A wireless channel modeling method based on environmental information

Abstract

The present application relates to a method and device for wireless channel modeling based on environmental information, the method comprising: performing three-dimensional environmental modeling on an actual wireless propagation environment to obtain wireless propagation environmental information; performing cluster core matching using the wireless propagation environmental information to calculate cluster core parameters, wherein the cluster core parameters include cluster core power parameters, cluster core delay parameters, and cluster core angle parameters; calculating the parameters of the sub-paths within the cluster core based on the cluster core parameters, the wireless propagation environmental information, and the statistical distribution characteristics of the sub-paths within the cluster; and calculating the channel coefficient matrix based on the cluster core parameters and the parameters of the sub-paths within the cluster core. According to the method and device of the present application, a high-precision, low-complexity channel modeling implementation scheme for future mobile communication systems that can adapt to various practical application scenarios is provided.

Description

Technical Field

[0001] The present application relates to the field of wireless channel modeling, and in particular to a wireless channel modeling method and device based on environmental information. Background Art

[0002] In the past decade, with the rapid popularization of smart terminals, new applications and services have also exploded. The introduction and development of technologies such as the Internet, cloud computing, smart cities, hybrid networking, and air-ground integration have officially brought human society into the era of big data. In order to meet the communication needs of massive big data services, the next generation of mobile communication technology has become a current research hotspot. As the communication medium between the transmitter and receiver in the communication system, the propagation characteristics of the wireless channel determine the ultimate performance upper limit of the communication system. Therefore, studying the channel propagation characteristics is a prerequisite for designing the network architecture of the communication system and designing high-performance communication technology.

[0003] As the carrier and medium of signal propagation, the characteristics of wireless channels will have an important impact on communication performance. Therefore, in the process of technical evaluation, equipment development, network deployment and performance testing of communication systems, the impact of channels needs to be fully considered. Due to the high cost of field testing and the difficulty in repeatedly locating problems, simulation or laboratory testing is often required in the development stage of communication systems to evaluate the performance of technologies and the advantages and disadvantages of solutions. Although this method is not affected by various external factors, it can significantly reduce the cost and shorten the development cycle in the development process, but it is necessary to accurately reproduce the characteristics of real wireless channels through channel simulation, so as to replace field testing to the greatest extent. Channel modeling and channel simulation are a pair of opposite processes. The process of parameterizing or mathematically abstracting the influence of real wireless channels on signal propagation is channel modeling, and the process of regenerating channel impulse response through parameters or mathematical models obtained by channel modeling is channel simulation. Therefore, the comprehensiveness and accuracy of the acquisition of channel propagation characteristics and the rationality of the abstract description of the channel will affect the accuracy of channel modeling.

[0004] At present, the mainstream channel modeling methods in mobile communication standards are deterministic modeling and statistical modeling. The idea of ​​deterministic modeling is to obtain specific geometric positioning information of each scatterer and the transmitting and receiving ends of the wireless link in a specific scenario, and then use the propagation characteristics of electromagnetic waves in space, such as Maxwell's equations or other similar algorithms, to accurately calculate the channel impulse response (CIR). The more commonly used deterministic modeling method is the ray tracing (RT) model. This method requires the establishment of a database that fully records the propagation terrain, the antenna configuration of the transmitting and receiving ends, and the detailed information of the scatterers (such as roads, buildings, trees, pedestrians, etc.) that may pass through during the propagation of radio waves, and then performs a large number of repeated calculations, carefully tracking each possible radio wave propagation path, and finally calculating the amplitude, phase, delay, etc. of each sub-path to obtain the corresponding channel impulse response. Compared with the channel impulse response and random modeling methods based on measurement, the shortcomings of deterministic modeling are mainly reflected in the following two aspects: 1) Too many operation parameters can easily lead to high simulation operation complexity. Generally, the more accurate the model is, the more relevant information is needed, which leads to an exponential increase in the amount of operation. 2) The simulation accuracy is easily affected by parameter settings.

[0005] Statistical modeling is mainly to calculate the corresponding channel impulse response by statistically analyzing some key parameters that can represent the channel fading characteristics, fitting the probability distribution of these parameters and analyzing them. Unlike the deterministic modeling method, this method does not necessarily need to accurately know the detailed values ​​of certain parameters, but only statistically analyzes the probability distribution of the parameters in a certain area and reconstructs the channel fading process based on this, thus greatly reducing the computational complexity of its simulation. Commonly used stochastic models include geometry-based statistical models (Geometry-based stochastic model, GBSM) and so on. The main modeling idea of ​​GBSM is: first, according to the geometric position of the transceiver and the scatterer (such as two-dimensional single ring, double ring, elliptical model and three-dimensional single sphere, double sphere, ellipsoid, cylinder, etc.), assume a reasonable probability distribution of the scatterer (such as Gaussian, uniform distribution, etc.); then, for each scattering path, generate key information such as delay, power, arrival angle, departure angle, etc. in sequence with specific statistical probabilities; finally, the channel model is built by simulating the relative movement of the transceiver and the scatterer (such as movement direction, speed, etc.). It can be seen that this simulation method only needs to obtain the statistical information of the corresponding parameters, and does not need to obtain complete and accurate environmental information to build the model.

