Multi-input multi-output channel model construction method and device, network equipment and medium

Abstract

The invention provides a multiple-input-multiple-output channel model construction method and device, network equipment and a medium, and relates to the technical field of wireless communication. The method comprises: according to at least one obtained model parameter, determining a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to a multiple-input-multiple-output (MIMO) channel, the at least one model parameter being obtained based on a three-dimensional (3D) MIMO channel model; elements in the spatial non-stationary matrix are used for describing the visibility of clusters in the MIMO channel to antenna array elements, and the near-field spherical wave matrix is used for describing the influence of spherical wave propagation on signal amplitude and phase; and according to the spatial non-stationary matrix and the near-field spherical wave matrix, a target MIMO channel model is constructed, and the target MIMO channel model is used for simulating a channel coefficient of the MIMO channel. According to the scheme of the invention, the problem that the existing MIMO channel model is not suitable for the simulation test of a large-scale MIMO channel is solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a method, apparatus, network device, and medium for constructing a multiple-input multiple-output (MIMO) channel model. Background Art

[0002] With the deployment of the fifth-generation (5G) mobile communication system globally, the research on the sixth-generation (6G) mobile communication system for 2030 and beyond has been fully launched. The number of antenna elements in a massive multiple-input multiple-output (MIMO) system is huge (from dozens to thousands), which can serve dozens of terminals within the same time-frequency resource block, has a large capacity, and relatively high spectral and energy efficiency, and can meet the higher requirements of 6G systems for performance indicators such as spectral efficiency and energy efficiency of communication systems.

[0003] A wireless channel model can be used to describe the amplitude and phase effects of the communication environment on signals, and can support base station deployment, technical evaluation, network optimization, and terminal performance testing, etc., and is a prerequisite for the design of each generation of mobile communication systems. In a massive MIMO system, due to the use of hundreds, thousands, or even tens of thousands of antenna elements, the massive MIMO channel will exhibit channel characteristics different from those of traditional channels.

[0004] However, the existing MIMO channel models have a high computational complexity and low operating efficiency, which is not conducive to the system design and performance testing of massive MIMO systems. Summary of the Invention

[0005] The objective of the present invention is to provide a method, apparatus, network device, and medium for constructing a multiple-input multiple-output channel model, which solves the problem that the existing MIMO channel model is not applicable to the simulation testing of massive MIMO channels.

[0006] To achieve the above objective, an embodiment of the present invention provides a method for constructing a multiple-input multiple-output channel model, including:

[0007] According to at least one obtained model parameter, determining a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to a multiple-input multiple-output (MIMO) channel, where the at least one model parameter is obtained based on a three-dimensional (3D) MIMO channel model, elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna array elements, and the near-field spherical wave matrix is used to describe the effects of spherical wave propagation on signal amplitude and phase;

[0008] Construct a target MIMO channel model according to the spatial non-stationary matrix and the near-field spherical wave matrix, where the target MIMO channel model is used to simulate the channel coefficients of the MIMO channel.

[0009] Optionally, the model parameters include cluster power.

[0010] Determine the spatial non-stationary matrix corresponding to the MIMO channel according to at least one obtained model parameter, including:

[0011] Determine the mean and variance corresponding to the visibility probability according to the cluster power, where the visibility probability is used to represent the visibility of the cluster to the antenna elements in the MIMO channel;

[0012] Generate Gaussian distributed random numbers according to the mean and the variance;

[0013] Determine the visibility probability corresponding to the first interval according to the Gaussian distributed random numbers, where the antenna array in the MIMO channel includes at least one interval, each interval includes at least one antenna element, and the first interval is any one of the at least one interval;

[0014] Determine the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster according to the visibility probability corresponding to the first interval;

[0015] Perform state transition using a Markov chain according to the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster, and obtain the spatial non-stationary factors corresponding to each antenna element in each interval other than the first interval in the at least one interval under each cluster;

[0016] Use the spatial non-stationary factors corresponding to each antenna element in the at least one interval under each cluster as matrix elements to construct the spatial non-stationary matrix.

[0017] Optionally, determine the near-field spherical wave matrix corresponding to the MIMO channel according to at least one obtained model parameter, including:

[0018] Determine the distances between each antenna element of the antenna array in the MIMO channel and the first-bounce scatterers (FBS), and obtain at least one spherical propagation distance according to the at least one model parameter;

[0019] Select any one of the at least one spherical propagation distance as a reference spherical propagation distance, and determine the near-field spherical wave factors corresponding to each antenna element in each cluster inner diameter of each cluster according to the reference spherical propagation distance and the spherical propagation distance corresponding to the antenna element;

[0020] Construct the near - field spherical - wave matrix with the near - field spherical - wave factor as matrix elements.

[0021] Optionally, the model parameters include: the relative delay of the cluster, the absolute delay of the cluster, the position information of the antenna elements, and the user position information.

[0022] Determining the distances between each antenna element of the antenna array in the MIMO channel and the first - hop scatterer FBS according to the at least one model parameter includes:

[0023] Determine the geometric distance between the antenna element and the terminal according to the position information of the antenna element and the user position information.

[0024] Determine the total length of the cluster according to the geometric distance, the relative delay of the cluster, and the absolute delay of the cluster.

[0025] Determine the distance between the antenna element and the FBS according to the total length.

[0026] Optionally, the target MIMO channel model is expressed as:

[0027]

[0028] Wherein, represents the channel coefficient of the k - th antenna element and the n - th cluster in the target MIMO channel model, S n,k represents the element of the spatial non - stationary matrix, represents the channel coefficient corresponding to the 3D MIMO channel model, A n,m,k represents the element of the near - field spherical - wave matrix, and M represents the number of cluster inner diameters in a cluster.

[0029] To achieve the above object, an embodiment of the present invention provides a multi - input multi - output channel model construction device, including:

[0030] A first processing module, configured to determine a spatial non - stationary matrix and a near - field spherical - wave matrix corresponding to a multi - input multi - output MIMO channel according to at least one obtained model parameter, where the at least one model parameter is obtained based on a 3D MIMO channel model corresponding to the MIMO channel, elements in the spatial non - stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near - field spherical - wave matrix is used to describe the influence of spherical - wave propagation on signal amplitude and phase.

[0031] A model construction module, configured to construct a target MIMO channel model according to the spatial non-stationary matrix and the near-field spherical wave matrix, where the target MIMO channel model is used to simulate the channel coefficients of the MIMO channel.

