CN115705455A

CN115705455A - Wireless channel modeling method and device, electronic equipment and storage medium

Info

Publication number: CN115705455A
Application number: CN202110940730.XA
Authority: CN
Inventors: 余泽浩; 吕星哉; 林伟; 芮华
Original assignee: ZTE Corp
Current assignee: ZTE Corp
Priority date: 2021-08-17
Filing date: 2021-08-17
Publication date: 2023-02-17
Also published as: WO2023020203A1

Abstract

The embodiment of the application provides a wireless channel modeling method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring channel data of a plurality of data acquisition points in a target space area; estimating channel parameters corresponding to each data acquisition point according to the channel data; and determining the channel parameters of the space points to be modeled according to the channel parameters corresponding to the data acquisition points. According to the embodiment of the application, the channel parameters of the space points to be modeled are calculated based on the actually measured channel data, so that the spatial correlation of the generated wireless channel parameters is improved, and the wireless channel parameters can describe the scene channel conditions more accurately and more precisely.

Description

Wireless channel modeling method and device, electronic equipment and storage medium

Technical Field

The embodiment of the application relates to the technical field of communication, in particular to a wireless channel modeling method, a wireless channel modeling device, electronic equipment and a storage medium.

Background

In a wireless communication system, system performance is greatly affected by a wireless channel. Therefore, when performing algorithmic research of a wireless communication system, accurate modeling of a wireless channel is essential.

In the related art, common channel models, such as a 3GPP NR (5G) model, a WINNER model, and the like, are not suitable for describing a more detailed and scenic channel condition because model parameters are obtained based on a large number of measurements in a large range of scenes, and are parameters in a statistical sense.

Disclosure of Invention

The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

The embodiment of the application provides a wireless channel modeling method, a wireless channel modeling device, electronic equipment and a storage medium, which can describe scene channel conditions more precisely and accurately.

In a first aspect, an embodiment of the present application provides a wireless channel modeling method, including:

acquiring channel data of a plurality of data acquisition points in a target space area;

estimating channel parameters corresponding to the data acquisition points according to the channel data;

and determining the channel parameters of the space points to be modeled according to the channel parameters corresponding to the data acquisition points.

In a second aspect, an embodiment of the present application provides a wireless channel modeling method, including:

selecting a space point to be modeled in a wireless channel as a wireless channel reference point;

calculating the position of the scattering cluster of each NLOS path according to the channel parameters of the reference points;

calculating to obtain a channel parameter corresponding to each NLOS path of other space points to be modeled in the wireless channel according to the scattering cluster position of each NLOS path;

and calculating to obtain the channel parameters of the LOS paths of other space points to be modeled in the wireless channel according to the channel parameters of the reference point.

In a third aspect, an embodiment of the present application provides a wireless channel modeling apparatus, including:

the system comprises a first module, a second module and a third module, wherein the first module is used for acquiring channel data of a plurality of data acquisition points in a target space area;

a second module, configured to estimate channel parameters corresponding to the data acquisition points;

and the third module is used for determining the channel parameters of the space points to be modeled according to the channel parameters corresponding to the data acquisition points.

In a fourth aspect, an embodiment of the present application provides an electronic device, including: memory, processor and computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing a method of wireless channel modeling as described in the first aspect or the second aspect.

In a fifth aspect, embodiments of the present application provide a computer-readable storage medium storing computer-executable instructions for:

the wireless channel modeling method of the first aspect or the second aspect is performed.

A first aspect of an embodiment of the present application provides a wireless channel modeling method, where the method includes: acquiring channel data of a plurality of data acquisition points in a target space area; estimating channel parameters corresponding to the data acquisition points according to the channel data; and determining the channel parameters of the space points to be modeled according to the channel parameters corresponding to the data acquisition points. According to the embodiment of the application, the channel parameters of the space points to be modeled are calculated based on the actually measured channel data, so that the spatial correlation of the generated wireless channel parameters is improved, and the wireless channel parameters can describe the scene channel conditions more accurately.

It is understood that the advantages of the second to fifth aspects compared with the related art are the same as the advantages of the first aspect compared with the related art, and reference may be made to the related description of the first aspect, which is not repeated herein.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments or the related technical descriptions will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

FIG. 1 is a schematic diagram of a system architecture for performing a wireless channel modeling method according to the present application;

fig. 2 is a schematic flow chart diagram of a wireless channel modeling method according to an embodiment of the present application;

FIG. 3 is a schematic diagram of a spatial structure to be modeled according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a spatial structure to be modeled according to another embodiment of the present application;

FIG. 5 is a schematic diagram illustrating a process of generating a channel parameter of a spatial point to be modeled according to an embodiment of the present application;

fig. 6 is a schematic flow chart diagram of a wireless channel modeling method according to another embodiment of the present application;

fig. 7 is a schematic flow chart diagram of a wireless channel modeling method according to another embodiment of the present application;

fig. 8 is a schematic diagram of a multipath channel structure provided by an embodiment of the present application;

fig. 9 is a schematic flow chart diagram of a wireless channel modeling method according to another embodiment of the present application;

fig. 10 is a diagram of a wireless channel provided by an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the embodiments of the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the embodiments of the present application with unnecessary detail.

It should be noted that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different from that in the flowcharts. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

It should also be appreciated that reference throughout the specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.

In the related art, common channel models, such as a 3GPP NR model, a WINNER model, and the like, are not suitable for describing a more detailed and scenized channel condition because model parameters are obtained based on a large number of measurements in a large range of scenes, and are parameters in a statistical sense. In addition, some common channel models are static models, and do not consider the correlation of radio channels when a UE (User Equipment) moves.

Based on this, the embodiment of the application provides a wireless channel modeling method, a wireless channel modeling device, an electronic device and a storage medium. The method comprises the following steps: acquiring channel data of a plurality of data acquisition points in a target space area; estimating channel parameters corresponding to each data acquisition point according to the channel data; and determining the channel parameters of the space points to be modeled according to the channel parameters corresponding to the data acquisition points. According to the embodiment of the application, the channel parameters of the space points to be modeled are calculated based on the actually measured channel data, so that the spatial correlation of the generated wireless channel parameters is improved, and the wireless channel parameters can describe the scene channel conditions more accurately and more precisely. In addition, in some embodiments, the present application provides a data-driven channel mobility modeling scheme, data required for modeling is easy to obtain, the data can be used for wireless channel modeling in any scene, and the model takes into account spatial and temporal correlations of wireless channels. The embodiment of the application can be applied to channel modeling of a wireless communication system, in particular to mobile communication systems, such as a 5G NR wireless communication system, for carrying out mobility modeling on wireless channels.

Referring to fig. 1, as shown in fig. 1, fig. 1 is a schematic diagram of a system architecture for performing a wireless channel modeling method according to an embodiment of the present application. In the example of fig. 1, the system architecture includes a processing module 110 and an acquisition module 120.

The acquisition module 120 is in communication connection with the processing module 110, and the acquisition module 120 is configured to acquire channel data of each data acquisition point and transmit the channel data to the processing module 110.

