Industrial internet of things scene integrated sensing channel modeling method, medium and equipment

Abstract

This invention discloses a method, medium, and device for modeling an integrated sensing channel in an industrial Internet of Things (IIoT) scenario. The method initializes the number of clusters and the number of rays within each cluster for both the communication and sensing channels. It constructs multiple scattering points for the target channel and generates radar cross-section values ​​for both the target scatterer and the environmental scatterer, while simultaneously determining the visibility of each antenna array to the clusters. Considering the differences between the communication and sensing channels and the motion of different mobile clusters in the IIoT scenario, a two-stage cluster birth and death process is designed. The birth and death processes of different clusters are applied to simulate the temporal and spatial non-stationary characteristics of the integrated sensing channel. This invention is of great significance for the standardization of integrated sensing channel models in the IIoT and provides effective theoretical support for channel design.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically to a method, medium, and device for integrated sensing channel modeling in industrial Internet of Things (IoT) scenarios. Background Technology

[0002] The Industrial Internet of Things (IIoT) is a typical application scenario for 6G wireless communication, aiming to transform traditional industrial manufacturing processes and achieve digital and intelligent industrial manufacturing. Integrated Sensing and Communication (ISAC), as an emerging technology, integrates sensing and communication functions into a unified system architecture, enabling resource sharing, efficient spectrum utilization, and real-time environmental monitoring. This improves the intelligence level of industrial systems and achieves more efficient spectrum management and multi-functional operation.

[0003] In Industrial Internet of Things (IIoT) scenarios, ISAC technology can be applied to equipment status monitoring, environmental sensing, and automated control. For example, it enables synchronous data transmission and object detection via wireless signals, reducing resource waste and latency issues present in traditional standalone systems. Furthermore, channel modeling plays a fundamental role in the physical layer of wireless communication, simulating various effects in the actual transmission environment by establishing mathematical models of signal propagation. Therefore, current research on channel modeling for industrial IoT scenarios or integrated sensing models includes, for example, Chinese invention patent CN117040669A, which discloses a geometric random channel modeling method for industrial IoT communication channels. This method divides the impulse response of industrial IoT channels into two parts: SMC and DMC, and models their delay, angle, and power separately. Alternatively, Chinese invention patent CN120454902A discloses a spatially consistent integrated sensing channel modeling method, which explores an integrated sensing channel modeling method that considers private clusters and shared clusters, and introduces the transmission probabilities of private clusters and shared clusters in the modeling. In addition, Chinese invention patent CN120074714A discloses a novel geometric random channel modeling method for integrated communication and sensing systems, which proposes a comprehensive integrated sensing wireless channel modeling method and verifies the model using RT simulation.

[0004] However, existing methods of this kind only consider channel modeling or integrated sensing channel modeling in industrial IoT scenarios, without addressing integrated sensing dual-station sensing channel modeling in industrial IoT scenarios. Furthermore, when simulating the temporal and spatial non-stationary characteristics of ISAC channels, the methods do not consider the velocity inconsistencies of target clusters and clusters in non-line-of-sight (NLOS) environments, as well as the impact of different proportions of mobile clusters in IIoT scenarios, when using cluster birth and death processes. Summary of the Invention

[0005] The present invention provides an integrated sensing channel modeling method, medium, and device for industrial IoT scenarios that can provide more comprehensive and reliable theoretical support for channel design, and can solve at least one of the above-mentioned technical problems.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for integrated sensing channel modeling in industrial IoT scenarios includes the following steps:

[0008] S1. Set up an industrial IoT scenario. The scenario content should include at least model parameters, antenna array configuration and signal propagation conditions, and calculate the propagation path loss under the current antenna array configuration.

[0009] S2. Based on the current industrial IoT scenario, generate large-scale channel parameters with spatial consistency. These large-scale channel parameters include at least shadowing fading, Rice factor, delay spread, and angle spread.

[0010] S3. For the communication channel and the sensing channel, initialize the number of clusters and the number of rays in the clusters, construct multiple scattering points for the target for the sensing channel, and generate the radar cross-section values ​​of the target scatterer and the environmental scatterer, while determining the visibility of each antenna array to the cluster.

