A method and device for quickly establishing a nakagami channel model

Abstract

The application relates to a fast establishment method and device of a Nakagami channel model, and relates to the communication field. The method comprises the following steps: obtaining a fading factor and signal average power of a to-be-simulated channel; determining a segmented calculation strategy of a power weight coefficient according to a numerical interval division result of the fading factor, wherein the segmented calculation strategy is a calculation relationship of the power weight coefficient based on the numerical size of the fading factor; constructing a first fading component and a second fading component based on the signal average power; configuring a power weight distribution ratio of the first fading component and the second fading component according to the power weight coefficient; and performing weighted superposition processing on the first fading component and the second fading component according to the power weight distribution ratio to obtain a channel fading coefficient sequence. The method can improve the generation efficiency of the channel fading coefficient sequence.

Description

Technical Field

[0001] This application relates to the field of communications, and in particular to a method and apparatus for rapidly establishing a Nakagami channel model. Background Technology

[0002] With the rapid development of wireless communication technology, channel modeling has become a crucial step in communication system design and performance evaluation. The Nakagami channel model, a widely used statistical channel model, effectively describes signal propagation characteristics under multipath fading conditions. It flexibly characterizes different degrees of channel fading through fading factor parameters, and has significant application value in mobile communication, satellite communication, and wireless sensor networks. Accurately establishing the Nakagami channel model is of great importance for the simulation verification and algorithm optimization of communication systems.

[0003] In existing technologies, the establishment of Nakagami channel models mainly employs inverse transform sampling (IBS) or accept-rejection sampling (ARS) methods to generate random variables that follow a Nakagami distribution. Specifically, IBS uses the inverse function of the cumulative distribution function of the Nakagami distribution to map uniformly distributed random numbers into sample points that follow a target distribution, thereby constructing a channel fading coefficient sequence. ARS filters the generated candidate samples by setting a proposal distribution and acceptance probability, retaining sample points that satisfy the characteristics of the Nakagami distribution. These methods establish the channel model by directly sampling from the Nakagami distribution.

[0004] However, the aforementioned existing technical solutions suffer from high computational complexity in practical applications. The inverse transform sampling method requires inverting the cumulative distribution function of the Nakagami distribution, an operation typically involving complex numerical integration and iterative solutions, resulting in lengthy generation times for each sample point. While the accept-reject sampling method is simple in principle, its sample acceptance rate fluctuates with different parameter settings. Under certain parameter conditions, it may require generating a large number of candidate samples to obtain sufficient effective samples, wasting computational resources. When generating a large number of channel fading coefficients to support long-term or multi-scenario communication simulations, this high computational complexity increases simulation time and impacts the efficiency of system design and testing. Summary of the Invention

[0005] This application provides a method and apparatus for rapidly establishing a Nakagami channel model, which can improve the generation efficiency of channel fading coefficient sequences.

[0006] In a first aspect, this application provides a method for rapidly establishing a Nakagami channel model, applied to a Nakagami channel model rapid establishment device. The method includes: obtaining the fading factor and average signal power of the channel to be simulated; determining a segmented calculation strategy for power weight coefficients based on the numerical interval division result of the fading factor, wherein the segmented calculation strategy is to set the calculation relationship of power weight coefficients in segments based on the numerical magnitude of the fading factor; constructing a first fading component and a second fading component based on the average signal power; configuring the power weight allocation ratio of the first fading component and the second fading component according to the power weight coefficients; and performing weighted superposition processing on the first fading component and the second fading component according to the power weight allocation ratio to obtain a channel fading coefficient sequence.

[0007] In the above embodiments, the internal generation logic can be adaptively adjusted according to the specific fading characteristics of the channel to be simulated. Specifically, by obtaining the two key parameters of fading factor and average signal power, it no longer relies on the traditional, computationally intensive inverse transform sampling method or accept-reject sampling method. Instead, the complex Nakagami distribution generation problem is transformed into a weighted superposition problem of two basic fading components. By establishing a direct mapping relationship from fading factor to weight allocation, the power weight coefficient shows a monotonically increasing trend as the fading factor increases. This allows the proportion of the two basic components in the final synthesized signal to transition smoothly, thereby accurately fitting the Nakagami distribution curves under different m values. This reduces the time complexity of the algorithm when generating the channel fading coefficient sequence, making it possible to achieve high-speed, real-time channel simulation on resource-constrained hardware platforms. At the same time, it also ensures that the generated channel model has a high degree of consistency with the theoretical values ​​in terms of statistical characteristics.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the numerical range of the fading factor includes a first numerical range, a second numerical range, a third numerical range, and a fourth numerical range. The first numerical range is the range where the fading factor is less than or equal to 0.25, the second numerical range is the range where the fading factor is greater than 0.25 and less than 0.75, the third numerical range is the range where the fading factor is greater than or equal to 0.75 and less than or equal to 2, and the fourth numerical range is the range where the fading factor is greater than 2.

[0009] In the above embodiment, the range of fading factor values ​​is divided into four specific numerical intervals. Optimized grouping is performed based on the shape variation characteristics of the probability density function (PDF) of the Nakagami distribution under different m values. In the interval with a small fading factor (m≤0.25), channel fading is severe, and the PDF exhibits a unique tailing characteristic; while in the interval with a large fading factor (m>2), the channel tends to be stable. This segmented processing allows for the adoption of the most suitable fitting strategy for the mathematical characteristics of different intervals, avoiding large errors caused by using a single, general fitting formula at certain extreme m values. This interval division strategy effectively balances the model's versatility and accuracy, ensuring high modeling accuracy whether simulating harsh deep fading environments or favorable line-of-sight communication environments, thus improving the dynamic adaptability of the channel simulator.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of determining the segmented calculation strategy of the power weight coefficient based on the numerical range division result of the fading factor specifically includes: when the fading factor is in the first numerical range, the power weight coefficient has a linear relationship with the fading factor; when the fading factor is in the second numerical range, the power weight coefficient directly takes the value of the fading factor; when the fading factor is in the third numerical range, the power weight coefficient has a square root relationship with the fading factor; when the fading factor is in the fourth numerical range, the power weight coefficient has a power function relationship with the fading factor.

