Super-large scale MIMO channel modeling method and device, medium and product

Abstract

The embodiment of the invention provides a super-large-scale MIMO channel modeling method and device, a medium and a product, and relates to the technical field of wireless communication. The method comprises the following steps: acquiring cluster power of each cluster according to a preset channel model; according to the power of each cluster or a preset statistical distribution model, determining a spatial non-stationary SnS state of each cluster; determining first power attenuation factors of different clusters of the base station end according to the SnS state of each cluster; determining a second power attenuation factor of each array element of the user equipment; and generating a channel coefficient according to the first power attenuation factor and the second power attenuation factor. According to the scheme, the channel modeling method which is easy to implement, clear in physical significance and efficient is provided, the defects existing in the prior art are effectively overcome, and reliable theoretical support is provided for design and optimization of a super-large-scale MIMO system.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method, device, medium and product for ultra-large-scale MIMO channel modeling. Background Art

[0002] With the rapid development of mobile communication technology, research on the sixth-generation mobile communication system (6G) has gradually begun. 6G systems aim to provide higher spectral efficiency, greater system capacity, and more reliable communication services to meet the needs of emerging applications such as immersive communications, holographic communications, and the intelligent Internet of Things. Extremely Large-Scale Multi-Input Multi-Output (XL-MIMO), one of the key candidate technologies for 6G, significantly increases the number of antenna elements (typically hundreds to thousands), improving the system's spectral efficiency, capacity, and reliability.

[0003] However, the introduction of XL-MIMO also brings new challenges. Because the XL-MIMO system is equipped with a large-scale antenna array, the propagation environment observed by the antenna elements at different spatial locations is significantly different, resulting in spatial non-stationarity (SnS) in the channel. This non-stationarity is mainly manifested in two aspects: first, objects of finite size (such as the human body, trees, etc.) may not be able to completely block the entire antenna array, resulting in the propagation path of some elements being blocked; second, finite-sized scatterers in a large-aperture array may no longer serve as a complete scattering source for the entire array, resulting in the power of the scattered signal being concentrated in one part of the array. This spatial non-stationarity places higher demands on the channel modeling of the XL-MIMO system.

[0004] Among existing channel modeling techniques, there are two main approaches for modeling spatial nonstationarity: the birth and death process (BD) and the visible range (VR). The BD simulates the appearance and disappearance of clusters along the array axis by defining a cluster generation rate and death rate at each antenna element. However, this approach suffers from the following drawbacks: 1) the cluster "birth rate" and "death rate" are difficult to obtain through measured or simulated data; 2) the model parameters are poorly correlated with the real physical environment, making it difficult to intuitively reflect the physical phenomena in the channel; and 3) when a cluster "births" or "dies," the cluster power exhibits abrupt changes, lacking a smooth transition. The visible range approach simulates cluster visibility by defining a region on the antenna array. However, this approach also suffers from the following issues: 1) the size, center point, and shape of the visible range depend on the propagation environment and the distribution of scatterers, making it difficult to accurately obtain the parameters; and 2) cluster power exhibits abrupt changes at the boundaries of the visible range, lacking a reasonable characterization of the transition region. Summary of the Invention

[0005] At least one embodiment of the present application provides a very large-scale MIMO channel modeling method, device, medium and product. For the modeling methods in the above-mentioned prior art, it solves the problems of accurately describing the spatial non-stationarity of the XL-MIMO channel, resulting in difficulty in obtaining model parameters, unclear physical meaning, and sudden changes in cluster power.

[0006] In order to solve the above technical problems, this application is implemented as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for modeling a very large-scale MIMO channel, including:

[0008] According to the preset channel model, the cluster power of each cluster is obtained;

[0009] Determining the spatial non-stationary SnS state of each cluster according to the power of each cluster or a preset statistical distribution model;

[0010] determining, according to the SnS state of each cluster, first power attenuation factors of different clusters at the base station end;

[0011] determining a second power attenuation factor for each array element of the user equipment;

[0012] A channel coefficient is generated according to the first power attenuation factor and the second power attenuation factor.

[0013] Optionally, determining, according to the SnS state of each cluster, first power attenuation factors of different clusters at the base station includes:

[0014] When the SnS state indicates that the cluster is in a non-stationary state, calculating a visibility probability and a visibility area of the corresponding SnS state cluster, and determining a first power attenuation factor of the SnS state cluster according to the visibility probability and the visibility area;

[0015] In a case where the SnS state cluster indicates that it is in a stable state, the first power attenuation factors of all array elements in the corresponding non-SnS state cluster are determined to be 1.

[0016] Optionally, determining the spatial non-stationary SnS state of each cluster according to the power of each cluster includes:

[0017] Calculating the spatial non-stationary probability of each cluster based on the power of each cluster by using at least one of a probability distribution obtained by multi-user joint analysis, a probability distribution obtained by single-user independent analysis, and a cluster normalized power correlation modeling method;

[0018] According to the spatial non-stationary probability of each cluster, a binary decision is performed on each cluster to determine the spatial non-stationary SnS state of each cluster.