[0011] S4. Generate the initial cluster and ray's related angles, delays, and power after initialization, and perform random coupling matching of the ray to generate the cross-polarization power ratio and the small-scale channel parameters of the antenna array;

[0012] S5. Update the positions of the three ends and the large-scale parameters of the channel based on the motion trajectories of the transmitting end, receiving end and sensing receiver in the antenna array.

[0013] S6. Considering the similarities and differences between communication channels and sensing channels, as well as the movement of different mobile clusters in the industrial IoT scenario, design a two-stage cluster birth and death process, and apply the birth and death of different clusters to simulate the time and space non-stationary characteristics of the integrated communication and sensing channel.

[0014] S7. Initialize the new cluster, allocate the relevant channel coefficients of the new cluster, and update the relevant angle, delay and power of the surviving cluster. Generate the total channel coefficients based on propagation path loss and shadow fading.

[0015] S8. Return to S5 until the motion trajectories of the transmitter, receiver, and sensing receiver have been traversed, and calculate and analyze the statistical characteristics of the communication channel model and sensing channel model in the industrial Internet of Things scenario.

[0016] Furthermore, in S1, the industrial IoT scenario is M under millimeter wave conditions.T ×M R ×M S The industrial IoT integrated sensing dual-station sensing scenario, where M T M R and M S The numbers represent the number of antennas for the transmitter Tx, receiver Rx, and sensing receiver Sx, respectively. All three are selected as large-scale MIMO antenna arrays.

[0017] Furthermore, the two-stage cluster birth and death process in S6 is divided into two stages: communication channel and sensing channel. The clusters in the communication channel and sensing channel are the superposition of surviving cluster components and newly formed cluster components.

[0018] Furthermore, when in a communication channel, the cluster birth and death process includes:

[0019] S6.a1, after a time interval and antenna spacing Then, the survival probability of a cluster is:

[0020]

[0021] in, The survival probability of the communication channel cluster. The recombination rate of the cluster. and These are the spacing between the unit antennas at the transmitting and receiving ends, respectively. This represents the percentage of mobile clusters in an Industrial Internet of Things (IIoT) scenario. and The average relative velocity between the transmitter and receiver and the scatterers in the cluster. and These are the scene correlation coefficients, which describe spatial correlation and temporal correlation, respectively.

[0022] S6.a2. Based on the birth-death process, newly formed clusters are generated according to a Poisson distribution:

[0023]

[0024] in, For mathematical expectation, The number of newly generated scattering clusters is given by t, where t is the current time and r is the current antenna status. The generation rate of the cluster;

[0025] When in a sensing channel, the cluster birth and death process includes:

[0026] S6.b1, after a time interval and antenna spacing Then, the survival probability of a cluster is:

[0027]

[0028] in, To sense the survival probability of channel clusters, To sense the unit antenna spacing at the receiving end, To determine the percentage of moving target clusters in the sensing channel, To determine the percentage of mobile environment clusters in the sensing channel, satisfying , To sense the average relative velocity between the receiver and the scatterers in the environmental cluster, and These are the average relative velocities between the transmitter and the sensing receiver and the scatterers in the target cluster, respectively.

[0029] S6.b2. Based on the birth-death process, newly formed clusters are generated according to a Poisson distribution:

[0030] .

[0031] Furthermore, in S6, the motion of different mobile clusters is considered, and the influence of different cluster environmental characteristics on the channel non-stationary characteristics is analyzed using different speed weights.

[0032] Furthermore, in S7, the communication channel matrix in the industrial IoT scenario and sensing channel matrix They are respectively:

[0033]

[0034]

[0035] In this model, both PL and SH are large-scale models; the former represents the propagation path loss model, and the latter represents the shadow fading model. and These are the small-scale fading channel matrices for the communication channel and the sensing channel, respectively, with the former having a matrix dimension of M. T ×M R The latter matrix has a dimension of M. T ×M S M T M R and M S These represent the number of antennas at the transmitter (Tx), receiver (Rx), and sensing receiver (Sx), respectively.

