A method and apparatus for modeling a multi-link channel based on shared clusters

Abstract

The application provides a shared cluster-based multi-link channel modeling method and device. The method provided by the application comprises the following steps: determining a multi-link scene comprising at least two communication links; collecting channel data related to a shared cluster, extracting and determining the physical characteristics of the shared cluster from the channel data; constructing a cross-correlation function based on the physical characteristics of the shared cluster; the cross-correlation function is used to describe the correlation characteristics between different links caused by the shared cluster, and the cross-correlation function comprises a joint power and cluster summation term, a joint angle-delay power distribution term, a phase offset term and a frequency-delay coupling term; processing the cross-correlation function to obtain a cross term, and integrating the cross term into a multi-cluster superposition model; correlating and superimposing the channel characteristics of each link based on the multi-cluster superposition model to construct a multi-link channel model; the diagonal blocks of the multi-link channel model are single-link channel matrices generated based on autocorrelation terms of each link, and the non-diagonal blocks are inter-link coupling matrices generated based on cross terms.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and in particular to a multi-link channel modeling method and device based on a shared cluster. BACKGROUND

[0002] With the evolution of mobile communication technologies such as 5G-A and 6G towards multi-connection and high-reliability scenarios, multi-link cooperation (such as vehicle-to-vehicle communication in vehicle networking and cluster communication of unmanned aerial vehicles) has become a core solution to improve system capacity and coverage. In such scenarios, different communication links often share the same scattering cluster (i.e., a shared cluster), and the signal propagation characteristics of the shared cluster will simultaneously affect the channel quality of multiple links. Therefore, it is necessary to model the multi-link channel based on the shared cluster to accurately depict the influence of the shared cluster on the correlation characteristics between links and provide channel support for key technologies such as multi-link resource scheduling and interference suppression.

[0003] Currently, the mainstream multi-link channel modeling methods mainly fall into two categories: one is statistical modeling based on measured data, which collects channel parameters in a multi-link scenario to construct an empirical correlation model; the other is an extension based on a geometric channel model, which simply superimposes the independent channel characteristics of multiple links or describes the relationship between links through a preset fixed correlation coefficient based on a single-link geometric cluster model. Some methods introduce the concept of a shared cluster, but only use it as a supplement to a single-link cluster without forming a systematic modeling framework for the shared cluster correlation.

[0004] Therefore, there is an urgent need for a method that can model the multi-link channel by fusing the characteristics of the shared cluster and quantifying the link correlation. SUMMARY

[0005] Therefore, the present application provides a multi-link channel modeling method and device based on a shared cluster to model the multi-link channel by fusing the characteristics of the shared cluster and quantifying the link correlation.

[0006] Specifically, the present application is implemented by the following technical solutions:

[0007] The first aspect of the present application provides a multi-link channel modeling method based on a shared cluster, which comprises:

[0008] determining a multi-link scenario including at least two communication links; the multi-link scenario including a shared cluster simultaneously perceived by multiple links;

[0009] under the multi-link scenario, collecting channel data related to the shared cluster, extracting and determining physical features of the shared cluster from the channel data;

[0010] based on the physical features of the shared cluster, constructing a cross-correlation function; the cross-correlation function is used to describe the correlation characteristics between different links caused by the shared cluster, and the cross-correlation function includes a joint power and cluster summation term, a joint angle-delay power distribution term, a phase offset term and a frequency-delay coupling term;

[0011] processing the cross-correlation function to obtain a cross term, and integrating the cross term into a multi-cluster superposition model;

[0012] based on the multi-cluster superposition model, correlating and superimposing channel characteristics of each link to construct a multi-link channel model; diagonal blocks of the multi-link channel model are single-link channel matrices generated based on autocorrelation terms of each link, and non-diagonal blocks are inter-link coupling matrices generated based on cross terms.

[0013] The second aspect of the application provides a multi-link channel modeling device based on a shared cluster, the device comprising a determination module, a processing module and a construction module;

[0014] The determination module is configured to determine a multi-link scenario including at least two communication links; the multi-link scenario includes a shared cluster simultaneously perceived by multiple links;

[0015] The processing module is configured to, under the multi-link scenario, collect channel data related to the shared cluster, extract and determine physical features of the shared cluster from the channel data;

[0016] The construction module is configured to, based on the physical features of the shared cluster, construct a cross-correlation function; the cross-correlation function is used to describe the correlation characteristics between different links caused by the shared cluster, and the cross-correlation function includes a joint power and cluster summation term, a joint angle-delay power distribution term, a phase offset term and a frequency-delay coupling term;

[0017] The processing module is further configured to process the cross-correlation function to obtain a cross term, and integrate the cross term into a multi-cluster superposition model; the multi-cluster superposition model is composed of the cross term and autocorrelation terms of each shared cluster in the corresponding link, the autocorrelation term is the independent contribution of a single shared cluster to a single link, and the cross term is the associated contribution of the shared cluster to different links;

[0018] The construction module is further configured to construct a multi-link channel model by associating and superimposing channel characteristics of each link based on the multi-cluster superposition model; diagonal blocks of the multi-link channel model are single-link channel matrices of each link generated based on autocorrelation terms, and non-diagonal blocks are inter-link coupling matrices generated based on cross terms.

[0019] The shared cluster-based multi-link channel modeling method and device provided in the application bring double improvements of precision and integrity to multi-link channel modeling by explicitly including joint power and cluster summation terms, joint angle-delay power distribution terms, phase offset terms and frequency-delay coupling terms in the cross-correlation function, and by constructing a matrix-based multi-link channel model that integrates autocorrelation terms and cross terms. From the composition of the cross-correlation function, the shared cluster association characteristics are fully described in multiple dimensions, solving the limitation of traditional modeling that only describes link association in a single dimension. The joint power and cluster summation terms quantify the total supporting effect of the shared cluster on the power association of the multi-link, avoiding power analysis fragmentation; the joint angle-delay power distribution terms can make up for the defects of traditional modeling that ignore the association of space and time dimensions; the phase offset terms can quantify the phase difference between links, and the frequency-delay coupling terms cover the association of the phase and frequency-delay dimensions. The cross-correlation function composed of the four parameters can fully capture the influence of the shared cluster on the multi-link from multiple dimensions of power, space-time, phase and frequency-delay, ensuring the comprehensiveness and precision of the association characteristic description. From the multi-link channel model that integrates autocorrelation terms and cross terms, the single-link independent characteristics and inter-link association characteristics are distinguished by the matrix structure, breaking the limitation of traditional modeling that only focuses on single-link modeling. The single-link channel matrix of the diagonal block is generated by the autocorrelation terms, which can retain the independent channel characteristics (such as signal power, delay, etc.) of each link, ensuring accurate description of single-link transmission rules; the inter-link coupling matrix of the non-diagonal block is generated by the cross terms, which can accurately present the association and coupling relationship (such as power association and phase coupling) between different links caused by the shared cluster. The combination of the two can cover both single-link independent propagation and multi-link association coupling, forming a unified matrix framework that not only conforms to the physical reality that the shared cluster simultaneously affects multiple links in actual multi-link scenarios, but also provides intuitive and accurate model support for subsequent multi-link system interference suppression, signal detection and performance simulation, avoiding simulation bias caused by the lack of association characteristic description in traditional models, and improving the reliability and effectiveness of multi-link system design. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of the shared cluster-based multi-link channel modeling method provided for Embodiment One of the application;