[0011] In the above embodiments, a linear relationship is adopted in the first interval, the value is directly taken in the second interval, a square root relationship is adopted in the third interval, and a power function relationship is adopted in the fourth interval. This takes advantage of the instruction set optimization of hardware processors (such as FPGA or DSP) for linear operation, square root operation and power operation, and ensures the time domain stability of the generated channel fading coefficient sequence, providing more reliable simulation data for the bit error rate test of the communication system.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of configuring the power weight allocation ratio of the first fading component and the second fading component according to the power weight coefficient specifically includes: generating a first complex Gaussian random process and a second complex Gaussian random process, the first complex Gaussian random process and the second complex Gaussian random process being independent of each other; performing an envelope operation on the first complex Gaussian random process to obtain the first fading component; performing an envelope operation on the second complex Gaussian random process to obtain the second fading component; and performing power normalization processing on the first fading component and the second fading component according to the average signal power.

[0013] In the above embodiments, a complex Gaussian process is first generated, and then it is transformed into a Rayleigh or Rayleigh-like fading component through envelope operation. Finally, it is normalized according to the average power of the signal to avoid subsequent weighted superposition overflow or accuracy loss caused by excessive signal amplitude fluctuations. By utilizing the statistical independence of the Gaussian process, it is ensured that the final synthesized Nakagami signal can truly reflect the random scattering characteristics in multipath propagation, so that the model not only meets the requirements in amplitude distribution, but also is closer to the real physical channel environment in second-order statistical characteristics such as correlation.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, both the first and second complex Gaussian random processes are generated using a sine wave superposition method. This sine wave superposition method involves superimposing multiple sine wave components with different initial phases and Doppler phases. The second complex Gaussian random process is generated by superimposing a deterministic direct path component on the scattering component generated using the sine wave superposition method, so that the second fading component has Ricean fading characteristics. The Doppler phase is calculated based on the maximum Doppler frequency shift and the incident angle. The initial phase settings of the first and second complex Gaussian random processes are different to ensure their independence from each other.

[0015] In the above embodiments, the Sine Superposition (SOS) method is used to generate the required complex Gaussian random process. Compared to the method of passing Gaussian white noise through a shaping filter, the SOS method does not require the design of complex filters, and the generated signal is strictly band-limited, eliminating the spectral aliasing problem. Furthermore, by setting different initial phases, the orthogonality and independence between the first and second complex Gaussian random processes can be easily guaranteed, thus providing a high-quality input source with strictly controlled statistical properties for subsequent weighted synthesis.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the step of weighting and superimposing the first fading component and the second fading component according to the power weight allocation ratio specifically includes: denoting the first fading component as r1(t), the second fading component as r2(t), and the channel fading coefficient sequence as r(t); calculating the channel fading coefficient sequence according to a first preset formula, wherein the first preset formula is:

[0017] .

[0018] In the above embodiments, the sum of squares of the first weighting factor and the second weighting factor is designed to dynamically adjust the energy proportion of the two fading components in the total signal, successfully avoiding the mathematical difficulties of directly generating Nakagami random variables. Instead, it achieves continuous coverage of any m parameter (especially non-integer m values) through simple weighted summation. This not only ensures the accurate matching of the model on the probability density function, but also preserves the time correlation of the original Gaussian process, thereby achieving a dual accurate simulation of the channel's first-order statistical characteristics (envelope distribution) and second-order statistical characteristics (autocorrelation function).

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of weighted superposition of the first fading component and the second fading component according to the power weight allocation ratio to obtain the channel fading coefficient sequence, the method further includes: determining whether the fading factor is an integer or a half-integer; when the fading factor is an integer or a half-integer, generating the channel fading coefficient sequence by the Gaussian random variable square sum method, without executing the segmented calculation strategy.

[0020] In the above embodiments, when m is detected to be an integer or half-integer, the segmented calculation strategy is automatically bypassed and the Gaussian random variable sum of squares generation mode is directly switched to, eliminating the approximation error and achieving theoretically zero-error generation. The dual-mode hybrid architecture enables the device to have high flexibility, providing a high-precision approximate solution when m is any real number and an exact solution when m is a specific value, thereby improving the simulation efficiency and accuracy in specific scenarios while ensuring full coverage.

[0021] In a second aspect, embodiments of this application provide a Nakagami channel model rapid establishment apparatus, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the Nakagami channel model rapid establishment apparatus to perform the method as described in the first aspect and any possible implementation thereof.

[0022] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a Nakagami channel model rapid establishment apparatus, cause the Nakagami channel model rapid establishment apparatus to perform the method described in the first aspect and any possible implementation thereof.

[0023] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a Nakagami channel model rapid establishment apparatus, cause the Nakagami channel model rapid establishment apparatus to perform the method described in the first aspect and any possible implementation thereof.

[0024] Understandably, the Nakagami channel model rapid establishment apparatus provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0025] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0026] 1. This application adaptively adjusts its internal generation logic based on the specific fading characteristics of the channel to be simulated. Specifically, by acquiring the two key parameters of fading factor and average signal power, it no longer relies on traditional, computationally intensive inverse transform sampling or accept-reject sampling methods. Instead, it transforms the complex Nakagami distribution generation problem into a weighted superposition problem of two basic fading components. By establishing a direct mapping relationship from the fading factor to the weight allocation, the power weight coefficient monotonically increases with the increase of the fading factor. This allows for a smooth transition in the proportion of the two basic components in the final synthesized signal, thereby accurately fitting the Nakagami distribution curves for different m values. This reduces the time complexity of the algorithm when generating the channel fading coefficient sequence, making it possible to achieve high-speed, real-time channel simulation on resource-constrained hardware platforms. It also ensures that the generated channel model has a high degree of consistency with the theoretical values ​​in terms of statistical characteristics.

[0027] 2. This application divides the fading factor range into four specific numerical intervals. Based on the optimized grouping of the probability density function (PDF) shape variation characteristics of the Nakagami distribution under different m values, the channel fading is severe in the interval with a small fading factor (m≤0.25), and the PDF exhibits a unique tailing characteristic. Conversely, the channel tends to be stable in the interval with a large fading factor (m>2). This segmented processing allows for the adoption of the most suitable fitting strategy for each interval's mathematical characteristics, avoiding large errors that can occur with a single, general fitting formula at certain extreme m values. This interval division strategy effectively balances the model's versatility and accuracy, ensuring high modeling accuracy in both harsh deep fading environments and favorable line-of-sight communication environments, thus improving the dynamic adaptability of the channel simulator.

[0028] 3. This application utilizes the instruction set optimization advantages of hardware processors (such as FPGAs or DSPs) for linear operations, square root operations, and exponentiation operations by adopting a linear relationship in the first interval, direct value taking in the second interval, a square root relationship in the third interval, and a power function relationship in the fourth interval. This ensures the time-domain stability of the generated channel fading coefficient sequence and provides more reliable simulation data for the bit error rate test of communication systems. Attached Figure Description

[0029] Figure 1 This is a block diagram illustrating the principle of generating Rayleigh fading channels based on the subcarrier superposition method in existing technologies.