[0006] The development of future mobile communication technology has led to a rapid expansion of various indicators that channel models need to support, such as bandwidth, frequency band, antenna scale and scenarios. It is difficult to continue with the existing channel modeling methods to meet the needs of future mobile communication technology. With the increase of frequency band, bandwidth, number of antennas, and the introduction of more new scenarios, the amount of channel data has increased exponentially, and the modeling complexity has increased; at the same time, new transmission technologies such as ultra-large-scale MIMO require higher precision and narrow beams, which requires further improvement in the accuracy of channel models. Therefore, it is urgent to adopt new high-precision, low-complexity channel modeling methods that can be applied to a variety of application scenarios to meet the needs of future communication systems. Summary of the invention

[0007] There are currently two methods for channel modeling: statistical channel modeling and deterministic channel modeling. Deterministic models are highly complex and highly dependent on the accuracy of environmental modeling; statistical channel models lack physical meaning and are difficult to accurately model for specific wireless scenarios. Future mobile communication systems will expand rapidly in multiple dimensions of time, frequency, space, and scenarios, and the amount of channel data will increase exponentially, increasing the complexity of modeling. At the same time, new transmission technologies will further increase the accuracy requirements of channel models, which will make it difficult for both modeling methods to meet the needs of future mobile communication systems for high-precision, low-complexity, and scenario-universal channel modeling.

[0008] The purpose of the present invention is to overcome the shortcomings of existing deterministic models and statistical models, and to provide a high-precision, low-complexity channel modeling implementation method for future mobile communication systems that can adapt to various practical application scenarios. The method is based on the cluster core modeling idea, provides a specific implementation framework and process, and provides a result verification method and model performance.

[0009] According to a first aspect of the present invention, there is provided a method for wireless channel modeling based on environmental information, comprising:

[0010] Conduct three-dimensional environment modeling of the actual wireless propagation environment to obtain wireless propagation environment information;

[0011] Perform cluster core matching by using the wireless propagation environment information to calculate cluster core parameters, wherein the cluster core parameters include cluster core power parameters, cluster core delay parameters and cluster core angle parameters;

[0012] Calculating the parameters of the sub-paths within the cluster core based on the cluster core parameters, the wireless propagation environment information and the statistical distribution characteristics of the sub-paths within the cluster; and

[0013] A channel coefficient matrix is ​​calculated according to the cluster core parameters and the parameters of the sub-paths within the cluster core.

[0014] According to a second aspect of the present invention, there is provided a device for wireless channel modeling based on environmental information, comprising:

[0015] An environment modeling unit, used to perform three-dimensional environment modeling on the actual wireless propagation environment to obtain wireless propagation environment information;

[0016] A first calculation unit, configured to perform cluster core matching by using the wireless propagation environment information, and calculate cluster core parameters, wherein the cluster core parameters include cluster core power parameters, cluster core delay parameters, and cluster core angle parameters;

[0017] a second calculation unit, configured to calculate parameters of the sub-paths within the cluster core based on the cluster core parameters, the wireless propagation environment information and the statistical distribution characteristics of the sub-paths within the cluster; and

[0018] The third calculation unit is used to calculate the channel coefficient matrix according to the cluster core parameters and the parameters of the sub-paths in the cluster core.

[0019] According to a third aspect of the present invention, there is provided an electronic device, comprising:

[0020] Processor; and

[0021] A memory storing computer instructions, which, when executed by the processor, causes the processor to execute the method described in the first aspect.

[0022] According to a fourth aspect of the present invention, there is provided a non-transitory computer storage medium storing a computer program, which, when executed by a plurality of processors, enables the processors to execute the method described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without exceeding the scope of protection required by the present application.

[0024] Figure 1 It is a schematic diagram of the cluster core modeling principle.

[0025] Figure 2 It is a flowchart of wireless channel modeling based on environmental information according to an embodiment of the present application.

[0026] Figure 3 It is a flowchart of a method for wireless channel modeling based on environmental information according to an embodiment of the present application.

[0027] Figure 4 It is a schematic diagram of a device for wireless channel modeling based on environmental information according to an embodiment of the present application.

[0028] Figure 5 It is a flowchart for model evaluation with three-tier validation.

[0029] Figure 6 This is a simulation diagram of a typical indoor conference room scene.

[0030] Figure 7 It is a comparison chart of large-scale parameter simulation results of deterministic model, statistical model and cluster core-based channel model.

[0031] Figure 8 It is a comparison chart of small-scale parameter simulation results of deterministic model, statistical model and cluster core-based channel model.

[0032] Fig. 9 It is a comparison chart of simulation results of performance parameters of deterministic model, statistical model and cluster core-based channel model.

[0033] Fig.10 It is a structural diagram of an electronic device provided by the present invention.

[0034] Definitions of Abbreviations and Key Terms

[0035] CN: Cluster-nuclei, cluster nuclei

[0036] CNCM: Cluster-nuclei based channel model, cluster-nuclei based channel model

[0037] RT:Ray-tracing

[0038] GBSM: Geometry-based stochastic model, Geometry-based statistical model

[0039] SLAM: Simultaneous localization and mapping, simultaneous positioning and mapping

[0040] CIR: Channel impulse response, channel impulse response

[0041] MPC: Multipath component

[0042] LOS: Line of sight

[0043] SBR: Single bounce refection, one reflection

[0044] DBR: Double bounce reflection, secondary reflection

[0045] DS: Delay spread

[0046] AS: Angular spread DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0048] The purpose of the present invention is to overcome the shortcomings of existing deterministic models and statistical models, and to provide a high-precision, low-complexity channel modeling implementation method for future mobile communication systems that can adapt to various practical application scenarios. The method is based on the cluster core modeling idea, provides a specific implementation framework and process, and provides a result verification method and model performance.