[0036] Furthermore, the communication channel propagates from the signal transmitted at the transmitter Tx to the receiver Rx via a line-of-sight (LOS) path and a non-line-of-sight (NLOS) path. The channel impulse response (CIR) of the communication channel model is determined by... The expression is:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] in, This represents the channel impulse response of the communication channel model. For the line-of-sight (LOS) path component, For non-line-of-sight (NLOS) path components, t and τ represent the current time and delay, respectively, K is the Rice factor of the channel, N and M are the number of clusters and the total number of rays in the clusters, respectively, and n and m are the cluster and the number of rays in the clusters, respectively.

[0045] , These represent the vertical and horizontal polarizations of the transmitting antenna, respectively. , These represent the vertical and horizontal polarizations of the receiving antenna, respectively. , These represent the azimuth and elevation angles of the transmitting antenna, respectively. , These represent the azimuth and elevation angles of the receiving antenna, respectively. Indicates the distance between the transmitting and receiving ends, (·) T Indicates matrix transpose. , Let Tx and Rx be the unit spherical vectors representing the line-of-sight (LOS) components of the transmitter (Tx) and receiver (Rx), respectively. , Let represent the position vectors of the q-th receiving antenna and the p-th transmitting antenna, respectively. The Doppler frequency shift represents the line-of-sight (LOS) component. Let represent the path delay of the line-of-sight (LOS) component of the communication channel, j represent the imaginary unit, λ0 represent the wavelength, and δ(·) represent the Dirac function;

[0046] This represents the average power of the m-th ray in the n-th cluster. , Representing the nth cluster C nThe m-th ray in the middle has an azimuth and elevation angle relative to the center of the transmitting antenna. , , and They represent The initial phase within the range follows a uniform distribution. Indicates the cross-polarization ratio, , Representing the nth cluster C n The azimuth and elevation angles of the m-th ray relative to the center of the receiving antenna. , Let Tx be the unit spherical vectors of the non-line-of-sight components of the transmitter Tx and the receiver Rx, respectively. Indicates the Doppler frequency shift of the non-line-of-sight NLOS component. The path delay represents the non-line-of-sight (NLOS) component of the communication channel;

[0047] The sensing channel propagates the signal transmitted by the transmitter Tx to the sensing receiver Sx through the target Tar path and the environment Env path. The channel impulse response (CIR) of the sensing channel model is determined by... The expression is:

[0048]

[0049]

[0050]

[0051] in, The channel impulse response of the sensing channel model. For the target Tar path component, For the Environment Env path component, n' and m' are the cluster and ray number indices in the sensing channel, respectively;

[0052] K1 and K2 are Rice factors for the path from the transmitter to the target Tar and the path from the target to the sensing receiver, respectively. , , and These represent the channel impulse responses of the four sub-paths in the target Tar path: LOS-Target-LOS, NLOS-Target-LOS, LOS-Target-NLOS, and NLOS-Target-NLOS, respectively.

[0053] Indicates the power of the environmental Env path. , Representing the n'th cluster C n'The m'-th ray's azimuth and elevation angles relative to the center of the transmitting antenna. , Representing the n'th cluster C n' The m'-th ray and the azimuth and elevation angles of the center of the sensing antenna are... , , and These represent the initial phases, Indicates the cross-polarization ratio of the sensing channel. , Let Sx and Tx represent the unit spherical vectors of the non-line-of-sight (NLOS) components of the sensing receiver and transmitter, respectively. This represents the position vector of the s-th sensing antenna. This indicates the Doppler frequency shift of the environment's Env path. This represents the delay component of the environment's Env path.

[0054] Furthermore, in S8, the statistical characteristics of the communication channel model and the sensing channel model include the cluster visibility of the communication channel receiver and the sensing channel receiver, the power delay spectrum of the communication channel and the sensing channel, and the spatiotemporal frequency correlation function of the communication channel and the sensing channel.

[0055] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the above-described integrated sensing channel modeling method for industrial Internet of Things scenarios.

