Geometric random modeling method and system for industrial internet of things channel

Abstract

The application relates to a geometric random modeling method and system of an industrial Internet of Things channel, wherein the method comprises the following steps: establishing a channel impulse response model representing the industrial Internet of Things channel, and constructing an effective cluster about a scatterer between a transmitting end and a receiving end of the industrial Internet of Things channel; modeling parameters of the channel impulse response model; based on the spatial non-stationary characteristics of the industrial Internet of Things channel, obtaining the motion direction of the transmitting end and the receiving end relative to the scatterer respectively, calculating the disappearance probability and the survival probability of the effective cluster relative to the transmitting end and the receiving end, and obtaining an updated effective cluster, wherein the updated effective cluster comprises a surviving cluster and a newborn cluster; and performing parameter updating on the updated effective cluster according to the parameters of the channel impulse response model. The non-stationary characteristics of the channel are fully considered when the model about the industrial Internet of Things channel is constructed, and the effect is proved to be good through experiments.

Description

Technical Field

[0001] This invention relates to the field of industrial Internet of Things (IoT) channel technology, and in particular to a geometric stochastic modeling method and system for industrial IoT channels. Background Technology

[0002] The Industrial Internet of Things (IIoT) represents a standard application scenario in sixth-generation (6G) communication systems. In IIoT scenarios, various machines are interconnected, greatly improving work efficiency. The IIoT environment involves numerous machines and mobile units. The complexity of its internal structure and channel fading conditions poses a significant challenge to establishing reliable channel models. Ultra-Reliable Low-Latency Communication (uRLLC) and Massive Machine-Type Communication (mMTC) are important foundations of IIoT. Establishing a universal IIoT channel model is necessary for both uRLLC and mMTC.

[0003] Over the past few decades, several channel models have been proposed for studying channels in the Industrial Internet of Things (IIoT). These models can be broadly categorized into two types: the first focuses on the study of channel propagation characteristics, with current research on industrial characteristics mainly concentrated in the low-frequency band; the second model models the CIR of IIoT channels, which essentially uses Poisson or Gaussian distributions to model the scatterers.

[0004] Currently, geometrical stochastic models have become a key technology for studying industrial channels due to their high accuracy and versatility. Furthermore, with the increasing scarcity of wireless spectrum resources, the development of millimeter-wave bands has become a research hotspot in the field of communications. Massive MIMO (Multi-input Multi-output) technology can simultaneously provide services to multiple users in industrial IoT systems. Therefore, combining MIMO technology with millimeter-wave communication technology holds promise for building ultra-reliable, low-latency industrial IoT communication systems. However, the non-stationary characteristics of industrial IoT channels cannot be ignored, and current simulations of these non-stationary characteristics lack consideration for the diversity of scenarios caused by movement directions. Therefore, establishing a geometrical stochastic model of industrial IoT channels that models the dynamic and non-stationary characteristics of industrial IoT channels based on movement directions is crucial. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the lack of consideration for the diversity of scenarios caused by the direction of movement in the simulation of the non-stationary characteristics of industrial Internet of Things channels in the prior art.

[0006] To address the aforementioned technical problems, this invention provides a geometric stochastic modeling method for industrial IoT channels, comprising:

[0007] Step S1: Establish a channel impulse response model characterizing the industrial IoT channel, and construct an effective cluster of scatterers between the transmitter and receiver of the industrial IoT channel;

[0008] Step S2: Model the parameters of the channel impulse response model, wherein the parameters include angle, position and time delay;

[0009] Step S3: Based on the non-stationary characteristics of the channel space in the Industrial Internet of Things, obtain the motion directions of the transmitter and receiver relative to the scatterer, calculate the survival probability of the effective clusters relative to the transmitter and receiver, and obtain the updated effective clusters, wherein the updated effective clusters include surviving clusters and newly formed clusters.

[0010] Step S4: Based on the parameters of the channel impulse response model constructed in step S2, update the angle, position, and time delay parameters of the updated effective cluster.