[0019] Optionally, calculate the visibility probability and visibility area of the corresponding SnS state cluster, including:

[0020] Determining a first ratio between the number of visible elements in the SnS state cluster and the total number of elements in the very large-scale multiple-input multiple-output system; establishing a functional relationship between visibility probability and the cluster power based on ray tracing simulation or channel measurement data; calculating the visibility probability of the SnS state cluster based on the functional relationship; or calculating the visibility probability of the SnS state cluster based on the preset statistical distribution model;

[0021] According to the visibility probability, the visible area of the SnS state cluster is calculated using at least one of a center-and-spoke approach, a corner-triggered approach, and a near-field preferential approach.

[0022] Optionally, determining a first power attenuation factor of the SnS state cluster according to the visibility probability and the visible area includes:

[0023] If the SnS state cluster is within the visible area, determining, according to a preset spatial attenuation function, a first power attenuation factor of the SnS state cluster to be 1;

[0024] If the SnS state cluster is outside the visible area, a spatial attenuation function is used to determine a first power attenuation factor value that decays smoothly to 0 along the spatial coordinates based on the relative position of the array element of the SnS state cluster and the boundary of the visible area; the attenuation rate of the spatial attenuation function is a preset adjustable parameter.

[0025] Optionally, determining a second power attenuation factor of each array element of the user equipment includes:

[0026] Determine the gripping condition of the user device according to a preset typical ratio; the gripping condition includes three conditions: head and one-hand occlusion, two-hand occlusion, and one-hand occlusion;

[0027] Based on the holding condition, obtaining an attenuation value of each antenna unit of the user equipment;

[0028] A second power attenuation factor of each array element of the user equipment is determined according to the attenuation value.

[0029] Optionally, generating a channel coefficient according to the first power attenuation factor and the second power attenuation factor includes:

[0030] According to the first power attenuation factor and the second power attenuation factor, a target formula for generating a channel coefficient is input to generate a channel coefficient.

[0031] In a second aspect, an embodiment of the present application provides a very large-scale MIMO channel modeling device, including:

[0032] A first acquisition module, configured to acquire the cluster power of each cluster according to a preset channel model;

[0033] A first determining module is configured to determine the spatial non-stationary SnS state of each cluster according to the power of each cluster or a preset statistical distribution model;

[0034] A second determining module is configured to determine a first power attenuation factor of different clusters at the base station end according to the SnS state of each cluster;

[0035] A first processing module, configured to determine a second power attenuation factor for each array element of the user equipment;

[0036] The second processing module is configured to generate a channel coefficient according to the first power attenuation factor and the second power attenuation factor.

[0037] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0038] In a fourth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0039] Compared with the prior art, the ultra-large-scale MIMO channel modeling method, device, medium and product provided by the embodiment of the present application obtain the cluster power of each cluster according to a preset channel model; determine the spatial non-stationary SnS state of each cluster according to each cluster power or a preset statistical distribution model, and determine whether SnS occurs in the cluster, thereby solving the problem of determining the spatial stationarity of the cluster; according to the SnS state of each cluster, for the array elements outside the visible area, determine the first power attenuation factor of different clusters at the base station end and the second power attenuation factor of each array element of the user equipment, thereby achieving smooth evolution of the cluster power at the boundary of the visible area of the ultra-large-scale MIMO array, and solving the problem of sudden change in cluster power between the visible area and the array elements of the base station method; the present application dynamically adjusts the attenuation factor according to the SnS state of the cluster to avoid model mismatch. The present application associates the power attenuation factor with the array element position of the user equipment (UE), thereby solving the problem that the traditional method ignores the spatial differences between the array elements, resulting in sudden change in the channel coefficient, and realizes smooth transition of channel parameters by refining the array element level mapping, thereby enhancing the physical interpretability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0041] Figure 1 A schematic diagram of a very large-scale MIMO channel modeling method provided in an embodiment of the present application;

[0042] Figure 2 This is a general flow chart of the ultra-large-scale MIMO channel modeling method provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the spatial non-stationary probability distribution of clusters provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of a spatial non-stationary probability function of a cluster provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of the probability density function of VP provided in an embodiment of the present application;

[0046] Figure 6 This is a structural diagram of the ultra-large-scale MIMO channel modeling device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0048] The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the requested result, etc. in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the requested result, etc. based on the judgment result.

[0049] As described in the background technology, the modeling methods in the prior art have the difficulty in accurately describing the spatial non-stationarity of the XL-MIMO channel, resulting in problems such as difficulty in obtaining model parameters, unclear physical meaning, and sudden changes in cluster power. To solve at least one of the above problems, the embodiments of the present application provide a very large-scale MIMO channel modeling method, device, medium and product, which have the advantages of being simple to implement and having practical physical meaning, and are expected to overcome the problems existing in the existing methods.