[0056] A computer device includes a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the above-described integrated sensing channel modeling method for industrial Internet of Things scenarios.

[0057] The beneficial effects of this invention are reflected in:

[0058] This invention models the integrated sensing and communication ISAC bistatic channel in the Industrial Internet of Things (IIoT) scenario using a geometry-based stochastic model (GBSM) and analyzes the relevant characteristics of this scenario. During the cluster birth and death process, it considers the speed inconsistency between the target cluster and the cluster in the non-line-of-sight (NLOS) environment, as well as the impact of different proportions of mobile clusters in the IIoT scenario, and analyzes the cluster visibility at the receivers of the communication and sensing channels. Attached Figure Description

[0059] The accompanying drawings, which are provided to further understand this application and constitute a part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application.

[0060] Figure 1This is a schematic diagram of the overall process of the integrated sensing channel modeling method for industrial IoT scenarios according to an embodiment of the present invention.

[0061] Figure 2 This is a scenario diagram of the integrated sensing channel model for industrial IoT scenarios according to an embodiment of the present invention.

[0062] Figure 3 This is a schematic diagram comparing the visibility of cluster 1 and cluster 30 at the receiving end in the communication channel of an embodiment of the present invention.

[0063] Figure 4 This is a schematic diagram comparing the visibility of cluster 1 and cluster 30 at the receiving end in the sensing channel according to an embodiment of the present invention.

[0064] Figure 5 This is a schematic diagram comparing the power delay spectrum of the communication channel and the sensing channel in an embodiment of the present invention.

[0065] Figure 6 This is a schematic diagram comparing the time correlation functions of the communication channel and the sensing channel in an embodiment of the present invention.

[0066] Figure 7 This is a structural block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] It should be noted that the meaning of "and / or" throughout the text includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution that simultaneously satisfies A and B. Furthermore, "multiple" refers to two or more. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0069] See Figure 1 This invention provides a method for integrated sensing channel modeling in industrial IoT scenarios, comprising the following steps:

[0070] S1. Set up an industrial IoT scenario. The scenario content should include at least model parameters, antenna array configuration and signal propagation conditions, and calculate the propagation path loss under the current antenna array configuration.

[0071] In S1, the industrial IoT scenario is M under millimeter wave conditions. T ×M R ×M S The industrial IoT integrated sensing dual-station sensing scenario, where M T M R and M S The numbers represent the number of antennas for the transmitter Tx, receiver Rx, and sensing receiver Sx, respectively. All three are selected as large-scale MIMO antenna arrays.

[0072] The antenna spacing unit in the above three antenna arrays is [unit missing]. The system operates in the 28 GHz band. The model uses a double-hop propagation mechanism to simulate the signal propagation process. That is, the nth propagation path includes the first bounce from the transmitter Tx to the first cluster, the last bounce from the last cluster to the receiver Rx, and an abstract virtual link consisting of multiple bounces between the first and last bounces. Therefore, the total delay of a complete channel path can be composed of the delay of the first bounce, the delay of the last bounce, and the delay of the virtual link. Specifically, the pth transmitting antenna uses... This indicates that the q-th receiving antenna is used This indicates that the s-th sensing and receiving antenna uses express.

[0073] Figure 2 The diagram shows a scenario of the integrated sensing channel model for industrial IoT applications according to the present invention. This scenario includes a transmitter Tx, a receiver Rx, and a sensing receiver Sx. The base station transmitter is fixed, while the receiver and sensing receiver are positioned as follows: and The system moves at a certain speed. The transmitting end's role is to send information to Rx and participate in environmental perception with Sx; the receiving end has two roles: first, to receive the communication signals sent by Tx; second, to act as a target on the target Tar path and reflect the signals back to Sx; the perception receiving end's role is to receive the target Tar path signals reflected by Tx through the target and the environment Env path signals reflected by the environment. From the system path perspective, the overall system is divided into a communication system and a perception system. The communication channel is divided into line-of-sight (LOS) paths and non-line-of-sight (NLOS) paths, and the perception channel is divided into target (Tar, Target) paths and environment (Env, Environment) paths. Among them, the target Tar path is further subdivided into four sub-paths: LOS-Target-LOS, LOS-Target-NLOS, NLOS-Target-LOS, and NLOS-Target-NLOS.