The processing module 110 is communicatively coupled to the acquisition module 120. The processing module 110 corresponding to the wireless channel modeling method provided by one embodiment of the present application may be run in a server or a terminal device. The server may be a separate physical entity or a logical entity. The server may include a management server, a database server, a streaming server, and the like. The terminal device may be a mobile terminal device or a non-mobile terminal device. The mobile terminal equipment can be a mobile phone, a tablet computer, a notebook computer, a palm computer, vehicle-mounted terminal equipment, wearable equipment, a super mobile personal computer, a netbook, a personal digital assistant and the like; the non-mobile terminal equipment can be a personal computer, a television, a teller machine or a self-service machine and the like; the embodiments are not specifically limited. The server or terminal device may include a processor, an external memory interface, an internal memory, a Universal Serial Bus (USB) interface, and the like.

In some implementations, the processing module includes three sub-modules: the device comprises a data acquisition sub-module, a data driving modeling sub-module and a channel mobility modeling sub-module. The method is suitable for wireless channel modeling in any scene, and can realize channel modeling related to space and time.

The data acquisition submodule is used for acquiring channel data from the acquisition module.

The data-driven modeling submodule is used for realizing channel parameter estimation and space point channel generation according to the channel data, the channel parameter estimation part selects different estimation methods based on the number of the actually measured data, and the space point channel generation part ensures the space correlation of the generated wireless channel.

The channel mobility modeling submodule is used for realizing two parts of modeling in channel segments and modeling between the channel segments, the effect of similarity of channel parameters at adjacent moments is realized, and the time correlation of the generated wireless channel is ensured.

The system architecture and the application scenario described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not form a limitation on the technical solution provided in the embodiment of the present application, and it is known by those skilled in the art that the technical solution provided in the embodiment of the present application is also applicable to similar technical problems with the evolution of the system architecture and the appearance of new application scenarios.

It will be appreciated by those skilled in the art that the above-described hardware platform is not limiting of the embodiments of the present application and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

In the hardware platform, the processing module 110 may call a radio channel modeling program stored therein to execute the radio channel modeling method.

Based on the system architecture, various embodiments of the wireless channel modeling method in the present application are provided.

Referring to fig. 2, in some embodiments, a wireless channel modeling method includes:

step S1100, acquiring channel data of a plurality of data acquisition points in a target space area;

step S1200, estimating channel parameters corresponding to each data acquisition point according to the channel data;

and step S1300, determining the channel parameters of the space points to be modeled according to the channel parameters corresponding to the data acquisition points.

In some embodiments, step S1100 may be performed by a data acquisition sub-module for acquiring channel data from the acquisition module. The acquisition module is responsible for acquiring channel data off-line in a space area (target space area) needing channel modeling, and the channel data can be a channel frequency domain response matrix or a channel impulse response and the like. For example, the acquisition module may be dedicated to the acquisition device to acquire the channel frequency domain response matrix of the data acquisition points. The acquisition position of the channel data (position of the data acquisition point) can be any position in the area to be modeled, such as random acquisition in a target space area, grid-based acquisition, acquisition based on environmental information (buildings, plains and the like) and the like. After the acquisition positions are determined, channel data is acquired statically at each acquisition position. The data acquisition quantity can be set according to equipment conditions, storage capacity and the like, and the data quantity of channel data acquired by different data acquisition points can be different. In general, the greater the number of acquisitions of channel data, the greater the accuracy of the channel modeling.

According to the embodiment of the application, the channel parameters of the spatial points to be modeled are calculated based on the actually measured channel data, so that the spatial correlation of the generated wireless channel parameters is improved, and the wireless channel parameters can describe the scene channel conditions more accurately and more precisely.

In some embodiments, step S1200, estimating channel parameters corresponding to each data acquisition point according to the channel data, including:

step S1210, when the data volume of the data acquisition point is less than the preset data volume, estimating the determined value of the channel parameter corresponding to the data acquisition point by a parameter estimation method;

step S1220, when the data volume of the data acquisition point is larger than the preset data volume, estimating the determined value of the channel parameter corresponding to the data acquisition point by a parameter estimation method, and further estimating the distribution value of the channel parameter according to the determined value of the channel parameter;

wherein the determined values of the channel parameters include one or more of: NCP (Number of Cluster Paths), delay, amplitude of each multipath, aoA (Azimuth of Arrival angle), aoD (Azimuth of Departure angle), zoA (Zenith of Arrival angle), zoD (Zenith of Departure angle);

the distributed values of the channel parameters include one or more of: DS (Delay Spread), KF (rice K-factor), PL (Path Loss), SF (Shadow Fading), ASD (Azimuth Spread of department, horizontal angle of Arrival Spread), ASA (Azimuth Spread of Arrival angle Spread), ZSD (Zenith Spread of department, vertical angle of Arrival Spread), ZSA (Zenith Spread of Arrival angle Spread), vertical angle of Arrival Spread).

In some embodiments, steps S1210 and S1220 may be performed by a data driven modeling submodule. In the channel parameter estimation step, different schemes may be used depending on the data amount of each data acquisition point. When the data quantity of the data acquisition point is less, the definite value of the channel parameter, such as NCP, the time delay, the amplitude, aoA, aoD, zoA, zoD and the like of each multipath can be estimated by a parameter estimation method; when the data amount of the data acquisition point is large, the distribution calculation may be performed, and the distribution value of the channel parameter, such as DS, KF, PL, SF, ASD, ASA, ZSD, ZSA, etc., may be further estimated according to the above estimated determination value of the channel parameter. The preset data volume can be set as required, and the data volume of the data acquisition point can be judged according to the preset data volume. The channel parameters may be estimated separately for the channels at different frequencies. For a specific parameter estimation method, the embodiment of the present application is not limited. For example, the estimation of the channel parameters may be performed by using an algorithm such as a signal processing method or a deep learning method.

In some embodiments, in step S1300, determining the channel parameters of the spatial point to be modeled according to the channel parameters corresponding to each data acquisition point, includes:

step 1310, when the space point to be modeled coincides with the position of the data acquisition point, determining the channel parameter estimated by the data acquisition point as the channel parameter of the space point to be modeled;

step S1320, when the space point to be modeled is not coincident with the data acquisition point, calculating to obtain the channel parameter of the space point to be modeled according to the channel parameter estimated by the data acquisition point near the space point to be modeled.

In some embodiments, steps S1310 and S1320 may be performed by a data driven modeling submodule. In the spatial point channel generation step, based on the result of the foregoing channel parameter estimation, the channel parameters of each data acquisition point within the region to be modeled (target spatial region) can be obtained, as shown in fig. 3. For modeling a channel on a certain UE moving route, it is necessary to determine channel parameters of a wireless channel on the route, the wireless channel is shown as a curve in fig. 3, and asterisks indicate spatial points to be modeled in the wireless channel. It should be noted that fig. 3 is only a schematic diagram of a three-dimensional space projected on a two-dimensional plane, and the actual position and route are both in the three-dimensional space. Due to spatial correlation, the channel parameters should be similar at two points that are close in spatial location. The spatial correlation is considered to be included in the actually acquired channel data, i.e. the closer the position of the acquired channel data is, the greater the correlation of the acquired channel data is. Therefore, when modeling the channel parameters of a certain spatial point to be modeled on the UE moving route, the channel parameters of the spatial point can be generated by referring to the estimated channel parameters of the partial channel data acquisition points near the spatial point by using the relationship.