[0050] Please refer to Figure 1 , an embodiment of the present application provides a very large-scale MIMO channel modeling method, including:

[0051] Step 11: Obtain the cluster power of each cluster according to the preset channel model.

[0052] In the embodiment of the present application, the multipath channel is decomposed into several clusters according to the cluster generation rules defined in the relevant standard protocol, and each cluster corresponds to a group of multipath components with similar delay and angle characteristics.

[0053] Specifically, the preset channel model is adopted and the formula for normalized relative power (Formula (1)) is used to calculate the cluster power of each cluster.

[0054]

[0055] Among them, Pn ' represents the current cluster power determined using the formula of normalized relative power; τ n is the known delay of the nth cluster, DS is the delay spread value in Table 7.5-6 of 3GPP TR 38.901, rτ is the delay distribution scaling factor, and Z n ~N(0,ξ 2 ) represents the shadow fading term of the nth cluster, in [dB].

[0056] Furthermore, the current cluster power is normalized so that the sum of all cluster powers is equal to 1, thereby determining the cluster power P n That is, use formula (2) to determine the cluster power P n .

[0057]

[0058] Step 12: Determine the spatial non-stationary SnS state of each cluster according to the power of each cluster or a preset statistical distribution model.

[0059] In the embodiment of the present application, a ray tracing simulation is performed, with multiple users simulated, each corresponding to several clusters. Some of these clusters are spatially non-stationary, while others are spatially non-stationary. Therefore, a spatial non-stationary probability is obtained for each user. The spatial non-stationary probabilities of all users are statistically fitted to obtain a possible normal distribution. A preset statistical distribution model is determined based on each user's fit within this normal statistical distribution. Using this preset statistical distribution model, the spatial non-stationary probability of each user can be randomly generated.

[0060] Step 13: determining a first power attenuation factor of different clusters at the base station end according to the SnS state of each cluster;

[0061] Step 14: determining a second power attenuation factor of each array element of the user equipment;

[0062] Step 15: Generate a channel coefficient according to the first power attenuation factor and the second power attenuation factor.

[0063] In an embodiment of the present application, based on a preset channel model (such as deterministic channel model 3), a clustering algorithm is used to extract multipath clusters and calculate the power distribution of each cluster. This power is formed by the superposition of multipath signals within the cluster and reflects the characteristics of the local propagation environment. Based on the cluster power or preset statistical distribution of step 1, the spatial non-stationarity (SnS) of the cluster is analyzed. For example, the stability of the cluster is judged by statistical model parameters (such as delay spread and angle spread), and static and dynamic propagation scenarios are distinguished. According to the SnS state of each cluster, combined with the spatial correlation of the base station antenna array (such as polarization modeling and antenna spacing effects), the path loss factors of different clusters are calculated. For example, a steeper attenuation model is used in high-correlation scenarios. For each array element (such as a millimeter wave MIMO antenna) of the user equipment (UE), the array element-level path loss and shadow fading are modeled based on the propagation environment (such as scatterer distribution, multipath density) and device mobility characteristics. The first attenuation factor at the base station end and the second attenuation factor at the user end are combined with multipath parameters (such as delay, angle, and polarization) to generate the final channel coefficient matrix through the channel model for link-level simulation or system performance evaluation.

[0064] Optionally, the above step 13 includes:

[0065] When the SnS state indicates that the cluster is in a non-stationary state, calculating a visibility probability and a visibility area of the corresponding SnS state cluster, and determining a first power attenuation factor of the SnS state cluster according to the visibility probability and the visibility area;

[0066] In a case where the SnS state cluster indicates that it is in a stable state, the first power attenuation factors of all array elements in the corresponding non-SnS state cluster are determined to be 1.

[0067] In the embodiment of the present application, the SnS state includes being in a non-stationary state and a stable state. Processing when the SnS state is non-stationary: When a cluster is determined to be in a spatial non-stationary (SnS) state, its visible probability and visible area need to be dynamically calculated. The first power attenuation factor in the visible area is set to 1, and the first power attenuation factor outside the visible area is set to a roll-off attenuation value. The farther away from the visible area, the closer the first power attenuation factor is to 0. The visible probability indicates the ratio of the number of visible elements of the cluster to the total number of elements in the massive MIMO array. The visible area refers to the specific observable area of the cluster in the ultra-large-scale MIMO array.

[0068] Processing of the steady state (non-SnS cluster): When the cluster is in the steady state, the cluster is visible at all elements of the ultra-large-scale MIMO array, and the first power attenuation factor of all elements is 1.

