Time delay Doppler domain channel modeling method based on measured data

Abstract

The invention discloses a time delay Doppler domain channel modeling method based on measured data, which comprises the following steps of: dividing different time varying scenes through collinearity calculation based on a scene self-adaptive division mechanism of the measured data to adapt to the high-speed moving characteristic of a high-speed railway; depicting a channel statistical characteristic stability rule, guiding model time scale selection, depicting a fading coefficient instantaneous invariant rule and guiding DDMC system symbol design through a quasi-stationary interval and quasi-invariant interval layered modeling architecture; based on a TDDL model structure of TDL expansion, generating a standard time delay Doppler domain channel model, and adapting to a mainstream DDMC system simulation tool; the method comprises the following steps: firstly inputting measured data, then dividing a quasi-stationary interval, then carrying out multipath parameter modeling, then carrying out quasi-invariant interval evaluation, and finally outputting a model. The method has the advantages of overcoming the defects that the modeling process is fragmented, all links are connected and disconnected, only paying attention to single dimension, and the whole-process design of the DDMC system cannot be supported.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of 6G high-speed mobile communication channel modeling, in particular to a time delay Doppler domain channel modeling method based on measured data. BACKGROUND

[0002] At present, traditional channel modeling is mostly focused on the time-frequency domain, and there are two major defects in time delay Doppler domain modeling: first, existing methods mostly focus on the mapping of channel amplitude distribution and environmental evolution in a long period, and insufficient attention is paid to the statistical modeling of multipath fading coefficients, time delay and Doppler in the quasi-stationary interval, which cannot adapt to the rapid time-varying characteristics of the channel in the high-speed mobile scenario; second, the differences between the quasi-stationary and stable statistical characteristics and the quasi-invariant fading coefficient are not distinguished, resulting in large errors when the model is used for time delay Doppler domain multi-carrier DDMC system design.

[0003] In the prior art, in the 6G communication system, the train running speed of the high-speed railway scenario can reach 400-1000km / h, the channel time-varying property is extremely strong, the multipath is rapidly generated, disappeared and Doppler spectrum dynamically shifted, and the traditional time-frequency domain model is difficult to accurately depict the channel characteristics. As a key domain for channel analysis of the 6G high-speed mobile scenario, the time delay Doppler domain has the advantages of quasi-stationarity and sparsity: quasi-stationarity refers to the fact that the statistical characteristics of the channel remain stable in a certain interval, and sparsity refers to the fact that the number of real multipaths is much less than the number of system distinguishable multipaths, which provides a basis for high-precision modeling.

[0004] The core of channel modeling is to extract statistical rules based on measured data, and its accuracy directly determines the performance verification effect of the DDMC system. The existing modeling scheme has obvious limitations: on the one hand, the existing modeling of the high-speed railway scenario mostly focuses on the time-frequency domain. For example, the time domain modeling based on CIR does not delve into the time delay Doppler domain and cannot support the design requirements of DDMC technologies such as OTFS; on the other hand, the existing modeling in the time delay Doppler domain does not design in layers for the "quasi-stationary-quasi-invariant" characteristics of the high-speed mobile scenario, the difference between the model parameters and the real channel is large, and the simulation results of the DDMC system deviate significantly from the actual scenario.

[0005] The existing related technologies mainly fall into two categories, neither of which can meet the high-precision modeling needs of the high-speed railway scenario:

[0006] Time-frequency domain channel modeling scheme: in academic research, a time-frequency domain CIR model is established based on measured data, and the channel time-varying characteristics are analyzed through multipath clustering. The defects of this scheme are: only focusing on the time-frequency domain, without involving time delay Doppler domain parameter extraction; without dividing the quasi-stationary interval, the model cannot reflect the stage-by-stage stable rules of the channel statistical characteristics in the high-speed mobile scenario, and the error is large when adapting to the DDMC system.

[0007] A general-purpose time-delay Doppler domain modeling scheme: Existing technologies propose a DD domain modeling method for 60GHz V2X scenarios, which transforms time-frequency domain measured data into the DD domain and evaluates the amplitude distribution through local scattering functions. The drawbacks of this scheme are: it is only suitable for low-speed V2X scenarios (vehicle speed ≤ 120km / h) and does not adapt to high-speed mobile scenarios; it lacks a quasi-invariant interval evaluation mechanism, cannot guide the symbol period selection of the DDMC system, and has poor compatibility with LTE-R systems.