Specifically, if the spatial point to be modeled coincides with a certain data acquisition point, the channel parameter estimated by the data acquisition point can be directly used as the channel parameter of the spatial point. If the positions of the space point to be modeled and the data acquisition point are not coincident, the channel parameter of the space point can be generated according to the channel parameter estimated by the data acquisition point near the space point.

In some embodiments, in step S1320, when the to-be-modeled space point does not coincide with the data acquisition point, calculating the channel parameter of the to-be-modeled space point according to the channel parameter estimated by the data acquisition point near the to-be-modeled space point, including:

step S1322, when the position of the space point to be modeled is not coincident with the position of the data acquisition point, selecting according to a first preset rule to obtain the data acquisition point near the space point to be modeled; the first preset rule includes one or more of the following: a space distance minimum rule and an environment information rule;

and step S1323, calculating to obtain the channel parameters of the space point to be modeled according to the channel parameters estimated by the data acquisition points near the space point to be modeled.

In some embodiments, the selection of nearby data collection points may be based on the amount of available environmental information. For example, when no environmental information is acquired except for the channel data, the nearby data acquisition point may be selected according to the spatial distance. When there is environment information, such as map information, in addition to the channel data, the nearby data collection points can be selected according to the spatial distance and the environment information. As shown in fig. 4, when generating channel parameters for a spatial point to be modeled, a measurement point (solid point) near the same scene (flat ground area) where the spatial point is located should be selected, and a measurement point (hollow point) in another scene (building area) should not be selected, although the measurement point in another scene (building area) may be closer to the spatial point to be modeled in spatial distance (star point in fig. 4).

In some embodiments, in step S1320, calculating the channel parameter of the to-be-modeled space point according to the channel parameter estimated from the data acquisition point near the to-be-modeled space point, includes:

step S1321, calculating the channel parameter of the space point to be modeled by adopting a KNN (K-Nearest Neighbor) algorithm or an interpolation method according to the channel parameter estimated by the data acquisition point near the space point to be modeled.

In some embodiments, in the correlation modeling of the spatial point to be modeled, for the determination of the channel parameter at a certain point on the UE moving route, KNN method and interpolation method may be adopted. Other alternatives that achieve similar effects may also be used, such as neural network based methods, upsampling methods, and other interpolation methods, which are not limited in this application.

In some embodiments, the channel parameters estimated by the data acquisition point comprise determined values of the channel parameters and distributed values of the channel parameters;

correspondingly, step S1321, calculating, by using a KNN algorithm or an interpolation method, a channel parameter of the to-be-modeled space point according to the channel parameter estimated by the data acquisition point near the to-be-modeled space point, including:

S1321-A, when the channel parameters of the nearby data acquisition points are the determined values of the channel parameters, KNN or interpolation is carried out on the determined values, and the channel parameters of the space points to be modeled are obtained through calculation;

step S1321-B, when the channel parameters of the nearby data acquisition points are all distribution values, KNN or interpolation is carried out on the distribution values, and then the channel parameter determination values of the space points to be modeled are generated according to distribution;

and S1321-C, when the channel parameters of the nearby data acquisition points are partially determined values and partially distributed values, firstly generating the determined values according to the distribution of the distributed values, and then carrying out KNN (K nearest neighbor) or interpolation on the determined values to calculate the channel parameters of the space points to be modeled.

In some embodiments, after determining the data acquisition points in the vicinity of the to-be-modeled space point, it is necessary to generate the channel parameters of the to-be-modeled space point according to the channel parameters of the data acquisition points. Alternatively, a KNN algorithm may be used, i.e., the average of the channel parameters of K nearby data acquisition points is selected as the channel parameter value of the spatial point, and K may be selected empirically. Alternatively, interpolation methods, such as trilinear interpolation and the like, may be employed. Since the estimation types of the channel parameters of different data acquisition points may be different, when the channel parameter of a certain spatial point on the UE moving route is specifically determined, the parameter generation flow needs to be determined according to the estimation types of the channel parameters of the nearby data acquisition points, as shown in fig. 5. When the channel parameter estimation results of the nearby data acquisition points are all determined values, KNN or interpolation is directly carried out on the determined values; when the channel parameter estimation results of the nearby data acquisition points are all distribution values (distribution functions), KNN or interpolation is carried out on the distributed parameter values, and then a determined value is generated according to the distribution; when the channel parameter estimation result of the nearby data acquisition point is partially a determined value and partially a distributed value, firstly, the determined value is generated according to the distribution, and then KNN or interpolation is carried out on the determined value.

In addition, the application also provides a wireless channel modeling method based on mobility modeling.

In some embodiments, as the UE moves over the course of movement, the wireless channels it experiences should also be continuous. In order to model this property, the embodiments of the present application further propose a channel segment concept. The channel segment means that, within a certain range of the UE moving, the position of the multipath scattering cluster of the channel does not change, i.e. the spatial domain characteristics of the channel remain relatively stable, and the channel formed within the range of the segment is called a channel segment. The length of the channel segments is set according to the real geographical environment. It should be noted that there is no necessary link between channel segments and channel scenes: one channel scenario may contain multiple channel segments. For example, in an outdoor wide scene, as the UE moves, the scattering clusters it experiences may also change, so the UE movement path may be divided into different channel segments, even though they all belong to the same channel scene. Also, a wireless channel may contain one channel segment or may contain a plurality of channel segments.

When the time correlation of the wireless channel needs to be modeled, a multiple scattering model can be adopted for determining the positions of scattering clusters in the channel segment. Other models, such as a single scattering model, may also be used, and are not limited in this application.

The following description will be given by taking the multiple scattering model as an example.

In some embodiments, the following channel mobility modeling steps may be implemented by a channel mobility modeling submodule that is divided into two parts: intra-channel segment modeling and inter-channel segment modeling.

Referring to fig. 6, in some embodiments, a wireless channel modeling method includes:

step S1400, selecting a space point to be modeled in a wireless channel as a wireless channel reference point;

step S1500, calculating the scattering cluster position of each NLOS (Non-line-of-sight) path according to the channel parameters of the reference points;

step S1600, calculating to obtain a corresponding channel parameter of each NLOS path of other to-be-modeled space points in the wireless channel according to the scattering cluster position of each NLOS path;

step S1700, according to the channel parameters of the reference points, calculating to obtain the channel parameters of LOS (Line-of-sight) paths of other to-be-modeled space points in the wireless channel.

In some embodiments, the wireless channel includes at least one channel segment, and the modeling within the channel segment may be accomplished by performing steps S1400 through S1700. In step S1500, the channel parameter of the reference point may be obtained according to the above embodiment, such as the wireless channel modeling method in steps S1100 to S1300 in fig. 2, or may be obtained based on other channel estimation methods, or may directly receive the reference point channel parameter input from the outside, which is not limited in this application. As shown in fig. 7, in some embodiments, the wireless channel modeling method based on mobility modeling includes steps S1100 to S1700.

In some embodiments, in the modeling portion within a channel segment, it is first necessary to determine the location of scatterers (i.e., scattering clusters) within the channel segment. Based on the result of the data-driven modeling submodule, a certain point in the channel segment can be used as a reference point to obtain the channel parameter of the point, and then the number of multipath of the point, the time delay of each path, aoA, aoD, zoA and ZoD are determined, and the positions of FBS (First Bounce Scatterer) and LBS (Last Bounce Scatterer) of each NLOS path can be calculated by adopting a multiple scattering model.