[0049] Radio waves are emitted from the transmitter, and after being reflected, scattered, and diffracted by various scatterers in the propagation path, they reach the receiver. These received radio waves will show multipath clustering characteristics. The shape, position, and material of the scatterer will have a significant impact on the propagation characteristics of the radio wave; at the same time, when the radio wave passes through the same scatterer, the parameters of the multipath component, such as delay and angle, will also show similar characteristics. Therefore, there must be a certain mapping relationship between the multipath cluster in the statistical model and the scatterer in the determination model. Based on this feature, the concept of cluster core is proposed. The cluster core is defined as a cluster that has a certain mapping relationship with the scatterer in the actual propagation environment, can dominate the generation of channels in various scenarios and configurations, and is aggregated by a large number of waves according to a certain rule. Based on the superposition of a finite number of cluster cores, the channel impulse response of the real scene can be calculated. For an understanding of the concept of cluster core, please refer to the application CN201610456787. The cluster core modeling principle is as follows Figure 1 shown.

[0050] Figure 1 In the sphere containing several points, it represents the cluster core, and its characteristic parameters can be obtained by the matching mapping relationship between it and the surrounding scatterers; the points in the cluster core represent the sub-path, and the sub-path characteristic parameters are determined by the cluster core parameters and environmental information. The channel impulse response expression of this channel model is as follows:

[0051]

[0052] Where f(·) represents the mapping relationship between cluster core and scatterer, CN n represents the characteristic parameter of the nth cluster core, Ω n represents the characteristic parameter of the sub-path in the nth cluster core, δ(τ) represents the Dirac function with time delay τ, and v n represents the Doppler shift of the cluster core.

[0053] Figure 2 FIG. 1 is a flow chart of wireless channel modeling based on environmental information according to an embodiment of the present application. Figure 2 As shown, the process of wireless channel modeling based on environmental information in the present application includes the following aspects: three-dimensional environment reconstruction, determining cluster core parameters using environmental mapping, generating sub-path parameters and generating channel coefficients.

[0054] exist Figure 2 In the present invention, the three-dimensional environment reconstruction includes using the actual wireless propagation environment to reconstruct the scatterers and the environment boundaries, and setting the material information of the scatterers and the environment boundaries, so as to obtain the three-dimensional wireless environment parameters. The three-dimensional environment reconstruction includes: 1) establishing a three-dimensional wireless propagation environment model to obtain the position, shape, size, material and other information of the scatterers in the wireless propagation environment; 2) obtaining the electromagnetic parameter information of the corresponding propagation frequency band through the material information, such as the complex dielectric constant.

[0055] exist Figure 2 In the process, the cluster core parameters (including geometric parameters and power parameters) are determined by using environmental mapping, including: 1) path calculation, which is used to determine the cluster core angle parameters and delay parameters; 2) electric field calculation, which is used to determine the cluster core power parameters; 3) cluster core power screening, which is used to determine the final number and parameters of cluster cores.

[0056] exist Figure 2 In the method, generating sub-path parameters includes: 1) obtaining the delay information of the sub-path within the cluster core according to the cluster core delay parameter; 2) obtaining the angle information of the sub-path within the cluster core according to the angle of the cluster core and the statistical distribution characteristics of the sub-path within the cluster core; 3) obtaining the power information of the sub-path within the cluster core according to the angle information of the sub-path within the cluster core, the power angle spectrum distribution and the cluster core power.

[0057] exist Figure 2 In the method, generating channel coefficients includes: 1) calculating the impulse response of the subchannel between any receiving antenna and any transmitting antenna through the parameters of the cluster core and the subpaths within the cluster core; and 2) superimposing each subchannel to obtain the channel coefficient matrix of the MIMO channel.

[0058] According to one aspect of the present application, a method for wireless channel modeling based on environmental information is provided. Figure 3 FIG. 1 is a flow chart of a method for wireless channel modeling based on environmental information according to an embodiment of the present application. Figure 3 As shown, the method includes the following steps.

[0059] Step S301, three-dimensional environment modeling is performed on the actual wireless propagation environment to obtain wireless propagation environment information.

[0060] In this step, 3D environment modeling is performed on scatterers, environment boundaries, etc. in the wireless propagation environment. This can be performed using 3D modeling software or technologies such as Google Sketchup, laser radar, and SLAM. This provides an environmental information basis for subsequent channel modeling. Step S301 specifically includes the following sub-steps:

[0061] Sub-step S3011, obtaining wireless environment and material information in an actual wireless propagation environment, and performing proportional three-dimensional environment modeling.

[0062] Specifically, the location, shape, size, material and other information of scatterers in the wireless propagation environment are obtained, and proportional three-dimensional environment modeling is performed.

[0063] Sub-step S3012, calculating electromagnetic parameter information in the corresponding radio wave propagation frequency band through wireless environment and material information.

[0064] Specifically, through the material information of scatterers, environmental boundaries, etc., the electromagnetic parameter information in the corresponding radio wave propagation frequency band, such as dielectric constant, is obtained as the basis for the subsequent calculation of cluster core parameters.

[0065] In step S301, the obtained wireless propagation environment information includes wireless environment and material information and electromagnetic parameter information calculated according to the material information.

[0066] Step S302 , performing cluster core matching using wireless propagation environment information, and calculating cluster core parameters, wherein the cluster core parameters include cluster core power parameters, cluster core delay parameters, and cluster core angle parameters.