[0030] Figure 2 This is a block diagram illustrating the principle of generating Ricean fading channels based on the subcarrier superposition method in existing technologies.

[0031] Figure 3 This is a flowchart illustrating a method for rapidly establishing a Nakagami channel model in an embodiment of this application.

[0032] Figure 4 This is another flowchart illustrating the method for rapidly establishing the Nakagami channel model in the embodiments of this application;

[0033] Figure 5 This is a structural diagram of the internal weighted synthesis principle of the Nakagami channel model fast establishment device in the embodiments of this application;

[0034] Figure 6 This is a simulation result diagram of the amplitude probability density of the fast Nakagami channel model establishment method according to the embodiments of this application under different m values;

[0035] Figure 7 This is a simulation result of the amplitude probability density of the fast Nakagami channel model establishment method according to the embodiments of this application under integer / half-integer m values;

[0036] Figure 8 This is a schematic diagram of the physical device structure of a Nakagami channel model rapid establishment device in the embodiments of this application. Detailed Implementation

[0037] 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 of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0038] 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.

[0039] To facilitate understanding, the application scenarios of the embodiments of this application are described below.

[0040] In the field of wireless channel modeling, in order to describe the signal fading characteristics caused by the Doppler effect in mobile communication, ideally, the theoretical Doppler power spectrum of the classic Rayleigh fading model conforms to the classic Jakes Doppler power spectrum model. The theoretical Doppler power spectrum curve is a typical U-shaped spectrum, and the effective bandwidth of the power spectrum is twice the maximum Doppler frequency shift.

[0041] The Rayleigh fading model is merely a special case of the more general Nakagami-m fading model (corresponding to the case where the fading factor m=1). The Nakagami-m model can describe a wider range of channel scenarios, from stronger than Rayleigh fading (m>1) to weaker than Rayleigh fading (m<1), by adjusting the fading factor m. Therefore, an ideal Nakagami-m channel generation method needs to accurately match its theoretical first-order statistical properties (i.e., the probability density function of the signal amplitude) for all values ​​of m.

[0042] Currently, in engineering implementations, the Sum of Sinusoids (SOS) method is commonly used to simulate Rayleigh or Ricean fading. For example, the Exact Doppler Extended Method (MEDS) and the Equal Area Method (MEA) generate complex Gaussian random processes by superimposing several sine waves with specific frequencies and phases.

[0043] The Sum of Sinusoids (SOS) method is a mainstream approach for simulating Rayleigh fading in wireless channel simulation. Its core idea is to simplify the infinite number of scatterers in the wireless channel into a linear superposition of a finite number (let's say M) of low-frequency sinusoidal oscillators. In this model, the Doppler phase component of the m-th sub-path... It is usually expressed as:

[0044] Where t represents time. This represents the maximum Doppler frequency shift at the current moment or on link n. Let be the angle of incidence (AngleofArrival) of the m-th sub-path.

[0045] Incident angle of each sub-path The selection strategy directly determines whether the statistical characteristics of the simulation sequence are close to the theoretical value. For the calculation of this angle, existing technologies mainly employ two classic discretization strategies: the Method of Exact Doppler Spread (MEDS) and the Method of Equal Areas (MEA). Both methods are based on the same signal superposition model; their core difference lies only in the sampling point index offset during incident angle discretization.

[0046]

[0047] The Precise Doppler Spread Method (MEDS): This method introduces a half-wave offset (i.e., ...) during calculation to ensure that the mean square value of the Doppler spread of the simulated sequence closely approximates the theoretical value. The formula for calculating its angle is as follows:

[0048]

[0049] Equal Area Method (MEA): This method is similar to MEDS, the only difference being the use of index m instead of... The formula for calculating its angle is as follows:

[0050]

[0051] In the above formula, the subscript m represents different subpath components; , , The equal phase parameter is usually set to... Uniformly distributed random variables within the interval are used to ensure the non-correlation between sub-paths. Other methods in the prior art (such as MCM, MSEM, etc.) are essentially based on the above. (Amplitude coefficient) and (Frequency parameters) can be discretized in different ways.

[0052] Based on the above-mentioned sinusoidal superposition (SOS) model, different configurations of the superposition components can flexibly simulate the fading statistical characteristics of different types of wireless channels. In specific implementation, time-domain fading waveforms can be generated by vector superposition of MMM sine waves (i.e., "subcarriers" or "subpaths") with specific Doppler frequency shifts and random phases.

[0053] Rayleigh fading is generated when there is no line-of-sight (LoS) component in the channel, and only multipath scattering components exist. This is achieved by directly superimposing the aforementioned MMM sine waves. According to the central limit theorem, when MMM is sufficiently large, its envelope follows a Rayleigh distribution.

[0054] like Figure 1 As shown, Figure 1 This is a block diagram illustrating the principle of generating Rayleigh fading channels based on the subcarrier superposition method in existing technologies.

[0055] Figure 1 This is a schematic diagram illustrating the principle of generating Rayleigh fading channels based on the subcarrier superposition (SOS) method in existing technologies. As shown in the figure, the model includes two parallel branches: an in-phase component (I-path) and a quadrature component (Q-path). Each branch simulates multipath scattering effects by superimposing multiple (N) low-frequency sinusoidal oscillators with different Doppler frequencies and random initial phases. The input parameters include the maximum Doppler frequency shift and the incident angle. The output signal is a complex superposition of the real and imaginary parts, and its envelope amplitude follows a Rayleigh distribution, making it suitable for simulating communication environments without a direct path (N-LOS).

[0056] As shown in the figure, the model contains two parallel branches: an in-phase component (I-path) and a quadrature component (Q-path). Each branch simulates multipath scattering effects by superimposing multiple (N) low-frequency sinusoidal oscillators with different Doppler frequencies and random initial phases. The input signal of the upper branch is... (M paths in total), each path with a coefficient Multiply and then sum; the input signals of the lower branch are the same as those of the upper branch, and the multiplication coefficient is... Similarly, the summation is combined with the result from the upper branch to form a complex number. The envelope amplitude of the final output signal follows a Rayleigh distribution, suitable for simulating communication environments without a direct path (N-LOS). Rice fading generation: When strong line-of-sight components exist in the channel, a fixed DC component (or deterministic sine component) needs to be superimposed on the Rayleigh scattering components as the main path. In this case, the signal envelope will follow a Rice distribution.