In some embodiments, the step S1400 of selecting a spatial point to be modeled in a wireless channel as a wireless channel reference point includes:

step S1410, when the wireless channel contains the data acquisition point, the data acquisition point is used as a reference point;

step S1420, when the wireless channel does not contain any data acquisition point, selecting the space point to be modeled with dense nearby data acquisition points in the wireless channel as a reference point.

In some embodiments, the selection of the reference point is not required by the present application. As an embodiment, the following method can be adopted for selection: if the path of the channel segment contains a certain data acquisition point, the point can be used as a reference point; if the path of the channel segment does not contain any data acquisition point, a certain point of the channel segment where nearby data acquisition points are dense can be selected as a reference point. As shown in FIG. 8, a schematic diagram of FBS and LBS of a certain NLOS path is shown.

In some embodiments, the channel parameters of the reference point include: NCP, time delay of each NLOS path, aoA, aoD, zoA and ZoD;

step S1500, calculating a scattering cluster position of each NLOS path according to the channel parameter of the reference point, including:

step S1510, calculating to obtain a direction of a first vector from the base station to the first bounce scattering cluster FBS according to the AoD and the ZoD;

step 1520, calculating to obtain a direction of a second vector from the first-time rebound scattering cluster FBS to the last-time rebound scattering cluster LBS according to the AoA and the ZoA;

step S1530, according to the first constraint condition, calculating to obtain a first vector from the base station to the first bounce scattering cluster FBS and a third vector from the LBS to the user equipment UE; wherein the first constraint condition comprises:

|a|+|b|+|c|＝τ*c ₀ ；

arg min _a，c |r|-|a|-|c|；

wherein, a is a first vector and a second vector,

is the direction of the first vector, | a | is the modulus of the first vector; b is a second vector of the second,

is the direction of the second vector, | b | is the modulus of the second vector; c is a third vector of the second order,

is the direction of the third vector, | c | is the modulus of the third vector; r is a fourth vector from the base station to the UE,

is the direction of the fourth vector, | r | is the modulus of the fourth vector; τ is the time delay, c ₀ Is the speed of light;

step S1540, calculating to obtain the FBS position of the NLOS path according to the position of the base station and the first vector;

step S1550, calculating to obtain LBS position of the NLOS path according to the position of the UE and the third vector;

step S1560, repeating the above steps S1510 to S1550 for other NLOS paths, and correspondingly obtaining the FBS position and the LBS position of each NLOS path.

In some embodiments, the specific calculation procedure for FBS and LBS positions for each NLOS path is as follows:

step P11, for a certain NLOS path, let its time delay be τ, vector from base station to UE be r, vector from base station to FBS be a (direction vector is

) Vector from FBS to LBS is b (direction vector is

) LBS to UE vector is c (direction vector is

). Wherein, the time delay tau, the firstFour vectors r, direction of the first vector

Direction of the third vector

Known, and satisfies | a | + | b | + | c | = τ c |) ₀ The first vector a and the third vector c need to be solved.

Step P12, solving the following constraint problem:

arg min _a，c |r|-|a|-|c|；

a first vector a, a third vector c may be obtained.

Step P13, calculates the position of the FBS based on the base station position and the first vector a, and calculates the position of the LBS based on the UE position and the third vector c.

And repeating the steps P11 to P13 for each NLOS diameter to correspondingly obtain the FBS position and the LBS position of each NLOS diameter.

In some embodiments, let the nth time UE move to the position P of the spatial point to be modeled _UE [n]The channel parameter of the 1 st NLOS path corresponding to the space point to be modeled comprises time delay tau ₁ [n]And amplitude alpha ₁ [n](ii) a Wherein N =1,2, ·, N; l =2,3, ·, L;

step S1600, calculating to obtain the corresponding channel parameter of each NLOS path of other to-be-modeled space points in the wireless channel according to the scattering cluster position of each NLOS path, including:

step S1610, according to the scattering cluster position of each NLOS path and the first calculation formula, calculating the time delay tau of each NLOS path of other space points to be modeled in the wireless channel ₁ [n](ii) a The first calculation formula includes:

τ ₁ [n]＝(|a|+|b|+|P _LBS -P _UE [n]|)/c ₀ ；

wherein a is a first vectorAnd | a | is the modulus of the first vector; b is the second vector, | b | is the modulus of the second vector; p _LBS LBS position for corresponding NLOS path, c ₀ Is the speed of light;

step S1620, according to the amplitude of the reference point S and the second calculation formula, calculating to obtain the amplitude alpha of each NLOS path of other to-be-modeled space points in the wireless channel ₁ [n](ii) a The second calculation formula includes:

α ₁ [n]＝β ₁ [n]*α ₁ [s]；

wherein, beta ₁ [n]For the amplitude adjustment coefficient, α ₁ [s]The amplitude of the 1 st NLOS path is the reference point s.

In some embodiments, after determining the positions of the FBS and the LBS of each NLOS path, the information of other spatial points to be modeled in the channel segment is calculated, and then the time delay and amplitude of the new NLOS path of the UE at the spatial points to be modeled in other channels can be calculated according to the criterion that the FBS/LBS in the channel segment is unchanged. Here, the new NLOS path refers to an NLOS path (shown by a dotted line in fig. 8) recalculated based on the FBS, the LBS, and the UE position when the UE position is changed. The specific calculation process is as follows:

step P21, assume that the current channel segment is divided into N time instants, time instant s (1)<＝s<= N) is the reference point of the channel segment, the amplitude of the l NLOS path at the point is alpha _l [s]Time delay of τ _l [s]L =2,3, L, where L is the number of multipaths. The position of the FBS calculated based on the above steps is P _FBS LBS position is P _LBS 。

Step P22, calculating the time delay tau of the nth time and the first path _l [n]. Suppose that the UE moves to position P at time n _UE [n]Then τ is _l [n]The following can be calculated:

T _l [n]＝(|a|+|b|+|P _LBs -P _UE [n]|)/c ₀ ；

step P23, now calculate the amplitude α of the nth diameter _l [n]. Based on τ calculated in step P22 _l [n]，α _l [n]The following can be calculated:

α _l [n]＝β _l [n]*α _l [s]；

wherein, beta _l [n]The amplitude adjustment coefficient can be selected empirically. In some embodiments, β _l [n]Involving a time delay τ _l [n]The influence on the phase also comprises the influence on the phase caused by Doppler frequency shift caused by the UE speed when the UE moves, and the method can be applied to channel modeling under different UE speeds.

For L =2,3, L, repeating the steps P21 to P23 to obtain the amplitude of each NLOS path of other spatial points to be modeled in the wireless channel.

In some embodiments, let the nth time instant UE move to position P _UE [n]The channel parameter of LOS path corresponding to the space point to be modeled comprises time delay tau ₁ [n]And amplitude alpha ₁ [n]；

Step S1700, according to the channel parameter of the reference point, calculating to obtain the channel parameter of the LOS path of other to-be-modeled space points in the wireless channel, including:

step S1710, calculating to obtain the channel parameters of the LOS paths of other space points to be modeled in the wireless channel according to the channel parameters of the reference point and a third calculation formula;

τ ₁ [n]＝r/c ₀ ；

α _l [n]＝β _l [n]*α _l [s]；

where r is the distance from the base station to the UE, β _l [n]For amplitude adjustment of the coefficient, α _l [s]The amplitude of the ith NLOS path is the reference point s.

