to traverse and locate the actual delay position of the multipath signal. This is an impulse function, valid only when the parameter within the parentheses is 0. This is the Doppler phase modulation term. The static cluster frequency offset in the frequency offset sequence at the current time The possible values ​​of ; Dynamic clusters in The specific processing formula for the signal at that time is as follows: ； in, This is an impulse function, valid only when the parameter within the parentheses is 0. This is the Doppler phase modulation term. The dynamic cluster frequency offset in the frequency offset sequence at the current time The value of , For the dynamic number of clusters, For dynamic cluster numbering, This is the value of the switching function.

8. The method according to claim 2, characterized in that, The step of linearly superimposing the static multipath signal copy, the dynamic multipath signal copy, and the channel noise to generate a channel output signal sequence includes: Retain a copy of the dynamic multipath signal with a switch function value of 1; Get the fixed latency of the static cluster issued by the parameter configuration interface. Based on the time delay diffusion coefficient, the time-varying time delay of each dynamic cluster is calculated using a dynamic cluster generator. The calculation method is as follows: ； in Indicates the first The time-varying delay of each dynamic cluster in the next sampling period Indicates the first A dynamic cluster at the current moment Time-varying delay, The time delay diffusion coefficient is... The signal sampling period, for The channel noise at that time; Based on the fixed delay of static clusters Time-varying delay of dynamic clusters Calculate the time offset between each of the static multipath signal replicas and the dynamic multipath signal replicas; Based on the time offset, the static multipath signal copy and the dynamic multipath signal copy are aligned on the same time axis. The static multipath signal copy and the dynamic multipath signal copy are linearly accumulated to obtain the multipath composite signal. The specific formula is as follows: ； Obtain the preset channel signal-to-noise ratio (SNR) requirement parameters, and calculate the amplitude adjustment coefficient of the channel noise based on the channel SNR requirement parameters; The channel noise is scaled according to the amplitude adjustment coefficient and then linearly superimposed with the multipath composite signal to obtain the initial channel output signal. The initial channel output signal is discretized and sampled according to a preset sampling period to obtain the instantaneous signal value at each sampling time. The instantaneous signal values ​​are arranged in chronological order to generate the channel output signal sequence.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the channel unification system, it causes the channel unification system to perform the method as described in any one of claims 2-8.

10. A computer program product, characterized in that, When the computer program product is run on the channel unification system, it causes the channel unification system to perform the method as described in any one of claims 2-8.

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