[0074] Figure 2 In this context, communication channels include line-of-sight paths (...) <3> ) and non-line-of-sight paths ( <1> + <2> The bistatic sensing channel includes target path-line-of-sight-target-line-of-sight (…). <4> + <5> ), Target path - line of sight - Target - non-line of sight ( <4> + <8> + <9> ), Target path - non-line-of-sight - target - line-of-sight ( <6> + <7> + <5> ), Target path - non-line-of-sight - target - non-line-of-sight ( <6> + <7> + <8> + <9> ) and environment path ( <10> + <11> ).

[0075] In addition, the positions and altitudes of the base station transmitter Tx, receiver Rx, and sensing receiver Sx are set, as well as the speed, azimuth angle, and elevation angle of movement of Tx, Rx, and Sx.

[0076] S2. Based on the current industrial IoT scenario, generate large-scale channel parameters with spatial consistency. These large-scale channel parameters include at least shadow fading, Rice factor, delay spread, and angle spread.

[0077] In S2, shadow fading is a variable that follows a Gaussian random distribution, optionally with a mean of 0 and a variance of . The standard deviation is determined based on whether it is a line-of-sight path and the type of industrial IoT scenario; the Rice factor is a variable that follows a Gaussian distribution and is optional, with a mean of 5 and a standard deviation of 3.

[0078] S3. For the communication channel and the sensing channel, initialize the number of clusters and the number of rays in the clusters, construct multiple scattering points for the target for the sensing channel, and generate the radar cross-section values ​​of the target scatterer and the environmental scatterer, while determining the visibility of each antenna array to the cluster.

[0079] In S3, for example, the initial number of clusters is set to... Birth rate Set to 80, recombination rate Set it to 4, which means the number of clusters and rays is initialized to 20.

[0080] Considering the influence of the angle, frequency, and random variations of the radar cross section (RCS) at the target location, the RCS is expressed as follows:

[0081]

[0082] Where A is the average intensity of the electromagnetic energy intercepted and scattered back by the target, i.e., the average value, B1 is the relative scattering gain affected by different angles, and B2 is the random fluctuation factor. For industrial IoT scenarios, this invention uses a log-normal distribution to characterize the random fluctuation part.

[0083] S4. Generate the initial cluster and ray's related angles, delays, and power after initialization, and perform random ray coupling matching to generate the cross-polarization power ratio and the small-scale channel parameters of the antenna array.

[0084] S5. Update the positions of the three ends and the large-scale parameters of the channel based on the motion trajectories of the transmitter, receiver and sensing receiver in the antenna array.

[0085] S6. Considering the similarities and differences between communication channels and sensing channels, and the target representation as a multi-scattering point cluster model, as well as the motion of different mobile clusters in the industrial IoT scenario, design a two-stage cluster birth and death process, and apply the birth and death of different clusters to simulate the temporal and spatial non-stationary characteristics of the integrated communication and sensing channel.

[0086] In S6, the two-stage cluster birth and death process is divided into two stages: communication channel and sensing channel. The clusters in the communication channel and sensing channel are the superposition of surviving cluster components and newly formed cluster components.

[0087] When in a communication channel, the cluster's birth and death process includes:

[0088] S6.a1, after a time interval and antenna spacing Then, the survival probability of a cluster is:

[0089]

[0090] in, The survival probability of the communication channel cluster. The recombination rate of the cluster. and These are the spacing between the unit antennas at the transmitting and receiving ends, respectively. This represents the percentage of mobile clusters in an Industrial Internet of Things (IIoT) scenario. and The average relative velocity between the transmitter and receiver and the scatterers in the cluster. and These are the scene correlation coefficients, which describe spatial correlation and temporal correlation, respectively.

