A vehicle channel modeling method based on geometric model and related equipment

Abstract

The present invention provides a vehicle channel modeling method based on a geometric model and related equipment. The method includes constructing a broadband GBSM based on a dual-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models; determining the position information of a target scatterer in the broadband GBSM; calculating the CIR from a first vehicle array to a second vehicle array based on the broadband GBSM; and calculating channel characteristics based on the CIR. The present invention provides a broadband GBSM constructed based on the dual-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models, which can flexibly and efficiently simulate the distribution positions of scatterers in a city; and sets a multipath delay partitioning scheme based on different propagation delay differences and time-varying Doppler frequency shifts, and uses time-varying Doppler frequency shifts in channel characteristic calculations in the channel model, thereby more accurately characterizing the wireless propagation environment of urban V2V communication and thereby better describing the characteristics of urban vehicle flow density.

Description

Technical Field

[0001] The present invention relates to the technical field of channel modeling, and in particular to a vehicle channel modeling method based on a geometric model and related equipment. Background Art

[0002] Currently, motor vehicles often use wireless data transmission (V2V) to model channels, but existing channel models cannot flexibly simulate communication between urban vehicles and cannot efficiently simulate traffic density. Summary of the Invention

[0003] In view of this, an embodiment of the present invention provides a vehicle channel modeling method and related equipment based on a geometric model to solve the problem in the prior art that the existing channel model cannot flexibly simulate the communication between urban vehicles and cannot efficiently simulate the traffic flow density.

[0004] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0005] A first aspect of an embodiment of the present invention provides a vehicle channel modeling method based on a geometric model, the method comprising:

[0006] A broadband geometry-based random model (GBSM) is constructed based on a double-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models.

[0007] Determining position information of a target scatterer in the broadband geometry-based stochastic model (GBSM);

[0008] Calculating a channel impulse response (CIR) from the first vehicle array to the second vehicle array based on the broadband geometry-based stochastic model (GBSM);

[0009] Channel characteristics are calculated based on the channel impulse response CIR.

[0010] Optionally, the process of constructing a broadband geometry-based stochastic model (GBSM) according to the double-ring model, the four quarter-cylinder models, and the multiple confocal semi-ellipsoid models includes:

[0011] A double-ring model is constructed with a first vehicle array of the first vehicle as a transmitting end circle center and a second vehicle array of the second vehicle as a receiving end circle center;

[0012] Construct four quarter-cylinder models at the four corners corresponding to the intersection;

[0013] a confocal semi-ellipsoidal surface model constructed with a first vehicle array of the first vehicle and a second vehicle array of the second vehicle as foci;

[0014] The dual-ring model, four quarter-cylinder models and multiple confocal semi-ellipsoidal models are combined to construct a broadband GBSM.

[0015] Optionally, determining the position information of the target scatterer in the broadband geometry-based stochastic model (GBSM) includes:

[0016] Determining position information of a target scatterer within a dual-ring model in the broadband GBSM model;

[0017] Determining position information of a target scatterer within a cylindrical surface model in the broadband GBSM model;

[0018] Determine the position information of the target scatterer in the semi-ellipsoidal surface model in the broadband GBSM model.

[0019] Optionally, determining the position information of the target scatterer in the semi-ellipsoidal surface model in the broadband GBSM model includes:

[0020] Obtain the horizontal angle and elevation angle information of the target scatterer relative to the semi-ellipsoidal model within the semi-ellipsoidal model;

[0021] Calculating an arrival angle of the target scatterer relative to the second vehicle array based on horizontal angle and elevation angle information of the target scatterer relative to the semi-ellipsoidal surface model;

[0022] Calculating a departure angle of the target scatterer relative to the first vehicle array based on an arrival angle of the target scatterer relative to the second vehicle array;

[0023] Processing is performed based on the arrival angle of the target scatterer relative to the second vehicle array and the departure angle relative to the first vehicle array to obtain position information of the target scatterer.

[0024] Optionally, the calculating a channel impulse response CIR from the first vehicle array to the second vehicle array based on the broadband geometry-based stochastic model (GBSM) includes:

[0025] Obtaining the first path between the scatterer in the dual-ring model in the broadband GBSM and the scatterer on the semi-ellipsoidal surface corresponding to the time delay tap;

[0026] Obtaining a second path between a scatterer in a dual-ring model in a broadband GBSM and a scatterer on a semi-ellipsoidal surface corresponding to a time delay tap;

[0027] The channel impulse response (CIR) from the first vehicle array to the second vehicle array is calculated using the delay taps corresponding to the first path and the second path.

[0028] Optionally, the calculating the channel characteristics based on the channel impulse response CIR includes:

[0029] Calculating a temporal autocorrelation function (ACF) based on a channel impulse response (CIR) from the first vehicle array to the second vehicle array at a current time and a channel impulse response (CIR) from the first vehicle array to the second vehicle array at a preset delay, where the ACF is a channel feature;

[0030] Performing Fourier transform on the ACF to determine a Doppler power spectrum density (DPSD) simulation curve, where the DPSD simulation curve is a channel characteristic;

[0031] Calculating a spatial cross-correlation function (CCF) based on a channel impulse response (CIR) from the first vehicle array to the second vehicle array in a first propagation link and a channel impulse response (CIR) from the first vehicle array to the second vehicle array in a second propagation link at a current time, where the CCF is a channel feature;

[0032] A frequency cross-correlation function FCF is calculated based on Fourier transform of a CIR channel impulse response from the first vehicle array to the second vehicle array at the current time, where the FCF belongs to a channel feature.