[0032] Optionally, the model parameters include cluster power; the first processing module includes:

[0033] A first processing sub-module, configured to determine the mean and variance corresponding to the visibility probability according to the cluster power, where the visibility probability is used to represent the visibility of the cluster in the MIMO channel to the antenna elements;

[0034] A second processing sub-module, configured to generate Gaussian distribution random numbers according to the mean and the variance;

[0035] A random generation sub-module, configured to determine the visibility probability corresponding to the first interval according to the Gaussian distribution random numbers, where the antenna array in the MIMO channel includes at least one interval, each interval includes at least one antenna element, and the first interval is any one of the at least one interval;

[0036] A third processing sub-module, configured to determine the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster according to the visibility probability corresponding to the first interval;

[0037] A fourth processing sub-module, configured to perform state transition using a Markov chain according to the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster, and obtain the spatial non-stationary factors corresponding to each antenna element in each of the intervals other than the first interval in the at least one interval under each cluster;

[0038] A fifth processing sub-module, configured to use the spatial non-stationary factors corresponding to each antenna element in the at least one interval under each cluster as matrix elements to construct the spatial non-stationary matrix.

[0039] Optionally, the first processing module includes:

[0040] A sixth processing sub-module, configured to determine the distances between each antenna element of the antenna array in the MIMO channel and the first-hop scatterer FBS according to the at least one model parameter, and obtain at least one spherical propagation distance;

[0041] A seventh processing sub-module, configured to select any one of the at least one spherical propagation distance as a reference spherical propagation distance, and determine the near-field spherical wave factors corresponding to each antenna element in each cluster inner diameter of each cluster according to the reference spherical propagation distance and the spherical propagation distance corresponding to the antenna element;

[0042] The eighth processing sub-module is used to construct the near-field spherical wave matrix by using the near-field spherical wave factor as matrix elements.

[0043] Optionally, the model parameters include: the relative time delay of the cluster, the absolute time delay of the cluster, the position information of the antenna array elements, and the user position information; the sixth processing sub-module includes:

[0044] The first processing unit is used to determine the geometric distance between the antenna array element and the terminal according to the position information of the antenna array element and the user position information;

[0045] The second processing unit is used to determine the total length of the cluster according to the geometric distance, the relative time delay of the cluster, and the absolute time delay of the cluster;

[0046] The third processing unit is used to determine the distance between the antenna array element and the FBS according to the total length.

[0047] Optionally, the target MIMO channel model is expressed as:

[0048]

[0049] Wherein, represents the channel coefficient of the k-th antenna array element and the n-th cluster in the target MIMO channel model, S n,k represents the element of the spatial non-stationary matrix, represents the channel coefficient corresponding to the 3D MIMO channel model, A n,m,k represents the element of the near-field spherical wave matrix, and M represents the number of cluster inner diameters in a cluster.

[0050] To achieve the above object, an embodiment of the present invention provides a network device, including a processor and a transceiver, wherein the processor is used for:

[0051] Determine a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to a multiple-input multiple-output (MIMO) channel according to at least one obtained model parameter, wherein the at least one model parameter is obtained based on a three-dimensional (3D) MIMO channel model, the elements in the spatial non-stationary matrix are used to describe the visibility of the clusters in the MIMO channel to the antenna array elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on the signal amplitude and phase;

[0052] Construct a target MIMO channel model according to the spatial non-stationary matrix and the near-field spherical wave matrix, and the target MIMO channel model is used to simulate the channel coefficients of the MIMO channel.

[0053] Optionally, the model parameters include cluster power; when determining the spatial non-stationary matrix corresponding to the MIMO channel according to at least one obtained model parameter, the processor is specifically configured to:

[0054] Determine the mean and variance corresponding to the visibility probability according to the cluster power, where the visibility probability is used to represent the visibility of the cluster in the MIMO channel to the antenna elements;

[0055] Generate Gaussian distributed random numbers according to the mean and the variance;

[0056] Determine the visibility probability corresponding to the first interval according to the Gaussian distributed random numbers, where the antenna array in the MIMO channel includes at least one interval, each interval includes at least one antenna element, and the first interval is any one of the at least one interval;

[0057] Determine the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster according to the visibility probability corresponding to the first interval;

[0058] Perform state transition using a Markov chain according to the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster, and obtain the spatial non-stationary factors corresponding to each antenna element in each interval other than the first interval in the at least one interval under each cluster;

[0059] Use the spatial non-stationary factors corresponding to each antenna element in the at least one interval under each cluster as matrix elements to construct the spatial non-stationary matrix.

[0060] Optionally, when determining the near-field spherical wave matrix corresponding to the MIMO channel according to at least one obtained model parameter, the processor is specifically configured to:

[0061] Determine the distances between each antenna element of the antenna array in the MIMO channel and the first-hop scatterer FBS according to the at least one model parameter, and obtain at least one spherical propagation distance;

[0062] Select any one of the at least one spherical propagation distance as a reference spherical propagation distance, and determine the near-field spherical wave factors corresponding to each antenna element in each cluster inner diameter of each cluster according to the reference spherical propagation distance and the spherical propagation distance corresponding to the antenna element;

[0063] Use the near-field spherical wave factors as matrix elements to construct the near-field spherical wave matrix.

[0064] Optionally, the model parameters include: the relative time delay of the cluster, the absolute time delay of the cluster, the position information of the antenna elements, and the user position information; when determining the distances between the respective antenna elements in the antenna array in the MIMO channel and the first-hop scatterer FBS according to the at least one model parameter, the processor is specifically configured to:

[0065] Determine the geometric distance between the antenna element and the terminal according to the position information of the antenna element and the user position information;

[0066] Determine the total length of the cluster according to the geometric distance, the relative time delay of the cluster, and the absolute time delay of the cluster;

[0067] Determine the distance between the antenna element and the FBS according to the total length.

[0068] Optionally, the target MIMO channel model is expressed as:

[0069]

[0070] Wherein, represents the channel coefficient of the k-th antenna element and the n-th cluster in the target MIMO channel model, S n,k represents the element of the spatial non-stationary matrix, represents the channel coefficient corresponding to the 3D MIMO channel model, A n,m,k represents the element of the near-field spherical wave matrix, and M represents the number of cluster inner diameters in one cluster.

[0071] To achieve the above object, an embodiment of the present invention provides a network device, including a transceiver, a processor, a memory, and a program or instruction stored on the memory and executable on the processor; when the processor executes the program or instruction, the above multi-input multi-output channel model construction method is implemented.