[0008] Existing time-delay Doppler domain modeling is mostly geared towards low-speed scenarios, such as V2X and ordinary highways, and is not adapted to high-speed rail-level mobile scenarios, failing to characterize the time-varying channel behavior under strong Doppler spread. It does not distinguish between the "quasi-stationary" and "quasi-invariant" characteristics of the channel, focusing only on statistically stable and quasi-stationary characteristics, neglecting the requirement for instantaneously invariant and quasi-invariant fading coefficients, resulting in large errors when the model is used for symbol design in DDMC systems. Summary of the Invention

[0009] The main objective of this invention is to provide a time-delay Doppler domain channel modeling method based on measured data, so as to solve at least one technical problem in the background art.

[0010] To achieve the above objectives, according to one aspect of the present invention, a time-delay Doppler domain channel modeling method based on measured data is provided, comprising: a scene adaptive partitioning mechanism based on measured data, which partitions different time-varying scenes through collinearity calculation to adapt to the high-speed movement characteristics of high-speed railways; a hierarchical modeling architecture of quasi-stationary intervals and quasi-invariant intervals to characterize the stability law of channel statistical characteristics, guide the selection of model time scale, characterize the instantaneous invariance law of fading coefficients, and guide the symbol design of DDMC system; a standard time-delay Doppler domain channel model is generated based on the TDDL model structure extended by TDL, which is compatible with mainstream DDMC system simulation tools; first, measured data is input, then quasi-stationary intervals are partitioned, then multipath parameter modeling is performed, then quasi-invariant interval evaluation is performed, and finally the model is output.

[0011] Preferably, for a generalized stationary uncorrelated scattering WSSUS channel, the channel correlation expression in the time delay domain is as follows:

[0012]

[0013] in, At time t, the delay The time-delay domain channel impulse response at the location; yes The conjugate accompaniment form, These represent another time and another delay, respectively. This represents the expectation operation. It's a time difference and delay Related functions, For Dirac function, only when When the value is non-zero, it indicates that the multipath components with different time delays are statistically independent, and only the components with the same time delay are correlated.

[0014] In the time-delayed Doppler domain, the scattering function Defined as about Fourier transform:

[0015]

[0016] in, Indicates delay Power density and Doppler frequency shift at the location The power density at that location describes the power of the scattered signal. , It's a time difference and delay Related functions, As a time integrator, the integration operation over the time difference enables the conversion from the time-dependent function to the Doppler frequency domain spectrum.

[0017] The time-delay Doppler domain channel correlation of a generalized stationary uncorrelated scattering channel is given by the following equation:

[0018]

[0019] in, At time t, the delay The time-delay domain channel impulse response at the location; yes The conjugate accompaniment form, These represent another time and another delay, respectively. This represents the expectation operation. Indicates delay Power density and Doppler frequency shift at the location Power density at that location , For Dirac function, only when , A non-zero value indicates that the multipath components of different time delays and Dopplers are statistically independent, and only the components of the same time delay and Doppler are correlated.

[0020] Preferably, spectral distance is used to quantify channel stationarity, specifically collinearity. Collinearity is a bounded metric γ∈[0,1] comparing the time-delay Doppler domains at different times. Collinearity close to 1 indicates very similar power spectra, while collinearity close to 0 indicates comparisons of two very dissimilar spectral densities. Temporal collinearity is defined as:

[0021]

[0022] where, represents the power of the channel at time , delay and Doppler shift , p is the delay index, p e [0, M-1], q is the Doppler index, q e [0, N-1], i, j both represent time index, M and N represent the index number of delay and Doppler respectively, represents the co-linearity value of the channel between time i and j .

[0023] Preferably, by constructing the co-linearity matrix, the quasi-stationary interval T QS in the delay-Doppler domain is identified by threshold processing, the specific steps are as follows:

[0024] Step 1: Predefine similarity threshold : Predefine threshold based on similarity of channel power , usually ranging from 0.7 to 0.9;

[0025] Step 2: Classify the delay-Doppler domain power according to co-linearity: for any two delay-Doppler domain powers, calculate the corresponding value, if is ≥ , then the two time instants are classified as the same quasi-stationary interval T QS .