[0033] A second aspect of an embodiment of the present invention shows a device for modeling an urban vehicle channel based on a geometric model, the device comprising:

[0034] A construction unit for constructing a broadband geometry-based random model GBSM based on a double-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models;

[0035] The simulation processing unit is used to determine the position information of the target scatterer in the broadband geometry-based random model (GBSM); calculate the channel impulse response (CIR) from the first vehicle array to the second vehicle array based on the broadband geometry-based random model (GBSM); and calculate the channel characteristics based on the channel impulse response (CIR).

[0036] Optionally, the construction unit is specifically configured to: construct a double-ring model with a first vehicle array of the first vehicle as a transmitting end circle center, and with a second vehicle array of the second vehicle as a receiving end circle center;

[0037] Construct four quarter-cylinder models at the four corners corresponding to the intersection;

[0038] a confocal semi-ellipsoidal surface model constructed with a first vehicle array of the first vehicle and a second vehicle array of the second vehicle as foci;

[0039] The dual-ring model, four quarter-cylinder models and multiple confocal semi-ellipsoidal models are combined to construct a broadband GBSM.

[0040] A third aspect of an embodiment of the present invention shows an electronic device, which is used to run a program, wherein when the program is run, the vehicle channel modeling method based on the geometric model as shown in the first aspect of the embodiment of the present invention is executed.

[0041] The fourth aspect of an embodiment of the present invention shows a computer storage medium, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the vehicle channel modeling method based on the geometric model as shown in the first aspect of the embodiment of the present invention.

[0042] Based on the above-mentioned embodiments of the present invention, a vehicle channel modeling method based on a geometric model and related equipment are provided. The method includes: constructing a broadband geometry-based random model (GBSM) based on a dual-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models; determining the position information of target scatterers in the broadband geometry-based random model (GBSM); calculating the channel impulse response (CIR) from a first vehicle array to a second vehicle array based on the broadband geometry-based random model (GBSM); and calculating channel characteristics based on the channel impulse response (CIR). Embodiments of the present invention provide a broadband GBSM constructed based on a dual-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models. This method can flexibly and efficiently simulate the scattering environment of a city, namely, the distribution of scatterers. It also sets a multipath delay partitioning scheme based on different propagation delay differences and time-varying Doppler shifts, and uses time-varying Doppler shifts in the channel characteristic calculation of the channel model to more accurately characterize the wireless propagation environment of urban V2V communication, thereby better describing the characteristics of urban vehicle traffic density. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0044] Figure 1 A schematic flow chart of a vehicle channel modeling method based on a geometric model according to an embodiment of the present invention;

[0045] Figure 2 A schematic diagram illustrating the architecture of a broadband geometry-based stochastic model (GBSM) according to an embodiment of the present invention is provided;

[0046] Figure 3 The figure is a schematic structural diagram of a vehicle channel modeling device based on a geometric model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] The terms "first," "second," "third," "fourth," and the like (if any) in the specification and claims of this application and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequential sequence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0049] It should be noted that the descriptions of "first", "second", etc. in the present invention are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0050] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0051] See also Figure 1 , which is a flow chart of a vehicle channel modeling method based on a geometric model according to an embodiment of the present invention.

[0052] Step S101: constructing a broadband geometry-based random model (GBSM) according to a double-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models.

[0053] In step S101, the dual-ring model is constructed with the first vehicle array of the first vehicle as the center of the transmitting end and the second vehicle array of the second vehicle as the center of the receiving end. The quarter-cylinder model is constructed with the four corners corresponding to the intersection respectively. The confocal semi-ellipsoid model is constructed with the first vehicle array of the first vehicle and the second vehicle array of the second vehicle as the focus.

[0054] It should be noted that the process of constructing a broadband geometry-based random model GBSM based on the double-ring model, the four quarter-cylinder models, and the multiple confocal semi-ellipsoid models includes:

[0055] Step S11: constructing a double-ring model with the first vehicle array of the first vehicle as the center of the transmitting end circle and the second vehicle array of the second vehicle as the center of the receiving end circle.

[0056] It is understandable that in a broadband communication system, a planar antenna array (UPA) with high space utilization is advantageous for deploying large-scale multiple input multiple output (MIMO). Therefore, the first vehicle array in the first vehicle is, that is, the receiving end M R and the second vehicle array of the second vehicle, i.e., the transmitting end M T Both are equipped with UPA arrays.

[0057] The first element of the UPA array is located at the focus of the semi-ellipsoid (the center of the double ring). T The UPA array consists of P rows and Q columns of antennas with a spacing of δ T The antenna array is composed of M R The UPA array consists of R rows and M columns of antennas with a spacing of δ R The antenna array is composed of .

[0058] In the specific implementation of step S11, in the urban vehicle communication scenario, a first vehicle and a second vehicle for communication are determined; the first vehicle array M of the first vehicle is T As the center point of a circle, the second vehicle array M of the second vehicle R As the point at the center of another circle, the distance from the position of the scatterer to the initial position of the first vehicle array of the first vehicle or the second vehicle array of the second vehicle is used as the radius. The scatterer in this case can be the traffic vehicles around the first vehicle or the second vehicle; then a double-ring model corresponding to the first vehicle and the second vehicle is drawn.