[0072] To achieve the above object, an embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps in the above multi-input multi-output channel model construction method are implemented.

[0073] The beneficial effects of the above technical solutions of the present invention are as follows:

[0074] According to the obtained at least one model parameter, the method of the embodiment of the present invention can determine the spatial non-stationary matrix and the near-field spherical wave matrix corresponding to the MIMO channel. The elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on the signal amplitude and phase. Furthermore, the target MIMO channel model can be constructed according to the spatial non-stationary matrix and the near-field spherical wave matrix. In this way, the target MIMO channel model can simulate the change of multipath visibility brought by the spatial non-stationary characteristics and the change of spherical wave phase and power caused by the near-field effect. Therefore, it can describe the changes of various channel characteristics brought by the near-field non-stationary characteristics in the large-scale MIMO system. Its calculation is simple, accurate and easy to use, and it can be applied to the system design and performance testing of the large-scale MIMO system. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a schematic diagram of the vision region (VR) in the existing large-scale MIMO channel model;

[0076] Figure 2 It is a flowchart of the method for constructing a multiple-input multiple-output channel model according to an embodiment of the present invention;

[0077] Figure 3 It is a schematic diagram of the partition when the number of antenna elements (i.e., the number of antenna array elements) is 8 within the interval according to an embodiment of the present invention;

[0078] Figure 4 It is a schematic diagram of the m-th multipath component (inner diameter of the cluster) in the n-th cluster according to an embodiment of the present invention;

[0079] Figure 5 It is a schematic diagram of the framework of the large-scale MIMO channel modeling process according to an embodiment of the present invention;

[0080] Figure 6 It is a structural diagram of the device for constructing a multiple-input multiple-output channel model according to an embodiment of the present invention;

[0081] Figure 7 It is a structural diagram of the network device according to an embodiment of the present invention;

[0082] Figure 8 It is a structural diagram of the network device according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0083] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0084] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures, or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, the "in one embodiment" or "in an embodiment" that appears throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics can be combined in one or more embodiments in any suitable manner.

[0085] In various embodiments of the present invention, it should be understood that the magnitude of the sequence numbers of the following processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0086] In addition, the terms "system" and "network" are often used interchangeably herein.

[0087] In the embodiments provided in the present application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0088] Next, the related technologies will be introduced first:

[0089] The existing large-scale MIMO channel models include the VR-based channel model. In this model, the calculation method of the channel coefficient is as follows:

[0090]

[0091] Among them, L is the path loss, which is related to the distance between the Base Station (BS) and the User Terminal (UT); l is the set of visible clusters; V n is the visibility gain of the nth visible cluster; S n is the shadow fading of the cluster; L n is the attenuation of the cluster, which is related to the delay of the cluster; a n,p is the complex Gaussian attenuation of the pth multipath component in the nth cluster; s t 、s r are the direction vectors of the transmitting antenna and the receiving antenna respectively, Ω n,p 、Ψ n,pThey are respectively the departure and arrival azimuth angles of the p-th multipath component in the n-th cluster, namely the azimuth angle of departure (AOD), the zenith angle of arrival (ZOD), the azimuth angle of arrival (AOA), and the zenith angle of arrival (ZOA). is the link delay of the n-th cluster. is the geometric path delay of the p-th multipath component in the n-th cluster.

[0092] Among them, the visibility of the multipath is determined by the visible region VR. As Figure 1 shown, the visible region VR is a series of circular regions evenly distributed in the simulation region. Each VR corresponds to a cluster. When the UT is located within the VR, the cluster corresponding to this VR is visible to this UT. The visibility is described by the visibility gain V n . As the UT enters the VR, V n grows from 0 to 1 according to the position of the UT within the VR. In the simulation environment, the VRs can overlap with each other. When the UT enters the overlapping region of multiple VRs, multiple clusters can be observed.

[0093] In the above scheme, a series of circular regions need to be randomly generated in the scene and associated with the clusters. Therefore, when the number of clusters is large, the number of regions to be generated is also large. In addition, the above scheme also needs to make a judgment according to the position of each antenna element in the UT to determine which visible regions this array element is in. Therefore, (the number of antenna elements × the number of visible regions) judgments are required. In this way, when using a large-scale antenna array or an ultra-large-scale antenna array (the number of antennas often reaches hundreds or even thousands), a large amount of calculation is required, and the calculation complexity is relatively high, which will affect the actual operation efficiency.

[0094] In addition, the generation of clusters and multipath components is related to specific scatterers. That is, after randomly generating scatterers, information such as the angles and powers of clusters and multipath components is calculated according to the geometric positions. In the 3D MIMO channel model, information such as the angles of clusters and multipaths has no specific physical meaning and is completely generated by a statistical model. Therefore, in practical applications, some concepts and implementation schemes in this technology cannot be well applied in the 3D MIMO channel model, and the compatibility with the 3D MIMO channel model is weak.

[0095] As Figure 2 shown, a method for constructing a multiple-input multiple-output channel model according to an embodiment of the present invention includes:

[0096] Step 201: Determine a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to a multiple-input multiple-output (MIMO) channel according to at least one obtained model parameter, where the at least one model parameter is obtained based on a three-dimensional (3D) MIMO channel model, elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on signal amplitude and phase.

[0097] Here, the 3D MIMO channel model may specifically be a 3D MIMO channel model based on the principle of a geometry-based stochastic model (GBSM).

[0098] Step 202: Construct a target MIMO channel model according to the spatial non-stationary matrix and the near-field spherical wave matrix, where the target MIMO channel model is used to simulate the channel coefficients of the MIMO channel.

[0099] In this step, for channel characteristics such as the near-field spatial non-stationary characteristics of a large-scale MIMO channel, a spatial non-stationary matrix and a near-field spherical wave matrix are introduced into the target MIMO channel model. Among them, the spatial non-stationary matrix can reduce the computational complexity of the target MIMO channel model, and the near-field spherical wave matrix enables the target MIMO channel model to describe the near-field characteristics of a large-scale MIMO channel. Therefore, the target MIMO channel model in the embodiments of the present invention can be better applied to the simulation of a large-scale MIMO channel.