[0026] Step 3: Form a complete quasi-stationary interval: after classifying all delay-Doppler domain powers, merge the consecutive time instants that consistently satisfy the threshold condition into a complete quasi-stationary interval T QS .

[0027] Preferably, within the stationary interval, the multipath components in the delay-Doppler domain exhibit stable statistical characteristics represented by the generalized stationary uncorrelated scattering properties, capture this feature, through a sliding window of size M x N along the Doppler dimension, step size D T , the number of sliding points is calculated as , where T max and T min represent the maximum and minimum index values of the M x N size data block within the stationary interval, D T is the step length, represents the floor, using the weighting coefficient given by the following formula to calculate the estimated physical delay and Doppler shift of the multipath within a stationary interval:

[0028]

[0029] wherein, denotes the weight coefficient of the delay index , i denotes the multipath index, j is the time index, j ∈ [0, a], a denotes the maximum time index, denotes the delay value of the ith path at jth time, denotes the fading coefficient of the ith path at jth time, denotes the modulo value.

[0030]

[0031] wherein, denotes the weight coefficient of the Doppler index , i denotes the multipath index, j is the time index, j ∈ [0, a], a denotes the maximum time index, denotes the Doppler value of the ith path at jth time, denotes the fading coefficient of the ith path at jth time, denotes the modulo value.

[0032] Preferably, the quantitative evaluation of the fitting degree of the selected candidate distribution with the observed power data adopts the KS test, which judges by comparing the maximum absolute difference Dn = SUP n |F x |F n (wherein, Dn is the core statistic of the KS test, measuring the maximum absolute deviation between the "empirical cumulative distribution" and the "theoretical cumulative distribution", SUP x denotes the upper bound for all possible x values, ensuring that the statistic Dn reflects the maximum deviation in the entire distribution interval, F n (X) denotes the empirical cumulative distribution function, based on the observed n sample data, the proportion of the number of samples less than or equal to x in the total sample number, which is an empirical description of the actual data distribution, F(X) denotes the theoretical cumulative distribution function, which is a theoretical hypothesis of the data distribution) to make a judgment.

[0033] Preferably, the delay Doppler domain time correlation coefficient DD-TCC is used to measure the similarity of the instantaneous delay Doppler domain fading coefficient.

[0034] Preferably, the time power correlation coefficient TPCC is extended from the delay domain to the delay Doppler domain to define DD-TCC, which measures the linear similarity of each multipath component of two delay Doppler domain fading coefficient matrices, and the formula is as follows:

[0035]

[0036] wherein, represents the linear similarity of multipath components, i is the multipath index, , represents the number of multipaths, t b , t c represents time, b, c is the time index, h i (t b ) and h i (t c ) represent the delay-Doppler domain channel fading coefficients of the ith multipath at the bth and cth time, represents taking the conjugate, represents taking the modulus value.

[0037] Preferably, by constructing a DD-TCC matrix, a quasi-invariant interval T QI is identified by threshold processing in the delay-Doppler domain, and the specific steps include: first, a similarity threshold is predefined, then the delay-Doppler domain fading coefficients are classified using the DD-TCC, and finally, the consecutive time instants that consistently satisfy the threshold condition are merged into a complete quasi-invariant interval T QI .

[0038] Preferably, the quasi-stationary interval T QS is calculated using collinearity, then the data within the quasi-invariant interval is subjected to delay-Doppler weighted calculation and power distribution fitting, and the most suitable distribution is selected using the KS test; finally, the quasi-invariant interval T QI is calculated using the DD-TCC, generating a TDDL model extended based on the TDL model.

[0039] The technical scheme of the application has the following technical effects:

[0040] It can accurately capture core features such as rapid change of multipath and dynamic shift of Doppler spectrum under high-speed movement for different time-varying scenarios, covering aspects such as "data input-feature extraction-model generation", solving the problem of fragmentation of existing technology modeling process and disconnection between links, and forming a complete technical solution that can be directly implemented.