[0059] It should be noted that, in the T The scatterers distributed on the circle centered at M describe the distribution of traffic vehicles around the transmitter, that is, the scatterers in the circle corresponding to the first vehicle array can be represented by n1; R The scatterers distributed on the circle with n as the center describe the distribution of traffic vehicles around the receiving end, that is, the scatterers in the circle corresponding to the second vehicle array can be represented by n2.

[0060] Scatterers can be static scatterers such as trees, billboards, etc. and dynamic scatterers such as pedestrians or vehicles, etc.

[0061] Step S12: construct four quarter-cylinder models at the four corners corresponding to the intersection.

[0062] In the specific implementation of step S12, the four corners corresponding to the intersection where the first vehicle and the second vehicle are located are equidistant from the intersection, and four quarter-cylinder models are constructed based on the height and axis of the preset cylinder.

[0063] It should be noted that the scatterers on the quarter cylinder and the semi-ellipsoid represent the scatterers on the surface of the building and other scatterers in the air (such as drones and billboards, etc.), respectively. That is to say, the scatterers within the quarter cylinder can be represented by n3; the scatterers on the semi-ellipsoid can be represented by n4.

[0064] Step S13: constructing a confocal semi-ellipsoidal surface model with the first vehicle array of the first vehicle and the second vehicle array of the second vehicle as foci.

[0065] In the specific implementation of step S13, in the urban vehicle communication scenario, a first vehicle and a second vehicle for communication are determined; the first vehicle array M of the first vehicle is set to T , and a second vehicle array M of the second vehicle R Multiple semi-ellipsoidal surface models are drawn as a common focus, i.e., confocal.

[0066] Step S14: combining the double-ring model, four quarter-cylinder models and multiple confocal semi-ellipsoidal models to construct a broadband GBSM.

[0067] Based on the implementation process of step S11 to step S14, as Figure 2 As shown, Figure 2 Only one confocal semi-ellipsoidal surface model is shown.

[0068] Figure 2 The identifiers N of the various symbols are also shown. i represents the scatterer n i The number of SBi Represents the scatterer n i The single-hop path component of DB ij Represents the scatterer n i and n j The two-hop path component of represents the scatterer n i The phase change of the single-hop path caused by represents the phase change of the double-hop path caused by scatterers n1 and n2, Indicates that from M T The antenna array element in the pth row and qth column is connected to the scatterer n i The displacement vector, Indicates that from M R The rth row and mth column antenna array element to the scatterer n i The displacement vector, represents the displacement vector from scatterer n2 to scatterer n1, Indicates that from M T(R) The first antenna element to the scatterer n i The displacement vector, v T and v R Denote the scatterer n i 、M T and M R The velocity vector, and Denote the scatterer n i The horizontal angle and pitch angle relative to the corresponding circle center, α s is the angle that controls the direction of vehicle movement, λ represents the wavelength, and f0 represents the distance from the initial position of the first antenna element at the transceiver end to the center of the intersection. Describes the scatterer n i The radius of the corresponding circle of the distribution, d1 represents the vertical distance from the center of the base of the quarter cylinder to the road.

[0069] Among them, i is 1, 2, 3, 4; j is 1, 2, 3, 4.

[0070] Step S102: determining position information of a target scatterer in the broadband geometry-based stochastic model (GBSM).

[0071] Optionally, for the convenience of simulation, it is assumed that the antenna arrays at the transmitting and receiving ends, that is, the first vehicle array and the second vehicle array, are both located on a horizontal plane. T The first antenna element to M T The displacement vector of the antenna array element in the pth row and qth column It can be expressed by formula (1), from M R The first antenna element to M RThe displacement vector of the antenna array element in the rth row and mth column It can be expressed by formula (2).

[0072] Formula (1):

[0073]

[0074] Formula (2):

[0075]

[0076] Among them, δ T is the antenna spacing of p rows and q columns in the first vehicle array, δ R is the antenna spacing of r rows and m columns in the second vehicle array.

[0077] It should be noted that the specific implementation of step S102 includes the following steps:

[0078] Step S21: determining the position information of the target scatterer in the dual-ring model in the broadband GBSM model.

[0079] In the specific process of implementing step S21 , the horizontal angle of the target scatterer relative to the circle in the double-ring model is used as the position information of the target scatterer.

[0080] Step S22: determining the position information of the target scatterer in the cylindrical surface model in the broadband GBSM model.

[0081] In the specific process of implementing step S22, the horizontal angle and the pitch angle of the center of the bottom surface of the cylindrical surface model corresponding to the target scatterer are used as the position information of the target scatterer.

[0082] Step S23: determining the position information of the target scatterer in the semi-ellipsoidal surface model in the broadband GBSM model.

[0083] It should be noted that the specific process of implementing step S23 includes the following steps:

[0084] Step S31: obtaining horizontal angle and elevation angle information of the target scatterer in the semi-ellipsoidal model relative to the semi-ellipsoidal model;

[0085] Step S32: Calculating the arrival angle of the target scatterer relative to the second vehicle array based on the horizontal angle and elevation angle information of the target scatterer relative to the semi-ellipsoidal surface model.

[0086] In the specific implementation of step S32, the horizontal angle and pitch angle information of the target scatterer relative to the semi-ellipsoidal model are substituted into formula (3) to calculate the arrival angle of the target scatterer relative to the second vehicle array, that is, the phase change of the single-hop path caused by the scatterer n4

[0087] Formula (3):

[0088]

[0089] in, is the horizontal angle of the target scatterer n4 relative to the semi-ellipsoidal model, is the pitch angle of the target scatterer n4 relative to the semi-ellipsoidal model.