[0100] In this embodiment, according to at least one obtained model parameter, a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to the MIMO channel can be determined. Among them, elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on signal amplitude and phase. Furthermore, a target MIMO channel model can be constructed according to the spatial non-stationary matrix and the near-field spherical wave matrix. In this way, the target MIMO channel model can simulate the change in multipath visibility brought by spatial non-stationary characteristics and the change in spherical wave phase and power caused by the near-field effect. Therefore, it can describe the changes in various channel characteristics brought by near-field non-stationary characteristics in a large-scale MIMO system. It is simple, accurate, and easy to use in calculation and can be applied to the system design and performance testing of a large-scale MIMO system.

[0101] Optionally, the model parameter includes cluster power; determining a spatial non-stationary matrix corresponding to the MIMO channel according to at least one obtained model parameter includes:

[0102] (1) Determine the mean and variance corresponding to the visibility probability based on the cluster power, where the visibility probability is used to represent the visibility of the cluster in the MIMO channel to the antenna elements.

[0103] In this step, the cluster power of the nth cluster obtained from the 3D MIMO channel model (denoted by p n ) can be used to calculate the above-mentioned mean μ n and variance σ n :

[0104] μ n (p n ) = a μ p n + b μ

[0105] σ n (p n ) = a σ p n + b σ

[0106] where {a, b} μ , {a, b} σ are intermediate coefficients, and their specific values can be obtained by performing distribution fitting on channel measurement or simulation data.

[0107] As an optional embodiment, for different transmission conditions: line of sight (LOS) and non-line of sight (NLOS), the reference values of the above intermediate coefficients can be taken as shown in Table 1:

[0108] Table 1 Reference values of intermediate parameters

[0109] <![CDATA[a μ > <![CDATA[b μ > <![CDATA[a σ > <![CDATA[b σ > LOS 0.0118 0.2992 0.0068 0.1981 NLOS 0.0321 0.3732 0.0282 0.3654

[0110] (2) Generate a Gaussian distribution random number (denoted by X) based on the mean and the variance. This process can be expressed as: X ∼ N(μ n (p n ), σ n (p n ))).

[0111] where X represents the Gaussian distribution random number, μ n (p n ) represents the mean corresponding to the visibility probability, and σ n (p n ) represents the variance corresponding to the visibility probability.

[0112] (3) Determine the visibility probability corresponding to the first interval according to the Gaussian distribution random number, where the antenna array in the MIMO channel includes at least one interval, each interval includes at least one antenna element, and the first interval is any one of the at least one interval.

[0113] It should be noted that the antenna array in the MIMO channel can be partitioned to obtain at least one interval. For example, some antenna elements that are close in distance can be grouped into the same interval. Among them, the number of antenna elements in each interval can be the same, or it can be that only the number of antenna elements in one interval is less than that in other intervals, and no specific limitation is made here. Taking the antenna array as a Uniform Linear Array (ULA) as an example, as Figure 3 shown, the ULA is divided into a series of "stationary intervals" (i.e., the above-mentioned intervals), each stationary interval is composed of a certain number of adjacent antenna elements, and the antenna elements located in the same stationary interval have the same non-stationary characteristics.

[0114] It should be noted that the process of determining the visibility probability corresponding to the first interval (denoted by ) according to the Gaussian distribution random number can be expressed as follows:

[0115]

[0116] It should be noted that after determining the visibility probability corresponding to the first interval , the invisibility probability corresponding to the first interval (denoted by ) can also be determined according to , and and satisfy:

[0117] (4) Determine the spatial non-stationary factor corresponding to each antenna element in the first interval under each cluster according to the visibility probability corresponding to the first interval;

[0118] (5) According to the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster, use the Markov chain for state transition to obtain the spatial non-stationary factors corresponding to each antenna element in each interval other than the first interval in the at least one interval under each cluster.

[0119] In this step, by using the Markov chain, the spatial non-stationary factors on other intervals except the first interval can be obtained through state transition between different intervals.

[0120] In this process, the state transition matrix used for state transition can be expressed as follows:

[0121]

[0122] Wherein, d is the distance between the center points of the current interval and the first interval.

[0123] (6) Using the spatial non-stationarity factors corresponding to each antenna element in each of the at least one interval under each cluster as matrix elements, construct the spatial non-stationarity matrix.

[0124] It should be noted that in the embodiments of the present invention, the elements (denoted by S n,k ) in the spatial non-stationarity matrix (denoted by S) can be used to describe the visibility of the nth cluster to the kth antenna element, and its definition is as follows:

[0125]

[0126] That is, S n,k being 1 indicates that the nth cluster is visible to the kth antenna element, and S n,k being 0 indicates that the nth cluster is invisible to the kth antenna element.

[0127] It should also be noted that in the embodiments of the invention, the cluster inner diameters located under the same cluster have the same visibility.

[0128] Optionally, according to the at least one obtained model parameter, determining the near-field spherical wave matrix corresponding to the MIMO channel includes:

[0129] (1) According to the at least one model parameter, determine the distances between each antenna element of the antenna array in the MIMO channel and the first-hop scatterer FBS, and obtain at least one spherical propagation distance (for example, the distance between the kth antenna element and the FBS: which can be denoted as d n,m,k ).

[0130] It should be noted that as the size of the antenna array increases, the transmission distance between the base station and the user will be within the Rayleigh distance. At this time, the traditional plane wave transmission assumption fails, and the phase of multipath transmission needs to be calculated through the spherical wave phase. Therefore, in the embodiments of the present invention, for the base station side, the multipath phase of spherical wave transmission can be obtained by calculating the distance between the antenna element at the base station side and the first-hop scatterer FBS in the cluster, and then obtaining the path difference between the clusters of different antenna elements. When the user side also uses an antenna array, the calculation method is the same, and only the FBS needs to be replaced with the last-hop scatterer (Last-bounce scatterer, LBS).

[0131] In the embodiments of the present invention, it is assumed that there is only one hop between the FBS and the LBS, such asFigure 4 As shown, the k-th antenna element at the base station end propagates within the m-th cluster inner diameter in the n-th cluster. Among them, is the vector pointing from the k-th antenna element at the base station end to the FBS, is the vector pointing from the k-th antenna element at the base station end to the LBS, is the vector pointing from the k-th antenna element at the base station end to the user end, is the vector pointing from the FBS to the LBS, is the vector pointing from the LBS to the user end.

[0132] Among them, the magnitude of (i.e., ) is unknown, but the direction is known (i.e., the angle of departure and the angle of arrival of this cluster can be obtained through at least one model parameter); can be directly obtained from the position coordinates of the base station and the user end.