[0041] The "quasi-stationary interval-quasi-invariant interval" double-layer architecture is innovatively divided, which not only depicts the stable law of channel statistical characteristics, but also captures the instantaneous invariant characteristics of fading coefficients, making up for the defects of existing technologies that only focus on a single dimension and cannot support the whole process design of DDMC system. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0043] Figure 1 A flowchart illustrating the time-delay Doppler domain channel modeling method based on measured data according to the present invention is shown. Detailed Implementation

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0045] like Figure 1 As shown, this embodiment of the invention provides a time-delay Doppler domain channel modeling method based on measured data, including: a scene adaptive partitioning mechanism based on measured data, which partitions different time-varying scenes through collinearity calculation to adapt to the high-speed movement characteristics of high-speed railways; a hierarchical modeling architecture of quasi-stationary intervals and quasi-invariant intervals to characterize the stable law of channel statistical characteristics, guide the selection of model time scale, characterize the instantaneous invariance law of fading coefficients, and guide the symbol design of DDMC system; a standard time-delay Doppler domain channel model is generated based on the TDDL model structure extended by TDL, which is compatible with mainstream DDMC system simulation tools; first, measured data is input, then quasi-stationary intervals are partitioned, then multipath parameter modeling is performed, then quasi-invariant interval evaluation is performed, and finally the model is output.

[0046] In this embodiment, based on measured data, a high-precision model can be constructed by dividing quasi-stationary intervals, statistical fitting of multipath parameters, and evaluation of quasi-invariant intervals. This provides reliable model support for bit error rate optimization and resource allocation in 6G DDMC systems (such as OTFS). Addressing the issues of neglecting the statistical characteristics of quasi-stationary intervals, failing to distinguish between quasi-stationary and quasi-invariant characteristics, and poor model adaptability in time-delay Doppler domain channel modeling in high-speed railway scenarios, this invention provides a time-delay Doppler domain channel modeling method based on measured data. The time-delay Doppler (DD) domain is a two-dimensional domain that simultaneously describes the channel delay (signal propagation time difference) and Doppler frequency shift (frequency shift caused by relative motion), and is a key dimension for channel analysis in 6G high-speed mobile scenarios. The tapped delay line (TDL) model is a classic channel modeling structure that represents the channel as a superposition of multiple discrete "tap" (corresponding to multipath), each tap containing delay and complex channel coefficients. This invention extends this to a time-delay Doppler domain tapped delay line (TDDL) model.

[0047] In this embodiment, based on measured data and considering the high-speed mobility characteristics of high-dynamic scenarios, a three-layer modeling architecture of "quasi-stationary interval division - multipath parameter fitting - quasi-invariant interval evaluation" is designed to fill the technical gap in high-precision modeling of time delay Doppler domain in high-speed railway scenarios. The time delay Doppler domain modeling scheme based on LTE-R measured data is adapted to high-speed mobility scenarios, compatible with commercial system data formats, and improves the engineering practicality of the model. The hierarchical evaluation mechanism of "quasi-stationary interval - quasi-invariant interval" accurately characterizes the dual requirements of stable channel statistical characteristics and invariant instantaneous characteristics, providing multi-dimensional parameter support for DDMC system design. This invention can generate high-precision models for different time-varying scenarios, covering the full-scenario channel characteristics of high-speed railway scenarios, providing comprehensive support for DDMC system performance verification.

[0048] In this embodiment, based on measured data, it is achieved through five steps: "measured data input - quasi-stationary interval division - multipath parameter modeling - quasi-invariant interval evaluation - model output". The core is to characterize the quasi-stationary and quasi-invariant characteristics of the time-delay Doppler domain channel in layers.