In some embodiments, for the LOS path, the UE may recalculate the delay τ of the LOS path at any time n based on the current location and the location of the base station ₁ [n]And amplitude alpha ₁ [n]The calculation method is similar to the above NLOS path, and is not described herein. Based on the recalculated time delay and amplitude of the NLOS path and the LOS path, the time delay tau of each multipath at any moment in the current channel segment is obtained _l [n]And amplitude alpha _l [n]L =1,2., L, N =1,2., N, thereby obtaining temporally correlated channel parameters.

In some embodiments, the wireless channel comprises a front channel segment and a back channel segment, the front channel segment and the back channel segment being concatenated to form a channel segment transition region;

referring to fig. 9, in some embodiments, the wireless channel modeling method further comprises:

step S1800, according to the channel parameters corresponding to the NLOS paths of the front channel segment and the channel parameters corresponding to the NLOS paths of the back channel segment, calculating the channel parameters corresponding to the NLOS paths of each space point to be modeled in the transition region of the channel segment, so that the amplitude of each NLOS path of the front channel segment is gradually reduced, and the amplitude of each NLOS path of the back channel segment is gradually increased.

In some embodiments, in the inter-channel segment modeling section, abrupt changes may occur at channel segment switches due to the fact that the scattering cluster positions of different channel segments are not necessarily the same. To avoid this problem, when different channel segments are combined in series, a channel segment transition region needs to be introduced, as shown in fig. 10. Wherein the length of the channel segment transition region can be set according to experience. In the channel segment transition region, the amplitude of each NLOS path of the front channel segment is gradually reduced, and the amplitude of each NLOS path of the rear channel segment is gradually increased. As an implementation method, at each time in the channel segment transition region, the amplitudes of all NLOS paths of the previous channel segment may be multiplied by a certain coefficient at the same time, and the coefficient is reduced from 1 to 0 in the channel segment transition region; the amplitudes of all NLOS paths for the post-channel segment are multiplied simultaneously by a coefficient that rises from 0 to 1 in the channel segment transition region. For the LOS path, the amplitude is adjusted so that at the end of the channel segment transition region, the amplitude is consistent with the amplitude of the next channel segment LOS path.

In some embodiments, the channel parameters of the spatial point to be modeled at any time in the pre-channel segment include the time delay and amplitude of each multipath, respectively τ _l [n]And alpha _l [n]The channel parameters of the space point to be modeled at any moment in the back channel segment comprise the time delay and amplitude of each multipath, and are tau' _l [n]And alpha' _l [n]The channel segment transition region is a region from M time to N time; wherein M is>＝1，M<＝N；

In some embodiments, step S1800, calculating, according to the channel parameter corresponding to the NLOS path of the front channel segment and the channel parameter corresponding to the NLOS path of the back channel segment, the channel parameter corresponding to the NLOS path of each spatial point to be modeled in the channel segment transition region, includes:

step S1810, in the front channel segment, starting from the time M, the amplitude alpha of the first NLOS path of the space point to be modeled at the time n is adjusted _l [n]Is k is ₁ [n]*α _l [n]Wherein L =2,3,.., L, N = M, M + 1.., N, coefficient k ₁ [n]Gradually decreasing from the M moment to the N moment;

step S1820, in the back channel segment, starting from the time M, adjusting the amplitude alpha 'of the l NLOS path of the space point to be modeled at the time n' _l [n]Is k ₂ [n]*α’ _l [n]Wherein L =2,3,.., L', N = M, M + 1.., N, coefficient k ₂ [n]Gradually rising from time M to time N.

The above specific process can be described as follows:

let the time delay and amplitude of each multipath at any time in the preceding channel segment be tau _l [n]And alpha _l [n]L =1,2., L, N =1,2., N, the time delay and amplitude of each multipath at any time instant within the back channel segment are τ' _l [n]And alpha' _l [n],l＝1,2,...,L’, n＝1,2,...,N’。

In the front channel segment, starting from a certain time M (M)>＝1，M<= N), adjusting α _l [n]Is k ₁ [n]*α _l [n]L =2,3,.., L, N = M, M +1,..., N. Where M is the starting time of the transition region of the channel segment, coefficient k ₁ [n]The gradual decrease from 1 to 0 can be selected empirically.

In the front channel segment, starting from a certain time M (M)>＝1，M<= N), L '-1 multipath is introduced, the time delay of which is tau' _l [1]L =2,3, L', with amplitude k ₂ [n]*α’ _l [1]L =2,3,.., L', N = M, M +1,..., N. Wherein the coefficient k ₂ [n]Gradually from 0 to 1, can be selected empirically.

For LOS paths, the amplitude is adjusted so that at the end of the channel segment transition region, the amplitude isThe degree is consistent with the amplitude of the next channel segment LOS path. Specifically, in the previous channel segment, starting from a certain time M (M)>＝1，M<= N), the LOS path amplitude alpha is adjusted ₁ [n]Is k ₃ [n]*α ₁ [n]N = M, M + 1. Wherein the coefficient k ₃ [n]Gradually changed from 1 to alpha' ₁ [1]/α ₁ [N]And can be selected empirically.

According to the adjusted path amplitudes, channel parameters of each channel segment at each moment can be generated and are connected in series, so that the channel parameters on the whole UE moving path are obtained, and mobility modeling is completed.

It should be noted that, in the channel time correlation modeling, for the transition between channel segments, the present application adopts a scheme that the amplitude on all NLOS paths of the front channel segment is simultaneously decreased and the amplitude on all NLOS paths of the back channel segment is simultaneously increased at the spatial point to be modeled at each time of the transition period, but does not constitute a limitation to the protection scope of the present application. Other schemes, such as decreasing the amplitude of the first channel segment on a certain/some NLOS path and increasing the amplitude of the second channel segment on a certain/some NLOS path at each time of the transition period, may also be used, which is not limited in this application.

In order to facilitate a clearer understanding of the inventive concepts of the present application, the following is further illustrated in two examples.

It should be noted that the following first example and second example are different in that:

1. the data acquisition points are different. Example one employs random data acquisition points; example two data acquisition points are selected based on historical grid partitioning).

2. The data acquisition amount is different. Example one employs 30 sets; example two employs 500 sets.

3. The channel parameter estimation modes are different. Example one employs a determined value; example two employs a distribution value.

4. The channel parameters of the space points to be modeled are generated in different modes. Example one uses the KNN method; example two employs an interpolation method.

5. The channel segments are divided differently. Example one is divided empirically; two data trellis partitions are illustrated.

6. The channel segment transition regions are of different lengths. The channel segment transition region length of example one is 30% of the previous channel segment length; the channel segment transition region length of example two is 50% of the length of the preceding channel segment.

7. The channel segment transition region amplitude coefficients are different. The amplitude coefficient of the transition region of the channel segment in the first example is linearly increased or decreased; the channel segment transition region amplitude coefficients of example one increase or decrease as a Sigmoid function).