[0011] In one embodiment of the present invention, the method for modeling the scatterers traversed by multiple paths between the transmitter and receiver in the industrial IoT channel as an effective cluster in step S1 is as follows:

[0012] For the scattering environment between the transmitter and receiver in an industrial IoT channel, scatterers whose paths pass through scatterers and whose scattering angles at multiple reflection points are less than a preset angle are modeled as effective clusters. n And for Cluster n Considering only the transmitter T x to the first rebound cluster and the receiving end R x The cluster before the last rebound Transmitter T x and receiver R x The multiple reflection processes in between are represented by virtual links.

[0013] In one embodiment of the present invention, the method for establishing the channel impulse response model characterizing the industrial Internet of Things channel in step S1 is as follows:

[0014] Use M T ×M R Matrix H = h qp (t,τ) represents the channel impulse response model, where M T M represents the number of transmitting antennas. R h represents the number of antennas at the receiving end. qp (t,τ) represents the transmitting antenna. With the receiving antenna The impulse response between them is expressed as:

[0015]

[0016] In the formula, K represents the Rice factor, N(t) represents the number of effective clusters, and M... n (t) represents Cluster n The number of rays within, δ(τ) represents the impulse response function, τ LOS (t) represents the delay of the LOS component, and τn(t) represents the cluster delay. n The delay Indicates Cluster n The relative delay of the middle ray, the LOS component and NLOS components They are respectively:

[0017]

[0018] In the formula, V and H represent vertical polarization and horizontal polarization, respectively, and the function F T (a,b) and F R (a,b) is the antenna radiation pattern. These are the position vectors of the transmitting antenna and the receiving antenna, respectively. Indicates LOS environment and Inter-vector, Indicates Cluster in NLOS environment n The m-th ray reflection point and Vectors between, Indicates Cluster n The m-th ray reflection point and Inter-vector, κ is the cross-polarization ratio, random phase They are evenly distributed in (0, 2π]. They represent and Space, Cluster n The m-th ray and Space, Cluster n The m-th ray and Doppler frequency shift between intervals.

[0019] In one embodiment of the present invention, the method for modeling the parameters of the channel impulse response model in step S2 is as follows:

[0020] Assume the initial number of scatterers follows a Gaussian distribution, and the number of rays in each scatterer follows a Poisson distribution;

[0021] Compute Cluster n The angle of the reflection point of the m-th ray: Cluster n initial angle Cluster is obtained by estimating using a Gaussian distribution and then adding an angle offset. n The angle of the m-th ray is given by the formula:

[0022]

[0023] in, It is the angular offset of the ray, which follows a Laplace distribution with a mean of zero and a standard deviation of 1 degree;

[0024] Since only Cluster is calculated n The three-dimensional coordinates are given, therefore only the transmitter T needs to be considered. x and receiver R x To Cluster n Angle Will Abbreviated as Based on the angle results, use the transmitter T x and receiver R x With Cluster n Vector representation of Cluster n The three-dimensional coordinates; where the transmitter T x and receiver R x To Cluster n The vectors between them are:

[0025]

[0026] In the formula, and Following an exponential distribution, representing the distance from the transmitter / receiver to the Cluster, respectively. n The distance, D, represents the vector from the transmitter to the receiver; for LOS components, the delay is the delay from the transmitter to the receiver; for NLOS components, the delay of a complete path consists of the delay of the first bounce, the delay of the last bounce, and the delay of the virtual link; and The time delays for obtaining the LOS and NLOS components are as follows:

[0027]

[0028]

[0029] In the formula, τ LOS τ represents the time delay of the NLOS components. n The time delay represents the LOS component, and c represents the speed of light. The latency of a virtual link follows an exponential distribution:

[0030]

[0031] In the formula, u n Following a uniform distribution in (0,1], r τ σ τ These are the scene-dependent latency factor and the randomly generated latency spread, respectively.

[0032] In one embodiment of the present invention, the method for calculating the disappearance probability and survival probability of the effective cluster relative to the transmitter and receiver in step S3 is as follows:

[0033] For Cluster n Calculate the transmitter T x Receiver R x With Cluster n relative velocity ΔV T and ΔV R :

[0034]

[0035] In the formula, V T Indicates the speed of the transmitting end. express Speed, V R Indicates the receiver speed. express speed;

[0036] For ΔV T and ΔV R Perform velocity decomposition to obtain ΔV R ΔV T Along T x R x Decomposition of the direction of the inter-connection and ΔV R ΔV T Along perpendicular to T x R x Decomposition of the direction of the inter-connection Calculate the velocity contribution in both directions:

[0037]

[0038] In the formula, Q H and Q V Contribute parameters to scene-dependent speeds, ΔV H Indicates along t x R x The velocity contribution along the connecting line, ΔV V Indicates perpendicular to T x R xThe velocity contribution along the connecting line direction; finally, at time t = t + Δt, the survival probability of the effective cluster is modeled as:

[0039]

[0040] In the formula, λ R The mortality rate of the effective cluster, The context correlation coefficients are given in the spatial and temporal domains; the number of new clusters at time t = t + Δt follows a mean of E[N]. new The Poisson distribution of [t+Δt)]:

[0041]

[0042] In the formula, λ G The generation rate of the cluster.

[0043] In one embodiment of the present invention, the method for updating the angle, position, and delay parameters of the updated effective cluster based on the parameters of the channel impulse response model constructed in step S2 in step S4 is as follows:

[0044] For the surviving clusters obtained in step S3, update the parameters:

[0045] At time t = t + Δt, the position parameters of the surviving clusters are updated. The new position parameters are calculated as follows:

[0046]

[0047] In the formula, Indicates Cluster n The speed of the first and last rebounds;

[0048] Based on the calculated new position parameters Calculate the update angle:

[0049]

[0050] according to Update the latency of the surviving clusters.

[0051] To address the aforementioned technical problems, this invention provides a geometric stochastic modeling system for industrial Internet of Things (IoT) channels, comprising:

[0052] Building module: Used to establish a channel impulse response model characterizing the industrial IoT channel, and to construct an effective cluster of scatterers between the transmitter and receiver of the industrial IoT channel;

[0053] Parameter modeling module: used to model the parameters of the channel impulse response model, wherein the parameters include angle, position and time delay;

[0054] Update module: Based on the non-stationary characteristics of the channel space in the Industrial Internet of Things, it obtains the motion directions of the transmitter and receiver relative to the scatterer, calculates the survival probability of the effective clusters relative to the transmitter and receiver, and obtains the updated effective clusters, wherein the updated effective clusters include surviving clusters and newly formed clusters;

[0055] Parameter update module: Used to update the angle, position and time delay parameters of the updated effective cluster based on the parameters of the channel impulse response model constructed by the parameter modeling module.

[0056] To address the aforementioned technical problems, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the geometric stochastic modeling method for industrial Internet of Things channels described above.

[0057] To address the aforementioned technical problems, the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the geometric random modeling method for industrial Internet of Things channels described above.

[0058] To address the aforementioned technical problems, the present invention provides a computer program product, comprising a computer program that, when executed by a processor, implements the steps of the aforementioned geometric stochastic modeling method for industrial Internet of Things channels.

[0059] The technical solution of the present invention has the following advantages over the prior art:

[0060] The geometric stochastic modeling method for industrial IoT channels described in this invention can model the differences in channel non-stationary characteristics caused by different movement directions based on velocity decomposition, thereby achieving accurate modeling of industrial IoT channel characteristics.

[0061] This invention demonstrates the effectiveness of the channel impulse response model for industrial IoT channels constructed on the front side in the millimeter-wave band through experimental comparison, showing that this invention can be used in practical engineering. Attached Figure Description

[0062] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0063] Figure 1 This is a flowchart of the method of the present invention;

[0064] Figure 2 This is a schematic diagram of the industrial IoT channel scattering environment in an embodiment of the present invention;

[0065] Figure 3It is a comparison chart of the inter-cluster delay cumulative distribution function curves of simulated, actual measured values ​​and reference models;

[0066] Figure 4 It is a comparison graph of the cumulative distribution function curves of the number of clusters in the simulated motion direction at a receiving end, the actual measured value, and the reference model;

[0067] Figure 5 This is a comparison chart of the cumulative distribution function curves of the number of clusters at the simulated, actual measured, and reference models at the other receiving motion direction end. Detailed Implementation

[0068] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0069] Example 1

[0070] Reference Figure 1 As shown, this invention relates to a geometric stochastic modeling method for industrial Internet of Things (IoT) channels, comprising:

[0071] Step S1: Establish a channel impulse response model to characterize the industrial IoT channel, and model the scatterers that pass through the multipath between the transmitter and receiver in the industrial IoT channel as an effective cluster;

[0072] Step S2: Model the parameters of the channel impulse response model, wherein the parameters include angle, position and time delay;

[0073] Step S3: Based on the non-stationary characteristics of the channel space in the Industrial Internet of Things, obtain the motion directions of the transmitter and receiver relative to the scatterer, calculate the survival probability of the effective clusters relative to the transmitter and receiver, and obtain the updated effective clusters, wherein the updated effective clusters include surviving clusters and newly formed clusters.