[0057] like Figure 2 The diagram shown is a block diagram illustrating the principle of generating Ricean fading channels based on the subcarrier superposition method in existing technologies.

[0058] Figure 2 This is a schematic diagram illustrating the principle of generating Rice fading channels based on subcarrier superposition in existing technologies. Figure 1 Compared to the Rayleigh model shown, this structure introduces a direct path (LOS) component or specular reflection component (represented as a constant or deterministic component in the figure) during the signal synthesis stage. This direct component is vector-added with the scattering component generated by the superposition of subcarriers, making the amplitude probability density function of the final output signal follow a Rice distribution, which is suitable for simulating communication environments with strong direct signals.

[0059] like Figure 2The diagram shown illustrates the principle of generating Ricean fading channels based on subcarrier superposition in existing technologies. Figure 1 Compared to the Rayleigh model shown, this structure retains the subcarrier superposition module (generating Rayleigh scattering components) of the in-phase and orthogonal branches, while adding a "direct-line-of-sight channel" branch: the input signal is , After coefficient multiplication and summation, the direct component is obtained. Finally, the Rayleigh scattering component and the direct component are vector-added, so that the amplitude probability density function of the output signal follows a Rice distribution, which is suitable for simulating communication environments with strong direct signals.

[0060] In existing Nakagami model building techniques, the CoDiT project proposed a method based on simulation of reflection and diffusion components. Its core idea is to calculate the complex tap coefficient E based on a specific time exponent k. k By adjusting the amplitude of the reflected component A0 and the amplitude of the diffused component A i To adapt to different values ​​of m. The calculation formula is as follows:

[0061] Statistical properties of the Nakagami-m channel: When a signal propagates in the channel, its amplitude probability density function is:

[0062]

[0063] The model yielded the Nakagami amplitude distribution. It is the gamma function of m, and r is the amplitude.

[0064] When m=1, the amplitude distribution follows a Rayleigh distribution. The larger the value of the m parameter, the smaller the deep fading near the mean and the smaller the amplitude fluctuations. As m→∞, the tap coefficients become constant, where... , where m is the fading factor; the larger m is, the smaller the fading. When m = 1 / 2, it follows a one-sided Gaussian distribution; when m = 1, it follows a Rayleigh distribution. Due to the variability of m, Nakagami-m has broad adaptability and better flexibility, encompassing Rice, Rayleigh, and log-shaded distributions.

[0065] In the Nakagami model, the scattering object is simulated according to the specifications in the CoDiT project. The complex tap coefficient at time index k is calculated using the following formula.

[0066]

[0067] Where Ek is the complex tap coefficient at time exponent k, A0 is the amplitude of the reflection component, and Ai is the amplitude of the diffuse wave. The initial phase of the specular reflection component. The initial phase of the diffused wave. The average angle of incidence of the scatterer relative to its moving path. The incident angle of the diffuse portion of the wave, the SD response sampling density, the number of impulse responses per half wavelength, and the N-part wavenumber = 10⁵. The time exponent k increases with the time step determined by the sampling density SD, as shown below:

[0068]

[0069]

[0070] in It is the expected value.

[0071] Some amplitudes are random, drawn from a truncated Gaussian distribution with zero mean and standard deviation, conforming to the preceding equation. All partial amplitudes have the same standard deviation, and the reflection component... initial phase and partial wave The initial phase is uniform, and the incident angle is... Also taken from the average value The truncated Gaussian distribution with standard deviation s = 0.15 rad = 8.59°.

[0072] However, the aforementioned existing technologies have significant limitations in practical applications. While methods such as the CoDiT model can simulate Nakagami fading to some extent, their accuracy is low, especially when the fading factor mmm varies, the amplitude distribution of the generated signal often deviates from the theoretical value. Furthermore, in pursuit of higher accuracy, it is often necessary to significantly increase the number of sine waves or employ complex inverse transform sampling, which leads to increased computational complexity and makes it difficult to meet the simulation requirements of scenarios with high real-time requirements or limited hardware resources (such as FPGA implementation).

[0073] To address the technical problems of low accuracy and high computational complexity in establishing Nakagami-m channel models in the prior art, this embodiment provides a fast method for establishing Nakagami-m channel models with high accuracy and fast computation speed.

[0074] This embodiment proposes a weighted synthesis algorithm based on a piecewise correction strategy. This method does not directly rely on complex nonlinear transformations, but instead uses Rayleigh and Ricean fading as fundamental components, synthesizing the fading through a nonlinear exponential weighting mechanism optimized for the value of m (involving the parameter gam_m). This scheme achieves high-precision fitting of the Nakagami-m channel statistical characteristics over a wide range of fading factors by finely adjusting the piecewise function while maintaining extremely low computational complexity.

[0075] The method involved in this application can be widely applied to scenarios such as baseband chip testing and channel simulator instrument development in 5G / 6G mobile communication systems. For ease of understanding, the method provided in this implementation is described below in conjunction with the aforementioned scenarios. Please refer to... Figure 3 This is a flowchart illustrating a method for rapidly establishing a Nakagami channel model in an embodiment of this application.

[0076] S301. Obtain the fading factor and average signal power of the channel to be simulated.

[0077] The fading factor *m* is a parameter characterizing the severity of channel fading, with a value greater than or equal to 0.5; a larger value indicates weaker channel fading. The average signal power *Ω* refers to the average energy level of the signal during transmission and is used for power normalization of the generated fading sequence.

[0078] Specifically, this step obtains two key parameters through external input or measurement: the fading factor *m* and the average signal power *Ω*. These two parameters together determine the statistical characteristics of the Nakagami channel model to be established. The fading factor *m* determines the distribution pattern of channel fading, while the average signal power *Ω* determines the scale of channel fading.

[0079] In some embodiments, the parameters can be obtained in several ways: optionally, the fading factor and average signal power can be extracted using moment estimation methods based on actual channel measurement data; alternatively, the theoretical values ​​of these parameters can be directly set according to the specific communication scenario and application requirements. It is understood that other statistical estimation or scenario mapping methods can also be used to obtain the required parameters, and this is not limited here.

[0080] S302. Determine the segmented calculation strategy for the power weight coefficient based on the numerical range division of the fading factor. This segmented calculation strategy sets the calculation relationship of the power weight coefficient in segments based on the numerical magnitude of the fading factor.