[0069] Optionally, step 12 of this application includes:

[0070] Calculating the spatial non-stationary probability of each cluster based on the power of each cluster by using at least one of a probability distribution obtained by multi-user joint analysis, a probability distribution obtained by single-user independent analysis, and a cluster normalized power correlation modeling method;

[0071] According to the spatial non-stationary probability of each cluster, a binarization decision is performed on each cluster to determine the spatial non-stationary state of each cluster.

[0072] In the embodiment of the present application, the specific steps for calculating the spatial non-stationary probability of each cluster are as follows: Define the SnS probability to model whether the cluster is non-stationary. Assume that the total number of base station (BS) XL-MIMO array elements is N all , the number of visible elements of a cluster is N n If a cluster is visible to some elements in the array (i.e. N n <N all ), then the cluster is spatially non-stationary; conversely, if a cluster is visible to all elements in the array (N n =N all ), then the cluster is not spatially nonstationary.

[0073] Based on ray tracing simulation or channel measurement data, the SnS probability of the cluster is calculated using formula (3)

[0074] Pr~D(μ,σ 2 )or Pr=f(P), formula (3);

[0075] Where Pr represents the SNS probability of a cluster, D is a probability distribution type (e.g., normal distribution, lognormal distribution, etc.), and μ and σ represent the mean and variance of the distribution. f(P) indicates that Pr can also be a function related to the normalized power of the cluster.

[0076] The specific implementation methods include the following three technical methods:

[0077] Method 1 (multi-user joint analysis to obtain probability distribution): Multi-user data preprocessing: Arrange the normalized power of clusters detected by all users in the target area in descending order; Power domain segmentation: Divide the sorted cluster sequence into K equal power intervals; Probabilistic statistical calculation: Within each power interval, calculate the probability of clusters meeting the SnS condition, that is, the probability of SnS clusters accounting for the total number of clusters in that power interval; Distribution fitting: Perform maximum likelihood estimation on the data set within the K power intervals to verify that it obeys a specific probability distribution type (such as normal distribution, lognormal distribution, etc.).

[0078] Method 2 (independent analysis of a single user to obtain probability distribution): Single-user cluster SNS probability calculation: Calculate the SnS probability based on the cluster data of user i: that is, the ratio of the number of non-stationary clusters of user i to the total number of clusters detected for this user; Multi-user distribution modeling: Perform maximum likelihood estimation on the SnS probability data set of user I to verify that it obeys a specific probability distribution type (such as normal distribution, lognormal distribution, etc.).

[0079] Method 3 (cluster normalized power association modeling method): Global data integration: Aggregate the detection clusters of all users, normalize the power of each cluster and arrange them in descending order to construct a unified power sequence across users; Dynamic power segmentation: Divide the power value into K continuous intervals, and the median of each interval must meet the statistical distribution constraints of the power value in the interval; Power interval probability calculation: Count the proportion of clusters with SnS characteristics in each power interval as the SnS probability observation value of the power segment; Function relationship fitting: Use weighted regression analysis based on sample size to establish a continuous function model in which the SnS probability changes with normalized power, including but not limited to: exponential decay model, polynomial model, and piecewise linear model.

[0080] Finally, according to the cluster power P n Substitute into formula (3) to calculate the SnS probability of the cluster.

[0081] According to the spatial non-stationary probability of each cluster, each cluster is binarized and the spatial non-stationary state of each cluster is determined using formula (4):

[0082]

[0083] Among them, S n =1 indicates that the cluster has generated SnS; otherwise, no SnS has been generated. x is a randomly generated number that obeys x~U(0,1).

[0084] Optionally, calculate the visibility probability and visibility area of the corresponding SnS state cluster, including:

[0085] Determining a first ratio between the number of visible elements in the SnS state cluster and the total number of elements in the very large-scale multiple-input multiple-output system; establishing a functional relationship between visibility probability and the cluster power based on ray tracing simulation or channel measurement data; calculating the visibility probability of the SnS state cluster based on the functional relationship; or calculating the visibility probability of the SnS state cluster based on the preset statistical distribution model;

[0086] According to the visibility probability, the visible area of the SnS state cluster is calculated using at least one of a center-and-spoke approach, a corner-triggered approach, and a near-field preferential approach.

[0087] In the embodiment of the present application, for a cluster in SnS state, the specific steps of calculating the visible probability (VP) of the SnS state cluster are as follows: define the number N of visible elements in the SnS state cluster n The total number of array elements N in the ultra-large-scale multiple-input multiple-output system all The first ratio of . Based on ray tracing simulation or channel measurement data, a function of VP is established, which is expressed as formula (5): V = f(p), where V is a function of the cluster power P; the preset statistical distribution model is expressed as V:Y(0,1), where V obeys the statistical distribution of the form Y, which is greater than 0 and less than 1.

[0088] According to formula (5) or the preset statistical distribution model, the VP of cluster n can be generated, that is, V n , that is: V n =f(P n )or V n =min(V(x),1).