[0021] Figure 2 A structural schematic diagram of the shared cluster-based multi-link channel modeling device provided for Embodiment Two of the application. DETAILED DESCRIPTION

[0022] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The description herein refers to the accompanying drawings, which show by way of illustration various

[0023] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0024] It is to be understood that the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0025] The specific embodiments are given as follows to introduce the technical scheme of the present application in detail.

[0026] Figure 1 The flow chart of the multi-link channel modeling method based on shared cluster provided by Embodiment One of the present application is shown in FIG. 1. Please refer to Figure 1 The method provided by the present embodiment can include:

[0027] S101, determining a multi-link scenario containing at least two communication links.

[0028] The multi-link scenario includes a shared cluster that is simultaneously perceived by multiple links.

[0029] Specifically, the multi-link scenario refers to a communication scenario in which there are at least two independent communication links, and each link is in the same or associated propagation environment. From the composition, each link contains a complete communication path of "transmitting end-receiving end" (such as the two links of "drone 1-drone 2" and "drone 1-drone 3" in a drone cluster). The signal propagation of multiple links in the multi-link scenario will be affected by the same batch of scatterers (such as buildings, trees, and obstacles), which has the physical conditions to produce a shared cluster.

[0030] Further, the shared cluster refers to a scattering cluster that can be simultaneously perceived by at least two communication links in a multi-link scenario, and is the core physical carrier for channel association between multi-links. The essence of the shared cluster is a set of scattering bodies with similar spatial positions and consistent scattering characteristics (for example, the wall surface of a building can form a scattering cluster); when the reflection / scattering signals of the scattering cluster can simultaneously reach the receiving ends of two links (or be utilized by the transmitting ends of two links for signal transmission), it is a shared cluster. For example, vehicle A and vehicle B communicate with the roadside unit through the reflection signals of the same high-rise building, and the scattering cluster corresponding to the high-rise building is the shared cluster of the "vehicle A-roadside unit" and "vehicle B-roadside unit" links.

[0031] Optionally, after determining the multi-link scenario containing at least two communication links, the method further comprises: performing feature extraction on the propagation environment of each link to obtain the angle parameter, delay parameter and power parameter of each cluster in each link; calculating the matching degree of the feature parameters of the clusters in any two links, for the angle parameter, calculating the comparison result of the absolute value of the angle difference and the preset angle threshold, for the delay parameter, calculating the comparison result of the absolute value of the delay difference and the preset delay threshold, and for the power parameter, calculating the comparison result of the power ratio and the preset power threshold; when the angle difference and the delay difference of the target cluster in the two links are both less than the corresponding threshold, and the power ratio is within the preset threshold range, it is determined that the target cluster is a shared cluster of the two links.

[0032] In specific implementation, for each communication link in the multi-link scenario, the propagation environment data thereof is collected, and three types of feature parameters of each scattering cluster in each link are extracted, including the angle parameter (average arrival angle, average departure angle), the delay parameter (average delay), and the power parameter (power weight coefficient). A cluster to be determined is selected from each of the two links as a target cluster, and the matching degree of the feature parameters of the two target clusters is calculated: for the angle parameter, the absolute value of the angle parameter difference of the target clusters of the two links is calculated, and then the absolute value is compared with the pre-set angle threshold, and the comparison result is recorded; for the delay parameter, the absolute value of the delay parameter difference of the target clusters of the two links is calculated, and then the absolute value is compared with the pre-set delay threshold, and the comparison result is recorded; for the power parameter, the power parameter ratio of the target clusters of the two links is calculated, and then it is determined whether the ratio is within the pre-set power threshold range, and the comparison result is recorded. Based on the comparison results of the above three types of parameters, if the absolute value of the angle difference is less than the angle threshold, the absolute value of the delay difference is less than the delay threshold, and the power ratio is within the power threshold range, it is determined that the target cluster is a shared cluster of the two links. The above comparison steps are repeated to determine all the remaining clusters in the two links one by one, so as to identify the shared clusters in the two links; if there are more links in the scenario, the matching determination of the clusters is performed according to the same process between two links, so as to determine all the shared clusters in the multi-link scenario.

[0033] S102, in the multi-link scenario, collect channel data related to the shared cluster, extract and determine the physical characteristics of the shared cluster from the channel data.

[0034] Specifically, the channel data refers to the original data and derived data obtained by the signal acquisition equipment (such as antenna array, signal analyzer) of the transmitting end and the receiving end in the multi-link scenario, which can reflect the characteristic changes of the signal in the "transmitting end-shared cluster-receiving end" propagation path. The channel data includes original signal data (such as baseband signal amplitude, phase, frequency, etc. time domain / frequency domain signal waveform data collected by the receiving end) and channel measurement data (such as channel impulse response, channel frequency response, signal arrival time, received power obtained by signal processing).

[0035] Further, the physical characteristics refer to the key parameters extracted from the channel data, which can quantitatively describe the properties of the shared cluster and the spatial relationship between the shared cluster and each link. The physical characteristics include the shared cluster attribute parameters (such as the power weight coefficient of the shared cluster, the delay dispersion, etc.), and the spatial correlation parameters between the shared cluster and the link (such as the average angle of departure of the shared cluster to the link transmitting end, the average angle of arrival of the shared cluster to the link receiving end, and the angle between the link motion direction and the signal incident direction of the shared cluster, etc.).