[0074] Step S4: Based on the parameters of the channel impulse response model constructed in step S2, update the angle, position, and time delay parameters of the updated effective cluster.

[0075] The following is a detailed description of this embodiment:

[0076] Step 1: Establish a channel impulse response model to characterize the channel of the Industrial Internet of Things based on the 3GPP standard model.

[0077] For the scattering environment between the transmitter and receiver in an industrial IoT channel, scatterers whose paths pass through scatterers and whose angles between the reflection points of multiple paths and the transmitter / receiver are less than a preset angle are modeled as effective clusters. nIn short, it involves modeling scatterers traversed by similar paths as effective clusters. n In the channel impulse response model, effective clusters are modeled based on a two-hop mechanism, i.e., for a Cluster... n Considering only the transmitter T x Clusters that bounce off the first scatterer and the receiving end R x Clusters that bounced off the scatterer after the last time Transmitter T x and receiver R x The multiple reflections in between are represented by virtual links, see details. Figure 2 .

[0078] It is important to note that there are several scatterers at both the first and last reflections after the transmitter and before the receiver. Therefore, the first reflection point differs for different paths (obtained through clustering). ) and the last reflection point (obtained after clustering) Some paths are similar, and their reflection points are also similar, thus they are clustered into an effective cluster.

[0079] This embodiment uses M T ×M R Matrix H = h qp (t,τ) represents the channel impulse response model, where M T M represents the number of transmitting antennas. R h represents the number of antennas at the receiving end. qp (t,τ) represents the transmitting antenna. With the receiving antenna The impulse response between them includes the LOS component (i.e., the direct component). Non-line-of-sight (Non-LOS, NLOS) components The proposed channel impulse response model is expressed as follows:

[0080]

[0081] In the formula, K represents the Rice factor, and N(t) represents the cluster. n The quantity of M n (t) represents Cluster n The number of rays within the cluster (a ray represents the path within the effective cluster after passing through the scatterer; it can be simply understood as the path itself), and δ(τ) represents the impulse response function. τ LOS (t) represents the delay of the LOS component, τ n (t) represents Cluster n The delay Indicates Cluster n The relative delay of the intermediate rays. LOS component. and NLOS components They are represented as follows:

[0082]

[0083] In the formula, V and H represent vertical polarization and horizontal polarization, respectively; the function F T (a,b) and F R (a,b) is the antenna radiation pattern; These are the position vectors of the transmitting antenna and the receiving antenna, respectively;

[0084] Indicates LOS environment and Inter-vector;

[0085] Indicates Cluster in NLOS environment n The m-th ray reflection point and Vectors between; Indicates Cluster n The m-th ray reflection point and Inter-vector. κ is the cross-polarization ratio, random phase. It is evenly distributed in (0, 2π]. They represent and Space, Cluster n The m-th ray and Space, Cluster n The m-th ray and Doppler frequency shift between intervals.

[0086] Step 2: Model the parameters (angle, position, delay, and power) of the channel impulse response model from Step 1. Specifically, this includes:

[0087] First, assume that the initial number of scatterers follows a Gaussian distribution, and the number of rays in each scatterer follows a Poisson distribution.

[0088] Secondly, calculate Cluster n The angle of the m-th ray reflection point, Cluster n initial angle It can be estimated using a Gaussian distribution. Then, by adding an angular offset, the Cluster can be obtained. n The angle of the m-th ray:

[0089]

[0090] in, This refers to the angular offset of the ray, which in this embodiment is assumed to follow a Laplace distribution with a mean of zero and a standard deviation of 1 degree (0.017 radians). The standard deviation of the angular offset can be modified based on the measurement results.