[0089] Where Pn is the power of cluster n, and x is a randomly generated number that obeys x~U(0,1).

[0090] It should be noted that the preset statistical distribution model for generating visible probability requires "combining a random number generator to generate random variables that follow a uniform distribution." The functional relationship for generating visible probability can be directly calculated based on the dependency between cluster power and the visible probability function.

[0091] For the cluster of SnS state, the specific steps of generating the visible range (VR) of the SnS state cluster are as follows: n The visible region (VR) of the SnS state cluster may be calculated using at least one of a center-and-spoke approach, a corner-triggered approach, and a near-field preferential approach.

[0092] In one implementation, the visible area of the SnS state cluster is calculated using a hub-and-spoke approach, including:

[0093] In N all The initial element is randomly selected from the elements as the geometric center; the length of the VR rectangle is randomly generated, and its minimum and maximum values are And the number of array elements in the horizontal dimension of the array, which obeys a uniform distribution within this range. According to the number of coverage elements, N n =V n ×N allThe width of the rectangle VR is calculated by adding the length of the rectangle. Based on the width and length of the VR, the initial array element is expanded outward in both directions until the required number of visible array elements is met. If one end of the bidirectional expansion reaches the array boundary, unidirectional expansion is performed.

[0094] In another implementation, the visible area of the SnS state cluster is calculated using a corner-triggered method, including: randomly selecting an expansion starting point from the four corner elements of the array, or selecting an expansion starting point based on a certain probability (for example, for a horizontal uniform plane array, the probability of selecting the left and right vertices of the array is 50%, and the probability of selecting the top and bottom is 80% and 20% respectively); performing directional area expansion: randomly generating the length of the VR rectangle, whose minimum and maximum values are respectively and the number of array elements in the horizontal dimension of the array, which obeys a uniform distribution within this range; according to the number of coverage elements reaching N n =V n ×N all The area enclosed by the length and width of the rectangle is VR.

[0095] In another implementation, the visible area of the SnS state cluster is calculated using a near-field preferential method, including: calculating the distance between all array elements and the user; selecting the array element with the smallest distance as the center and performing radial expansion; when the cumulative number of array elements N n =V n ×N all When , the expansion stops and the last complete annular area is used as the VR boundary.

[0096] Optionally, determining a first power attenuation factor of the SnS state cluster according to the visibility probability and the visible area includes:

[0097] If the SnS state cluster is within the visible area, determining, according to a preset spatial attenuation function, a first power attenuation factor of the SnS state cluster to be 1;

[0098] If the SnS state cluster is outside the visible area, a spatial attenuation function is used to determine a first power attenuation factor value that decays smoothly to 0 along the spatial coordinates based on the relative position of the array element of the SnS state cluster and the boundary of the visible area; the attenuation rate of the spatial attenuation function is a preset adjustable parameter.

[0099] In this embodiment of the present application, for a cluster in the SnS state, the first power attenuation factor (PAF) of different XL-MIMO array elements is calculated as follows: within the VR, the cluster power remains unchanged, PAF = 1; outside the VR, the cluster PAF is modeled as a function that slowly decreases to 0 between XL-MIMO array elements to ensure a smooth transition between the visible and non-visible areas. The function for establishing the PAF can be expressed as:

[0100] Where f(x,y) represents the function of PAF, which is a function of the VR horizontal (x) and vertical (y) directions in uniform plane array coordinates.

[0101] Optionally, the above step 14 includes:

[0102] Determine the gripping condition of the user device according to a preset typical ratio; the gripping condition includes three conditions: head and one-hand occlusion, two-hand occlusion, and one-hand occlusion;

[0103] Based on the holding condition, obtaining an attenuation value of each antenna unit of the user equipment;

[0104] A second power attenuation factor of each array element of the user equipment is determined according to the attenuation value.

[0105] In an embodiment of the present application, the physical occlusion scenario of the user equipment (UE) is identified, including three typical situations: the first state of head and single-hand occlusion (such as the device is close to the head and held with one hand during a call); the second state of double-hand occlusion (such as both hands covering both sides of the device during a horizontal screen game); and the third state of single-hand occlusion (such as holding the device with one hand causing part of the antenna to be blocked by the palm). For example, the probability of occurrence of the three states is set to 13%, 29% and 58%. During the generation process, a value in the range of 0-1 is randomly generated, that is, x~U(0,1), and it is determined whether x falls within the range of 0-13%, 13%-42%, or 42%-100%, then it is in that state. According to the state corresponding to the physical occlusion scenario, the preset table is queried to determine the fixed attenuation factor of each array element of the UE, and it is converted to a linear value (that is, falling between 0-1). That is, the present application provides a processing method for obtaining the attenuation value of each antenna unit of the user equipment based on the holding condition and using a table lookup method.