[0067] The cluster core is given actual physical meaning through the mapping relationship between the environmental information and the cluster core. Through the actual propagation environment mapping, the cluster core is matched using the wireless propagation environment information, and the cluster core parameters are calculated. In step S302, the power, delay, angle and other parameters of the cluster core are obtained through geometric optics and electromagnetic calculations, which specifically includes the following sub-steps:

[0068] Sub-step S3021, using geometric optics to calculate the propagation path, obtain the spatial position of the cluster core that matches the actual scatterer in the environment, and then calculate the angle and delay parameters of the cluster core propagation path.

[0069] In one embodiment, the law of rectilinear propagation and reflection of light is used to calculate the LOS path and all primary and secondary reflection paths between the transmitting and receiving antennas to obtain the angle and delay parameters of each propagation path, and determine the cluster core angle parameter and cluster core delay parameter.

[0070] Geometric optics is used to calculate the direct path, primary reflection path, and secondary reflection path. The law of linear propagation and reflection of light is used to perform geometric calculations on the LOS path between the transmitting and receiving antennas and all primary and secondary reflection paths to obtain the angle and delay parameters of each propagation path:

[0071]

[0072] Where N is the number of all paths, Type represents the type of propagation mechanism {LOS, SBR, DBR}, are the horizontal launch angle, vertical launch angle, horizontal arrival angle, vertical arrival angle and delay of the nth cluster core respectively. The number of all paths calculated by the above mechanism is:

[0073]

[0074] Sub-step S3022, calculating the power parameters of the cluster core according to the angle and delay parameters of the cluster core propagation path.

[0075] For the results obtained from the above path calculation, Friis's law is used to calculate the power of the cluster core generated by the LOS path:

[0076]

[0077] Among them, P r is the received power, P t is the transmission power, λ is the wavelength, d is the propagation distance between the transmitter and receiver, and F tx and F rx are the antenna gains of the transmitting antenna and the receiving antenna respectively. The power calculation of the cluster core generated by a single reflection is performed using the Fresnel formula:

[0078] E r =E t ·A(s)·R

[0079]

[0080]

[0081]

[0082]

[0083] Among them, E r and E t is the electric field strength at the transmitting and receiving ends. A(s) is the divergence factor, s i is the distance from the transmitter to the reflection point, s ris the distance from the receiving end to the reflection point, R is the reflection coefficient of the electric field strength, R ⊥ and R ∥ are the Fresnel reflection coefficients in the vertical and horizontal directions, respectively, and ε1 and ε2 are the complex dielectric constants of different media.

[0084] Similarly, the power of the secondary reflection is also calculated using the above method. Then, all the cluster core power values ​​are calculated, that is,

[0085]

[0086] in, Indicates the number of cluster cores obtained through all path calculations.

[0087] After obtaining the number of all cluster cores, the cluster cores can be screened so that the remaining cluster cores have sufficient power, thereby reducing the amount of calculation and complexity of channel modeling while ensuring the accuracy of channel modeling.

[0088] Thus, step S302 may further include the following sub-steps:

[0089] Sub-step S3023 , screening the cluster cores based on the cluster core power parameters, and determining the final cluster cores and the number of cluster cores.

[0090] Sub-step S3023 includes removing cluster cores whose cluster core power values ​​are less than the preset power value to obtain the number of cluster cores obtained after power calculation; in response to the number of cluster cores obtained after power calculation being less than or equal to the number of cluster cores of the corresponding scenario in the channel model standard, determining the number of cluster cores obtained after power calculation as the final number of cluster cores; in response to the number of cluster cores obtained after power calculation being greater than the number of cluster cores of the corresponding scenario in the channel model standard, retaining the number of cluster cores of the corresponding scenario in the channel model standard in order of cluster core power values ​​from high to low.

[0091] The specific means adopted in sub-step S3023 may be to first sort the powers P′ of all cluster cores from high to low,

[0092]

[0093] Among them, sort{·} means sorting all power values ​​in P′ from high to low. sort is the result of sorting.

[0094] Considering that the cluster cores with too low power have little effect on channel generation, the cluster cores with power lower than the set power can be eliminated. For example, the cluster cores with a difference of more than 25dB from the maximum power can be eliminated. At the same time, considering reducing the complexity of the model, an upper limit is set for the number of final cluster cores, that is,

[0095]

[0096] in, is the number of cluster cores after final screening, is the number of cluster cores obtained after power calculation, Indicates the number of clusters of the corresponding scenario in the channel model standard (for example, the 5G channel model standard).

[0097] In addition, considering that the sum of the powers of all cluster cores should be equal to 1, the power of the selected cluster cores is normalized, that is,

[0098]

[0099] Then all parameters of the final cluster core are obtained, including power parameters, angle parameters and delay parameters.

[0100] Step S303, calculating the parameters of the sub-paths within the cluster core based on the cluster core parameters, the wireless propagation environment information and the statistical distribution characteristics of the sub-paths within the cluster.

[0101] Specifically, step S303 includes the following sub-steps:

[0102] Sub-step S3031, obtaining the delay information of the sub-paths in the cluster core according to the cluster core delay parameter.

[0103] According to the cluster core delay parameter obtained in step S302, the delay parameter of each sub-path in the cluster core is calculated. The sub-path delay in the cluster core is equal to the cluster core delay τ n .

[0104] Sub-step S3032, calculating the angle information of the sub-diameters within the cluster core according to the cluster core angle parameter and the statistical distribution characteristics of the sub-diameters within the cluster.

[0105] According to the statistical distribution characteristics of the sub-paths within the cluster and the cluster core angle parameter calculated in step S302, the angle information of each multipath sub-path within the cluster core is calculated.