[0049] For a generalized stationary uncorrelated scattering (WSSUS) channel, the channel correlation in the time delay domain can be expressed as follows:

[0050]

[0051] in, At time t, the delay The time-delay domain channel impulse response at the location; yes The conjugate accompaniment form, These represent another time and another delay, respectively. This represents the expectation operation. It's a time difference and delay Related functions, For Dirac function, only when A non-zero value indicates that the multipath components with different time delays are statistically independent, and only components with the same time delay are correlated. In the time-delay Doppler domain, we will use the scattering function... Defined as about Fourier transform:

[0052]

[0053] in, Indicates delay Power density and Doppler frequency shift at the location The power density at that location describes the power of the scattered signal. , It's a time difference and delay Related functions, Using the time integral element, the integration operation over the time difference achieves the transformation from the time correlation function to the Doppler frequency domain spectrum. Therefore, the time-delay Doppler domain channel correlation of the generalized stationary uncorrelated scattering channel is given by the following equation:

[0054]

[0055] in, At time t, the delay The time-delay domain channel impulse response at the location; yes The conjugate accompaniment form, These represent another time and another delay, respectively. This represents the expectation operation. Indicates delay Power density and Doppler frequency shift at the location Power density at that location , For Dirac function, only when , A non-zero value indicates that the multipath components with different time delays and Doppler frequencies are statistically independent, with correlation only existing between components with the same time delay and Doppler frequency. This suggests that within the stationary interval, the time delay and Doppler frequency shift of the multipath signal in the time-delay Doppler domain do not change significantly, and no multipath generation or extinction phenomenon occurs. Therefore, we can assume that the statistical performance of multipath remains the same within the stationary interval. We use a spectral distance metric to quantify the stationarity of the channel, called collinearity. Collinearity is a bounded metric γ∈[0,1] comparing the time-delay Doppler domain at different times. Collinearity close to 1 is the result of very similar power spectra, while collinearity close to 0 is the result of comparing two very dissimilar spectral densities. Temporal collinearity is defined as:

[0056]

[0057] in, Indicates time ,Delay and Doppler shift The power is given by p, where p is the time delay index (p∈[0, M-1]), q is the Doppler index (q∈[0, N-1]), i and j represent time indices, and M and N represent the number of time delay and Doppler indices, respectively. Indicates t i and t j The collinearity of the channel across time intervals. By constructing a collinearity matrix and applying a threshold, the quasi-stationary interval T in the time-delay Doppler domain is identified. QS The specific steps are as follows:

[0058] Step 1: Predefine similarity threshold Predefined threshold based on channel power similarity The typical range is from 0.7 to 0.9;

[0059] Step 2: Classify the time-delay Doppler domain power according to collinearity: For any two time-delay Doppler domain powers, calculate the corresponding... Value. If ≥ These two moments are then classified as the same quasi-stationary interval T. QS .

[0060] Step 3: Forming a completely quasi-stationary interval: After classifying all time-delayed Doppler domain powers, consecutive time instants that consistently satisfy the threshold condition are merged into a complete quasi-stationary interval T. QS .

[0061] Within the stationary region, the multipath components in the time-delayed Doppler domain exhibit stable statistical characteristics represented by the generalized stationary uncorrelated scattering properties. To capture these characteristics, we use a sliding window of size M×N along the Doppler dimension with a step size of D. T The number of sliding points is calculated as follows: T max and T min D represents the maximum and minimum index values ​​of a data block of size M×N within a stationary interval. T The step length, This indicates rounding down, using weighted coefficients to calculate the estimated physical delay and Doppler shift of multipath over a stationary interval. These weighted coefficients are given by the following formula:

[0062]

[0063]

[0064] in, Indicates delay index The weighting coefficients, Indicates Doppler index The weighting coefficients are given by: where i represents the multipath index, j is the time index (j∈[0, a]), and a represents the maximum time index. This represents the time delay value at time j of the i-th path. This represents the Doppler value at time j of the i-th path. This represents the fading coefficient at time j of the i-th path. This represents the modulo value.

[0065] This process improves the estimation of the delay and Doppler parameters of each multipath component in the time-delay Doppler domain, producing a more accurate representation of its central tendency.

[0066] While average time delay and Doppler parameters effectively characterize the temporal stability of multipath components, power characteristics require further statistical analysis. Assuming that multipath components within a stationary interval have the same statistical properties, we focus on fitting the probability distribution to the amplitudes of all multipath components within a single data packet captured within that stationary interval. This invention chooses classic probability distributions to fit the amplitude distributions, namely the Rice, Rayleigh, upper-middle, and Weibull distributions, as shown in the table below.