[0106] Optionally, the above step 15 includes:

[0107] According to the first power attenuation factor and the second power attenuation factor, a target formula for generating a channel coefficient is input to generate a channel coefficient.

[0108] In the embodiment of the present application, the target formula is preferably the "target formula of the three-dimensional wireless channel model of 3GPP TR 38.901", and the first power attenuation factor and the second power attenuation factor are input into the target formula for generating the channel coefficient to obtain the generated channel coefficient. The channel coefficient here is preferably generated line-of-sight (LOS) and non-line-of-sight (NLOS):

[0109] The formula for LOS is:

[0110]

[0111] The formula for NLOS is:

[0112]

[0113] Among them, and Denote the impulse response of LOS and NLOS conditional channels respectively, τ and t denote the propagation delay and time; K R represents Rice's K factor; N represents the total number of clusters, and represents the impulse response of the LOS cluster and the nth NLOS cluster. The above parameters can be obtained from the "3D Wireless Channel Model of 3GPP TR 38.901". s,n represents the first power attenuation factor between the cluster and the base station antenna unit, s represents the base station antenna unit index, and n represents the cluster index; β u represents the second power attenuation factor on the user side (in some cases, the second power attenuation factor is the antenna unit attenuation value), and u represents the UE antenna unit index.

[0114] Reference Figure 2 As shown, the embodiment of the present application provides a specific flow diagram, including the following steps:

[0115] Step 1, obtain cluster power;

[0116] Step 2, calculate the SnS probability of the cluster;

[0117] Step 3, determine the SnS state of the cluster;

[0118] Step 4: For clusters in non-SnS state, set the PAF of all XL-MIMO elements to 1;

[0119] Step 5: For the cluster in SnS state, calculate the VP of the cluster in SnS state;

[0120] Step 6: For the cluster in the SnS state, generate a VR of the SnS state cluster;

[0121] Step 7: For the cluster in SnS state, calculate the PAF of different array elements;

[0122] Step 8: Generate PAFs for different elements of the UE.

[0123] Step 9: Substitute the PAF into the generated channel coefficients.

[0124] In a specific embodiment of a spatial non-stationary channel modeling solution based on ray tracing simulation data, the specific simulation process and parameters are as follows:

[0125] Step 1: Get cluster power. In this step, the cluster power P is generated according to Step 6 and Step 12 in "3GPP TR 38.901". n .

[0126] Step 2: Calculate the SnS probability of the cluster (Pr SNS ). Based on the ray tracing simulation results, Pr SNS It is obtained by calculating the proportion of SNS clusters in all samples within a specific power range. Since clusters are unevenly distributed in the linear power domain (most clusters have low power, and a few clusters have high power), the spatial non-stationary probability of clusters within a specific power range is statistically analyzed in the dB domain of max(Pn)-Pn (where Pn is the power of the nth cluster in dB). Figure 3 As shown, it can be observed that the SNS probability of a cluster follows a truncated normal distribution. Therefore, the SNS probability generation of a single cluster can be expressed as: Pr n =N(μ,σ 2 ).

[0127] Among them, Pr SNS It represents the probability that the nth cluster has the SNS characteristic. The probability obeys the truncated normal distribution with mean and variance, and its value range is limited to the interval [0,1].

[0128] In addition, the functional relationship between the cluster space non-stationary probability and cluster power can be established, such as Figure 4 shown.

[0129] Step 3: Determine the SnS state of the cluster. n , generate the SnS probability of the cluster, and then determine whether the cluster generates SnS.

[0130] Step 4: For clusters in non-SnS state, set the PAF of all XL-MIMO elements to 1; if S n =0, then the corresponding PAF of all XL-MIMO elements is set to 1, that is, α s,n =1.

[0131] Step 5: For the clusters in the SnS state, calculate the VP of the SnS state cluster. One of the options is to generate VP based on the statistical distribution of the simulation data, refer to Figure 5 As shown in the figure, it is found that as VP increases, its probability density gradually decreases and approximately obeys the exponential distribution. n =min(-μlnx,1), and the mean of its distribution is μ=0.42.

[0132] Step 6: For the cluster in the SnS state, generate a VR of the SnS state cluster;

[0133] Step 7: For the clusters in the SnS state, calculate the PAFs of different XL-MIMO array elements. One option is to generate the PAFs of different XL-MIMO array elements using the following formula.

[0134]

[0135] in, (x A,n ,y B,n ) is the coordinate of another corner point on the diagonal of the antenna array (the VR reference corner point is (x 0,n ,y 0,n ));(x a,n ,y b,n ) is the coordinate of the other corner point on the diagonal of the rectangular visible area (VR), d s,n represents the minimum distance between the sth antenna element and the visible region (VR); C is the roll-off factor between the visible and invisible regions. Adjusting the roll-off factor allows for varying visible regions and slow power changes between elements, ensuring consistent power changes across them.