[0036] In the implementation, in the determined multi-link scenario, the signal acquisition equipment (such as antenna array, spectrum analyzer, etc.) deployed at the transmitting end and the receiving end of each link is used to synchronously collect the signal data propagated through the shared cluster, including the original channel data such as the baseband signal waveform, signal strength, phase change, etc. of each link receiving end. From the collected channel data, for the shared cluster determined in the above embodiments, the average angle of departure of the shared cluster to each link transmitting end, and the average angle of arrival to the receiving end are calculated by angle estimation algorithm (such as MUSIC algorithm), the average delay and delay dispersion of the shared cluster are determined by delay estimation algorithm, the power weight coefficient of the shared cluster is obtained by power estimation algorithm, and the spatial correlation parameters such as the angle between the link motion direction and the antenna array are calculated in combination with the link motion state data. The specific implementation process and principles of the physical characteristics extraction can be referred to the description in related technologies, which will not be repeated here.

[0037] S103, based on the physical characteristics of the shared cluster, construct a cross-correlation function.

[0038] The cross-correlation function is used to describe the correlation characteristics between different links caused by the shared cluster, and the cross-correlation function includes joint power and cluster summation term, joint angle-delay power distribution term, phase offset term and frequency-delay coupling term.

[0039] Specifically, the cross-correlation function is a mathematical expression based on the physical characteristics of the shared cluster, which is used to quantitatively describe the channel correlation characteristics between different communication links due to sharing the same scattering cluster (shared cluster) in a multi-link scenario. The essence is to convert the abstract correlation between links into a calculable and analyzable mathematical form by integrating the multi-dimensional parameters of the shared cluster. The cross-correlation function includes a joint power and cluster summation term, a joint angle-delay power distribution term, a phase offset term, and a frequency-delay coupling term. Among them, the joint power and cluster summation term is a mathematical term that accumulates the power contribution of all shared clusters in a multi-link scenario; the joint angle-delay power distribution term is a distribution characteristic term that describes the common influence of the shared cluster on different links in the angle (average arrival angle / departure angle) and delay (average delay) dimensions; the phase offset term is a phase difference term calculated based on the geometric relationship (such as antenna spacing, angle parameter) between the shared cluster and each link; the frequency-delay coupling term is a mathematical term describing the interaction between signal frequency and shared cluster delay parameters.

[0040] In a specific implementation, the cross-correlation function is constructed based on the physical characteristics of the shared cluster, including:

[0041] (1) Based on the total power of each shared cluster in different links and the power correlation coefficient, the joint power and cluster summation term is obtained.

[0042] Specifically, the power correlation coefficient is a parameter for quantifying the correlation strength between the power contributions of the same shared cluster in different links in a multi-link scenario. Its core role is to reflect the consistency or coupling degree of the power characteristics of the shared cluster between different links, rather than independently describing the power size of a single link.

[0043] In a specific implementation, for each shared cluster in a multi-link scenario, the total power of each shared cluster in each link is obtained, i.e. the power contribution value of the shared cluster to the scattered signal of each link. By statistically analyzing the correlation of the total power of each shared cluster in two links, the power correlation coefficient of the same shared cluster between any two links is determined. For each shared cluster, multiply the total power in two links, and then multiply the corresponding power correlation coefficient to obtain the contribution value of the shared cluster to the joint power of the two links. The sum of the joint power contribution values of all shared clusters is the joint power and cluster summation term.

[0044] (2) Based on the angle distribution characteristics and delay distribution characteristics of each shared cluster in different links, the joint angle-delay power distribution term is obtained.

[0045] Specifically, the angle distribution characteristic is a statistical distribution characteristic of a propagation direction (an angle parameter) of a scattered signal of the same shared cluster in different links, and core reflects a spatial orientation association between the shared cluster and a transmission end / reception end of each link. The angle distribution characteristic includes an average angle of arrival (an average direction angle of a signal from the shared cluster to a reception end of a link), an average angle of departure (an average direction angle of a signal from a transmission end of a link to the shared cluster), and an angle dispersion (a dispersion degree of different scattered signal angles in a shared cluster composed of multiple scatterers).

[0046] Further, the delay distribution characteristic is a statistical distribution characteristic of a time delay (a delay parameter) of a scattered signal of the same shared cluster in different links to a reception end, and core reflects a propagation distance association between the shared cluster and a transmission end / reception end of each link. The delay distribution characteristic includes an average delay (an average propagation time of a signal from a transmission end to a reception end through the shared cluster), a delay dispersion (a time difference range of scattered signals of different scatterers in the shared cluster to the reception end).

[0047] In a specific implementation, a type of the shared cluster is determined; the type of the shared cluster includes a Cauchy- Rayleigh cluster and a Rayleigh cluster; link-specific parameters of the shared cluster in at least two target links and a cross-link association parameter between the two target links are respectively acquired; the link-specific parameters include an angle distribution parameter and a delay distribution parameter, the angle distribution parameter includes an angle dispersion or an angle standard deviation, the delay distribution parameter includes a delay dispersion or a delay standard deviation, and the cross-link association parameter includes a correlation coefficient of the angle distribution and a correlation coefficient of the delay distribution between the two target links; when the shared cluster is the Cauchy- Rayleigh cluster, the angle dispersion, the delay dispersion, and the cross-link association parameter are substituted into a bivariate Cauchy distribution formula to obtain a joint angle-delay power distribution term; and when the shared cluster is the Rayleigh cluster, the angle standard deviation, the delay standard deviation, and the cross-link association parameter are substituted into a bivariate Gaussian distribution formula to obtain the joint angle-delay power distribution term.