[0067] Table 1 Probability Distribution Model

[0068]

[0069] To quantitatively assess the goodness of fit between the selected candidate distributions and the observed power data, the Kolmogorov-Smirnov (KS) test was used. The KS test compares the empirical cumulative distribution function (ECDF) F of the sample data. n The maximum absolute difference Dn = SUP between (X) and the cumulative distribution function (CDF) F(X) of the theoretical distribution. x |F n (Where, Dn is the core statistic of the KS test, which measures the maximum absolute deviation between the "empirical cumulative distribution" and the "theoretical cumulative distribution," and SUP...) x This indicates that the supremum is taken for all possible values ​​of x, ensuring that the statistic Dn reflects the maximum deviation over the entire distribution interval. F n (X) represents the empirical cumulative distribution function, which is based on the observed n sample data and counts the proportion of samples less than or equal to x to the total number of samples. It is an empirical description of the actual data distribution. F(X) represents the theoretical cumulative distribution function, which is a theoretical assumption about the data distribution.

[0070] The core focus during the stationary interval is whether the statistical characteristics of the channel change significantly, rather than the channel fading coefficient h in the delay-Doppler domain. i Fluctuations. Even in the time-delay Doppler domain, the channel fading coefficient h... i Slight random fluctuations exist within the interval, but as long as the statistical characteristics remain stable, this interval can still be identified as a stationary interval. However, for delay-Doppler domain multicarrier data packets, h i The change is undesirable, and h i Excessive fluctuations may reduce data transmission performance.

[0071] To address this issue, we further define a more stringent quasi-invariant interval based on the already established statistically stationary interval. This quasi-invariant interval not only requires the channel to maintain stable statistical properties but also imposes constraints on the channel fading coefficients in the time-delay Doppler domain to ensure they do not change significantly over time. Specifically, the time-delay Doppler domain time correlation coefficient (DD-TCC) will be used to measure the similarity of instantaneous time-delay Doppler domain fading coefficients. This invention extends the time power correlation coefficient (TPCC) from the delay domain to the time-delay Doppler domain, defining the DD-TCC. This coefficient measures the linear similarity of each multipath component of two time-delay Doppler domain fading coefficient matrices, as shown in the following formula:

[0072]

[0073] in, This represents the linear similarity of multipath components, where i is the multipath index. , t represents the number of multipaths. b , t c Indicates time, b and c are time indices, h i (t b ) and h i (t c () represents the time delay Doppler domain channel fading coefficient of the i-th multipath at times b and c. Indicates taking the conjugate. This represents the modulus value. By constructing the DD-TCC matrix, the quasi-invariant interval T in the time-delay Doppler domain is identified through thresholding. QI The specific steps are similar to the quasi-stationary interval modeling described above, that is, firstly, a similarity threshold is predefined. Then, DD-TCC is used to classify the time-delay Doppler domain fading coefficients, and finally, consecutive moments that consistently satisfy the threshold condition are merged into a complete quasi-invariant interval T. QI .

[0074] In summary, the complete process of the time-delay Doppler domain channel modeling method of this invention can be summarized as follows: First, the quasi-stationary interval T is calculated using collinearity. QS Then, time-delay Doppler weighted calculations and power distribution fitting are performed on the data within the quasi-invariant interval, and the most suitable distribution is selected using the KS test; finally, the quasi-invariant interval T is calculated using DD-TCC. QIBased on the above modeling methods, a TDDL model based on an extension of the TDL model can be generated. An adaptive scenario partitioning mechanism based on measured data can divide different time-varying scenarios through collinearity calculation, adapting to the high-speed movement characteristics of high-speed railways. The hierarchical modeling architecture of "quasi-stationary interval - quasi-invariant interval" not only describes the stable laws of channel statistical characteristics (guiding the selection of model time scale) but also the instantaneous invariance of fading coefficients (guiding the symbol design of DDMC systems). Based on the TDDL model structure extended from TDL, a standard time-delay Doppler domain channel model can be generated, compatible with mainstream DDMC system simulation tools.