[0133] In a specific embodiment, the model parameters include: the relative delay of the cluster, the absolute delay of the cluster, the position information of the antenna element, and the user position information; the specific steps for determining the distance between each antenna element of the antenna array in the MIMO channel and the first-hop scatterer FBS according to the at least one model parameter include:

[0134] (1) According to the position information of the antenna element and the user position information, determine the geometric distance between the antenna element and the terminal (i.e., ).

[0135] (2) According to the geometric distance, the relative delay of the cluster, and the absolute delay of the cluster, determine the total length of the cluster.

[0136] Specifically, in this step, the total length of the cluster can be calculated by the formula: where D n,m represents the total length of the cluster, represents the geometric distance between the antenna element and the terminal, τ n and τ Δ are the relative delay and the absolute delay of the cluster, respectively.

[0137] (3) According to the total length, determine the distance between the antenna element and the FBS.

[0138] Here, the distance between the base station end antenna element and the FBS is which can be obtained by solving the following optimization problem:

[0139]

[0140]

[0141]

[0142]

[0143] Among them, d min is the minimum distance limit. In an outdoor scenario, the value of d min can be 1 m. In an indoor scenario, the value of d min can be 0.1 m.

[0144] (2) Select any one of the at least one spherical propagation distance as the reference spherical propagation distance (which can be denoted as d n,m,ref ), and determine the near-field spherical wave factor corresponding to each cluster inner diameter of the antenna element in each cluster according to the reference spherical propagation distance and the spherical propagation distance corresponding to the antenna element (which can be denoted as A n,m,k ).

[0145] In this step, it can be determined according to the formula: the near-field spherical wave matrix factor A n,m,k for the k-th antenna element at the m-th cluster inner diameter in the n-th cluster.

[0146] (3) Construct the near-field spherical wave matrix with the near-field spherical wave factor as the matrix element.

[0147] That is, use A n,m,k as the matrix element of the near-field spherical wave matrix (denoted as A).

[0148] In some embodiments, the target MIMO channel model is expressed as:

[0149]

[0150] Among them, represents the channel coefficient of the k-th antenna element in the n-th cluster in the target MIMO channel model, S n,k represents the element of the spatial non-stationary matrix, represents the channel coefficient corresponding to the 3D MIMO channel model, A n,m,k represents the element of the near-field spherical wave matrix, and M represents the number of cluster inner diameters in a cluster.

[0151] Here, it is assumed that the antenna array used at the base station side is a ULA with K antenna elements, and the user side uses a single antenna. Then there are N clusters in the channel, and there are M cluster inner diameters in each cluster. Among them, k ≤ K, n ≤ N, S n,k is mainly used to describe the visibility of the current cluster to the current antenna element, An,m,k It mainly describes the impact of spherical wave propagation on the signal amplitude and phase.

[0152] In this embodiment, by introducing a spatial non-stationary matrix and a near-field spherical wave matrix, an expression of the target MIMO channel model considering the near-field non-stationary characteristics is proposed, which is applicable to the simulation of large-scale MIMO channels. Based on this expression of the target MIMO channel model, the large-scale MIMO channel coefficients with near-field effects and spatial non-stationary characteristics can be obtained (i.e., ).

[0153] As Figure 5 shown, when simulating the near-field spatial non-stationary characteristics of a large-scale MIMO channel using the target MIMO channel model based on statistical principles provided by the embodiments of the present invention, the specific process can be as follows:

[0154] Step 1: Set layout parameters such as the scenario and antenna array.

[0155] In this step, the simulation scenario (such as urban macrocell, urban microcell, indoor, etc.), the antenna array layout (such as panel layout, interval size, etc.), and other system-related configurations (such as base station, user location, center frequency, bandwidth, etc.) can be set.

[0156] Step 2: Set the transmission conditions.

[0157] In this step, the transmission conditions from the base station to the user (i.e., the terminal) (LOS or NLOS) can be set, and specifically, it can be allocated according to the LOS probability given by the standard model or set directly.

[0158] Step 3: Calculate the path loss.

[0159] Here, the path loss can be calculated based on the path loss model given by the 3D MIMO channel model, as well as the scenario, distance between the base station and the user, frequency, etc. in Step 1.

[0160] Step 4: Calculate large-scale parameters, such as delay spread (DS), angular spread (AS), Rician K-factor (K), and shadow fading (SF).

[0161] In this step, according to the scenario selected in Step 1, the mean values, variances, cross-correlation coefficients between parameters, and other model parameters of the large-scale parameters (i.e., delay spread, angular spread, Rician K-factor, shadow fading) can be obtained from the parameter table of the 3D MIMO channel model, and then the cross-correlated large-scale parameters can be randomly generated.

[0162] Step 5: Calculate the cluster delay.

[0163] According to the delay spread parameter, using the formula given by the 3D MIMO channel model, the relative delay of each cluster can be calculated.

[0164] Step 6: Calculate the cluster power.

[0165] According to the cluster delay obtained in Step 5, using the formula given in the 3D MIMO channel model, the power of each cluster can be calculated, and further normalization processing can be performed on it.

[0166] Step 7: Calculate the departure angle and arrival angle of the cluster center (i.e., ZOA, ZOD, AOA, AOD).

[0167] In this step, according to the angle spread parameter given in Step 4 and the cluster power calculated in Step 6, using the formula given in the 3D MIMO channel model, the departure angle and arrival angle of the cluster center of each cluster can be generated.

[0168] Step 8: Calculate the departure angle and arrival angle of the cluster inner diameter.

[0169] In this step, centered on the cluster center angle in Step 7, according to the expansion coefficient given in the 3D MIMO channel model, the departure angle and arrival angle of the inner diameter of each cluster (i.e., ZOA, ZOD, AOA, AOD) are calculated.

[0170] Step 9: Calculate the cross-polarization power ratio and the initial phase.

[0171] In this step, according to the scenario set in Step 1, the standard deviation of the cross-polarization power ratio is obtained from the 3D MIMO channel model, and then the cross-polarization power ratio of the inner diameter of each cluster is randomly generated, and the initial phase of the inner diameter of the cluster is generated according to the uniform distribution.

[0172] Step 10: Calculate the value of the spatial non-stationary matrix S.

[0173] Step 11: Calculate the value of the near-field spherical wave matrix A.

[0174] Step 12: Calculate the channel coefficient to obtain the channel coefficient with spatial non-stationary characteristics.