[0048] Specifically, for the identified shared cluster in the multi-link scenario, the type is determined according to the difference in the angle and time delay distribution characteristics of the scattered signals: if the signal angle and time delay distribution presents a thick tail characteristic (a large degree of dispersion and a high proportion of extreme values), it is classified as a Cauchy-Rayleigh cluster; if it presents a concentrated distribution (a small degree of dispersion and a symmetric distribution around the mean), it is classified as a Rayleigh cluster. For at least two target links (such as link 1 and link 2) that need to calculate the correlation characteristics, the original angle data (such as the angle of arrival and departure measurement value at each time) and the original time delay data (such as the signal delay measurement value at each time) of the shared cluster in the two links are collected. Further, link-specific parameters are calculated based on the original data: for Cauchy-Rayleigh clusters, the angle dispersion (the difference between the maximum and minimum values of the angle data or the difference between the 90th percentile and the 10th percentile) is calculated by counting the dispersion range of the angle data; similarly, the delay dispersion is calculated by counting the dispersion range of the time delay data. For Rayleigh clusters, the angle standard deviation (reflecting the degree of deviation of the angle data from the mean) is calculated as the angle standard deviation; similarly, the time delay standard deviation is calculated as the delay standard deviation. Further, cross-link correlation parameters are calculated: the angle data of the shared cluster in link 1 and the angle data in link 2 are paired and counted, and the linear correlation degree between the two is calculated by the Pearson correlation coefficient formula to obtain the angle distribution correlation coefficient. The same calculation method as the angle distribution correlation coefficient is used to calculate the time delay distribution correlation coefficient based on the time delay data of the shared cluster in the two links. If the shared cluster is a Cauchy-Rayleigh cluster, the angle dispersion, delay dispersion, and angle distribution correlation coefficient, time delay distribution correlation coefficient are substituted into the bivariate Cauchy distribution formula to calculate the joint angle-delay power distribution term by the formula. If the shared cluster is a Rayleigh cluster, the angle standard deviation, delay standard deviation, and angle distribution correlation coefficient, time delay distribution correlation coefficient are substituted into the bivariate Gaussian distribution formula to calculate the joint angle-delay power distribution term by the formula. The specific expressions of the bivariate Cauchy distribution formula and the bivariate Gaussian distribution formula can be referred to the description in related technologies, which will not be repeated here.

[0049] (3) Based on the geometric position parameters of each link, the antenna spacing, and the angle parameters of the shared cluster, the phase offset term is calculated.

[0050] Specifically, the geometric position parameter is used to quantify the spatial position relationship among the transmitting end, the receiving end and the shared cluster in the multi-link scenario. The geometric position parameter specifically includes: the distance between the transmitting end and the shared cluster, the distance between the receiving end and the shared cluster, the direct distance between the transmitting end and the receiving end, and the coordinates of the transmitting end and the receiving end in the three-dimensional space, etc. The angle parameter is used to describe the signal propagation direction between the shared cluster and the communication link, and reflects the angle characteristics of the signal in the "transmitting end-shared cluster-receiving end" path. The angle parameter specifically includes: the average departure angle of the shared cluster to the transmitting end, the average arrival angle of the shared cluster to the receiving end, and the angle dispersion (such as the angle dispersion of the Cauchy- Rayleigh cluster, or the angle standard deviation of the Rayleigh cluster).

[0051] In a specific implementation, the geometric position parameter of each link is obtained; the geometric position parameter includes the transmitting antenna array spacing, the receiving antenna array spacing, and the link spatial position related parameter; the angle parameter of the shared cluster relative to each link is determined; the angle parameter includes the average departure angle of the shared cluster to the transmitting end of the link, the average arrival angle of the shared cluster to the receiving end of the link, and the included angle between the link motion direction and the antenna array; the sine value of the average arrival angle and the sine value of the difference between the average arrival angle and the included angle are calculated based on the link spatial position related parameter, the product of the quotient of the two and the receiving antenna array spacing is taken as the variation, and the receiving end phase offset component of a single link is obtained; the sine value of the average departure angle and the transmitting antenna array spacing are taken as the variation based on the link spatial position related parameter, and the transmitting end phase offset component of a single link is obtained; the transmitting end phase offset component and the receiving end phase offset component of the same link are superimposed to obtain the differential phase offset subterm; the difference between the differential phase offset subterms of any two links is calculated to obtain the phase offset term.

[0052] Specifically, the geometric position parameters of each link are collected and determined, including the transmitting antenna array spacing, the receiving antenna array spacing, and the link spatial position related parameters. The average angle of departure from the transmitting end of the link and the average angle of arrival at the receiving end of the shared cluster are calculated by an angle estimation algorithm, and the angle between the link motion direction and the transmitting / receiving antenna array is determined by combining the link motion state data. Based on the link spatial position related parameters, the quotient of the sine value of the average angle of arrival and the sine value of the difference between the average angle of arrival and the angle between the link motion direction and the antenna array is calculated first, and then the quotient is multiplied by the receiving antenna array spacing to obtain the phase offset component of the receiving end of the link. Based on the link spatial position related parameters, the sine value of the average angle of departure is calculated, and then the sine value is multiplied by the transmitting antenna array spacing to obtain the phase offset component of the transmitting end of the link. The phase offset components of the transmitting end and the receiving end of the same link are superimposed to obtain the differentiated phase offset subterm of the link. Any two target links are selected from the multi-link scene, and the difference between the differentiated phase offset subterms of the two links is calculated, which is the phase offset term. If there are more links in the scene, repeat the step to calculate two by two to obtain the phase offset terms between all links.

[0053] For example, in an embodiment, the receiving end phase offset component is:

[0054] ;

[0055] wherein, is the receiving end phase offset component; is the link spatial position related parameter; is the receiving antenna array spacing; is the average angle of arrival; is the angle between the link motion direction and the antenna array.

[0056] (4) Based on the frequency difference of different links and the delay characteristics of the shared cluster, the frequency-delay coupling term is obtained.

[0057] Specifically, the delay characteristics refer to a set of key parameters extracted from the shared cluster related channel data, which quantitatively describe the influence of the shared cluster on signal propagation in the time dimension, and core reflect the inherent properties of the signal scattered by the shared cluster in the delay dimension. The delay characteristics include the average delay (the average propagation time of the signals scattered by all scatterers in the shared cluster) and the delay dispersion (the degree of delay difference of the signals scattered by different scatterers in the shared cluster).

[0058] In a specific implementation, the target link combination in the multi-link scenario is determined, and any two links (such as link A and link B) that need to calculate the correlation characteristics are selected as the basis for calculating the frequency difference. The carrier frequency of the transmitting end of link A and the carrier frequency of the transmitting end of link B are read by a signal analyzer, the difference between the two is calculated, and the frequency difference of different links is obtained. From the determined shared cluster physical characteristics, the average delay and delay dispersion of the shared cluster are obtained. If the shared cluster is a Cauchy- Rayleigh cluster, a coupling model containing delay dispersion is used; if it is a Rayleigh cluster, a coupling model containing delay standard deviation is used. The obtained frequency difference, average delay, and delay characteristic parameters are substituted into the selected calculation model (such as the formula , is the frequency-delay coupling term, is the frequency difference, is the average delay, is the delay characteristic parameter), and the frequency-delay coupling term of the two target links is obtained through numerical operation. For all other two-combination links in the multi-link scenario, the above steps are sequentially executed to calculate the frequency-delay coupling term corresponding to all link combinations.

[0059] (5) The joint power and cluster summation term, the joint angle-delay power distribution term, the phase offset term, and the frequency-delay coupling term are multiplied and integrated to obtain the cross-correlation function.