[0081] Among them, the power weighting coefficient This is a parameter used to adjust the weights of the two basic fading components, with a value ranging from 0 to 1. The segmented calculation strategy refers to using different mathematical relationships to calculate based on different value ranges of the fading factor m. The method. Monotonically increasing trend indicates... It increases as m increases.

[0082] Specifically, this step first divides the range of values ​​for the fading factor m into four intervals: the first interval [0, 0.25], the second interval (0.25, 0.75), the third interval [0.75, 2], and the fourth interval (2, ∞). Then, based on the specific interval in which m falls, the appropriate calculation formula is selected to determine the power weighting coefficient. And ensure that γm increases monotonically as m increases.

[0083] In some embodiments, the weighting coefficients can be calculated in several ways: optionally, a linear relationship can be used when m is in the first interval. =1.5m; when m is in the second interval, the value is taken directly. =m; when m is in the third interval, the square root relation is used. = When m is in the fourth interval, a power function relationship is used. =m^(1 / 2.5). It is understandable that other mathematical relationships can also be used for piecewise calculations; this is not limited here.

[0084] S303. Construct the first fading component and the second fading component based on the average signal power.

[0085] The first fading component and the second fading component are two statistically independent stochastic processes, denoted as r1(t) and r2(t), respectively. Each fading component has a specific statistical distribution characteristic, and its power is controlled by the normalization of the average signal power Ω.

[0086] Specifically, this step first generates two independent complex Gaussian random processes, and then performs envelope operations on these processes to obtain the fundamental fading components. The power levels of these two fading components are normalized using the average signal power Ω to ensure that the power of the final synthesized signal meets the requirements.

[0087] In some embodiments, the fading component can be constructed in several ways: optionally, a sinusoidal superposition method can be used to generate a complex Gaussian random process by superimposing multiple sinusoidal waves with different initial phases and Doppler phases; alternatively, Gaussian white noise can be used to generate the desired random process through a shaping filter. It is understood that other random process generation methods can also be used, and are not limited here.

[0088] S304. Configure the power weight allocation ratio between the first fading component and the second fading component according to the power weight coefficient.

[0089] The power weight allocation ratio refers to the energy proportion of the two fading components in the final synthesized signal. This ratio is derived from the power weight coefficients calculated earlier. The decision is made to ensure that the synthesized signal has the required statistical properties.

[0090] Specifically, this step is based on the power weighting coefficient. Calculate two exponential weighting factors: the first weighting factor is The second weighting factor is These two weighting factors act on the first fading component and the second fading component, respectively, controlling their power ratio in the final signal.

[0091] In some embodiments, weight allocation can be implemented in several ways: optionally, the power weight coefficients can be directly converted into exponential weight factors; optionally, the weight factors can be calculated by performing a logarithmic transformation followed by an exponentiation; optionally, the weight factors can be quickly obtained using a lookup table. It is understood that other mathematical transformation methods can also be used to implement weight allocation, and this is not limited here.

[0092] S305. The first fading component and the second fading component are weighted and superimposed according to the power weight allocation ratio to obtain the channel fading coefficient sequence.

[0093] The weighted superposition process refers to the vector summation of the two weighted fading components. The channel fading coefficient sequence is the final generated time-domain random sequence that follows a Nakagami-m distribution.

[0094] Specifically, this step multiplies the first fading component r1(t) by the first weighting factor. Multiply the second fading component r2(t) by the second weighting factor. Then, the two weighted components are added together to obtain the final channel fading coefficient sequence r(t).

[0095] In some embodiments, weighted superposition can be achieved in several ways: optionally, vector superposition can be performed directly using complex addition; optionally, the signal can be decomposed into in-phase and quadrature components, which are then weighted and superimposed separately; optionally, weighted superposition can be performed in the frequency domain and then transformed back to the time domain. It is understood that other signal synthesis methods can also be used to achieve weighted superposition, and this is not limited here.

[0096] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 4 This is another flowchart illustrating the method for rapidly establishing the Nakagami channel model in this application.

[0097] S401. Obtain the fading factor and average signal power of the channel to be simulated. The numerical range of the fading factor includes a first numerical range, a second numerical range, a third numerical range, and a fourth numerical range. The first numerical range is the range where the fading factor is less than or equal to 0.25. The second numerical range is the range where the fading factor is greater than 0.25 and less than 0.75. The third numerical range is the range where the fading factor is greater than or equal to 0.75 and less than or equal to 2. The fourth numerical range is the range where the fading factor is greater than 2.

[0098] The fading factor m is a parameter characterizing the fading characteristics of a wireless channel, its value ranges over positive real numbers, and a larger value indicates weaker channel fading. The average signal power Ω represents the average energy level of a signal during transmission. The numerical interval division includes four intervals: the severe fading interval [0, 0.25], the moderate fading interval (0.25, 0.75), the mild fading interval [0.75, 2] and the good channel interval (2, ∞).

[0099] In this step, the fading factor m and the average signal power Ω are first obtained through channel measurement or scene setting. The fading factor is obtained by analyzing the statistical characteristics of the received signal. The specific method is to calculate the high-order moment of the received signal envelope, and extract the value of m by using the moment estimation method. The average signal power is obtained by averaging the received signal power within a certain time window. According to the obtained value of the fading factor, it is mapped to the corresponding numerical interval. For example, when the measured m=0.2, it is classified into the first numerical interval; when m=1, it is classified into the third numerical interval. This interval division method reflects the physical characteristics of the channel under different fading degrees.

[0100] S402, determining a segmented calculation strategy for the power weight coefficient according to the numerical interval division result of the fading factor, wherein the segmented calculation strategy is to set the calculation relationship of the power weight coefficient in segments based on the numerical value of the fading factor.

[0101] The step of determining the segmented calculation strategy for the power weight coefficient according to the numerical interval division result of the fading factor specifically comprises: when the fading factor is in the first numerical interval, the power weight coefficient has a linear relationship with the fading factor; when the fading factor is in the second numerical interval, the power weight coefficient directly takes the value of the fading factor; when the fading factor is in the third numerical interval, the power weight coefficient has a square root relationship with the fading factor; when the fading factor is in the fourth numerical interval, the power weight coefficient has a power function relationship with the fading factor.

[0102] In order to efficiently and accurately fit the Nakagami distribution under different m values, an intermediate variable, the power weight coefficient γ, is introduced m , and a piecewise function strategy is adopted to calculate it. This strategy is an optimal fitting relationship obtained based on a large number of experimental simulations, and can effectively balance the simplicity of calculation and the accuracy of the model.