[0175] In this step, the near-field spatial non-stationary channel coefficient can be calculated by using the spatial non-stationary matrix S, the near-field spherical wave matrix A, and the channel coefficient of the 3D MIMO channel model, and the large-scale MIMO channel coefficient with near-field spatial non-stationary characteristics can be obtained.

[0176] Step 13: Add path loss and shadow fading.

[0177] In this step, the path loss calculated in step 3 and the shadow fading calculated in step 4 can be added to the channel coefficient calculated in step 12.

[0178] It should be noted that the above steps 1-9 and step 13 are consistent with the channel model calculation steps given in the 3D MIMO channel model, and will not be elaborated here.

[0179] In this embodiment, based on the 3D MIMO channel model and on the basis of the simulation framework of the 3D MIMO channel model, by adding steps, it not only ensures the compatibility between the target MIMO channel model and the standard model, but also effectively reduces the computational amount of the simulation by simplifying some simulation steps, and can support the simulation of the near-field effect and spatial non-stationary characteristics of the large-scale MIMO channel, realizing the calculation of the large-scale MIMO channel coefficient with these two characteristics.

[0180] In addition, since the steps of using this target MIMO channel model to simulate the large-scale MIMO channel are relatively simple, the parameters and models can be adjusted according to subsequent actual requirements, and it does not conflict with other steps of the existing 3D MIMO model, further improving the usability and versatility of the present invention.

[0181] For the method for constructing a multiple-input multiple-output channel model in this embodiment, according to at least one obtained model parameter, the spatial non-stationary matrix and the near-field spherical wave matrix corresponding to the MIMO channel can be determined. Among them, the elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to the antenna array elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on the signal amplitude and phase. Furthermore, the target MIMO channel model can be constructed according to the spatial non-stationary matrix and the near-field spherical wave matrix. In this way, the target MIMO channel model can simulate the change in multipath visibility brought by the spatial non-stationary characteristics and the change in spherical wave phase and power caused by the near-field effect. Therefore, it can describe the changes in various channel characteristics brought by the near-field non-stationary characteristics in the large-scale MIMO system. Its calculation is simple, accurate and easy to use, and can be applied to the system design and performance testing of the large-scale MIMO system.

[0182] As Figure 6 shown, a multiple-input multiple-output channel model construction device according to an embodiment of the present invention includes:

[0183] The first processing module 610 is configured to determine a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to a multiple-input multiple-output (MIMO) channel according to at least one obtained model parameter, where the at least one model parameter is obtained based on a 3D MIMO channel model corresponding to the MIMO channel, elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on signal amplitude and phase;

[0184] A model construction module 620 is configured to construct a target MIMO channel model according to the spatial non-stationary matrix and the near-field spherical wave matrix, and the target MIMO channel model is used to simulate channel coefficients of the MIMO channel.

[0185] In this embodiment, according to at least one obtained model parameter, a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to a MIMO channel can be determined. Elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on signal amplitude and phase. Furthermore, a target MIMO channel model can be constructed according to the spatial non-stationary matrix and the near-field spherical wave matrix. In this way, the target MIMO channel model can simulate the change in multipath visibility brought by spatial non-stationary characteristics and the change in spherical wave phase and power caused by the near-field effect. Therefore, it can describe the changes in various channel characteristics brought by near-field non-stationary characteristics in a large-scale MIMO system. Its calculation is simple, accurate, and easy to use, and it can be applied to system design and performance testing of a large-scale MIMO system.

[0186] Optionally, the model parameter includes cluster power; the first processing module 610 includes:

[0187] A first processing sub-module is configured to determine the mean and variance of the visibility probability according to the cluster power, where the visibility probability is used to represent the visibility of the cluster in the MIMO channel to the antenna element;

[0188] A second processing sub-module is configured to generate Gaussian distribution random numbers according to the mean and the variance;

[0189] A random generation sub-module is configured to determine the visibility probability corresponding to a first interval according to the Gaussian distribution random numbers, where the antenna array in the MIMO channel includes at least one interval, each interval includes at least one antenna element, and the first interval is any one of the at least one interval;

[0190] A third processing sub-module is configured to determine spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster according to the visibility probability corresponding to the first interval;

[0191] A fourth processing sub-module, configured to perform state transition by using a Markov chain according to the spatial non-stationary factors corresponding to each antenna element in each cluster in the first interval, so as to obtain the spatial non-stationary factors corresponding to each antenna element in each cluster in each interval other than the first interval among the at least one interval;

[0192] A fifth processing sub-module, configured to construct the spatial non-stationary matrix by using the spatial non-stationary factors corresponding to each antenna element in each cluster in the at least one interval as matrix elements.

[0193] Optionally, the first processing module 610 includes:

[0194] A sixth processing sub-module, configured to determine the distances between each antenna element of the antenna array in the MIMO channel and the first-hop scatterer FBS according to the at least one model parameter, so as to obtain at least one spherical propagation distance;

[0195] A seventh processing sub-module, configured to select any one of the at least one spherical propagation distance as a reference spherical propagation distance, and determine the near-field spherical wave factors corresponding to each antenna element under each cluster inner diameter in each cluster according to the reference spherical propagation distance and the spherical propagation distance corresponding to the antenna element;

[0196] An eighth processing sub-module, configured to construct the near-field spherical wave matrix by using the near-field spherical wave factors as matrix elements.

[0197] Optionally, the model parameters include: the relative delay of the cluster, the absolute delay of the cluster, the position information of the antenna element, and the user position information; the sixth processing sub-module includes:

[0198] A first processing unit, configured to determine the geometric distance between the antenna element and the terminal according to the position information of the antenna element and the user position information;

[0199] A second processing unit, configured to determine the total length of the cluster according to the geometric distance, the relative delay of the cluster, and the absolute delay of the cluster;

[0200] A third processing unit, configured to determine the distance between the antenna element and the FBS according to the total length.