[0091] Since only Cluster is calculated n The three-dimensional coordinates are given, therefore only the transmitter T needs to be considered. x and receiver R x To Cluster n Angle Will Abbreviated as Based on the angle results, use the transmitter T x and receiver R x With Cluster n Vector representation of Cluster n 3D coordinates;

[0092] Transmitter T x and receiver R x To Cluster n The vectors are respectively:

[0093]

[0094] In the formula, and Following an exponential distribution, representing the distance from the transmitter / receiver to the Cluster, respectively. n The distance, D, represents the vector from the transmitter to the receiver. For example... Figure 2 As shown, for LOS components, the delay is the delay from the transmitter to the receiver; for NLOS components, the delay of a complete path consists of the delay of the first bounce, the delay of the last bounce, and the delay of the virtual link. and The time delays of the LOS and NLOS components can be obtained as follows:

[0095]

[0096] In the formula, τ LOS τ represents the time delay of the NLOS components. n represents the time delay of the LOS component, and c represents the speed of light; The latency of a virtual link follows an exponential distribution:

[0097]

[0098] In the formula, u n Following a uniform distribution in (0,1], rτ σ τ These are the scene-dependent delay scalar and the randomly generated delay spread, respectively.

[0099] Step 3: At time t = t + Δt, update the number of clusters in the channel impulse response model. Specifically, this includes:

[0100] First, for Cluster n Calculate T x R x With Cluster n relative velocity ΔV T and ΔV R :

[0101]

[0102] In the formula, V T Indicates the speed of the transmitting end. express Speed, V R Indicates the receiver speed. express speed;

[0103] Subsequently, regarding ΔV T and ΔV R Perform velocity decomposition to obtain ΔVR 、 ΔV T Along T x R x Decomposition of the direction of the inter-connection and ΔV R ΔV T Along perpendicular to T x R x Decomposition of the direction of the inter-connection Calculate the velocity contribution in both directions:

[0104]

[0105] In the formula, Q H and Q V Contribute parameters to scene-dependent speeds, ΔV H Indicates along T x R x The velocity contribution along the connecting line, ΔV V Indicates perpendicular to T x R x The velocity contribution along the connecting line. Finally, at time t = t + Δt, the survival probability of the effective cluster is modeled as:

[0106]

[0107] In the formula, λ R The mortality rate of the effective cluster, The scenario correlation coefficient is defined in both the spatial and temporal domains. The number of newly formed clusters at time t = t + Δt follows a mean of E[N]. new The Poisson distribution of [t+Δt)]:

[0108]

[0109] In the formula, λ G This represents the cluster generation rate (i.e., the generated clusters represent newly formed clusters).

[0110] Step 4: Update the parameters for the updated cluster.

[0111] For the surviving clusters obtained in step three, update the parameters:

[0112] At time t = t + Δt, the position parameters of the surviving clusters are updated. The new position parameters are calculated as follows:

[0113]

[0114]

[0115] In the formula, Indicates Cluster n The speed of the first bounce and the last bounce.

[0116] Based on the calculated new position parameters Calculate the update angle:

[0117]

[0118] Subsequently, the latency of the surviving clusters is updated according to the following formula:

[0119]

[0120] It should be noted that for the newly generated clusters (new clusters) obtained in step three, the initial parameters should be set according to the method in step two.

[0121] Experimental comparison and analysis:

[0122] This invention selects a machine room, representative of most industrial environments, as the reference environment. To verify the accuracy of the proposed model, the simulation results were fitted with the actual measured cumulative distribution function (CDF) results of inter-cluster delay. A general geometrical random channel model was selected as the reference model for comparison with the proposed model, such as... Figure 3 As shown, "Proposed" represents the model proposed in this invention, "Measurement" represents the actual measured value, and "Reference" represents the reference model. The results show that the simulated CDF value of the inter-cluster delay of the proposed model has good agreement with the measured value, and its fitting effect is better than that of the reference model, verifying the accuracy of the proposed model.

[0123] Figure 4 and Figure 5 The cumulative distribution functions of cluster numbers in channel modeling for IoT scenarios with different receiver motion directions are displayed. Results show that, under the same set of birth and death rates, the proposed model fits the cluster numbers well for both processes. In contrast, the reference model only fits one process well and the other poorly. This demonstrates the effectiveness of the invented method for industrial IoT channel modeling based on a geometrical stochastic model in the millimeter-wave band.