Example 1

Step E101, selecting data acquisition points in the region to be modeled (e.g., 500m × 500m rectangular region), where the number and positions of the data acquisition points may be selected according to a scene, for example, randomly selected in the region to be modeled.

And E102, continuously collecting the channel frequency domain response matrix H data 30 groups at each data acquisition point.

And E103, for each data acquisition point, estimating the channel parameter determination value through 30 groups of acquired channel frequency domain response matrix H data. For the estimation of the number NCP of the multiple paths and the delay of each path, a spectrum estimation algorithm, such as MUSIC, ESPRIT, etc., can be adopted. For the estimation of the amplitude of each path, the phase can be calculated based on the previously estimated time delay of each path, and then the least square method is used for amplitude estimation. For the estimation of the angle domain parameters, namely the estimation of AoD, aoA, zoD and ZoA, a power spectrum analysis method can be adopted to obtain the spatial angle energy distribution, so as to obtain the angle domain parameters.

And step E104, determining a UE moving path in the channel scene to be generated, such as a straight path with the length of 200 m.

Step E105, dividing the UE moving path into channel segments, which can be divided according to the complex situation and experience of the channel scene. E.g., into 5 channel segments, each 40m in length.

Step E106, determining a reference point of each channel segment. For a certain channel segment, if the path of the channel segment contains a certain data acquisition point, the point is taken as a reference point; if the path of the channel segment does not contain any data acquisition point, a certain point in the channel segment where nearby data acquisition points are dense can be selected as a reference point.

And step E107, acquiring channel parameters at the reference point of each channel segment. If 2 channel segments of the divided 5 channel segments contain data acquisition points, the channel parameters estimated by the data acquisition points can be directly used as the channel parameters of the reference points. For the rest 3 channel segments, adopting a KNN method, taking K =4, that is, adopting an average value of channel parameters estimated by 4 data acquisition points closest to a spatial distance in the vicinity of a reference point of a channel segment, as a channel parameter of the point. Specifically, assume that the reference point of the channel segment is point a, and the set of 4 data acquisition points whose spatial distances in the vicinity are closest is N _a The channel parameter of the space point to be estimated is theta _a Then the following can be calculated:

if the parameter to be estimated is an integer (such as NCP), rounding the result of the above formula.

And E108, calculating the positions of the FBS and the LBS of each NLOS path in each channel segment by adopting a multiple scattering model based on the channel parameters at the reference point of each channel segment.

Step E109, fix the positions of FBS and LBS of each NLOS path, consider as unchanged in the channel segment, and the UE recalculates the time delay and amplitude of the NLOS path at any moment based on the current position and the fixed scattering cluster position.

Step E110, the UE recalculates the delay and the amplitude of the LOS path at any moment based on the current location and the base station location.

Step E111, based on the repetitionThe calculated time delay and amplitude of NLOS path and LOS path can obtain the time delay tau of each multi-path at any time in the current channel segment _l [n]And amplitude alpha _l [n],l＝1,2,...,L,n＝1,2,...,N。

And E112, setting a channel segment transition region, wherein the length of the channel segment transition region can be set according to scenes and experience. For example, set to 30% of the front channel segment length, then

Operator

Indicating a rounding down. At each time within the transition region of the channel segment, the amplitudes of all NLOS paths of the preceding channel segment are simultaneously multiplied by a coefficient k ₁ [n]，k ₁ [n]Can be chosen to be a linear decrease:

multiplying the amplitudes of all NLOS paths of the post-channel segment by a coefficient k at the same time ₂ [n]，k ₂ [n]Can be chosen to be a linear increase:

coefficient k for LOS path ₃ [n]It can be chosen to be a linear variation:

and E113, generating channel parameters of the space points to be modeled at each moment of each channel segment, and connecting the channel parameters in series to obtain the channel parameters on the whole UE moving path to complete modeling.

Example two

Step E201, selecting data acquisition points in the region to be modeled (such as 500m × 500m rectangular region), wherein the number and the positions of the data acquisition points can be selected according to historical experience information, for example, according to historical report information collected by a base station, performing grid division on the region to be modeled, and acquiring at each grid center point.

And E202, continuously collecting a channel frequency domain response matrix H data 500 group at each data acquisition point.

And E203, for each data acquisition point, estimating the determined value of the channel parameter by using 500 groups of acquired channel frequency domain response matrix H data. For the estimation of the number NCP of the multiple paths and the delay of each path, a spectrum estimation algorithm, such as MUSIC, ESPRIT, etc., can be adopted. For the estimation of the amplitude of each path, the phase can be calculated based on the previously estimated time delay of each path, and then the least square method is used for amplitude estimation. For the estimation of the angle domain parameters, namely the estimation of AoD, aoA, zoD and ZoA, a power spectrum analysis method can be adopted to obtain the spatial angle energy distribution, so as to obtain the angle domain parameters.

And step E204, performing parameter distribution estimation on the 500 groups of estimated channel parameter determination values. Firstly, calculating PL and SF by amplitude, calculating KF and DS by the amplitude of delay of each diameter, and calculating ASD, ASA, ZSD and ZSA by AoD, aoA, zoD and ZoA; then, according to the distribution of each parameter, a parameter describing the distribution of each parameter is estimated. For example, if DS satisfies a log-normal distribution, the mean and variance of the log-normal distribution can be estimated from the calculated DS value.

Step E205, determining a UE moving path in the channel scene to be generated, such as a straight path with a length of 200 m.

Step E206, dividing the UE moving path into channel segments. Alternatively, the channel segment division may be performed according to the grid divided at the time of data acquisition in step E201. For example, the UE moving path is considered to be in the same grid as the same channel segment, and the channel segment is divided accordingly. Assume a division into 6 channel segments according to a grid.

Step E207, the reference point of each channel segment is determined. For a certain channel segment, if the path of the channel segment contains a certain data acquisition point, the point is taken as a reference point; if the path of the channel segment does not contain any data acquisition point, a certain point in the channel segment where nearby data acquisition points are dense can be selected as a reference point.

Step E208, acquiring channel parameters at the reference point of each channel segment. Assuming that a path of 3 channel segments among the divided 6 channel segments includes a data acquisition point, a distribution value of a channel parameter estimated by the data acquisition point may be used to randomly generate a determined value of the channel parameter as a channel parameter of a reference point. For the remaining 3 channel segments, first, the determined values of the channel parameters are randomly generated based on the distribution of the channel parameters estimated by the data acquisition points near the channel segment reference point. Then, an interpolation method is adopted, the determined value of the channel parameter of the data acquisition point near the reference point of the channel segment is used for interpolation, and the interpolated result is used as the channel parameter of the point. Specifically, a trilinear interpolation algorithm can be adopted, 8 data acquisition points near a channel segment reference point are selected, linear interpolation is firstly carried out in pairs in the x direction to obtain 4 corresponding parameter values, linear interpolation is carried out in pairs in the y direction to obtain 2 corresponding parameter values, and finally linear interpolation is carried out in the z direction to obtain a final parameter value. The specific formula of linear interpolation is:

wherein, theta _a For the parameter to be estimated, x _a Reference point coordinates, x, for channel segments ₁ 、x ₂ For nearby data acquisition point coordinates, θ ₁ 、θ ₂ Are nearby data acquisition point parameter values. If the parameter to be estimated is an integer (such as NCP), the result is rounded.