[0090] Step S33: Based on the arrival angle of the target scatterer relative to the second vehicle array Calculate the target scatterer relative to the first vehicle array M T The departure angle, that is, the phase change of the single-hop path caused by the scatterer n4

[0091] In the specific implementation of step S33, due to the geometric characteristics of the ellipsoid, the scatterer n4 located on the semi-ellipsoid is relatively close to the first vehicle array, that is, the transmitting end M T Departure angle and Satisfy a certain numerical relationship, that is, Substituting into formula (4) we can get

[0092] Formula (4):

[0093]

[0094] in, The parameter κ0 is the inverse of the eccentricity of the ellipse;

[0095] Step S34: performing processing based on the arrival angle of the target scatterer relative to the second vehicle array and the departure angle relative to the first vehicle array to obtain position information of the target scatterer.

[0096] In the specific implementation of step S34, the arrival angle of the target scatterer relative to the second vehicle array and the departure angle relative to the first vehicle array are substituted into formula (5) to calculate the position information of the target scatterer, that is, the displacement vector of the target scatterer to the second vehicle array

[0097] Formula (5):

[0098]

[0099] in, is the horizontal angle of the target scatterer n4 relative to the semi-ellipsoidal model, is the pitch angle of the target scatterer n4 relative to the semi-ellipsoidal model; is the departure angle of the target scatterer relative to the first vehicle array, is the arrival angle of the target scatterer relative to the second vehicle array.

[0100] Optionally, since the scatterers in the dual-ring model and on some semi-ellipsoidal surfaces in the broadband GBSM are moving, the time delay can be represented by τ, and the vector in the position information can be expressed using the following formula.

[0101] Wherein, formula (6) represents the circular ring corresponding to the first vehicle array of the dual-ring model in the broadband GBSM. At this time, the displacement vector of the scatterer moving in the circular ring is

[0102] Formula (6):

[0103]

[0104] Wherein, formula (7) represents the circular ring corresponding to the second vehicle array of the dual-ring model in the broadband GBSM. At this time, the displacement vector of the scatterer moving in the circular ring is

[0105] Formula (7):

[0106]

[0107] Wherein, formula (8) represents the displacement vector of the scatterer moving in the semi-ellipsoid model in the broadband GBSM:

[0108] Formula (8):

[0109]

[0110] It should be noted that the formulas (6), (7) and (8) v T and v R Denote the scatterer n i 、M T and M R The velocity vector of ; τ is the time delay; is the first vehicle array M T The antenna array element in the pth row and qth column is connected to the scatterer n i The displacement vector of is the second vehicle array M R The rth row and mth column antenna array element to the scatterer n i The displacement vector of Displacement vector from scatterer n2 to scatterer n1.

[0111] Optionally, based on the position information of different scatterers obtained by the above-mentioned step S102, the density of the scatterer distribution can be determined; by describing the power distribution of different multipath components and the density of the scatterer distribution, the vehicle traffic density (VTD) characteristics in the vehicle-to-vehicle (V2V) communication scenario can be characterized. For example, in a low VTD scenario, assuming that the vehicle moves faster, the power of the direct path component is greater, and the power of the received scattering path mainly comes from the static scattering environment around the road, and the power of the single-hop path is higher than that of the double-hop path. The present invention fully considers the impact of vehicles on the scattering environment through this VTD feature simulation method, and can effectively reflect the distribution of vehicles.

[0112] It should be noted that in actual urban V2V communication scenarios, the larger the VTD, the more scatterers there are around the vehicle in the transceiver section, resulting in a decrease in the single-hop component in the multipath components and an increase in the proportion of the double-hop path components in the effective multipath. That is, in high-VTD scenarios, the double-hop path components should be allocated a higher normalized power than the single-hop path components. Combined with the scatterer distribution characteristics on the dual ring, the present invention uses a uniform distribution of scatterers on the dual ring and a higher single-hop path power allocation to simulate vehicle distribution in low-VTD scenarios, and uses a concentrated distribution of scatterers on the dual ring near the center of the intersection and a higher double-hop path power allocation to simulate vehicle distribution in high-VTD urban scenarios.

[0113] Step S103: Calculating a channel impulse response CIR based on the broadband geometry-based stochastic model GBSM.

[0114] It should be noted that the specific implementation of step S104 includes the following steps:

[0115] Existing broadband models imperfectly partition multipath components. For example, two-hop paths passing through scatterers on a double loop do not have identical propagation delays; their delays should fall within a certain range. Assigning these two-hop paths to a fixed delay will lead to bias in the broadband model. Therefore, it is necessary to consider the same multipath components under different delay taps. Flexible delay partitioning is implemented for multipath components in urban vehicular communication scenarios. This approach uses L delay taps, which equal the number of semi-ellipsoidal models.

[0116] Step S41: obtaining a first path between a scatterer in a dual-ring model in a broadband GBSM and a scatterer on a semi-ellipsoidal surface corresponding to a time delay tap.

[0117] In the specific implementation of step S41, the first path between the scatterer in the dual-ring model in the broadband GBSM and the scatterer on the semi-ellipsoidal surface corresponding to the delay tap is collected based on L2 delay taps of the first delay.

[0118] It should be noted that the first delay is a preset shorter delay.