[0075] As can be seen from the above description, the embodiments of the present invention achieve the following technical effects:

[0076] It can accurately capture core features such as rapid changes in multipath and dynamic shifts in Doppler spectrum under high-speed movement in different time-varying scenarios, covering the links of "data input-feature extraction-model generation", solving the problems of fragmentation and disconnect between links in the existing modeling process, and forming a complete technical solution that can be directly implemented.

[0077] The innovative two-layer architecture of "quasi-stationary interval - quasi-invariant interval" not only describes the stable law of channel statistical characteristics, but also captures the instantaneous invariant characteristics of fading coefficient, making up for the shortcomings of existing technologies that only focus on a single dimension and cannot support the entire process design of DDMC system.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A time-delay Doppler domain channel modeling method based on measured data, characterized in that, include: Based on measured data, an adaptive scenario partitioning mechanism is used to divide different time-varying scenarios through collinearity calculation, adapting to the high-speed movement characteristics of high-speed railways. Through a hierarchical modeling architecture of quasi-stationary and quasi-invariant intervals, the stability law of channel statistical characteristics is characterized, guiding the selection of model time scale and characterizing the instantaneous invariance law of fading coefficient, guiding the symbol design of DDMC system. Based on the TDDL model structure extended by TDL, a standard time-delay Doppler domain channel model is generated, which is compatible with mainstream DDMC system simulation tools. First, measured data is input, then quasi-stationary intervals are partitioned, then multipath parameter modeling is performed, then quasi-invariant interval evaluation is performed, and finally the model is output.

2. The time-delay Doppler domain channel modeling method based on measured data as described in claim 1, characterized in that, For a generalized stationary uncorrelated scattering WSSUS channel, the channel correlation expression in the time delay domain is as follows: ； in, At time t, the delay Channel impulse response in the time delay domain; yes The conjugate accompaniment form, These represent another time and another delay, respectively. This represents the expectation operation. It's a time difference and delay Related functions, For Dirac function, only when A non-zero value indicates that the multipath components with different time delays are statistically independent, and only the components with the same time delay are correlated; in the time-delay Doppler domain, the scattering function Defined as about Fourier transform: ； in, Indicates delay Power density and Doppler frequency shift at the location The power density at that location describes the power of the scattered signal. , It's a time difference and delay Related functions, As a time integrator, the integration operation over the time difference enables the conversion from the time-correlation function to the Doppler frequency domain spectrum; The time-delay Doppler domain channel correlation of a generalized stationary uncorrelated scattering channel is given by the following equation: ； in, At time t, the delay Channel impulse response in the time delay domain; yes The conjugate accompaniment form, These represent another time and another delay, respectively. This represents the expectation operation. Indicates delay Power density and Doppler frequency shift at the location Power density at that location , For Dirac function, only when , A non-zero value indicates that the multipath components of different time delays and Dopplers are statistically independent, and only the components of the same time delay and Doppler are correlated.

3. The time-delay Doppler domain channel modeling method based on measured data as described in claim 1, characterized in that, The stationarity of the channel is quantified using a spectral distance metric, specifically collinearity. Collinearity is a bounded metric γ∈[0,1] comparing the time-delay Doppler domains at different times. Collinearity close to 1 indicates very similar power spectra, while collinearity close to 0 indicates comparisons of two very dissimilar spectral densities. Temporal collinearity is defined as: ； in, Indicates time ,Delay and Doppler shift The power is given by p, where p is the time delay index (p∈[0, M-1]), q is the Doppler index (q∈[0, N-1]), i and j represent time indices, and M and N represent the number of time delay and Doppler indices, respectively. Indicates t i and t j The collinearity of the channel over time.

4. The time-delay Doppler domain channel modeling method based on measured data as described in claim 1, characterized in that, By constructing a collinearity matrix and applying thresholding, the quasi-stationary interval T in the time-delay Doppler domain is identified. QS The specific steps are as follows: Step 1: Predefine similarity threshold Predefined threshold based on channel power similarity The typical range is from 0.7 to 0.9; Step 2: Classify the time-delay Doppler domain power according to collinearity: For any two time-delay Doppler domain powers, calculate the corresponding... Value, if ≥ These two moments are then classified as the same quasi-stationary interval T. QS ; Step 3: Forming a completely quasi-stationary interval: After classifying all time-delayed Doppler domain powers, consecutive time instants that consistently satisfy the threshold condition are merged into a complete quasi-stationary interval T. QS .