[0201] Optionally, the target MIMO channel model is expressed as:

[0202]

[0203] Wherein, represents the channel coefficient of the k-th antenna element and the n-th cluster in the target MIMO channel model, S n,k represents the element of the spatial non-stationary matrix represents the channel coefficient corresponding to the 3D MIMO channel model, A n,m,k represents the element of the near-field spherical wave matrix, and M represents the number of cluster inner diameters in a cluster

[0204] It should be noted here that the above multi-input multi-output channel model construction device provided by the embodiments of the present invention can implement all the method steps implemented by the above-mentioned multi-input multi-output channel model construction method embodiments, and can achieve the same technical effects. The same parts and beneficial effects as those in the method embodiments will not be specifically described in this embodiment

[0205] Such as Figure 7 As shown, a network device 700 according to an embodiment of the present invention includes a processor 710 and a transceiver 720. Among them, the processor 710 is used for

[0206] According to at least one obtained model parameter, determine the spatial non-stationary matrix and the near-field spherical wave matrix corresponding to the multi-input multi-output MIMO channel. Among them, the at least one model parameter is obtained based on the three-dimensional 3D MIMO channel model. The elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on the signal amplitude and phase

[0207] According to the spatial non-stationary matrix and the near-field spherical wave matrix, construct a target MIMO channel model, and the target MIMO channel model is used to simulate the channel coefficients of the MIMO channel

[0208] In this embodiment, according to at least one obtained model parameter, the spatial non-stationary matrix and the near-field spherical wave matrix corresponding to the MIMO channel can be determined. Among them, the elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on the signal amplitude and phase. Furthermore, a target MIMO channel model can be constructed according to the spatial non-stationary matrix and the near-field spherical wave matrix. In this way, the target MIMO channel model can simulate the change of multipath visibility caused by spatial non-stationary characteristics and the change of spherical wave phase and power caused by near-field effects. Therefore, it can describe the changes of various channel characteristics brought by near-field non-stationary characteristics in a large-scale MIMO system. Its calculation is simple, accurate and easy to use, and it can be applied to the system design and performance testing of a large-scale MIMO system

[0209] Optionally, the model parameters include cluster power; when determining the spatial non-stationary matrix corresponding to the MIMO channel according to at least one obtained model parameter, the processor 710 is specifically configured to:

[0210] Determine the mean and variance corresponding to the visibility probability according to the cluster power, where the visibility probability is used to represent the visibility of the cluster in the MIMO channel to the antenna elements;

[0211] Generate Gaussian distribution random numbers according to the mean and the variance;

[0212] Determine the visibility probability corresponding to the first interval according to the Gaussian distribution random numbers, where the antenna array in the MIMO channel includes at least one interval, each interval includes at least one antenna element, and the first interval is any one of the at least one interval;

[0213] Determine the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster according to the visibility probability corresponding to the first interval;

[0214] Perform state transition using a Markov chain according to the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster, and obtain the spatial non-stationary factors corresponding to each antenna element in each interval other than the first interval in the at least one interval under each cluster;

[0215] Use the spatial non-stationary factors corresponding to each antenna element in the at least one interval under each cluster as matrix elements to construct the spatial non-stationary matrix.

[0216] Optionally, when determining the near-field spherical wave matrix corresponding to the MIMO channel according to at least one obtained model parameter, the processor 710 is specifically configured to:

[0217] Determine the distances between each antenna element of the antenna array in the MIMO channel and the first-hop scatterer FBS according to the at least one model parameter, and obtain at least one spherical propagation distance;

[0218] Select any one of the at least one spherical propagation distance as a reference spherical propagation distance, and determine the near-field spherical wave factors corresponding to each antenna element in each cluster inner diameter of each cluster according to the reference spherical propagation distance and the spherical propagation distance corresponding to the antenna element;

[0219] Use the near-field spherical wave factors as matrix elements to construct the near-field spherical wave matrix.

[0220] Optionally, the model parameters include: the relative delay of the cluster, the absolute delay of the cluster, the position information of the antenna elements, and the user position information; when determining, according to the at least one model parameter, the distances between the respective antenna elements of the antenna array in the MIMO channel and the first-hop scatterer FBS, the processor 710 is specifically configured to:

[0221] Determine the geometric distance between the antenna element and the terminal according to the position information of the antenna element and the user position information;

[0222] Determine the total length of the cluster according to the geometric distance, the relative delay of the cluster, and the absolute delay of the cluster;

[0223] Determine the distance between the antenna element and the FBS according to the total length.

[0224] Optionally, the target MIMO channel model is expressed as:

[0225]

[0226] Wherein, represents the channel coefficient of the k-th antenna element and the n-th cluster in the target MIMO channel model, S n,k represents the element of the spatial non-stationary matrix, represents the channel coefficient corresponding to the 3D MIMO channel model, A n,m,k represents the element of the near-field spherical wave matrix, and M represents the number of cluster inner diameters in a cluster.

[0227] It should be noted here that the above network device provided by the embodiments of the present invention can implement all the method steps implemented by the above-mentioned multi-input multi-output channel model construction method embodiments, and can achieve the same technical effects. The same parts and beneficial effects as those in the method embodiments are not specifically described in this embodiment.

[0228] A network device according to another embodiment of the present invention, as Figure 8 shown, includes a transceiver 810, a processor 800, a memory 820, and a program or instruction stored on the memory 820 and executable on the processor 800; when the processor 800 executes the program or instruction, the above-mentioned multi-input multi-output channel model construction method is implemented.

[0229] The transceiver 810 is configured to receive and send data under the control of the processor 800.

[0230] Wherein, in Figure 8Among them, the bus architecture may include any number of interconnected buses and bridges, specifically, various circuits of one or more processors represented by the processor 800 and the memory represented by the memory 820 are linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art, and thus will not be further described herein. The bus interface provides an interface. The transceiver 810 may be a plurality of components, that is, including a transmitter and a receiver, and provides a unit for communicating with various other devices on the transmission medium. The processor 800 is responsible for managing the bus architecture and general processing, and the memory 820 may store data used by the processor 800 when executing operations.

[0231] A readable storage medium according to an embodiment of the present invention, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps in the above-mentioned multi-input multi-output channel model construction method are implemented, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here. Among them, the computer-readable storage medium, such as a read-only memory (ROM for short), a random access memory (RAM for short), a magnetic disk or an optical disc, etc.

[0232] It should be further noted that the terminals described in this specification include but are not limited to smart phones, tablet computers, etc., and many of the described functional components are referred to as modules to more particularly emphasize the independence of their implementation methods.

[0233] In an embodiment of the present invention, a module can be implemented by software so as to be executed by various types of processors. For example, an identified executable code module may include one or more physical or logical blocks of computer instructions. For example, it may be constructed as an object, a process, or a function. Nevertheless, the executable code of the identified module does not need to be physically located together, but may include different instructions stored in different locations. When these instructions are logically combined together, they constitute the module and achieve the specified purpose of the module.