[0119] L2 is a pre-set number. This method uses this to simulate various intersection scattering environments. For example, when the delay range of the path through the scatterers on the dual ring is small, a smaller L2 can be set, using fewer delay taps to simulate shorter delays. This flexible multipath delay partitioning allows for more rational allocation of effective multipath components to different delay taps, enabling detailed simulation of actual urban vehicle communication scenarios. The number of L2s is smaller than the number of Ls.

[0120] The first path, i.e. the shorter path, includes SB1, SB2, SB4 and DB 12 .

[0121] SB1 represents a single-hop path through scatterer n1, SB2 represents a single-hop path through scatterer n2, SB4 represents a single-hop path through scatterer n4, and DB 12 represents a two-hop path passing through scatterers n1 and n2.

[0122] A single-hop path refers to a propagation path from the transmitter to the receiver that passes through only one scatterer; a double-hop path refers to a propagation path from the transmitter to the receiver that passes through two scatterers, i.e., two scatterings.

[0123] Step S42: obtaining a second path between a scatterer in the dual-ring model in the broadband GBSM and a scatterer on the semi-ellipsoidal surface corresponding to the time delay tap.

[0124] During the specific matter step S42, L-L2 delay taps based on the second delay collect the second path between the scatterer on the double ring in the broadband GBSM, the scatterer on the semi-ellipsoidal surface corresponding to the delay tap, and the farther path of the scatterer on the quarter-cylinder surface.

[0125] It should be noted that the second delay is a preset longer delay.

[0126] The second path, i.e. the longer path, specifically includes SB3, SB4, DB 13 , DB 32 , DB 14 and DB 42 .

[0127] Among them, SB3 represents the single-hop path through scatterer n3, SB4 represents the single-hop path through scatterer n4, and DB 13represents the two-hop path through scatterers n1 and n3, DB 32 represents a two-hop path through scatterers n3 and n2; DB 14 represents the two-hop path through scatterers n1 and n4, DB 42 represents a two-hop path passing through scatterers n4 and n2.

[0128] Step S43: Calculate the channel impulse response CIR from the first vehicle array to the second vehicle array using the delay taps corresponding to the first path and the second path, that is, determine the channel impulse response CIR from the first vehicle array M T The antenna array element of the pth row and qth column is connected to the second vehicle array M R The channel impulse response of the link with the rth row and mth column antenna array element.

[0129] In the specific implementation of step S43, the delay taps corresponding to the first path and the second path are substituted into formula (9) for calculation to obtain the channel impulse response.

[0130] Formula (9):

[0131]

[0132] Among them, τ' refers to the preset delay independent variable, τ l ' is the delay value of the corresponding lth delay tap, for the first path the corresponding delay tap Where l = 1, ..., L2; for the second path, the corresponding delay tap h l,rm,pq (t); Where l=L2+1,...,L.

[0133] Furthermore, it should be noted that For SB i The channel impulse response is shown in formula (10).

[0134] Formula (10):

[0135]

[0136] in, Represents the single-hop path component SB i The normalized power of SB i Doppler frequency SB i The receiving phase in,

[0137] Furthermore, it should be noted that For DB ij The channel impulse response is shown in formula (11).

[0138] Formula (11):

[0139]

[0140] in, Represents the two-hop path component DB ij Normalized power of . DB ij Doppler frequency DB ij The receiving phase in,

[0141] Step S104: Calculate channel characteristics based on the channel impulse response CIR.

[0142] The channel characteristics include the time autocorrelation function (ACF), the spatial cross-correlation function (CCF), the Doppler power spectral density (DPSD) simulation curve, and the frequency cross-correlation function (FCF).

[0143] It should be noted that the specific process of implementing step S105 includes the following steps:

[0144] Step S51: calculating a temporal autocorrelation function (ACF) based on a channel impulse response (CIR) from the first vehicle array to the second vehicle array at the current time and a channel impulse response (CIR) from the first vehicle array to the second vehicle array at a preset delay.

[0145] In the specific implementation of step S51, based on the current time t, the first vehicle array M T The antenna array element of the pth row and qth column is connected to the second vehicle array M R The channel impulse response of the link of the rth row and mth column antenna array element, and the current time t determined under the preset delay τ from the first vehicle array M T The antenna array element of the pth row and qth column is connected to the second vehicle array M R Substitute the channel impulse response of the link with the rth row and mth column antenna array element into formula (12) to calculate the ACF.

[0146] Formula (12):

[0147]

[0148] in, is ACF, h rm,pq (t) is the time from the first vehicle array M at the current time t T The antenna array element of the pth row and qth column is connected to the second vehicle array M R The channel impulse response of the link with the r-th row and m-th column antenna array element; is the number of vehicles from the first vehicle array M determined at the current time t under the preset time delay τ T The antenna array element of the pth row and qth column is connected to the second vehicle array M R The channel impulse response of the link with the rth row and mth column antenna array element.

[0149] It should be noted that ACF is usually used to measure the correlation of the same signal propagation link at different times.

[0150] The transmission link refers to the sending end M T The antenna array element in the pth row and qth column is connected to the receiving end M R A physical line of the rth row and mth column antenna array element without any other switching nodes in the middle.

[0151] Step S52: Perform Fourier transform on the ACF to determine a Doppler power spectrum density DPSD simulation curve.

[0152] In the specific implementation of step S52, the ACF is substituted into formula (13) for Fourier transformation to obtain the Doppler power spectrum density DPSD simulation curve S rm,pq (f).

[0153] Formula (13):

[0154]

[0155] Wherein, f represents the preset Doppler frequency.