And E209, calculating the positions of the FBS and the LBS of each NLOS path in each channel segment by adopting a multiple scattering model based on the channel parameters at the reference point of each channel segment.

Step E210, fix the positions of FBS and LBS of each NLOS path, consider as unchanged in the channel segment, and the UE recalculates the time delay and amplitude of the NLOS path at any moment based on the current position and the fixed scattering cluster position.

Step E211, the UE recalculates the delay and the amplitude of the LOS path at any time based on the current location and the base station location.

Step E212, based on the recalculated time delay and amplitude of NLOS path and LOS path, obtaining the time delay tau of each multi-path at any moment in the current channel segment _l [n]And amplitude alpha _l [n],l＝1,2,...,L,n＝1,2,...,N。

Step E213, setting a channel segment transition region, wherein the length thereof can be set according to the scene and experience. E.g. set to 50% of the front channel segment length, then

Meaning rounding down. At each time within the transition region of the channel segment, the amplitudes of all NLOS paths of the preceding channel segment are simultaneously multiplied by a coefficient k ₁ [n]，k ₁ [n]Can be selected according to Sigmoid function:

multiplying the amplitudes of all NLOS paths of the post-channel segment by a coefficient k at the same time ₂ [n]，k ₂ [n]Can be selected according to Sigmoid function:

coefficient k for LOS path ₃ [n]The following can be selected according to the Sigmoid function:

and step E214, generating channel parameters of the space points to be modeled at each moment of each channel segment, and connecting the channel parameters in series to obtain the channel parameters on the whole UE moving path to complete modeling.

In some embodiments, the data-driven modeling method provided by the application is suitable for modeling any wireless channel scene, and the data-driven modeling method can realize scene-based and refined channel modeling. In some embodiments, the spatial point channel generation method provided by the present application can achieve the effect similar to the channel parameters generated at the close positions in space, and meet the characteristics of the actual channel. In some embodiments, the mobility modeling module provided by the present application can achieve an effect similar to channel parameters at adjacent times, and the position of a scattering cluster changes with the movement of a UE but does not hop during handover, which conforms to the characteristics of an actual channel. In some embodiments, the input data required by the channel modeling method provided by the application is relatively easy to obtain, and the method is convenient to implement.

In addition, an embodiment of the present application provides a wireless channel modeling apparatus, including:

the second module is used for estimating channel parameters corresponding to the data acquisition points;

In some embodiments, the first module is configured to perform step S1100; the second module is used for executing the step S1200; the third module is configured to perform the step S1300. It should be noted that the wireless channel modeling apparatus in this embodiment can be applied to the wireless channel modeling apparatus in the system architecture of the embodiment shown in fig. 1; in addition, the wireless channel modeling apparatus in the present embodiment may perform the wireless channel modeling method in the embodiment shown in fig. 2. That is, the wireless channel modeling apparatus in the present embodiment, the wireless channel modeling apparatus in the system architecture of the embodiment shown in fig. 1, and the wireless channel modeling method in the embodiment shown in fig. 2 all belong to the same inventive concept, so these embodiments have the same implementation principle and technical effect, and are not described in detail here.

The above described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, an embodiment of the present application further provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing a method of wireless channel modeling as described above.

The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

It should be noted that the electronic device in this embodiment may be applied to an electronic device in the system architecture of the embodiment shown in fig. 1; in addition, the electronic device in this embodiment may perform the wireless channel modeling method in the embodiment shown in fig. 2. That is, the electronic device in the embodiment, the electronic device in the system architecture of the embodiment shown in fig. 1, and the wireless channel modeling method in the embodiment shown in fig. 2 all belong to the same inventive concept, so that these embodiments have the same implementation principle and technical effect, and are not described in detail here.

The non-transitory software programs and instructions required to implement the radio channel modeling method of the above-described embodiments are stored in the memory, and when executed by the processor, perform the radio channel modeling method of the above-described embodiments, e.g., performing the above-described method steps S1100 to S1300 in fig. 2, the method steps S1400 to S1700 in fig. 6, the method steps S1100 to S1700 in fig. 7, and the method steps S1100 to S1800 in fig. 9.

In addition, embodiments of the present application further provide a computer-readable storage medium storing computer-executable instructions, where the computer-executable instructions are configured to:

the aforementioned wireless channel modeling method is performed.

In some embodiments, the computer-readable storage medium stores computer-executable instructions, which are executed by a processor or controller, for example, by a processor in the above-mentioned electronic device embodiment, and cause the processor to execute the wireless channel modeling method in the above-mentioned embodiments, for example, as steps S1100 to S1300 in fig. 2, method steps S1400 to S1700 in fig. 6, method steps S1100 to S1700 in fig. 7, and method steps S1100 to S1800 in fig. 9.

It will be understood by those of ordinary skill in the art that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.

While the preferred embodiments of the present invention have been described in detail, it will be understood, however, that the invention is not limited to those precise embodiments, and that various changes and modifications may be effected therein by one skilled in the art without departing from the scope of the invention as defined by the appended claims.

Claims

1. A wireless channel modeling method, comprising:

2. The method according to claim 1, wherein said estimating channel parameters corresponding to each of said data acquisition points based on said channel data comprises:

when the data volume of the data acquisition point is less than the preset data volume, estimating a determination value of a channel parameter corresponding to the data acquisition point by a parameter estimation method;

when the data volume of the data acquisition point is larger than the preset data volume, estimating a determined value of a channel parameter corresponding to the data acquisition point by a parameter estimation method, and further estimating a distribution value of the channel parameter according to the determined value of the channel parameter;

wherein the determined values of the channel parameters include one or more of: NCP, time delay, amplitude, aoA, aoD, zoA, zoD of each multipath;

the distribution values of the channel parameters include one or more of: DS, KF, PL, SF, ASD, ASA, ZSD, ZSA.

3. The method according to claim 1, wherein the determining the channel parameters of the spatial points to be modeled according to the channel parameters corresponding to each data acquisition point comprises:

when the space point to be modeled is superposed with the data acquisition point, determining the channel parameter estimated by the data acquisition point as the channel parameter of the space point to be modeled;

and when the position of the space point to be modeled is not coincident with the position of the data acquisition point, calculating to obtain the channel parameter of the space point to be modeled according to the channel parameter estimated by the data acquisition point near the space point to be modeled.

4. The method according to claim 3, wherein the calculating the channel parameters of the to-be-modeled space point according to the channel parameters estimated from the data acquisition points near the to-be-modeled space point comprises:

and calculating to obtain the channel parameters of the space point to be modeled by adopting a KNN algorithm or an interpolation method according to the channel parameters estimated by the data acquisition points near the space point to be modeled.