[0124] Example 2

[0125] This embodiment provides a geometric stochastic modeling system for industrial IoT channels, including:

[0126] Building module: Used to establish a channel impulse response model characterizing the industrial IoT channel, and to construct an effective cluster of scatterers between the transmitter and receiver of the industrial IoT channel;

[0127] Parameter modeling module: used to model the parameters of the channel impulse response model, wherein the parameters include angle, position and time delay;

[0128] Update module: Based on the non-stationary characteristics of the channel space in the Industrial Internet of Things, it obtains the motion directions of the transmitter and receiver relative to the scatterer, calculates the survival probability of the effective clusters relative to the transmitter and receiver, and obtains the updated effective clusters, wherein the updated effective clusters include surviving clusters and newly formed clusters;

[0129] Parameter update module: Used to update the angle, position and time delay parameters of the updated effective cluster based on the parameters of the channel impulse response model constructed by the parameter modeling module.

[0130] Example 3

[0131] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the geometric random modeling method for industrial Internet of Things channels described in Embodiment 1.

[0132] Example 4

[0133] This embodiment provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the geometric random modeling method for industrial IoT channels described in Embodiment 1.

[0134] Example 5

[0135] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the geometric stochastic modeling method for industrial IoT channels described in Embodiment 1.

[0136] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0137] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0138] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0139] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0140] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0141] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A geometric stochastic modeling method for industrial Internet of Things (IoT) channels, characterized in that: The method comprises the following steps: Step S1: establishing a channel impulse response model representing an industrial Internet of Things channel, and modeling scatterers as effective clusters between a transmitting end and a receiving end of the industrial Internet of Things channel; Step S2: modeling parameters of the channel impulse response model, wherein the parameters include angle, position and time delay; Step S3: based on the spatial non-stationary characteristics of the industrial Internet of Things channel, obtaining the motion direction of the transmitting end and the receiving end relative to the scatterers respectively, calculating the disappearance probability and the survival probability of the effective clusters relative to the transmitting end and the receiving end, and obtaining updated effective clusters, wherein the updated effective clusters include surviving clusters and new clusters; The method for calculating the disappearance probability and the survival probability of the effective clusters relative to the transmitting end and the receiving end in step S3 is as follows: For valid clusters , the relative velocity , of the transmitting end , the receiving end , and , and are calculated ； ； wherein represents the transmitting end velocity, represents the transmitting end to the first bounce velocity, represents the receiving end velocity, represents the receiving end to the last bounce ; To and decompose the velocity, get the decomposition amount along , interconnection direction , , and the decomposition amount along the direction perpendicular to , interconnection direction , , calculate the velocity contribution amount of two directions: ； ； In the formula, and Contribute parameters to scene-dependent speeds. Indicates along , The velocity contribution in the direction of the connecting line. Indicates perpendicular to , The velocity contribution in the direction of the connecting line; finally, in At time t, the survival probability of the effective cluster is modeled as: ； where is the mortality rate of the effective cluster, is the scenario-dependent correlation coefficient in the spatial and temporal domain; The number of newly born clusters at time t obeys a Poisson distribution with mean ​ ； In the formula, is the generation rate of effective clusters; Step S4: performing parameter updating of the updated effective clusters in terms of angle, position and time delay based on the parameters of the channel impulse response model constructed in step S2, and the method is as follows: For the surviving clusters obtained in step S3, the parameters are updated as follows: At time, the location parameters of the surviving clusters are updated, and the new location parameters are calculated as ； ； According to the calculated new position parameters , Calculate update angle: ； ； According to updating the latency of the surviving cluster; wherein, , are the angles of the transmitting end and the receiving end to , respectively; and are the vectors of the transmitting end and the receiving end to , respectively; and are subject to an exponential distribution, representing the distance of the transmitting end / receiving end to , respectively; represents the vector of the transmitting end to the receiving end; represents the speed of light; represents the delay of the virtual link.