[0156] Step S53: Calculate the spatial cross-correlation function CCF based on the channel impulse response CIR from the first vehicle array to the second vehicle array in the first propagation link and the channel impulse response CIR from the first vehicle array to the second vehicle array in the second propagation link at the current time.

[0157] It should be noted that the first transmission link and the second transmission link are the sending end M T The antenna array element in the pth row and qth column is connected to the receiving end M R Different physical lines of the r-th row and m-th column antenna array element.

[0158] In the specific implementation of step S53, based on the current time t, the first vehicle array M TThe antenna array element of the pth row and qth column is connected to the second vehicle array M R The channel impulse response h of the rth row and mth column antenna array element in the first propagation link rm,pq (t), and the current time t from the first vehicle array M T The antenna array element of the pth row and qth column is connected to the second vehicle array M R The channel impulse response of the rth row and mth column antenna array element in the second propagation link is Substitute into formula (14) to calculate and obtain the spatial cross-correlation function CCF.

[0159]

[0160] Among them, ρ rmpq,r'm'p'q' (δ T ,δ R ,t) is CCF.

[0161] Optionally, the ACF may be calculated based on the ACF of the delay tap. Specifically, the delay tap of the first delay is substituted into formula (15) to obtain the time correlation function ACF.

[0162] Formula (15):

[0163]

[0164] The delay taps include a first delay tap and a second delay tap.

[0165] The delay tap of the first delay is l is 1,2...,L2.

[0166] The delay tap of the second delay is Where l=L2+1,...,L.

[0167] Furthermore, it should be noted that in the calculation of multipath components, the calculation of different double-hop path components is similar, and only the single-hop path component SB is given here. i and two-hop path component DB ij calculation process.

[0168] Single-hop path component SB i The ACF of can be expressed as in,

[0169] Single-hop path component SB i The CCF can be expressed as

[0170] in,

[0171] Two-hop path component DB12 The ACF of can be expressed as

[0172] in,

[0173] Two-hop path component DB 12 The CCF can be expressed as

[0174] in,

[0175] Step S54: Calculating a frequency cross-correlation function (FCF) based on the Fourier transform of the CIR channel impulse response from the first vehicle array to the second vehicle array at the current time.

[0176] In the specific implementation of step S54, the first vehicle array M T The antenna array element of the pth row and qth column is connected to the second vehicle array M R Perform Fourier transform on the CIR of the rth row and mth column antenna array element to obtain the Fourier transform of CIR H rm,pq (t,f'); and the Fourier transform H of CIR rm,pq (t,f') is conjugated to obtain Based on the Fourier transform H of the CIR rm,pq (t,f') and Substituting into formula (16) for calculation, we can obtain the frequency cross-correlation function FCF.

[0177] Formula (16):

[0178]

[0179] It should be noted that

[0180] Where V' is the frequency interval, P l It means dividing the delay into L time points / TAPs / delay taps. The sum of the power of all propagation paths of the lth time delay / tap / TAP is P l , which is equivalent to the normalized power of the propagation path / multipath corresponding to a certain time delay when the power of all propagation paths is 1.

[0181] In this embodiment of the present invention, since the broadband channel model uses an infinite number of scatterers to simulate the distribution, the computational complexity of the simulation is relatively high. Therefore, a modified equal-area method is used to approximate the distribution information of a finite number of scatterers in the simulation model. Therefore, it is necessary to first calculate various statistical characteristics of the broadband channel model, namely, channel characteristics. The channel characteristics of the simulated model are then compared with those of the original model to verify their consistency.

[0182] Furthermore, it should be noted that the original model assuming infinite scatterers is used for comparison in the simulation. i Taking a single-hop component as an example, the calculation process of the ACF of the original model is shown in formula (17); the calculation process of the CCF of the original model is shown in formula (18).

[0183] Formula (17):

[0184]

[0185] Formula (18):

[0186]

[0187] in, is the scatterer n i Probability density function of the horizontal angle distribution relative to the corresponding circle center.

[0188] In an embodiment of the present invention, a broadband GBSM is constructed based on a dual-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models, which can flexibly and efficiently simulate the scattering environment of the city, that is, the distribution position of the scatterers; and set a multipath delay partitioning scheme under different propagation delay differences and time-varying Doppler frequency shifts, and use time-varying Doppler frequency shifts in the channel characteristic calculation of the channel model to more accurately characterize the wireless propagation environment of urban V2V communication, thereby better describing the traffic density characteristics of the city.

[0189] Based on the vehicle channel modeling method based on the geometric model shown in the above embodiment of the present invention, the embodiment of the present invention also discloses a structural diagram of an urban vehicle channel modeling device based on the geometric model, as shown in FIG. Figure 3 As shown, the device includes:

[0190] A construction unit 301 is configured to construct a broadband geometry-based stochastic model (GBSM) based on a double-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models;

[0191] The simulation processing unit 302 is used to determine the position information of the target scatterer in the broadband geometry-based random model (GBSM); calculate the channel impulse response (CIR) from the first vehicle array to the second vehicle array based on the broadband geometry-based random model (GBSM); and calculate channel characteristics based on the channel impulse response (CIR).

[0192] It should be noted that the specific principles and execution processes of each unit in the urban vehicle channel modeling device based on the geometric model disclosed in the above embodiment of the present application are the same as the vehicle channel modeling method based on the geometric model shown in the above embodiment of the present application. Please refer to the corresponding parts of the commodity processing method disclosed in the above embodiment of the present application, and no further details will be given here.