[0060] Specifically, the joint power and cluster summation term, the joint angle-delay power distribution term, the phase offset term, and the frequency-delay coupling term corresponding to the same group of target links (such as link 1 and link 2) are extracted. The phase offset term is exponentially processed (if the formula contains an exponential form), and if the phase offset term needs to be operated in a complex exponential form in the cross-correlation function model, the phase offset term is converted to a complex exponential form using Euler's formula to obtain an exponential phase component. If the phase offset term is already exponential, the "joint power and cluster summation term", "joint angle-delay power distribution term", "exponential phase component", and "frequency-delay coupling term" are sequentially multiplied by numerical values to obtain the cross-correlation function.

[0061] For example, in an embodiment, the cross-correlation function is:

[0062] ;

[0063] wherein, is the cross-correlation function; , are different shared clusters; is the total number of clusters; is the joint power of shared cluster and shared cluster ; and is the joint power of shared cluster and shared cluster a joint angle-delay power profile term; is an angle of arrival, an angle of departure, a time of arrival, respectively; is an imaginary unit; is a distance between base station antennas; is a mean of the angle of departure of the i-th path; is a mean of the angle of arrival of the i-th path; is a distance between a mobile terminal and an antenna; is a mean of the angle of departure of the i-th path; is a mean of the angle of arrival of the i-th path; is a Doppler frequency; is a time duration; is an angle between a link movement direction and an antenna array; is a frequency interval.

[0064] The method provided by the embodiment integrates a joint power and cluster summation term, a joint angle-delay power profile term, a phase offset term, and a frequency-delay coupling term to construct a cross-correlation function. Compared with the previous method of only aiming at independent clusters, the multi-dimensional parameters accurately depict the sharing cluster correlation characteristics, and a more comprehensive and more realistic scenario modeling of the correlation relationship of the multi-link channel is realized. The previous construction method aiming at independent clusters can only describe the influence of a single cluster on a single link in isolation, and cannot reflect the correlation of different links due to sharing the same cluster. However, the joint power and cluster summation term can quantify the total supporting effect of the sharing cluster on the power correlation of the multi-link, and avoid the fragmentation of power analysis in the independent cluster modeling. At the same time, the joint angle-delay power profile term combines the angle / delay distribution characteristics of the sharing cluster and the space-time correlation parameters, can depict the constraints of the sharing cluster on the multi-link correlation in the space (angle) and time (delay) dimensions, and makes up for the defects of ignoring the space-time dimension correlation in the independent cluster modeling. Then, the phase offset term calculates the phase difference between the links based on the geometric position of the link and the angle parameter of the sharing cluster, and the frequency-delay coupling term associates the frequency difference between the links and the delay characteristics of the sharing cluster, further covering the phase and frequency-delay dimension correlation not involved in the independent cluster modeling. Finally, the product of the four parameters integrates the power, space-time, phase, and frequency-delay correlation characteristics of the sharing cluster to the multi-link into the cross-correlation function, so that the function can more truly reflect the complex correlation of the multi-link due to the sharing cluster in the actual communication scenario, provide more accurate model support for channel simulation and performance optimization of multi-antenna, multi-user MIMO and other technologies, and improve the reliability and effectiveness of system design.

[0065] S104, processing the cross-correlation function to obtain a cross term, and integrating the cross term into a multi-cluster superposition model.

[0066] ​​The numerator of the cross term is a double summation of all the shared clusters, each summation term is composed of a power weight coefficient of the shared cluster and a joint distribution function between the two links, the joint distribution function is the correlation characteristics of the shared cluster in the angle and delay dimensions of the two links; and the denominator of the cross term is the product of the square roots of the summation results of the autocorrelation power weight coefficients of each shared cluster.

[0067] The multi-cluster superposition model is composed of the cross term and the autocorrelation term of each shared cluster in the corresponding link, the autocorrelation term is the independent contribution of a single shared cluster to a single link, and the cross term is the correlation contribution of the shared cluster to different links.

[0068] Specifically, the cross term is a mathematical term obtained from the cross-correlation function processing and used to quantify the correlation contribution of all shared clusters to two links, and the core reflects the coupling relationship between different links due to sharing the same cluster, rather than the independent characteristics of a single link. The numerator of the cross term is a double summation of all the shared clusters, each summation unit is composed of two parts, one is the power weight coefficient of the shared cluster (reflecting the contribution intensity of the cluster to the link signal), and the other is the joint distribution function (describing the correlation characteristics of the cluster to two links through the parameters of the angle and delay dimensions); the denominator of the cross term is the product of the two summation results after the autocorrelation power weight coefficients of each shared cluster are summed respectively, and then the product is squared (used to normalize the correlation contribution of the numerator, to ensure that the cross term value conforms to the physical meaning).

[0069] Further, the multi-cluster superposition model is a channel modeling framework integrating single-link independent contribution and multi-link correlation contribution, and is used to comprehensively describe the comprehensive action of all shared clusters in a multi-link scenario. The multi-cluster superposition model includes two parts: one is the autocorrelation term, corresponding to the independent contribution of a single shared cluster to a single link (only describing the signal influence of the cluster on a certain link, irrelevant to other links); the other is the cross term, corresponding to the correlation contribution of all shared clusters to different links (describing the coupling relationship between different links due to sharing the cluster).

[0070] In specific implementation, the cross term obtained by processing the cross-correlation function includes: performing cluster-level decomposition on the cross-correlation function to extract components representing the correlation between different links due to the same shared cluster; multiplying the components by the power weight coefficients of the shared clusters to obtain correlation components; the correlation component is the intensity of the correlation contribution of the shared cluster to the links; and integrating the correlation components corresponding to all shared clusters to obtain the cross term.

[0071] Specifically, a cluster-level decomposition operation is performed on the obtained cross-correlation function: Multiple independent components are extracted from the cross-correlation function using signal separation algorithms (such as cluster-based filtering or orthogonal decomposition). Components that represent the correlation between different links due to the same shared cluster are selected and extracted (excluding interference components unrelated to the shared cluster). The contribution strength of the shared cluster to the link signal is determined through cluster power ratio analysis, and a corresponding shared cluster power weight coefficient is matched for each extracted correlation component. The link correlation component corresponding to a specific shared cluster is multiplied by the determined power weight coefficient of that shared cluster; the result is the quantified value of the contribution strength of that shared cluster to the correlation between the two links (i.e., the correlation component of a single shared cluster). The correlation components of all obtained shared clusters (one correlation component per shared cluster) are summed, and the sum of all component values ​​is the cross term. For the specific implementation process of cluster-level decomposition, please refer to the description in related technologies; it will not be elaborated here.