5. The time-delay Doppler domain channel modeling method based on measured data as described in claim 1, characterized in that, Within the stationary interval, the multipath components in the time-delayed Doppler domain exhibit stable statistical characteristics represented by the generalized stationary uncorrelated scattering properties. This characteristic is captured by using a sliding window of size M×N along the Doppler dimension with a step size of D. T The number of sliding points is calculated as , among which, T max and T min D represents the maximum and minimum index values ​​of a data block of size M×N within a stationary interval. T The step length, This indicates rounding down, using weighted coefficients to calculate the estimated physical delay and Doppler shift of multipath over a stationary interval. These weighted coefficients are given by the following formula: ； in, Indicates delay index The weighting coefficients are given by: where i represents the multipath index, j is the time index (j∈[0, a]), and a represents the maximum time index. This represents the time delay value at time j of the i-th path. This represents the fading coefficient at time j of the i-th path. Indicates the modulo value; ； in, Indicates Doppler index The weighting coefficients are given by: where i represents the multipath index, j is the time index (j∈[0, a]), and a represents the maximum time index. This represents the Doppler value at time j of the i-th path. This represents the fading coefficient at time j of the i-th path. This represents the modulo value.

6. The time-delay Doppler domain channel modeling method based on measured data as described in claim 1, characterized in that, The KS test was used to quantitatively evaluate the goodness of fit between the selected candidate distributions and the observed power data. The KS test compares the empirical cumulative distribution function (ECDF) F of the sample data. n The maximum absolute difference Dn = SUP between (X) and the cumulative distribution function (CDF) F(X) of the theoretical distribution. x |F n Where Dn is the core statistic of the KS test, measuring the maximum absolute deviation between the empirical cumulative distribution and the theoretical cumulative distribution, SUP x This indicates that the supremum is taken for all possible values ​​of x, ensuring that the statistic Dn reflects the maximum deviation over the entire distribution interval. F n (X) represents the empirical cumulative distribution function, which is based on the observed n sample data and counts the proportion of samples less than or equal to x to the total number of samples. It is an empirical description of the actual data distribution. F(X) represents the theoretical cumulative distribution function, which is based on the theoretical assumptions of the data distribution.

7. The time-delay Doppler domain channel modeling method based on measured data as described in claim 6, characterized in that, The time-delay Doppler domain correlation coefficient (DD-TCC) is used to measure the similarity of instantaneous time-delay Doppler domain fading coefficients.

8. The time-delay Doppler domain channel modeling method based on measured data as described in claim 1, characterized in that, The time power correlation coefficient (TPCC) is extended from the delay domain to the time-delay Doppler domain. DD-TCC is defined, whereby the time power correlation coefficient measures the linear similarity of each multipath component of the fading coefficient matrices in two time-delay Doppler domains. The formula is as follows: ； in, This represents the linear similarity of multipath components, where i is the multipath index. , t represents the number of multipaths. b , t c Indicates time, b and c are time indices, h i (t b ) and h i (t c () represents the time delay Doppler domain channel fading coefficient of the i-th multipath at times b and c. Indicates taking the conjugate. This represents the modulo value.

9. The time-delay Doppler domain channel modeling method based on measured data as described in claim 1, characterized in that, By constructing the DD-TCC matrix and using thresholding, the quasi-invariant interval T in the time-delay Doppler domain is identified. QI The specific steps include: first, predefining a similarity threshold. Then, DD-TCC is used to classify the time-delay Doppler domain fading coefficients, and finally, consecutive moments that consistently satisfy the threshold condition are merged into a complete quasi-invariant interval T. QI .

10. The time-delay Doppler domain channel modeling method based on measured data as described in claim 1, characterized in that, Use collinearity to calculate the quasi-stationary interval T QS Then, time-delay Doppler weighted calculations and power distribution fitting are performed on the data within the quasi-invariant interval, and the most suitable distribution is selected using the KS test; finally, the quasi-invariant interval T is calculated using DD-TCC. QI Generate a TDDL model based on an extension of the TDL model.

Images