[0106]

[0107] θ ZOA,n,m =θ rx,n +θ' ZOA,n,m

[0108]

[0109]

[0110] in, and θ ZOA，n，m are the horizontal arrival angle and vertical arrival angle of the mth subpath of the nth cluster core, respectively. and θ rx，nare the horizontal arrival angle and vertical arrival angle of the nth cluster core respectively. ASA and C ZSA are the RMS horizontal and vertical angle of arrival spread within the cluster, respectively. n Obeys normal distribution N(0,1), W n It obeys the uniform distribution U(0,1).

[0111] The horizontal departure angle of each subpath in the cluster core and vertical departure angle θ ZOD，n，m It can also be calculated in the same way.

[0112] Sub-step S3033, calculating the power information of each sub-path according to the angle information of the sub-paths within the cluster, the power angle spectrum distribution and the cluster core power parameter.

[0113] The power information of each multipath subpath is calculated based on the power angular spectrum (PAS) distribution, the cluster core power parameter calculated in step S302, and the angle information of the subpath within the cluster obtained in substep S3032. The power angular spectrum of all multipath subpaths within the cluster core follows a winding Gaussian distribution in the horizontal dimension and a Laplace distribution in the vertical dimension.

[0114]

[0115] Among them, C ASD and C ZSD They are the within-cluster RMS horizontal departure angle spread and vertical departure angle spread, respectively.

[0116] Normalized sub-path power, that is, the sum of all sub-path powers in the cluster core is equal to the cluster core power:

[0117]

[0118] In the process of calculating the parameters of the sub-paths in the cluster core, it is not necessary to calculate the parameters of each sub-path one by one, but the parameters of the sub-paths in the cluster core are obtained from the cluster core parameters by a calculation method with lower calculation complexity.

[0119] Step S304, calculating the channel coefficient matrix according to the cluster core parameters and the parameters of the sub-paths in the cluster core.

[0120] Step S304 includes the following sub-steps:

[0121] Sub-step S3041, calculating the impulse response of a sub-channel between a receiving antenna and a transmitting antenna according to the cluster core parameters, the parameters of the sub-paths in the cluster core, and the antenna radiation pattern.

[0122] The impulse response h of the subchannel between the uth receiving antenna and the sth transmitting antenna is obtained by superimposing and calculating the various parameters of the cluster core and the sub-paths in the cluster core and the antenna pattern obtained in step S302 and step S303 using the following formula: u,s (t,τ).

[0123]

[0124]

[0125] Among them, F rx and F rx are the antenna patterns of the receiving and transmitting ends, respectively, p n，m and φ n，m are the power and phase of the mth subpath of the nth cluster core, r rx and r tx are the spherical coordinate unit vectors of the mth subpath at the receiving and transmitting ends, respectively, d rx，u and d tx，s are the spatial position vectors of the u-th receiving antenna element and the s-th transmitting antenna element respectively.

[0126] Sub-step S3042, superimposing each sub-channel to obtain a channel coefficient matrix of the MIMO channel.

[0127] Each sub-channel is superimposed to obtain the channel coefficient matrix H of the MIMO channel.

[0128] According to another aspect of the present application, a device for wireless channel modeling based on environmental information is provided. Figure 4 is a schematic diagram of a device for wireless channel modeling based on environmental information according to an embodiment of the present application. Figure 4 As shown, the device includes the following units.

[0129] The environment modeling unit 401 is used to perform three-dimensional environment modeling on the actual wireless propagation environment to obtain wireless propagation environment information.

[0130] In this unit, 3D environment modeling is performed on scatterers, environment boundaries, etc. in the wireless propagation environment. 3D modeling software or technologies such as Google Sketchup, laser radar, and SLAM can be used to provide environmental information basis for subsequent channel modeling. The environment modeling unit 401 specifically includes the following subunits:

[0131] The acquisition subunit is used to obtain the wireless environment and material information in the actual wireless propagation environment and perform proportional three-dimensional environment modeling.

[0132] Specifically, the location, shape, size, material and other information of scatterers in the wireless propagation environment are obtained, and proportional three-dimensional environment modeling is performed.

[0133] The electromagnetic parameter information calculation subunit is used to calculate the electromagnetic parameter information in the corresponding radio wave propagation frequency band through the wireless environment and material information.

[0134] Specifically, through the material information of scatterers, environmental boundaries, etc., the electromagnetic parameter information in the corresponding radio wave propagation frequency band, such as dielectric constant, is obtained as the basis for the subsequent calculation of cluster core parameters.

[0135] In the environment modeling unit 401, the obtained wireless propagation environment information includes wireless environment and material information and electromagnetic parameter information calculated according to the material information.

[0136] The first calculation unit 402 is used to perform cluster core matching by using the wireless propagation environment information to calculate cluster core parameters, wherein the cluster core parameters include cluster core power parameters, cluster core delay parameters and cluster core angle parameters.

[0137] The cluster core is given actual physical meaning through the mapping relationship between the environmental information and the cluster core. Through the actual propagation environment mapping, the cluster core is matched using the wireless propagation environment information to calculate the cluster core parameters. In the first calculation unit 402, the power, delay, angle and other parameters of the cluster core are obtained through geometric optics and electromagnetic calculation, which specifically includes the following subunits:

[0138] The first calculation subunit is used to calculate the propagation path using geometric optics to obtain the spatial position of the cluster core that matches the actual scatterer in the environment, and then calculate the angle and delay parameters of the cluster core propagation path.