[0103] The specific calculation relationship is shown in the following formula, where m is the fading factor obtained in step S201:

[0104] If 0 < m ≤ 0.25 (the first numerical interval), = 1.5*m; if 0.25 < m < 0.75 (the second numerical interval) = m; if 0.75 ≤ m ≤ 2 (the third numerical interval), =sqrt(m); if m>2 (fourth numerical interval) =m^(1 / 2.5);

[0105] When the fading factor m is in the first value range (m≤0.25), the channel experiences severe fading, and at this time the power weighting coefficient γ m It has a linear relationship with m.

[0106] When m is in the second numerical range (0.25) <m<0.75),γ m Take the value of m directly.

[0107] When m is in the third numerical interval (0.75≤m≤2), γ m It has a square root relationship with m.

[0108] When m is in the fourth numerical interval (m>2), the channel conditions are better and the fading is shallower, γ m It has a power function relationship with m.

[0109] S403. Construct the first fading component and the second fading component based on the average signal power.

[0110] The first fading component r1(t) and the second fading component r2(t) are the fundamental stochastic processes that constitute the final channel fading characteristics. The average signal power Ω is used as a normalization parameter to control the power levels of the two fading components.

[0111] This step constructs two fading components with different statistical characteristics based on a set average signal power. Specifically, the power normalization coefficient is first determined according to the average signal power Ω. Then, two independent random processes are constructed as the base fading components, with statistical characteristics corresponding to Rayleigh and Rice distributions, respectively. Power normalization ensures that the sum of the average powers of the two components equals the set average signal power Ω. For example, when Ω=1, the generated base component is normalized so that its average power satisfies the condition E[r1²(t)]+E[r2²(t)]=1.

[0112] S404. Generate a first complex Gaussian random process and a second complex Gaussian random process, wherein the first complex Gaussian random process and the second complex Gaussian random process are independent of each other.

[0113] A complex Gaussian random process is a Gaussian distributed random signal that contains both real and imaginary parts. The first and second complex Gaussian random processes must satisfy statistical independence, meaning that there is no correlation between samples at any time point in the two processes.

[0114] This step is achieved by generating two independent sequences of complex Gaussian random variables. Specifically, four independent Gaussian random sequences with zero mean and unit variance are generated. Two of these sequences serve as the real and imaginary parts of the first complex Gaussian random process, and the other two serve as the real and imaginary parts of the second complex Gaussian random process. The number of sampling points for each sequence is determined based on the simulation time and sampling rate. By ensuring the orthogonality and independence of the generated sequences, the absence of correlation between the two complex Gaussian random processes is guaranteed.

[0115] S405. Perform envelope operation on the first complex Gaussian random process to obtain the first fading component.

[0116] Envelope operation refers to the mathematical operation used to extract the amplitude characteristics of a complex signal. The first fading component is obtained by performing an envelope operation on the first complex Gaussian random process.

[0117] This step extracts the envelope of the generated first complex Gaussian random process. Specifically, the complex Gaussian random process is represented as x(t) + jy(t), where x(t) is the real part and y(t) is the imaginary part. This is achieved through calculation... The signal envelope is obtained, yielding the first fading component. This process ensures that the generated fading component possesses the desired statistical distribution characteristics.

[0118] S406. An envelope operation is performed on the second complex Gaussian random process to obtain the second fading component. Both the first and second complex Gaussian random processes are generated using a sine wave superposition method. The second complex Gaussian random process is generated by superimposing a deterministic direct path component on the scattering component generated using the sine wave superposition method, so that the second fading component has Ricean fading characteristics. The sine wave superposition method involves superimposing multiple sine wave components with different initial phases and Doppler phases. The Doppler phase is calculated based on the maximum Doppler frequency shift and the incident angle. The initial phase settings of the first and second complex Gaussian random processes are different to ensure their independence.

[0119] In this step, the first fading component r1(t) corresponds to the Rayleigh fading component, and the second fading component r2(t) corresponds to the Ricean fading component or a fading component containing a line-of-sight (LoS) component. Both are generated based on the sinusoidal superposition (SoS) method, but with different parameter settings:

[0120] The first complex Gaussian random process (corresponding to r1) is composed of N (e.g., N=8~12) sine waves with equal amplitude and random initial phases superimposed. The second complex Gaussian random process (corresponding to r2) is based on the superposition of the above sine waves, with an additional deterministic DC component (i.e., direct path component) superimposed, making its envelope follow Rice distribution characteristics.

[0121] To ensure the statistical independence of r1(t) and r2(t), two sets of independent and uncorrelated initial phases are used to generate these two processes. For example, the initial phases of the first and second processes are generated by two different random number seeds, or orthogonal sampling is performed in the interval [0, 2π).

[0122] The Doppler phase of each sinusoidal component is still calculated based on the maximum Doppler frequency shift and the incident angle. Finally, by taking the modulus (finding the envelope) of the two complex Gaussian random processes respectively, we can obtain r1(t) and r2(t).

[0123] S407. Perform power normalization processing on the first fading component and the second fading component based on the average signal power.

[0124] Power normalization is a mathematical operation that adjusts the power of a random signal to a specified level. The average signal power Ω is used as the target value for normalization to ensure that the generated fading components meet the power requirements.

[0125] This step performs power normalization on the two generated fading components. Specifically, it first calculates the actual average power of each fading component, denoted as P1 and P2. Then, it calculates the normalization coefficients for each component. and Multiply the first fading component by k1 and the second fading component by k2, such that the sum of the powers of the two components equals the target power Ω. For example, when the target power Ω = 1, the sum of the powers of the two components after normalization is 1.

[0126] S408. Let the first fading component be denoted as r1(t), the second fading component as r2(t), the channel fading coefficient sequence as r(t), and the power weighting coefficient as γ. m The channel fading coefficient sequence is calculated according to a first preset formula, which is:

[0127] .

[0128] This step implements nonlinear dynamic weighting of the Rayleigh and Rice components.

[0129] The first weighting factor is defined as ω1, ω1= Used to adjust the first fading component The percentage.

[0130] Second weighting factor ω2: defined as ω2= Used to adjust the second fading component The percentage.

[0131] when When it approaches 1 (corresponding to the Rayleigh fading scenario with m=1). As the value approaches 0, the first weighting factor ω1→e^0=1, and the second weighting factor ω2→1−1=0. The formula automatically degenerates into... This is perfectly consistent with the theoretical Rayleigh fading model, eliminating the calculation errors at parameter boundaries in existing techniques, through step S402. The precise segmented calculation, combined with the unweighted exponentiation in this step, makes the probability density function (PDF) of the synthesized sequence r(t) at different m values ​​(especially small m values) significantly better than the theoretical Nakagami-m distribution, effectively solving the problem of fitting distortion of traditional approximation algorithms at certain fading depths.