[0136] Step 8: Generate PAFs for different elements of the UE.

[0137] Step 9: The PAF on the base station side and the PAF on the UE side are used to generate a channel with a spatial non-stationary effect according to the formula for LOS and the formula for NLOS.

[0138] In summary, this application proposes a stochastic-based approach for spatial non-stationarity modeling (SA-SnS). By introducing the SnS probability of power-related clusters, it determines whether SnS occurs in the cluster, thereby solving the problem of determining the spatial stationarity of the cluster.

[0139] The SA-SnS method proposed in this application, for SnS clusters, demarcates VR through VP, solving the problem that parameters of VR and BD methods are difficult to obtain. The SA-SnS method proposed in this application introduces a distance-related power attenuation factor for array elements outside VR, achieving a smooth evolution of cluster power at the boundary of the visible area, solving the problem of sudden changes in cluster power in VR and BD methods. The SA-SnS method proposed in this application realizes the forward extension of the existing 3GPP TR 38.901 channel model, and only the power attenuation factor needs to be added to the original channel coefficient generation to achieve modeling of spatial non-stationary effects.

[0140] The above describes various methods of the embodiments of the present application. The following further provides apparatuses for implementing the above methods.

[0141] Reference Figure 6 As shown, the present application also provides a very large-scale MIMO channel modeling device, including:

[0142] A first acquisition module 61 is configured to acquire the cluster power of each cluster according to a preset channel model;

[0143] A first determining module 62 is configured to determine the spatial non-stationary SnS state of each cluster according to the power of each cluster or a preset statistical distribution model;

[0144] A second determining module 63 is configured to determine a first power attenuation factor of different clusters at the base station end according to the SnS state of each cluster;

[0145] A first processing module 64 is configured to determine a second power attenuation factor for each array element of the user equipment;

[0146] The second processing module 65 is configured to generate a channel coefficient according to the first power attenuation factor and the second power attenuation factor.

[0147] Optionally, the second determining module 63 includes:

[0148] a first determining unit, configured to calculate, when the SnS state representation is in a non-stationary state, a visibility probability and a visibility area of a corresponding SnS state cluster, and determine a first power attenuation factor of the SnS state cluster according to the visibility probability and the visibility area;

[0149] The second determining unit is configured to determine, when the SnS state cluster indicates that the cluster is in a stable state, that the first power attenuation factors of all array elements in the corresponding non-SnS state cluster are 1.

[0150] Optionally, the second determining module 63 includes:

[0151] a third determining unit, configured to calculate the spatial non-stationary probability of each cluster based on the power of each cluster by using at least one of a probability distribution obtained by multi-user joint analysis, a probability distribution obtained by single-user independent analysis, and a cluster normalized power correlation modeling method;

[0152] The fourth determining unit is configured to perform a binarization decision on each cluster according to the spatial non-stationary probability of each cluster, and determine the spatial non-stationary SnS state of each cluster.

[0153] Optionally, the first determining unit is specifically configured to:

[0154] Determining a first ratio between the number of visible elements in the SnS state cluster and the total number of elements in the very large-scale multiple-input multiple-output system; establishing a functional relationship between visibility probability and the cluster power based on ray tracing simulation or channel measurement data; calculating the visibility probability of the SnS state cluster based on the functional relationship; or calculating the visibility probability of the SnS state cluster based on the preset statistical distribution model;

[0155] According to the visibility probability, the visible area of the SnS state cluster is calculated using at least one of a center-and-spoke approach, a corner-triggered approach, and a near-field preferential approach.

[0156] Optionally, the first determining unit is further specifically configured to:

[0157] If the SnS state cluster is within the visible area, determining, according to a preset spatial attenuation function, a first power attenuation factor of the SnS state cluster to be 1;

[0158] If the SnS state cluster is outside the visible area, a spatial attenuation function is used to determine a first power attenuation factor value that decays smoothly to 0 along the spatial coordinates based on the relative position of the array element of the SnS state cluster and the boundary of the visible area; the attenuation rate of the spatial attenuation function is a preset adjustable parameter.

[0159] Optionally, the first processing module 64 includes:

[0160] The first processing unit is configured to determine a gripping condition of the user device according to a preset typical ratio; the gripping condition includes three conditions: head and one hand occlusion, two hands occlusion, and one hand occlusion;

[0161] an acquiring unit, configured to acquire an attenuation value of each antenna unit of the user equipment based on the holding condition;

[0162] The second processing unit is configured to determine a second power attenuation factor of each array element of the user equipment according to the attenuation value.

[0163] Optionally, the second processing module 65 includes:

[0164] The third processing unit is configured to input a target formula for generating a channel coefficient according to the first power attenuation factor and the second power attenuation factor, so as to generate a channel coefficient.