[0234] In fact, the executable code module may be a single instruction or many instructions, and may even be distributed on multiple different code segments, distributed in different programs, and distributed across multiple memory devices. Similarly, the operation data can be identified within the module, and can be implemented in any appropriate form and organized in any appropriate type of data structure. The operation data may be collected as a single data set, or may be distributed at different locations (including on different storage devices), and at least partially may only exist as an electronic signal in the system or network.

[0235] When a module can be implemented by software, considering the level of existing hardware technology, for a module that can be implemented by software, without considering cost, those skilled in the art can build a corresponding hardware circuit to implement the corresponding function. The hardware circuit includes conventional very large scale integration (VLSI) circuits or gate arrays, as well as existing semiconductors such as logic chips and transistors, or other discrete components. The module can also be implemented using programmable hardware devices, such as field programmable gate arrays, programmable array logic, programmable logic devices, etc.

[0236] The above exemplary embodiments are described with reference to these drawings. Many different forms and embodiments are possible without departing from the spirit and teachings of the present invention. Therefore, the present invention should not be construed as being limited to the exemplary embodiments presented herein. Rather, these exemplary embodiments are provided so that the present invention will be complete and full, and will convey the scope of the present invention to those skilled in the art. In these figures, the dimensions of components and relative dimensions may be exaggerated for clarity. The terms used herein are for the purpose of describing particular exemplary embodiments only and are not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms as well. It will be further understood that the terms "comprising" and / or "including" when used in this specification, specify the presence of the stated features, integers, steps, operations, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, groups thereof, and / or their combinations. Unless otherwise indicated, when stating a value range, the range includes the upper and lower limits thereof and any sub-ranges therebetween.

[0237] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for constructing a multiple-input multiple-output (MIMO) channel model, characterized in that, it includes: Determine a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to the MIMO channel according to at least one obtained model parameter, wherein the at least one model parameter is obtained based on a three-dimensional (3D) MIMO channel model, and the elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on the signal amplitude and phase; Construct a target MIMO channel model according to the spatial non-stationary matrix and the near-field spherical wave matrix, and the target MIMO channel model is used to simulate the channel coefficients of the MIMO channel.

2. The method according to claim 1, characterized in that, the model parameter includes cluster power; Determining a spatial non-stationary matrix corresponding to the MIMO channel according to at least one obtained model parameter includes: Determine the mean and variance of the visibility probability according to the cluster power, and the visibility probability is used to represent the visibility of the cluster in the MIMO channel to the antenna element; Generate Gaussian distributed random numbers according to the mean and the variance; Determine the visibility probability corresponding to a first interval according to the Gaussian distributed random numbers, wherein the antenna array in the MIMO channel includes at least one interval, each interval includes at least one antenna element, and the first interval is any one of the at least one interval; Determine the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster according to the visibility probability corresponding to the first interval; Perform state transition using a Markov chain according to the spatial non-stationary factors corresponding to each antenna element in the first interval under each cluster, and obtain the spatial non-stationary factors corresponding to each antenna element in each of the intervals other than the first interval in the at least one interval under each cluster; Use the spatial non-stationary factors corresponding to each antenna element in the at least one interval under each cluster as matrix elements to construct the spatial non-stationary matrix.

3. The method according to claim 1, characterized in that, Determining a near-field spherical wave matrix corresponding to the MIMO channel according to at least one obtained model parameter includes: Determine the distances between each antenna element of the antenna array in the MIMO channel and the first-hop scatterer (FBS) according to the at least one model parameter, and obtain at least one spherical propagation distance; Select any one of the at least one spherical propagation distance as a reference spherical propagation distance, and determine the near-field spherical wave factors corresponding to each antenna element in each cluster inner diameter of each cluster according to the reference spherical propagation distance and the spherical propagation distance corresponding to the antenna element; Use the near-field spherical wave factors as matrix elements to construct the near-field spherical wave matrix.

4. The method according to claim 3, characterized in that, the model parameter includes: the relative delay of the cluster, the absolute delay of the cluster, the position information of the antenna element, and the user position information; Determining the distances between respective antenna elements of an antenna array in the MIMO channel and a first-hop scatterer FBS according to the at least one model parameter includes: Determining the geometric distance between the antenna element and the terminal according to the position information of the antenna element and the user position information; Determining the total length of the cluster according to the geometric distance, the relative time delay of the cluster, and the absolute time delay of the cluster; Determining the distance between the antenna element and the FBS according to the total length.

5. The method according to claim 1, wherein, the target MIMO channel model is expressed as: Among them, represents the channel coefficient of the k-th antenna element and the n-th cluster in the target MIMO channel model, S n,k represents the element of the spatial non-stationary matrix, represents the channel coefficient corresponding to the 3D MIMO channel model, A n,m,k represents the element of the near-field spherical wave matrix, and M represents the number of cluster inner diameters in a cluster.

6. A multi-input multi-output channel model construction device, wherein, comprises: A first processing module, configured to determine a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to a multi-input multi-output MIMO channel according to at least one obtained model parameter, wherein the at least one model parameter is obtained based on a 3D MIMO channel model corresponding to the MIMO channel, elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on signal amplitude and phase; A model construction module, configured to construct a target MIMO channel model according to the spatial non-stationary matrix and the near-field spherical wave matrix, where the target MIMO channel model is used to simulate the channel coefficients of the MIMO channel.

7. A network device, wherein, comprises: A transceiver and a processor; The processor is configured to: Determine a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to a multi-input multi-output MIMO channel according to at least one obtained model parameter, wherein the at least one model parameter is obtained based on a 3D MIMO channel model corresponding to the MIMO channel, elements in the spatial non-stationary matrix are used to describe the visibility of clusters in the MIMO channel to antenna elements, and the near-field spherical wave matrix is used to describe the influence of spherical wave propagation on signal amplitude and phase; Construct a target MIMO channel model according to the spatial non-stationary matrix and the near-field spherical wave matrix, where the target MIMO channel model is used to simulate the channel coefficients of the MIMO channel.

8. A network device, comprises: A transceiver, a processor, a memory, and a program or instruction stored on the memory and executable on the processor; wherein, when the processor executes the program or instruction, the multi-input multi-output channel model construction method according to any one of claims 1-5 is implemented.

9. A readable storage medium, on which a program or instruction is stored, wherein, when the program or instruction is executed by a processor, the steps in the multi-input multi-output channel model construction method according to any one of claims 1-5 are implemented.

Images