2. The method of claim 1, wherein: The method for modeling the scatterers between the transmitting end and the receiving end of the industrial Internet of Things channel as effective clusters in step S1 is as follows: For a scattering environment between a transmitting end and a receiving end in an industrial Internet of Things channel, scattering bodies whose angles between corresponding reflection points and the transmitting end / receiving end after passing through the scattering bodies are less than a preset angle are modeled as effective clusters , and for only the transmitting end to the cluster of the first bounce , and the receiving end before the cluster of the last bounce , multiple reflection processes between the transmitting end and the receiving end are represented by virtual links.

3. The method of claim 2, wherein: The method for establishing the channel impulse response model representing the industrial Internet of Things channel in step S1 is as follows: Using matrix denotes a channel impulse response model, where, denotes the number of transmit antennas, denotes the number of receive antennas, is the impulse response between the transmit antenna and the receive antenna is denoted as: ； wherein denotes the RIS factor, denotes the number of effective clusters, denotes the number of rays within denotes the impulse response function, denotes the delay of the LOS component, denotes the delay of denotes the relative delay of the rays in the LOS component and the NLOS component, respectively, ； ； where V and H represent vertical polarization and horizontal polarization respectively, the functions and are the antenna patterns, , are the position vectors of the transmitting and receiving antennas respectively, represents the vector between and in the LOS environment, represents the vector between the reflection point of the mth ray in and in the NLOS environment, represents the vector between the reflection point of the mth ray in and , is the cross-polarization ratio, and the random phases are uniformly distributed in , represent the Doppler shifts between and , the mth ray in and , and the mth ray in , respectively.

4. The method of claim 3, wherein: The method for modeling the parameters of the channel impulse response model in step S2 is as follows: It is assumed that the initial number of scatterers follows a Gaussian distribution, and the number of rays in each scatterer follows a Poisson distribution; Computing the angle of the mth ray reflection point in the middle: the initial angle of the mth ray reflection point By estimating through a Gaussian distribution, and then adding an angle offset, we get the angle of the mth ray in the middle, the formula is: ； wherein is an angular deflection of the ray, which obeys a Laplace distribution with mean zero and standard deviation of 1 degree; Because only calculation The three-dimensional coordinates are given, therefore only the transmitter needs to be considered. and receiving end arrive Angle ,Will Abbreviated as , Based on the angle results, the transmitter is used. and receiving end and Vector representation between The three-dimensional coordinates; where the transmitting end and receiving end arrive The vectors between them are: ； ； wherein, and are subject to exponential distribution, respectively representing the distance from the transmitting end / receiving end to , represents the vector from the transmitting end to the receiving end; for the LOS component, the time delay is the time delay from the transmitting end to the receiving end; for the NLOS component, the time delay of a complete path is composed of the time delay of the first bounce, the time delay of the last bounce and the time delay of the virtual link; is obtained by and the time delays of the LOS component and the NLOS component are respectively: ； ； wherein denotes the delay of the NLOS component, denotes the delay of the LOS component, denotes the speed of light, denotes the delay of the virtual link, following an exponential distribution: ； wherein follows a uniform distribution, are a scenario-dependent delay factor and a randomly generated delay spread, respectively.

5. A system for geometrically random modeling of an industrial internet of things channel for implementing the method of geometrically random modeling of an industrial internet of things channel according to any one of claims 1 to 4, characterized in that: The method comprises the following steps: A construction module is configured to establish a channel impulse response model representing an industrial Internet of Things channel, and to construct effective clusters of scatterers between a transmitting end and a receiving end of the industrial Internet of Things channel; A parameter modeling module is configured to model parameters of the channel impulse response model, wherein the parameters include angle, position and time delay; An updating module is configured to, based on the spatial non-stationary characteristics of the industrial Internet of Things channel, obtain the motion direction of the transmitting end and the receiving end relative to the scatterers respectively, calculate the survival probability of the effective clusters relative to the transmitting end and the receiving end, and obtain updated effective clusters, wherein the updated effective clusters include surviving clusters and new clusters; A parameter updating module is configured to perform parameter updating of the updated effective clusters in terms of angle, position and time delay based on the parameters of the channel impulse response model constructed by the parameter modeling module.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that: The processor executes the computer program to implement the steps of the geometric random modeling method of the industrial Internet of Things channel according to any one of claims 1 to 4.

7. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the geometric random modeling method of the industrial Internet of Things channel according to any one of claims 1 to 4.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the geometric random modeling method of the industrial Internet of Things channel according to any one of claims 1 to 4.

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