[0072] For example, in one embodiment, the cross term is:

[0073] ;

[0074] in, These are intersecting terms; , For different shared clusters; The total number of clusters; For shared clusters and shared clusters The combined power; For shared clusters and shared clusters The joint distribution For the first The rich gain of the link, For the first The latency of the link, For the first Cluster rich gain, For the first Cluster delay, For the first Cluster rich gain, For the first Cluster latency; For shared clusters The autocorrelation power; For shared clusters The autocorrelation power.

[0075] The method provided by the embodiment solves the defect that only the single-link autocorrelation characteristics are focused on and the inter-link correlation is ignored in traditional modeling by accurately splitting and quantifying the correlation contribution of shared clusters, and realizes the dual improvement of modeling accuracy and scene fitting degree. Specifically, the construction method of the cross term first accurately separates the correlation components of the same shared cluster to different links from the cross-correlation function through cluster-level decomposition, avoiding the influence of non-shared cluster interference terms on the correlation description, then binds the correlation components with the actual contribution strength of the shared cluster through the power weight coefficient, ensuring that the correlation effect of each shared cluster is truly quantified, and finally integrates the correlation components of all clusters to form the cross term, which completely covers the coupling relationship between multiple links caused by shared clusters; the multi-cluster superposition model combines the correlation contribution (cross term) with the independent contribution of a single shared cluster to a single link (autocorrelation term), which not only retains the description ability of the traditional autocorrelation term to the independent channel characteristics of a single link, but also supplements the description of the inter-link correlation characteristics through the cross term, so that the model can reflect both the single-link independent propagation and the multi-link associated coupling two core channel characteristics, and is more suitable for the phenomenon that a shared scattering cluster simultaneously affects multiple links in an actual multi-link scene than a model containing only the autocorrelation term.

[0076] In S105, based on the multi-cluster superposition model, the channel characteristics of each link are associated and superimposed to construct a multi-link channel model.

[0077] The diagonal blocks of the multi-link channel model are single-link channel matrices generated based on the autocorrelation terms of each link, and the non-diagonal blocks are inter-link coupling matrices generated based on the cross terms.

[0078] Specifically, the channel characteristics refer to various key attributes that affect signal transmission in a communication link, and core reflect the propagation law of the signal from the transmitting end to the receiving end through the channel (including shared cluster scattering), and specifically include two types of single-link independent characteristics and inter-link correlation characteristics. The single-link independent characteristics are embodied by the autocorrelation term, and cover the signal power attenuation, time delay, phase shift, frequency response and other attributes related only to a single link; the inter-link correlation characteristics are embodied by the cross term, and cover the power correlation, angle-delay joint correlation, phase coupling, frequency-delay coupling and other attributes reflecting the mutual influence between different links caused by shared clusters.

[0079] Further, the multi-link channel model is a matrix model for describing the overall channel transmission law of multiple links, which is constructed by associating and superimposing the channel characteristics of each link based on the multi-cluster superposition model. The core structure of the model is in the form of a matrix, which includes diagonal blocks and off-diagonal blocks. The diagonal block is a single-link channel matrix, which is generated by the autocorrelation terms of each link. Each diagonal block corresponds to a link and is used to describe the independent channel characteristics of the link. The off-diagonal block is an inter-link coupling matrix, which is generated by the cross terms. Each off-diagonal block corresponds to two different links and is used to describe the associated coupling characteristics (such as power association and phase coupling) of the two links due to the shared clusters.

[0080] In a specific implementation, a matrix framework of the multi-link channel model is constructed. The dimension of the matrix is determined by the number of links, and each element in the matrix corresponds to the channel correlation characteristics between a pair of links. For each link, the autocorrelation contributions of all shared clusters within the link are superimposed to obtain a single-link autocorrelation term, which is filled into the diagonal position of the corresponding link in the matrix. For any two different links, the cross-correlation contributions of all shared clusters between the two links are superimposed to obtain an inter-link cross term, which is filled into the off-diagonal position of the corresponding two links in the matrix. The autocorrelation terms of all diagonal blocks and the cross terms of all off-diagonal blocks in the matrix are integrated to form the multi-link channel model.

[0081] Specifically, the total number of links (such as N links) in the multi-link scenario is counted, and an N×N dimension matrix is constructed as the basic framework of the multi-link channel model. The rows and columns of the matrix correspond to different links, and each element (i, j) in the matrix corresponds to the channel correlation characteristics between the ith link and the jth link. For each link (such as the ith link), the autocorrelation terms (the independent contribution of a single shared cluster to the link) of all shared clusters within the link are collected, and these autocorrelation terms are numerically superimposed to obtain the single-link autocorrelation term of the ith link. The single-link autocorrelation term is filled into the diagonal element (i, i) position of the matrix, and this operation is repeated to complete the filling of all diagonal positions of the links. Select any two different links (such as the ith link and the jth link), collect the cross terms (the associated contribution of a single shared cluster to two links) of all shared clusters between the two links, and numerically superimpose these cross terms to obtain the inter-link cross term of the ith link and the jth link. The cross term is filled into the off-diagonal element (i, j) position of the matrix, and the same cross term is filled into the (j, i) position (if the link correlation has symmetry). Repeat this operation to complete the filling of all off-diagonal positions. Check the completeness of the filling of all diagonal block autocorrelation terms and off-diagonal block cross terms in the matrix, and the entire matrix is used as the final multi-link channel model.

[0082] The method provided by the embodiment, in the first aspect, comprehensively characterizes the shared cluster association characteristics through multiple dimensions, decomposes the cross-correlation function into a joint power and cluster summation term, a joint angle-delay power distribution term, a phase offset term and a frequency-delay coupling term, and solves the limitation of only describing the link association in a single dimension in traditional modeling. The joint power and cluster summation term integrates the total power and the power correlation coefficient of each shared cluster, quantifies the total supporting effect of the shared cluster on the power association of multiple links, and avoids the fragmentation of power analysis in independent cluster modeling. The joint angle-delay power distribution term is substituted into the bivariate Cauchy / Gaussian distribution formula according to the type of the shared cluster, and combines the angle dispersion / standard deviation, the delay dispersion / standard deviation and the cross-link association parameter, so as to characterize the constraints of the shared cluster on the association of multiple links in the space (angle) and time (delay) dimensions, and make up for the defects of traditional modeling that ignore the association in the space and time dimensions. The phase offset term is based on the link geometric position parameter, the antenna spacing and the shared cluster angle parameter, calculates the phase offset components of the transmitting end and the receiving end, and calculates the difference between the links, so as to quantify the phase difference between the links. The frequency-delay coupling term is associated with the frequency difference of different links and the delay characteristics (average delay and delay dispersion) of the shared cluster, covers the association in the phase and frequency-delay dimensions, and the cross-correlation function formed by the product integration of the four parameters can completely capture the association of the shared cluster on multiple links from the power, space-time, phase and frequency-delay dimensions, so as to ensure the comprehensiveness and accuracy of the association characteristic characterization, and lay a reliable foundation for subsequent extraction of cross terms.