[0132] In a preferred embodiment, the Rayleigh component is used as the first fading component r1(t), and the Rice component (or another independent Rayleigh component) is used as the second fading component r2(t). The final synthesized Nakagami channel fading coefficient sequence r(t) is given by the following equation:

[0133] .

[0134] The two generated fundamental fading components are superimposed using two exponential nonlinear weighting factors. This is combined with the γ... m Through precise segmentation calculations, this weighting scheme enables the final synthesized sequence r(t) to achieve a statistical distribution that closely matches the theoretical Nakagami-m distribution with extremely high accuracy.

[0135] After obtaining the basic Gaussian stochastic process, the core weighted synthesis stage begins. For example... Figure 5 As shown, this image illustrates a model block diagram with the addition of a line-of-sight (LOS) component to the Rayleigh distribution map, corresponding to the weighted superposition physical implementation logic in this application.

[0136] Figure 5 This is a structural diagram of the internal weighted synthesis principle of the Nakagami channel model fast establishment device in the embodiments of this application;

[0137] As shown in the figure, this structure is Figure 2 Based on the Ricean channel, a weighted control module is added: after the output of the Rayleigh component branch (the subcarrier superposition module above / below in the diagram), a weighting factor is applied through a multiplication module. After the output of the direct-look path branch, a weighting factor is applied through the multiplication module. (in For amplitude parameters, (This refers to the phase coefficient). Finally, the two weighted signals are vector synthesized, and the output is the fading coefficient sequence of the Nakagami channel. This structure intuitively reflects the logic of superimposing the weighted Rayleigh and line-of-sight components in S409-S411, realizing flexible fitting of the Nakagami distribution under different m values.

[0138] After obtaining the basic Gaussian stochastic process, the core weighted synthesis stage begins. For example... Figure 5 As shown, this image illustrates a model block diagram with the addition of a line-of-sight (LOS) component to the Rayleigh distribution map, corresponding to the weighted superposition physical implementation logic in this application.

[0139] S409. Determine whether the fading factor is an integer or a half-integer;

[0140] S410. When the fading factor is an integer or half-integer, the channel fading coefficient sequence is generated by the Gaussian random variable sum of squares method, and the segmented calculation strategy is not executed.

[0141] Integer and half-integer refer to cases where m is a positive integer or a positive integer plus 0.5. The Gaussian random variable sum-of-squares method is a method for generating random variables with a specific distribution based on the sum of squares of independent Gaussian random variables.

[0142] These two steps provide an alternative implementation for a special case. When the fading factor m is an integer or half-integer, the channel fading coefficient sequence is directly generated using the Gaussian random variable sum-of-squares method. Specifically, 2m independent standard Gaussian random variables X1(t), X2(t), ..., X_{2m}(t) are first generated. Then, the sum of squares of these random variables is calculated and normalized to obtain the channel fading coefficient sequence.

[0143]

[0144] Where i ranges from 1 to 2m. This method is more computationally efficient for specific values ​​of m and can guarantee the accuracy of the statistical properties of the generated sequence.

[0145] To achieve the theoretically highest accuracy in a specific scenario, this embodiment also includes a preferred dual-mode working strategy. Before executing S402, it is first determined whether the input fading factor m satisfies the condition that 2m is a positive integer (i.e., m is an integer or half-integer, such as 0.5, 1, 1.5, 2, etc.).

[0146] If this condition is met, the weighted synthesis method from S402 to S411 is bypassed, and the classic Gaussian random variable sum-of-squares method is used to directly generate the Nakagami sequence. Its mathematical expression is:

[0147]

[0148] Where n=2m, and X i (t) is a set of n independent, zero-mean, unit-variance standard Gaussian random processes. This method is a theoretical definition of the Nakagami distribution for generating specific integer / half-integer m-values, and its results are statistically accurate.

[0149] By introducing this dual-mode strategy, the best balance between "universality" and "accuracy" is achieved: for any value of m, an efficient and high-precision exponential weighting method is used; for special values ​​of m that have a theoretically exact solution, the exact solution method is adopted, thus forming a channel modeling system that is both flexible and universal as well as accurate and reliable.

[0150] To verify the effectiveness of the rapid modeling method proposed in this application, the statistical characteristics of the generated channel fading coefficient sequence were analyzed, focusing on the core objective of "fitting accuracy of the Nakagami distribution under different m values". The core statistical feature of the Nakagami channel is the amplitude probability density function (PDF). The accuracy of the model was verified mainly by "comparing simulation results with theoretical PDF".

[0151] like Figure 6 As shown, Figure 6 The above diagram shows the simulation results of the amplitude probability density of the Nakagami channel model fast establishment method according to different m values ​​in the embodiments of this application.

[0152] like Figure 6 As shown, Figure 6 These are simulation results of the amplitude probability density of the Nakagami channel model fast establishment method according to the embodiments of this application under different m values, including 4 sub-figures (a)-(d):

[0153] Subplot (a) corresponds to (m=0.5) (first numerical interval, severe fading scenario): the blue star points in the figure are the theoretical Nakagami-mPDF curves, and the red curve is the simulation result generated based on the "exponential weighting method"; the two have a highly consistent trend in the (rin[0, 3.5]) interval, with only slight fluctuations in local areas, which verifies the fitting accuracy of the model under the severe fading scenario;

[0154] Subplot (b) corresponds to (m=0.75) (second numerical interval, moderate fading scenario): the peak position and descent slope of the theoretical PDF (blue star) and the simulation curve (red) are completely consistent, which reflects the stability of the model when m is in the transition interval;

[0155] Subgraph (c) corresponds to (m=1) (third numerical interval, Rayleigh fading scenario): The theoretical PDF and the simulation curve almost completely overlap—at this time, the Nakagami distribution degenerates into the Rayleigh distribution, further verifying the model's compatibility with the classic fading scenario;

[0156] Subplot (d) corresponds to (m=2) (third numerical interval, mild fading scenario): the peak shape and decay rate of the theoretical PDF (blue star) and the simulation curve (red) are highly matched, indicating that the model can still maintain high accuracy in the mild fading scenario.