[0139] In one embodiment, the law of rectilinear propagation and reflection of light is used to calculate the LOS path and all primary and secondary reflection paths between the transmitting and receiving antennas to obtain the angle and delay parameters of each propagation path, and determine the cluster core angle parameter and cluster core delay parameter.

[0140] Geometric optics is used to calculate the direct path, the first reflection path, and the second reflection path. The law of linear propagation and reflection of light is used to perform geometric calculations on the LOS path between the transmitting and receiving antennas and all the first reflection and second reflection paths to obtain the angle and delay parameters of each propagation path.

[0141] The second calculation subunit is used to calculate the power parameter of the cluster core according to the angle and delay parameters of the cluster core propagation path.

[0142] After obtaining the number of all cluster cores, the cluster cores can be screened so that the remaining cluster cores have sufficient power, thereby reducing the amount of calculation and complexity of channel modeling while ensuring the accuracy of channel modeling.

[0143] Thus, the first calculation unit 402 may further include the following subunits:

[0144] The determination subunit is used to screen the cluster cores based on the cluster core power parameters and determine the final cluster cores and the number of cluster cores.

[0145] The determination subunit is used to: remove cluster cores whose cluster core power values ​​are less than the preset power value to obtain the number of cluster cores obtained after power calculation; in response to the number of cluster cores obtained after power calculation being less than or equal to the number of cluster cores of the corresponding scenario in the channel model standard, determine the number of cluster cores obtained after power calculation as the final number of cluster cores; and in response to the number of cluster cores obtained after power calculation being greater than the number of cluster cores of the corresponding scenario in the channel model standard, retain the number of cluster cores of the corresponding scenario in the channel model standard in descending order of cluster core power values.

[0146] The specific means for determining the subunit may be to first sort the powers P' of all cluster cores from high to low.

[0147] Considering that cluster cores with too low power have little effect on channel generation, cluster cores with power lower than the set power can be eliminated, for example, cluster cores with a difference of more than 25dB from the maximum power can be eliminated. At the same time, considering reducing the complexity of the model, an upper limit is set for the number of final cluster cores. The upper limit of the final number of cluster cores is the minimum value of the number of cluster cores obtained after power calculation and the number of clusters in the corresponding scenario in the channel model standard (for example, the 5G channel model standard).

[0148] In addition, considering that the sum of the powers of all cluster cores should be equal to 1, the power of the screened cluster cores is normalized.

[0149] Then all parameters of the final cluster core are obtained, including power parameters, angle parameters and delay parameters.

[0150] The second calculation unit 403 is used to calculate the parameters of the sub-paths in the cluster core based on the cluster core parameters, the wireless propagation environment information and the statistical distribution characteristics of the sub-paths in the cluster.

[0151] Specifically, the second calculation unit 403 includes the following subunits:

[0152] The obtaining subunit is used to obtain the delay information of the subpath in the cluster core according to the cluster core delay parameter.

[0153] The delay parameter of each sub-path in the cluster core is calculated according to the cluster core delay parameter obtained by the first calculation unit 402, and the sub-path delay in the cluster core is equal to the cluster core delay.

[0154] The third calculation subunit is used to calculate the angle information of the sub-diameters within the cluster core according to the cluster core angle parameter and the statistical distribution characteristics of the sub-diameters within the cluster.

[0155] The fourth calculation subunit is used to calculate the power information of each sub-path according to the angle information of the sub-paths in the cluster, the power angle spectrum distribution and the cluster core power parameter.

[0156] The power information of each multipath subpath is calculated based on the power angle spectrum distribution, the cluster core power parameter obtained by the first calculation unit 402 and the angle information of the subpath within the cluster obtained by the third calculation subunit.

[0157] In the process of calculating the parameters of the sub-paths in the cluster core, it is not necessary to calculate the parameters of each sub-path one by one, but the parameters of the sub-paths in the cluster core are obtained from the cluster core parameters by a calculation method with lower calculation complexity.

[0158] The third calculation unit 404 is used to calculate the channel coefficient matrix according to the cluster core parameters and the parameters of the sub-paths in the cluster core.

[0159] The third calculation unit 404 includes the following sub-units:

[0160] The fifth calculation subunit is used to calculate the impulse response of the subchannel between a receiving antenna and a transmitting antenna according to the cluster core parameters, the parameters of the subpaths in the cluster core, and the antenna radiation pattern.

[0161] The impulse response of the subchannel between the uth receiving antenna and the sth transmitting antenna is obtained by superimposing and calculating the various parameters of the cluster core and the subpaths in the cluster core and the antenna pattern etc. obtained by the first calculating unit 402 and the second calculating unit 403 .

[0162] The channel coefficient matrix acquisition subunit is used to superimpose each subchannel to obtain the channel coefficient matrix of the MIMO channel.

[0163] Each sub-channel is superimposed to obtain the channel coefficient matrix H of the MIMO channel.

[0164] After obtaining the channel coefficient matrix, the channel model (referred to as the cluster core model) obtained by modeling the wireless channel based on the environmental information of the present application can be verified by the following evaluation method. Figure 5 This is a flow chart for establishing a three-tier validation model evaluation. Figure 5 As shown in Figure 2, the evaluation of the channel model includes two aspects:

[0165] 1. Establish a three-layer model evaluation method, and evaluate the models established by the cluster core channel modeling implementation method (CNCM) proposed in this application and the existing mainstream modeling methods, namely statistical modeling (GBSM) and deterministic modeling (RT), from three levels: large-scale parameters, small-scale parameters and performance parameters. Among them, the large-scale parameters include time spread (DS) and angle spread (AS), the small-scale parameters include multipath delay, power, horizontal angle and vertical angle parameters, and the performance parameters include channel capacity and modeling complexity;

[0166] 2. Compare and verify the cluster core channel modeling implementation method (CNCM) proposed in this application with statistical modeling (GBSM) and deterministic modeling (RT).