[0083] In the second aspect, the single-link independent characteristics and the inter-link association characteristics are distinguished through a matrix structure, a multi-dimensional matrix model is constructed, and the problem that multiple links are regarded as simple combinations of single links in traditional modeling is solved. The diagonal block generates a single-link channel matrix by superimposing the autocorrelation contributions of all shared clusters in the single link, can retain the independent channel characteristics (such as signal power, delay and phase) of each link itself, and ensure the accurate characterization of the single-link transmission law. The non-diagonal block generates an inter-link coupling matrix by superimposing the cross-association contributions of all shared clusters between any two links, can accurately present the association coupling relationship (such as power association and phase coupling) between different links caused by the shared cluster, and the combination of the two can cover both single-link independent propagation and multiple-link association coupling, which not only conforms to the physical reality that the shared cluster simultaneously affects multiple links in the actual multiple-link scenario, but also provides intuitive and accurate model support for subsequent interference suppression, signal detection and performance simulation of the multiple-link system, avoids the simulation deviation caused by the lack of association characteristic characterization in traditional models, and improves the reliability and effectiveness of the multiple-link system design.

[0084] Corresponding to the foregoing embodiment of the method for modeling a multiple-link channel based on a shared cluster, the application also provides an embodiment of a device for modeling a multiple-link channel based on a shared cluster.

[0085] Figure 2This is a schematic diagram of the structure of the multi-link channel modeling device based on shared clusters provided in Embodiment 2 of this application. Please refer to... Figure 2 The apparatus provided in this embodiment includes a determining module 210, a processing module 220, and a constructing module 230;

[0086] The determining module 210 is used to determine a multi-link scenario that includes at least two communication links; the multi-link scenario includes a shared cluster that is simultaneously perceived by multiple links.

[0087] The processing module 220 is used to collect channel data related to the shared cluster in the multi-link scenario, and extract and determine the physical characteristics of the shared cluster from the channel data;

[0088] The construction module 230 is used to construct a cross-correlation function based on the physical characteristics of the shared cluster; the cross-correlation function is used to describe the correlation characteristics between different links caused by the shared cluster, and the cross-correlation function includes a joint power and cluster summation term, a joint angle-delay power distribution term, a phase offset term, and a frequency-delay coupling term;

[0089] The processing module 220 is further configured to process the cross-correlation function to obtain cross terms, and integrate the cross terms into the multi-cluster superposition model; the multi-cluster superposition model consists of the cross terms and the autocorrelation terms of each shared cluster in the corresponding link, the autocorrelation terms are the independent contributions of a single shared cluster to a single link, and the cross terms are the correlation contributions of the shared cluster to different links;

[0090] The construction module 230 is further configured to associate and superimpose the channel characteristics of each link based on the multi-cluster superposition model to construct a multi-link channel model; the diagonal block of the multi-link channel model is a single-link channel matrix generated by each link based on autocorrelation terms, and the off-diagonal block is an inter-link coupling matrix generated based on cross terms.

[0091] The apparatus of this embodiment can be used to perform... Figure 1 The steps of the method embodiment shown are similar in principle and process, and will not be repeated here.

[0092] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0093] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0094] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method of modeling a shared cluster based multi-link channel, the method comprising: The method comprises: determining a multi-link scenario comprising at least two communication links; the multi-link scenario comprises a shared cluster which is simultaneously perceived by multiple links; under the multi-link scenario, collecting channel data related to the shared cluster, extracting and determining physical characteristics of the shared cluster from the channel data; based on the physical characteristics of the shared cluster, constructing a cross-correlation function; the cross-correlation function is used to describe the correlation characteristics between different links caused by the shared cluster, and the cross-correlation function comprises a joint power and cluster summation term, a joint angle-delay power distribution term, a phase offset term and a frequency-delay coupling term; processing the cross-correlation function to obtain a cross term, and integrating the cross term into a multi-cluster superposition model; based on the multi-cluster superposition model, correlating and superimposing the channel characteristics of each link to construct a multi-link channel model; the diagonal blocks of the multi-link channel model are single-link channel matrices generated based on autocorrelation terms of each link, and the non-diagonal blocks are inter-link coupling matrices generated based on cross terms; the cross-correlation function is constructed based on the physical characteristics of the shared cluster, which comprises: based on the total power and power correlation coefficient of each shared cluster in different links, a joint power and cluster summation term is obtained; based on the angle distribution characteristics and delay distribution characteristics of each shared cluster in different links, a joint angle-delay power distribution term is obtained; based on the geometric position parameters, antenna spacing of each link and the angle parameters of the shared cluster, the phase offset term is calculated; based on the frequency difference of different links and the delay characteristics of the shared cluster, a frequency-delay coupling term is obtained; the joint power and cluster summation term, the joint angle-delay power distribution term, the phase offset term and the frequency-delay coupling term are multiplied and integrated to obtain the cross-correlation function; the multi-cluster superposition model is composed of the cross term and the autocorrelation term of each shared cluster in the corresponding link; the autocorrelation term is the independent contribution of a single shared cluster to a single link, and the cross term is the associated contribution of the shared cluster to different links.

2. The method of claim 1, wherein, the joint angle-delay power distribution term is obtained based on the angle distribution characteristics and delay distribution characteristics of each shared cluster in different links, which comprises: determining the type of the shared cluster; the type of the shared cluster comprises Cauchy- Rayleigh cluster and Rayleigh cluster; obtaining link-specific parameters of the shared cluster in at least two target links and cross-link correlation parameters between the two target links; wherein the link-specific parameters comprise angle distribution parameters and delay distribution parameters, the angle distribution parameters comprise angle dispersion or angle standard deviation, the delay distribution parameters comprise delay dispersion or delay standard deviation, and the cross-link correlation parameters comprise correlation coefficients of angle distribution and correlation coefficients of delay distribution between the two target links; when the shared cluster is a Cauchy-Rayleigh cluster, the angle dispersion, delay dispersion and cross-link correlation parameters are substituted into the bivariate Cauchy distribution formula to calculate the joint angle-delay power distribution term; when the shared cluster is a Rayleigh cluster, the angle standard deviation, delay standard deviation and cross-link correlation parameters are substituted into the bivariate Gaussian distribution formula to calculate the joint angle-delay power distribution term.