[0091] S6.a2. Based on the birth-death process, newly formed clusters are generated according to a Poisson distribution:

[0092]

[0093] in, For mathematical expectation, The number of newly generated scattering clusters is given by t, where t is the current time and r is the current antenna status. The generation rate of the cluster.

[0094] When in a sensing channel, the cluster birth and death process includes:

[0095] S6.b1, after a time interval and antenna spacing Then, the survival probability of a cluster is:

[0096]

[0097] in, To sense the survival probability of channel clusters, To sense the unit antenna spacing at the receiving end, To determine the percentage of moving target clusters in the sensing channel, To determine the percentage of mobile environment clusters in the sensing channel, satisfying , To sense the average relative velocity between the receiver and the scatterers in the environmental cluster, and These are the average relative velocities between the transmitter and the sensing receiver and the scatterers in the target cluster, respectively.

[0098] S6.b2. Based on the birth-death process, newly formed clusters are generated according to a Poisson distribution:

[0099] .

[0100] In S6, the motion of different mobile clusters is considered, and the influence of different cluster environmental characteristics on the channel non-stationary characteristics is analyzed using different speed weights.

[0101] Based on research, this invention categorizes industrial IoT scenarios into the following four types according to speed:

[0102] 1) Static clusters – speed is 0

[0103] For example, walls, shelves, large fixed machinery, pipes, constant metal structures, etc.; static clusters account for the majority of the clusters.

[0104] 2) Slow-moving cluster – speed v slow

[0105] For example, automated guided vehicles (AGVs) and small handling robots account for a large proportion of mobile scattering bodies.

[0106] 3) Medium-speed moving clusters – with a speed of v medium

[0107] For example, workers walking in a workshop / warehouse; a moderate proportion of moving scatterers.

[0108] 4) Faster moving clusters – with a speed of v fast

[0109] For example, forklifts and trailers used indoors / in the factory area account for a relatively small proportion of mobile scattering.

[0110] Therefore, the average relative velocities of the transmitter, receiver, sensing receiver, and scatterers in the environmental cluster are as follows:

[0111]

[0112]

[0113]

[0114] Where p1, p2, and p3 are contribution coefficients of each speed cluster related to the industrial IoT scenario, that is, the ratio of slow, medium, and fast environment clusters to the total clusters, satisfying p1+p2+p3=1; similarly, the average relative speed formula between the transmitter, receiver, sensing receiver and target cluster can also be obtained.

[0115] S7. Initialize the new cluster, allocate the relevant channel coefficients of the new cluster, and update the relevant angle, delay and power of the surviving cluster. Generate the total channel coefficients based on propagation path loss and shadow fading.

[0116] In S7, the communication channel matrix in the industrial IoT scenario and sensing channel matrix They are respectively:

[0117]

[0118]

[0119] In this model, both PL and SH are large-scale models; the former represents the propagation path loss model, and the latter represents the shadow fading model. and These are the small-scale fading channel matrices for the communication channel and the sensing channel, respectively, with the former having a matrix dimension of M. T ×M R The latter matrix has a dimension of M. T ×M S M T M R and M S These represent the number of antennas at the transmitter (Tx), receiver (Rx), and sensing receiver (Sx), respectively.

[0120] In S7, the communication channel propagates from the signal transmitted at the transmitter Tx to the receiver Rx via a line-of-sight (LOS) path and a non-line-of-sight (NLOS) path. The channel impulse response (CIR) of the communication channel model is determined by... The expression is:

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] in, This represents the channel impulse response of the communication channel model. For the line-of-sight (LOS) path component, For non-line-of-sight (NLOS) path components, t and τ represent the current time and delay, respectively, K is the Rice factor of the channel, N and M are the number of clusters and the total number of rays in the clusters, respectively, and n and m are the cluster and the number of rays in the clusters, respectively.