[0193] In an embodiment of the present invention, a broadband GBSM is constructed based on a dual-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models, which can flexibly and efficiently simulate the scattering environment of the city, that is, the distribution position of the scatterers; and set a multipath delay partitioning scheme under different propagation delay differences and time-varying Doppler frequency shifts, and use time-varying Doppler frequency shifts in the channel characteristic calculation of the channel model to more accurately characterize the wireless propagation environment of urban V2V communication, thereby better describing the traffic density characteristics of the city.

[0194] Optionally, in a vehicle channel modeling method based on a geometric model, the construction unit 301 is specifically configured to: construct a double-ring model with a first vehicle array of the first vehicle as a transmitting end circle center and a second vehicle array of the second vehicle as a receiving end circle center;

[0195] Construct four quarter-cylinder models at the four corners corresponding to the intersection;

[0196] a confocal semi-ellipsoidal surface model constructed with a first vehicle array of the first vehicle and a second vehicle array of the second vehicle as foci;

[0197] The dual-ring model, four quarter-cylinder models and multiple confocal semi-ellipsoidal models are combined to construct a broadband GBSM.

[0198] Optionally, in the vehicle channel modeling method based on a geometric model, the simulation processing unit 302 for determining the position information of the target scatterer in the broadband geometry-based stochastic model (GBSM) is specifically configured to:

[0199] Determining position information of a target scatterer within a dual-ring model in the broadband GBSM model;

[0200] Determining position information of a target scatterer within a cylindrical surface model in the broadband GBSM model;

[0201] Determine the position information of the target scatterer in the semi-ellipsoidal surface model in the broadband GBSM model.

[0202] Optionally, in the vehicle channel modeling method based on a geometric model, the simulation processing unit 302 for determining the position information of the target scatterer within the semi-ellipsoidal surface model in the broadband GBSM model is specifically configured to:

[0203] Obtain the horizontal angle and elevation angle information of the target scatterer relative to the semi-ellipsoidal model within the semi-ellipsoidal model;

[0204] Calculating an arrival angle of the target scatterer relative to the second vehicle array based on horizontal angle and elevation angle information of the target scatterer relative to the semi-ellipsoidal surface model;

[0205] Calculating a departure angle of the target scatterer relative to the first vehicle array based on an arrival angle of the target scatterer relative to the second vehicle array;

[0206] Processing is performed based on the arrival angle of the target scatterer relative to the second vehicle array and the departure angle relative to the first vehicle array to obtain position information of the target scatterer.

[0207] Optionally, in the vehicle channel modeling method based on a geometric model, the processing unit 302 for calculating the channel impulse response CIR from the first vehicle array to the second vehicle array based on the broadband geometry-based stochastic model (GBSM) is specifically configured to:

[0208] Obtaining the first path between the scatterer in the dual-ring model in the broadband GBSM and the scatterer on the semi-ellipsoidal surface corresponding to the time delay tap;

[0209] Obtaining a second path between a scatterer in a dual-ring model in a broadband GBSM and a scatterer on a semi-ellipsoidal surface corresponding to a time delay tap;

[0210] The channel impulse response (CIR) from the first vehicle array to the second vehicle array is calculated using the delay taps corresponding to the first path and the second path.

[0211] Optionally, in the vehicle channel modeling method based on a geometric model, the simulation processing unit 302 for calculating channel characteristics based on the channel impulse response CIR is specifically configured to:

[0212] Calculating a temporal autocorrelation function (ACF) based on a channel impulse response (CIR) from the first vehicle array to the second vehicle array at a current time and a channel impulse response (CIR) from the first vehicle array to the second vehicle array at a preset delay, where the ACF is a channel feature;

[0213] Performing Fourier transform on the ACF to determine a Doppler power spectrum density (DPSD) simulation curve, where the DPSD simulation curve is a channel characteristic;

[0214] Calculating a spatial cross-correlation function (CCF) based on a channel impulse response (CIR) from the first vehicle array to the second vehicle array in a first propagation link and a channel impulse response (CIR) from the first vehicle array to the second vehicle array in a second propagation link at a current time, where the CCF is a channel feature;

[0215] A frequency cross-correlation function FCF is calculated based on Fourier transform of a CIR channel impulse response from the first vehicle array to the second vehicle array at the current time, where the FCF belongs to a channel feature.

[0216] The embodiment of the present invention also discloses an electronic device for running a database storage process, wherein the above-mentioned Figure 1 A vehicle channel modeling method based on a disclosed geometric model.

[0217] The embodiment of the present invention also discloses a computer storage medium, wherein the storage medium includes a storage database storage process, wherein when the database storage process is running, the device where the storage medium is located is controlled to execute the above Figure 1 A vehicle channel modeling method based on a disclosed geometric model.

[0218] In the context of the present disclosure, computer storage media can be tangible media that can contain or store programs for use by or in conjunction with an instruction execution system, device or equipment. Machine-readable media can be machine-readable signal media or machine-readable storage media. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0219] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0220] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0221] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle channel modeling method based on a geometric model, characterized in that: The method comprises: A broadband geometry-based random model (GBSM) is constructed based on a double-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models. Determining position information of a target scatterer within a dual-ring model in the broadband GBSM model; Determining position information of a target scatterer within a cylindrical surface model in the broadband GBSM model; Obtain the horizontal angle and elevation angle information of the target scatterer relative to the semi-ellipsoidal model within the semi-ellipsoidal model; Calculating an arrival angle of the target scatterer relative to the second vehicle array based on horizontal angle and elevation angle information of the target scatterer relative to the semi-ellipsoidal surface model; Calculating a departure angle of the target scatterer relative to the first vehicle array based on an arrival angle of the target scatterer relative to the second vehicle array; Processing is performed based on an arrival angle of the target scatterer relative to the second vehicle array and a departure angle relative to the first vehicle array to obtain position information of the target scatterer; Calculating a channel impulse response (CIR) from the first vehicle array to the second vehicle array based on the broadband geometry-based stochastic model (GBSM); Channel characteristics are calculated based on the channel impulse response CIR.