3. The method of claim 1, wherein, The phase offset term is calculated based on the geometric position parameters of each link, the antenna spacing, and the angle parameters of the shared cluster, and includes: Obtaining geometric position parameters of each link; the geometric position parameters include transmitting antenna array spacing, receiving antenna array spacing, and link spatial position related parameters; Determining the angle parameters of the shared cluster relative to each link; the angle parameters include the average angle of departure of the shared cluster to the transmitting end of the link, the average angle of arrival of the shared cluster to the receiving end of the link, and the angle between the link movement direction and the antenna array; Taking the link spatial position related parameters as the reference, calculating the quotient of the sine value of the average angle of arrival and the sine value of the difference between the average angle of arrival and the angle, and taking the product of the quotient and the receiving antenna array spacing as the variation to obtain the receiving end phase offset component of a single link; Taking the link spatial position related parameters as the reference, and taking the product of the sine value of the average angle of departure and the transmitting antenna array spacing as the variation to obtain the transmitting end phase offset component of a single link; Superimposing the transmitting end phase offset component and the receiving end phase offset component of the same link to obtain a differential phase offset subterm; Calculating the difference between the differential phase offset subterms of any two links to obtain a phase offset term.

4. The method of claim 1, wherein, The cross term is obtained by processing the cross-correlation function, including: Cluster-level decomposition of the cross-correlation function is performed to extract components representing the correlation between different links caused by the same shared cluster; Multiplying the components by the power weight coefficients of the shared cluster to obtain correlation components; the correlation components represent the strength of the contribution of the shared cluster to the correlation between links; Integrating the correlation components corresponding to all shared clusters to obtain a cross term.

5. The method of claim 1, wherein, The numerator of the cross term is a double summation term for all shared clusters, and each summation term is composed of the power weight coefficient of the shared cluster and the joint distribution function between two links; the joint distribution function represents the correlation characteristics of the shared cluster in the angle and delay dimensions; the denominator of the cross term is the product of the square roots of the summation results of the autocorrelation power weight coefficients of each shared cluster.

6. The method of claim 1, wherein, Based on the multi-cluster superposition model, the channel characteristics of each link are correlated and superimposed to construct a multi-link channel model, including: Constructing a multi-link channel model matrix framework; the dimension of the matrix is determined by the number of links, and each element in the matrix corresponds to the channel correlation characteristics between a pair of links; For each link, the autocorrelation contributions of all shared clusters within the link are superimposed to obtain a single-link autocorrelation term, and the single-link autocorrelation term is filled into the diagonal position of the corresponding link in the matrix; For any two different links, the cross-correlation contributions of all shared clusters between the two links are superimposed to obtain an inter-link cross term, and the inter-link cross term is filled into the non-diagonal position of the corresponding two links in the matrix; Integrating all diagonal block autocorrelation terms and non-diagonal block cross terms in the matrix to form a multi-link channel model.

7. The method of claim 1, wherein, After determining the multi-link scenario containing at least two communication links, the method further includes: Extracting features of the propagation environment of each link to obtain angle parameters, delay parameters, and power parameters of each cluster in each link; The matching degrees of the characteristic parameters of the clusters in any two links are calculated, for the angle parameter, a comparison result of an absolute value of an angle difference and a preset angle threshold is calculated, for the delay parameter, a comparison result of an absolute value of a delay difference and a preset delay threshold is calculated, and for the power parameter, a comparison result of a power ratio and a preset power threshold is calculated; When the angle difference and the delay difference of the target cluster in the two links are both less than the corresponding threshold, and the power ratio is within the preset threshold range, the target cluster is determined as a shared cluster of the two links.

8. A multi-link channel modeling device based on shared clusters, characterized in that, The device comprises a determination module, a processing module and a construction module; The determination module is configured to determine a multi-link scene comprising at least two communication links; the multi-link scene comprises a shared cluster that is simultaneously perceived by multiple links; The processing module is configured to, in the multi-link scene, collect channel data related to the shared cluster, and extract and determine physical characteristics of the shared cluster from the channel data; The construction module is configured to construct a cross-correlation function based on the physical characteristics of the shared cluster; the cross-correlation function is used to describe the correlation characteristics between different links caused by the shared cluster, and comprises a joint power and cluster summation term, a joint angle-delay power distribution term, a phase offset term and a frequency-delay coupling term; The processing module is further configured to process the cross-correlation function to obtain a cross term, and integrate the cross term into a multi-cluster superposition model; the multi-cluster superposition model is composed of the cross term and the autocorrelation terms of each shared cluster in the corresponding link; the autocorrelation term is the independent contribution of a single shared cluster to a single link, and the cross term is the associated contribution of the shared cluster to different links; The construction module is further configured to associate and superimpose the channel characteristics of each link based on the multi-cluster superposition model, and construct a multi-link channel model; the diagonal blocks of the multi-link channel model are single-link channel matrices generated based on the autocorrelation terms, and the non-diagonal blocks are inter-link coupling matrices generated based on the cross terms; The construction of the cross-correlation function based on the physical characteristics of the shared cluster comprises: Based on the total power and the power correlation coefficient of each shared cluster in different links, the joint power and cluster summation term is obtained; Based on the angle distribution characteristics and the delay distribution characteristics of each shared cluster in different links, the joint angle-delay power distribution term is obtained; Based on the geometric position parameters, the antenna spacing of each link and the angle parameters of the shared cluster, the phase offset term is calculated; Based on the frequency difference of different links and the delay characteristics of the shared cluster, the frequency-delay coupling term is obtained; The joint power and cluster summation term, the joint angle-delay power distribution term, the phase offset term and the frequency-delay coupling term are multiplied and integrated to obtain the cross-correlation function; The multi-cluster superposition model is composed of the cross term and the autocorrelation terms of each shared cluster in the corresponding link; the autocorrelation term is the independent contribution of a single shared cluster to a single link, and the cross term is the associated contribution of the shared cluster to different links.

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