[0167] By simulating typical indoor conference room scenes, the simulation scenes are as follows: Figure 6 (a). The geometric dimensions of the conference room are 10.97x6.62x2.40m3. The transmitter is set in the northeast corner of the conference room, and 16 receivers are evenly distributed in the conference room. Figure 6 (b) shows the simulation results. The height of the transmitter and receiver is set to 1.55m, and the simulation frequency band is 28GHz. In addition, both the RT and GBSM models are calibrated with measured data, and the RT simulation results are very close to the measured results.

[0168] i. Comparison of large-scale parameter simulation results of three models:

[0169] By simulating and comparing the large-scale parameters of the RT, CNCM and GBSM models at each receiving point, we can obtain the following: Figure 7 Simulation results. Among them, the simulation results of RT are used as the target value for comparison. As shown in the figure, the delay spread and angle spread simulation results of CNCM are highly consistent with the results of the RT model, while the results of GBSM deviate greatly from RT. The simulation results are further quantitatively described, and the NMSE indicator is used to measure the accuracy of CNCM and GBSM compared with RT. The delay spread NMSE of CNCM and RT is 5.2%, while the delay spread NMSE of GBSM and RT is 32.5%. The angle spread NMSE of CNCM and RT is 0.3%, while the angle spread NMSE of GBSM and RT is 4.4%.

[0170] ii. Comparison of simulation results of small-scale parameters of three models:

[0171] By comparing the large-scale parameters of the RT, CNCM and GBSM models at a certain receiving point (the 13th receiving point is selected in this experiment), the following is obtained: Figure 8Simulation results. Through the power angle delay profile (PADP), we can get a conclusion similar to the large-scale results. The simulation results of CNCM and RT are consistent on the main multipath with high power and low delay. In PADP, the additional multipath of RT compared to CNCM is mainly caused by high-order reflection and diffraction. The power of these multipaths is almost lower than -25dB of the maximum path, which has little impact on the channel, so it can be basically ignored in modeling. Compared with RT, GBSM has greater multipath randomness and greater delay spread, which is obviously inconsistent with the multipath distribution of RT.

[0172] iii. Comparison of simulation results of performance parameters of three models:

[0173] By comparing the MIMO channel capacities of the RT, CNCM and GBSM models, we can obtain the following results: Fig. 9 Simulation results. The channel capacity of the CNCM model is very close to that of the RT model, with a difference of 2.7% at the 50% CDF point. However, due to the randomness of cluster generation in the GBSM modeling process, the channel capacity distribution span of the model is larger, with a difference of 18.6% at the 50% CDF point from the RT. Therefore, the channel capacity of the CNCM at the 50% CDF point is improved by 14.3% compared with the GBSM.

[0174] Comparison of modeling complexity of three models:

[0175] On the same hardware platform (i7 8750H CPU, 16GB DDR4-2666MHz RAM), the RT, CNCM and GBSM models were modeled and simulated multiple times to obtain the average modeling time, and then their model complexity was compared, as shown in Table 1. It can be seen that the model complexity of CNCM is only slightly higher than that of GBSM, which is 1.6 times. The model complexity of RT is much higher than that of GBSM and CNCM, and its simulation time is 663 times that of GBSM.

[0176] Table 1

[0177] Modeling approach RT CNCM GBSM Simulation time(s) 1260.3 3.1 1.9

[0178] Compared with the prior art, the present invention has the following advantages:

[0179] 1. Based on the concept of cluster core, the actual propagation environment is mapped to find the main scatterers and calculate the cluster core parameters, and channel modeling is performed based on the limited cluster core. This method gives the cluster core a physical meaning and combines the physical properties of the main scatterers in the environment (spatial position, shape, material, etc.) with the electromagnetic properties of the cluster core (propagation direction, power, delay, etc.).

[0180] 2. This method combines the advantages of deterministic modeling and statistical modeling to perform high-precision, low-complexity modeling for actual application scenarios. The model accuracy is significantly higher than the traditional statistical modeling method, while the complexity is only slightly higher than statistical modeling, but much lower than deterministic modeling.

[0181] 3. This method is more suitable for the future mobile communication system's needs for a wide variety of application scenarios, is more scenario-applicable, and can perform efficient and accurate channel modeling for specific actual application scenarios.

[0182] See also Fig.10 , Fig.10 Provided is an electronic device, comprising a processor; and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the processor executes the computer instructions to implement the following Figure 3 The method and refinement scheme shown.

[0183] It should be understood that the above device embodiments are only illustrative, and the device disclosed in the present invention can also be implemented in other ways. For example, the division of units / modules described in the above embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0184] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present invention may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0185] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. If not otherwise specified, the processor or chip may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. If not otherwise specified, the on-chip cache, the off-chip memory, and the memory may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc.

[0186] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0187] The present application also provides a non-transitory computer storage medium storing a computer program. When the computer program is executed by a plurality of processors, the processors execute the following Figure 3 The method and refinement scheme shown.

[0188] The embodiments of the present application are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and its core idea of ​​the present application. At the same time, changes or deformations made by those skilled in the art based on the ideas of the present application, the specific implementation methods and the scope of application of the present application, all belong to the scope of protection of the present application. In summary, the content of this specification should not be construed as a limitation on the present application.