2. The method according to claim 1, characterized in that The process of constructing a broadband geometry-based random model GBSM according to the double-ring model, the four quarter-cylinder models and the multiple confocal semi-ellipsoid models includes: A double-ring model is constructed with a first vehicle array of the first vehicle as a transmitting end circle center and a second vehicle array of the second vehicle as a receiving end circle center; Construct four quarter-cylinder models at the four corners corresponding to the intersection; a confocal semi-ellipsoidal surface model constructed with a first vehicle array of the first vehicle and a second vehicle array of the second vehicle as foci; The dual-ring model, four quarter-cylinder models and multiple confocal semi-ellipsoidal models are combined to construct a broadband GBSM.

3. The method according to claim 1, characterized in that The calculating, based on the broadband geometry-based stochastic model (GBSM), a channel impulse response (CIR) from the first vehicle array to the second vehicle array includes: Obtaining the first path between the scatterer in the dual-ring model in the broadband GBSM and the scatterer on the semi-ellipsoidal surface corresponding to the time delay tap; Obtaining a second path between a scatterer in a dual-ring model in a broadband GBSM and a scatterer on a semi-ellipsoidal surface corresponding to a time delay tap; The channel impulse response (CIR) from the first vehicle array to the second vehicle array is calculated using the delay taps corresponding to the first path and the second path.

4. The method according to claim 1, wherein The calculating the channel characteristics based on the channel impulse response CIR includes: Calculating a temporal autocorrelation function (ACF) based on a channel impulse response (CIR) from the first vehicle array to the second vehicle array at a current time and a channel impulse response (CIR) from the first vehicle array to the second vehicle array at a preset delay, where the ACF is a channel feature; Performing Fourier transform on the ACF to determine a Doppler power spectrum density (DPSD) simulation curve, where the DPSD simulation curve is a channel characteristic; Calculating a spatial cross-correlation function (CCF) based on a channel impulse response (CIR) from the first vehicle array to the second vehicle array in a first propagation link and a channel impulse response (CIR) from the first vehicle array to the second vehicle array in a second propagation link at a current time, where the CCF is a channel feature; A frequency cross-correlation function FCF is calculated based on Fourier transform of a CIR channel impulse response from the first vehicle array to the second vehicle array at the current time, where the FCF belongs to a channel feature.

5. A vehicle channel modeling device based on a geometric model, characterized in that: The device comprises: A construction unit for constructing a broadband geometry-based random model GBSM based on a double-ring model, four quarter-cylinder models, and multiple confocal semi-ellipsoid models; a simulation processing unit, configured to determine position information of a target scatterer in the broadband geometry-based stochastic model (GBSM); calculate a channel impulse response (CIR) from the first vehicle array to the second vehicle array based on the broadband geometry-based stochastic model (GBSM); and calculate channel characteristics based on the channel impulse response (CIR); The simulation processing unit for determining the position information of the target scatterer in the broadband geometry-based stochastic model GBSM is specifically used to: Determining position information of a target scatterer within a dual-ring model in the broadband GBSM model; Determining position information of a target scatterer within a cylindrical surface model in the broadband GBSM model; Determining position information of a target scatterer within a semi-ellipsoidal surface model in the broadband GBSM model; The simulation processing unit for determining the position information of the target scatterer in the semi-ellipsoidal surface model in the broadband GBSM model is specifically used to: Obtain the horizontal angle and elevation angle information of the target scatterer relative to the semi-ellipsoidal model within the semi-ellipsoidal model; Calculating an arrival angle of the target scatterer relative to the second vehicle array based on horizontal angle and elevation angle information of the target scatterer relative to the semi-ellipsoidal surface model; Calculating a departure angle of the target scatterer relative to the first vehicle array based on an arrival angle of the target scatterer relative to the second vehicle array; Processing is performed based on the arrival angle of the target scatterer relative to the second vehicle array and the departure angle relative to the first vehicle array to obtain position information of the target scatterer.

6. The device according to claim 5, characterized in that The construction unit is specifically configured to: construct a double-ring model with the first vehicle array of the first vehicle as the center of the transmitting end circle and the second vehicle array of the second vehicle as the center of the receiving end circle; Construct four quarter-cylinder models at the four corners corresponding to the intersection; a confocal semi-ellipsoidal surface model constructed with a first vehicle array of the first vehicle and a second vehicle array of the second vehicle as foci; The dual-ring model, four quarter-cylinder models and multiple confocal semi-ellipsoidal models are combined to construct a broadband GBSM.

7. An electronic device, characterized in that: The electronic device is used to run a program, wherein the program, when running, executes the vehicle channel modeling method based on a geometric model as described in any one of claims 1 to 4.

8. A computer storage medium, characterized in that The storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the vehicle channel modeling method based on a geometric model according to any one of claims 1 to 4.

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