[0165] It should be noted that the device in this embodiment is a device corresponding to the above-mentioned method, and the implementation methods in the above-mentioned embodiments are all applicable to the embodiments of this device and can achieve the same technical effects. The above-mentioned device provided in the embodiment of this application can implement all the method steps implemented in the above-mentioned method embodiment and can achieve the same technical effects. The parts and beneficial effects that are the same as those in the method embodiment in this embodiment will not be specifically described here.

[0166] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned ultra-large-scale MIMO channel modeling method embodiment are implemented, and the same technical effects are achieved. To avoid repetition, the details are not described here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0167] An embodiment of the present application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the various processes of the above-mentioned ultra-large-scale MIMO channel modeling method embodiment are implemented, and the same technical effects can be achieved. To avoid repetition, they are not repeated here.

[0168] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0169] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network equipment, etc.) to execute the methods described in each embodiment of the present application.

[0170] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A method for modeling ultra-large-scale MIMO channels, characterized in that: include: According to the preset channel model, the cluster power of each cluster is obtained; Determining the spatial non-stationary SnS state of each cluster according to the power of each cluster or a preset statistical distribution model; determining, according to the SnS state of each cluster, first power attenuation factors of different clusters at the base station end; determining a second power attenuation factor for each array element of the user equipment; A channel coefficient is generated according to the first power attenuation factor and the second power attenuation factor.

2. The method according to claim 1, characterized in that Determining, according to the SnS state of each cluster, first power attenuation factors of different clusters at the base station end, comprising: When the SnS state indicates that the cluster is in a non-stationary state, calculating a visibility probability and a visibility area of the corresponding SnS state cluster, and determining a first power attenuation factor of the SnS state cluster according to the visibility probability and the visibility area; In a case where the SnS state cluster indicates that it is in a stable state, the first power attenuation factors of all array elements in the corresponding non-SnS state cluster are determined to be 1.

3. The method according to claim 1, characterized in that Determining the spatial non-stationary SnS state of each cluster according to the power of each cluster includes: Calculating the spatial non-stationary probability of each cluster based on the power of each cluster by using at least one of a probability distribution obtained by multi-user joint analysis, a probability distribution obtained by single-user independent analysis, and a cluster normalized power correlation modeling method; According to the spatial non-stationary probability of each cluster, a binary decision is performed on each cluster to determine the spatial non-stationary SnS state of each cluster.

4. The method according to claim 2, characterized in that Calculate the visibility probability and visibility area of the corresponding SnS state cluster, including: Determining a first ratio between the number of visible elements in the SnS state cluster and the total number of elements in the very large-scale multiple-input multiple-output system; establishing a functional relationship between visibility probability and the cluster power based on ray tracing simulation or channel measurement data; calculating the visibility probability of the SnS state cluster based on the functional relationship; or calculating the visibility probability of the SnS state cluster based on the preset statistical distribution model; According to the visibility probability, the visible area of the SnS state cluster is calculated using at least one of a center-and-spoke approach, a corner-triggered approach, and a near-field preferential approach.

5. The method according to claim 2, characterized in that Determining a first power attenuation factor of the SnS state cluster according to the visibility probability and the visibility area includes: If the SnS state cluster is within the visible area, determining, according to a preset spatial attenuation function, a first power attenuation factor of the SnS state cluster to be 1; If the SnS state cluster is outside the visible area, a spatial attenuation function is used to determine a first power attenuation factor value that decays smoothly to 0 along the spatial coordinates based on the relative position of the array element of the SnS state cluster and the boundary of the visible area; the attenuation rate of the spatial attenuation function is a preset adjustable parameter.

6. The method according to claim 1, characterized in that Determining a second power attenuation factor of each array element of the user equipment includes: Determine the gripping condition of the user device according to a preset typical ratio; the gripping condition includes three conditions: head and one-hand occlusion, two-hand occlusion, and one-hand occlusion; Based on the holding condition, obtaining an attenuation value of each antenna unit of the user equipment; A second power attenuation factor of each array element of the user equipment is determined according to the attenuation value.

7. The method according to claim 1, characterized in that Generating a channel coefficient according to the first power attenuation factor and the second power attenuation factor includes: According to the first power attenuation factor and the second power attenuation factor, a target formula for generating a channel coefficient is input to generate a channel coefficient.

8. A very large-scale MIMO channel modeling device, characterized in that include: A first acquisition module, configured to acquire the cluster power of each cluster according to a preset channel model; A first determining module is configured to determine the spatial non-stationary SnS state of each cluster according to the power of each cluster or a preset statistical distribution model; A second determining module is configured to determine a first power attenuation factor of different clusters at the base station end according to the SnS state of each cluster; A first processing module, configured to determine a second power attenuation factor for each array element of the user equipment; The second processing module is configured to generate a channel coefficient according to the first power attenuation factor and the second power attenuation factor.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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