Claims

1. A method for wireless channel modeling based on environmental information, comprising: Conduct three-dimensional environment modeling of the actual wireless propagation environment to obtain wireless propagation environment information; Perform cluster core matching by using the wireless propagation environment information to calculate cluster core parameters, wherein the cluster core parameters include cluster core power parameters, cluster core delay parameters and cluster core angle parameters; Calculating the parameters of the sub-paths within the cluster core based on the cluster core parameters, the wireless propagation environment information and the statistical distribution characteristics of the sub-paths within the cluster core; and Calculating a channel coefficient matrix according to the cluster core parameters and the parameters of the sub-paths within the cluster core; The using the wireless propagation environment information to perform cluster core matching and calculate cluster core parameters includes: The propagation path is calculated using geometric optics to obtain the spatial position of the cluster core that matches the actual scatterer in the environment, and then the angle and delay parameters of the cluster core propagation path are calculated; Calculating the power parameter of the cluster core according to the angle and delay parameters of the cluster core propagation path; Screening cluster cores based on the cluster core power parameters to determine final cluster cores and the number of cluster cores; The step of screening the cluster cores based on the cluster core power parameters to determine the final cluster cores and the number of cluster cores includes: Remove the cluster cores whose power values ​​are less than the preset power value, and obtain the number of cluster cores obtained after power calculation; In response to the number of cluster cores obtained after the power calculation being less than or equal to the number of cluster cores of the corresponding scenario in the channel model standard, determining the number of cluster cores obtained after the power calculation as the final number of cluster cores; and In response to the number of cluster cores obtained after the power calculation being greater than the number of cluster cores of the corresponding scenario in the channel model standard, retaining the number of cluster cores of the corresponding scenario in the channel model standard in descending order of cluster core power values; The calculating of the parameters of the sub-paths in the cluster core based on the cluster core parameters, the wireless propagation environment information and the statistical distribution characteristics of the sub-paths in the cluster core includes: Obtaining the delay information of the sub-paths in the cluster core according to the cluster core delay parameter; Calculating the sub-diameter angle information within the cluster core according to the cluster core angle parameter and the sub-diameter statistical distribution characteristics within the cluster core; and The power information of each sub-path is calculated based on the angle information of the sub-paths in the cluster core, the power angle spectrum distribution and the cluster core power parameter.

2. The method of claim 1, wherein: The three-dimensional environment modeling of the actual wireless propagation environment to obtain wireless propagation environment information includes: Acquire wireless environment and material information in the actual wireless propagation environment, and perform proportional three-dimensional environment modeling; The electromagnetic parameter information in the corresponding radio wave propagation frequency band is calculated through the wireless environment and material information.

3. The method of claim 1, wherein: The calculating of the channel coefficient matrix according to the cluster core parameters and the parameters of the sub-paths in the cluster core comprises: Calculating an impulse response of a subchannel between a receiving antenna and a transmitting antenna according to the cluster core parameter, the parameters of the subchannels in the cluster core, and the antenna pattern; and Each sub-channel is superimposed to obtain the channel coefficient matrix of the MIMO channel.

4. A device for wireless channel modeling based on environmental information, comprising: An environment modeling unit, used to perform three-dimensional environment modeling on the actual wireless propagation environment to obtain wireless propagation environment information; A first calculation unit, configured to perform cluster core matching by using the wireless propagation environment information, and calculate cluster core parameters, wherein the cluster core parameters include cluster core power parameters, cluster core delay parameters, and cluster core angle parameters; a second calculation unit, configured to calculate parameters of the sub-paths within the cluster core based on the cluster core parameters, the wireless propagation environment information, and statistical distribution characteristics of the sub-paths within the cluster core; and A third calculation unit, used for calculating a channel coefficient matrix according to the cluster core parameters and the parameters of the sub-paths in the cluster core; Wherein, the first computing unit is further used for: The propagation path is calculated using geometric optics to obtain the spatial position of the cluster core that matches the actual scatterer in the environment, and then the angle and delay parameters of the cluster core propagation path are calculated; Calculating the power parameter of the cluster core according to the angle and delay parameters of the cluster core propagation path; Screening cluster cores based on the cluster core power parameters to determine final cluster cores and the number of cluster cores; Remove the cluster cores whose power values ​​are less than the preset power value, and obtain the number of cluster cores obtained after power calculation; In response to the number of cluster cores obtained after the power calculation being less than or equal to the number of cluster cores of the corresponding scenario in the channel model standard, determining the number of cluster cores obtained after the power calculation as the final number of cluster cores; and In response to the number of cluster cores obtained after the power calculation being greater than the number of cluster cores of the corresponding scenario in the channel model standard, retaining the number of cluster cores of the corresponding scenario in the channel model standard in descending order of cluster core power values; Wherein, the second computing unit is further used for: Obtaining the delay information of the sub-paths in the cluster core according to the cluster core delay parameter; Calculating the sub-diameter angle information within the cluster core according to the cluster core angle parameter and the sub-diameter statistical distribution characteristics within the cluster core; and The power information of each sub-path is calculated based on the angle information of the sub-paths in the cluster core, the power angle spectrum distribution and the cluster core power parameter.

5. An electronic device comprising: processor; as well as A memory storing computer instructions, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 3.

6. A non-transitory computer storage medium storing a computer program, which, when executed by a plurality of processors, causes the processors to execute the method according to any one of claims 1 to 3.

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