[0157] like Figure 7 As shown, Figure 7 This is a simulation result diagram of the amplitude probability density of the fast Nakagami channel model establishment method according to the embodiments of this application under integer / half-integer m values, including 3 sub-figures (a)-(c):

[0158] Subgraph (a) corresponds to (m=0.5) (half-integer): the black curve is the theoretical PDF of the Gaussian random variable sum of squares method, and the orange dots are the simulation results; the two almost completely overlap in the interval (rin[0,12]), which reflects the statistical unbiasedness of the exact solution in the dual-mode strategy;

[0159] Subgraph (b) corresponds to (m=1) (integer, Rayleigh distribution): the peak position and distribution pattern of the black theoretical curve and the orange simulated star points are completely consistent, which verifies the effectiveness of the exact solution in the classic scenario;

[0160] Subgraph (c) corresponds to (m=1.5) (half-integer): The theoretical PDF curve and the simulated star point have a very high degree of fit, which further proves the theoretical accuracy of the dual-mode strategy at integer / half-integer m values.

[0161] The following describes the Nakagami channel model rapid establishment apparatus in the embodiments of this invention from the perspective of hardware processing. Please refer to [link to relevant documentation]. Figure 8 This is a schematic diagram of the physical device structure of a Nakagami channel model rapid establishment device in the embodiments of this application.

[0162] It should be noted that, Figure 8 The structure of the Nakagami channel model rapid establishment device 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.

[0163] like Figure 8As shown, the Nakagami channel model rapid establishment device includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 802 or a program loaded from storage section 808 into random access memory (RAM) 803, such as performing the methods described in the above embodiments. The RAM 803 also stores various programs and data required for system operation. The CPU 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0164] The following components are connected to I / O interface 805: input section 806 including audio input devices, push-button switches, etc.; output section 807 including liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 808 including hard disks, etc.; and communication section 809 including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 809 performs communication processing via a network such as the Internet. Drive 810 is also connected to I / O interface 805 as needed. Removable media 811, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 810 as needed so that computer programs read from them can be installed into storage section 808 as needed.

[0165] 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 a computer program 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 809, and / or installed from removable medium 811. When the computer program is executed by central processing unit (CPU) 801, it performs the various functions defined in the present invention.

[0166] 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 disk 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.

[0167] 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.

[0168] Specifically, the Nakagami channel model rapid establishment device 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 Nakagami channel model rapid establishment method provided in the above embodiment.

[0169] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the Nakagami channel model rapid establishment apparatus described in the above embodiments; or it may exist independently and not assembled into the Nakagami channel model rapid establishment apparatus. The storage medium carries one or more computer programs that, when executed by a processor of the Nakagami channel model rapid establishment apparatus, cause the Nakagami channel model rapid establishment apparatus to implement the Nakagami channel model rapid establishment method provided in the above embodiments.

[0170] 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.

[0171] 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)".

[0172] 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 method for rapidly establishing a Nakagami channel model, characterized in that, The method, applied to a rapid Nakagami channel model establishment apparatus, includes: Obtain the fading factor and average signal power of the channel to be simulated; The segmented calculation strategy for the power weight coefficient is determined based on the numerical range division result of the fading factor. The segmented calculation strategy is to set the calculation relationship of the power weight coefficient in segments based on the numerical magnitude of the fading factor. The first fading component and the second fading component are constructed based on the average power of the signal; Configure the power weight allocation ratio between the first fading component and the second fading component according to the power weight coefficient; The first fading component and the second fading component are weighted and superimposed according to the power weight allocation ratio to obtain the channel fading coefficient sequence.

2. The method according to claim 1, characterized in that, The fading factor's numerical range includes a first numerical range, a second numerical range, a third numerical range, and a fourth numerical range. The first numerical range is the range where the fading factor is less than or equal to 0.25, the second numerical range is the range where the fading factor is greater than 0.25 and less than 0.75, the third numerical range is the range where the fading factor is greater than or equal to 0.75 and less than or equal to 2, and the fourth numerical range is the range where the fading factor is greater than 2.

3. The method according to claim 2, characterized in that, The steps for determining the segmented calculation strategy of the power weight coefficient based on the numerical interval division results of the fading factor specifically include: When the fading factor is within the first numerical range, the power weighting coefficient is linearly related to the fading factor; When the fading factor is within the second numerical range, the power weighting coefficient is directly taken as the value of the fading factor; When the fading factor is in the third numerical range, the power weighting coefficient has a square root relationship with the fading factor; When the fading factor is in the fourth numerical range, the power weighting coefficient has a power function relationship with the fading factor.

4. The method according to claim 1, characterized in that, The step of configuring the power weight allocation ratio between the first fading component and the second fading component according to the power weight coefficient specifically includes: Generate a first complex Gaussian random process and a second complex Gaussian random process, wherein the first complex Gaussian random process and the second complex Gaussian random process are independent of each other; The first fading component is obtained by performing an envelope operation on the first complex Gaussian random process. The second fading component is obtained by performing an envelope operation on the second complex Gaussian random process. The first fading component and the second fading component are normalized based on the average power of the signal.

5. The method according to claim 4, characterized in that, Both the first and second complex Gaussian random processes are generated using a sine wave superposition method. This method involves superimposing multiple sine wave components with different initial phases and Doppler phases. The second complex Gaussian random process is generated by superimposing a deterministic direct path component on the scattering component generated using the sine wave superposition method, thus giving the second fading component Riceian fading characteristics. The Doppler phase is calculated based on the maximum Doppler frequency shift and the incident angle. The initial phases of the first and second complex Gaussian random processes are set differently to ensure their independence.

6. The method according to claim 1, characterized in that, The step of weighting and superimposing the first fading component and the second fading component according to the power weight allocation ratio specifically includes: Let the first fading component be denoted r1(t), the second fading component be denoted r2(t), the channel fading coefficient sequence be denoted r(t), and the power weight coefficient be denoted γ m ; The channel fading coefficient sequence is calculated according to a first preset formula, which is: 。 7. The method according to claim 1, characterized in that, After the step of weighting and superimposing the first fading component and the second fading component according to the power weight allocation ratio to obtain the channel fading coefficient sequence, the method further includes: Determine whether the fading factor is an integer or a half-integer; When the fading factor is an integer or a half-integer, the channel fading coefficient sequence is generated by the Gaussian random variable sum of squares method, and the segmented calculation strategy is not executed.

8. A device for rapidly establishing a Nakagami channel model, characterized in that, The Nakagami channel model rapid establishment apparatus includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the Nakagami channel model rapid establishment apparatus to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the Nakagami channel model rapid establishment device, the Nakagami channel model rapid establishment device performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the Nakagami channel model rapid establishment device, the Nakagami channel model rapid establishment device performs the method as described in any one of claims 1-7.

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