5. The method of claim 4, wherein the channel parameters estimated by the data acquisition points comprise determined values of channel parameters and distribution values of channel parameters;

correspondingly, the calculating the channel parameters of the space point to be modeled by adopting a KNN algorithm or an interpolation method according to the channel parameters estimated by the data acquisition points near the space point to be modeled includes:

when the channel parameters of the nearby data acquisition points are the determined values of the channel parameters, carrying out KNN or interpolation on the determined values, and calculating to obtain the channel parameters of the space point to be modeled;

when the channel parameters of the nearby data acquisition points are all distribution values, KNN or interpolation is carried out on the distribution values, and then channel parameter determination values of the space points to be modeled are generated according to distribution;

when the channel parameters of the nearby data acquisition points are partially determined values and partially distributed values, the distributed values are firstly generated into determined values according to distribution, and then KNN or interpolation is carried out on the determined values to calculate the channel parameters of the space points to be modeled.

6. The method according to claim 3, wherein when the to-be-modeled space point and the data acquisition point are not coincident in position, calculating the channel parameter of the to-be-modeled space point according to the channel parameter estimated by the data acquisition point near the to-be-modeled space point, includes:

when the space point to be modeled is not coincident with the data acquisition point, selecting according to a first preset rule to obtain a data acquisition point near the space point to be modeled; the first preset rule comprises one or more of the following: a spatial distance minimum rule and an environmental information rule;

and calculating to obtain the channel parameters of the space point to be modeled according to the channel parameters estimated by the data acquisition points near the space point to be modeled.

7. A wireless channel modeling method, comprising:

calculating to obtain a corresponding channel parameter of each NLOS path of other space points to be modeled in the wireless channel according to the scattering cluster position of each NLOS path;

8. The method according to claim 7, wherein the selecting the spatial point to be modeled in the wireless channel as a wireless channel reference point comprises:

when the wireless channel contains a data acquisition point, taking the data acquisition point as a reference point;

and when the wireless channel does not contain any data acquisition point, selecting the space point to be modeled with dense nearby data acquisition points in the wireless channel as a reference point.

9. The method of claim 7, wherein the channel parameters of the reference point comprise: NCP, time delay of each NLOS path, aoA, aoD, zoA and ZoD;

calculating the scattering cluster position of each NLOS path according to the channel parameters of the reference point, wherein the method comprises the following steps:

calculating to obtain the direction of a first vector from the base station to the FBS (first bounce scattering cluster) according to the AoD and the ZoD;

calculating to obtain the direction of a third vector from the last rebound scattering cluster LBS to the user equipment UE according to the AoA and the ZoA;

according to the first constraint condition, calculating to obtain a first vector from a base station to a first bounce scattering cluster (FBS) and a third vector from an LBS to a user terminal (UE); wherein the first constraint condition comprises:

|a|+|b|+|c|＝τ*c ₀ ；

arg min _a，c |r|-|a|-|c|；

wherein, a is a first vector,

is the direction of the second vector, | b | is the modulus of the second vector; c is a third vector of the third,

calculating to obtain the FBS position of the NLOS path according to the position of the base station and the first vector;

calculating the LBS position of the NLOS path according to the position of the UE and the third vector;

and repeating the calculation steps for other NLOS paths to correspondingly obtain the FBS position and the LBS position of each NLOS path.

10. The method according to claim 7, wherein let the UE move to the position P of the to-be-modeled spatial point at the nth time _UE [n]The channel parameter of the first NLOS path corresponding to the space point to be modeled comprises time delay tau _l [n]And amplitude alpha _l [n](ii) a Wherein N =1,2, · N; l =2,3, ·, L;

the method for calculating and obtaining the corresponding channel parameter of each NLOS path of other space points to be modeled in the wireless channel according to the scattering cluster position of each NLOS path comprises the following steps:

calculating the time delay tau of each NLOS path of other to-be-modeled space points in the wireless channel according to the scattering cluster position of each NLOS path and a first calculation formula _l [n](ii) a The first calculation formula includes:

τ _l [n]＝(|a|+|b|+|P _LBS -P _UE [n]|)/c ₀ ；

wherein a is a first vector and | a | is a modulus of the first vector; b is the second vector, | b | is the modulus of the second vector; p _LBS LBS position for corresponding NLOS path, c ₀ Is the speed of light;

according to the amplitude of the reference point and a second calculation formula, calculating to obtain the amplitude alpha of each NLOS path of other to-be-modeled space points in the wireless channel _l [n](ii) a The second calculation formula includes:

α _l [n]＝β _l [n]*α _l [s]；

wherein, beta _l [n]For amplitude adjustment of the coefficient, α _l [s]The amplitude of the ith NLOS path is the reference point s.

11. The method of claim 7, wherein the UE moves to the position p at the nth time _UE [n]The channel parameter of LOS path corresponding to the space point to be modeled comprises time delay tau ₁ [n]And amplitude alpha ₁ [n]；

The calculating to obtain the channel parameters of the LOS paths of other to-be-modeled space points in the wireless channel according to the channel parameters of the reference point includes:

calculating to obtain the channel parameters of the LOS paths of other to-be-modeled space points in the wireless channel according to the channel parameters of the reference point and a third calculation formula;

τ ₁ [n]＝r/c ₀ ；

α _l [n]＝β _l [n]*α _l [s]；

wherein r is the distance from the base station to the UE, beta _l [n]For amplitude adjustment of the coefficient, α _l [s]The amplitude of the ith NLOS path is the reference point s.

12. The method of claim 7, wherein the wireless channel comprises a front channel segment and a back channel segment, and wherein the front channel segment and the back channel segment are concatenated to form a channel segment transition region;

the method further comprises the following steps:

and calculating the corresponding channel parameters of the NLOS paths of each space point to be modeled in the channel segment transition region according to the corresponding channel parameters of the NLOS paths of the front channel segment and the corresponding channel parameters of the NLOS paths of the rear channel segment, so that the amplitude of each NLOS path of the front channel segment is gradually reduced, and the amplitude of each NLOS path of the rear channel segment is gradually increased.

13. The method of claim 12,

the channel parameters of the space point to be modeled at any moment in the front channel segment comprise the time delay and amplitude of each multipath, and are tau respectively _l [n]And alpha _l [n]The channel parameters of the space point to be modeled at any moment in the back channel segment comprise the time delay and amplitude of each multipath, and are tau' _l [n]And α' _l [n]The channel segment transition region is a region from M time to N time; wherein, M>＝1，M<＝N；

The calculating the channel parameters corresponding to the NLOS paths of the spatial points to be modeled in the channel segment transition region according to the channel parameters corresponding to the NLOS paths of the front channel segment and the channel parameters corresponding to the NLOS paths of the rear channel segment comprises the following steps:

in the front channel segment, starting from the time M, the amplitude alpha of the first NLOS path of the space point to be modeled at the time n is adjusted _l [n]Is k ₁ [n]*α _l [n]Wherein L =2,3,.., L, N = M, M + 1.., N, coefficient k ₁ [n]Gradually decreasing from the time M to the time N;

in the back channel segment, starting from the time M, adjusting the amplitude alpha 'of the l NLOS path of the space point to be modeled at the time n' _l [n]Is k ₂ [n]*α _l ’[n]Wherein L =2,3,.., L', N = M, M + 1.., N, coefficient k ₂ [n]Gradually rising from the time M to the time N.

14. A wireless channel modeling apparatus, comprising:

15. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the wireless channel modeling method according to any of claims 1 to 13 when executing the computer program.

16. A computer-readable storage medium storing computer-executable instructions for:

-performing the wireless channel modeling method of any one of claims 1 to 13.