[0129] , These represent the vertical and horizontal polarizations of the transmitting antenna, respectively. , These represent the vertical and horizontal polarizations of the receiving antenna, respectively. , These represent the azimuth and elevation angles of the transmitting antenna, respectively. , These represent the azimuth and elevation angles of the receiving antenna, respectively. Indicates the distance between the transmitting and receiving ends, (·) T Indicates matrix transpose. , Let Tx and Rx be the unit spherical vectors representing the line-of-sight (LOS) components of the transmitter (Tx) and receiver (Rx), respectively. , Let represent the position vectors of the q-th receiving antenna and the p-th transmitting antenna, respectively. The Doppler frequency shift represents the line-of-sight (LOS) component. Let represent the path delay of the line-of-sight (LOS) component of the communication channel, j represent the imaginary unit, λ0 represent the wavelength, and δ(·) represent the Dirac function;

[0130] This represents the average power of the m-th ray in the n-th cluster. , Representing the nth cluster C n The m-th ray in the middle has an azimuth and elevation angle relative to the center of the transmitting antenna. , , and They represent The initial phase within the range follows a uniform distribution. Indicates the cross-polarization ratio, , Representing the nth cluster C n The azimuth and elevation angles of the m-th ray relative to the center of the receiving antenna. , Let Tx be the unit spherical vectors of the non-line-of-sight components of the transmitter Tx and the receiver Rx, respectively. Indicates the Doppler frequency shift of the non-line-of-sight NLOS component. The path delay represents the non-line-of-sight (NLOS) component of the communication channel;

[0131] The sensing channel propagates the signal transmitted from transmitter Tx to sensing receiver Sx via the target Tar path and the environment Env path. The CIR of the p-th transmitting antenna and the s-th sensing receiving antenna can be modeled as the superposition of these two paths. The channel impulse response CIR of the sensing channel model is given by... The expression is:

[0132]

[0133] in, The channel impulse response of the sensing channel model. For the target Tar path component, For the Environment Env path component, n' and m' are the cluster and ray number indices in the sensing channel, respectively;

[0134] The target Tar path can be further subdivided into four sub-paths: LOS-Target-LOS, LOS-Target-NLOS, NLOS-Target-LOS, and NLOS-Target-NLOS. The target Tar path components... The expression is as follows:

[0135]

[0136] Where K1 and K2 are the Rice factors of the path from the transmitter to the target Tar and the path from the target to the sensing receiver, respectively. , , and These represent the channel impulse responses of the four sub-paths in the target Tar path: LOS-Target-LOS, NLOS-Target-LOS, LOS-Target-NLOS, and NLOS-Target-NLOS, respectively.

[0137] Environment Env path components The expression is as follows:

[0138]

[0139] in, Indicates the power of the environmental Env path. , Representing the n'th cluster C n' The m'-th ray's azimuth and elevation angles relative to the center of the transmitting antenna. , Representing the n'th cluster C n' The m'-th ray and the azimuth and elevation angles of the center of the sensing antenna are... , , and These represent the initial phases, Indicates the cross-polarization ratio of the sensing channel. , Let Sx and Tx represent the unit spherical vectors of the non-line-of-sight (NLOS) components of the sensing receiver and transmitter, respectively. This represents the position vector of the s-th sensing antenna. This indicates the Doppler frequency shift of the environment's Env path. This represents the delay component of the environment's Env path.

[0140] S8. Return to S5 until the motion trajectories of the transmitter, receiver, and sensing receiver have been traversed, and calculate and analyze the statistical characteristics of the communication channel model and sensing channel model in the industrial Internet of Things scenario.

[0141] In S8, the statistical properties of the communication channel model and the sensing channel model include the cluster visibility of the communication channel receiver and the sensing channel receiver, the power delay spectrum of the communication channel and the sensing channel, and the space-time-frequency correlation function of the communication channel and the sensing channel. The power delay spectrum represents the average power received under different arrival delays; the space-time-frequency correlation function (STF-CF) is a statistical property characterizing the channel correlation at different times, antenna locations, and frequencies. The STF-CF can be simplified to a time autocorrelation function to reflect the time-varying nature of the channel.

[0142] To further highlight the feasibility and superiority of the integrated sensing channel modeling method for industrial IoT scenarios, this invention conducts a simulation experiment in an implementation case and analyzes and elaborates on the experimental results.

[0143] like Figures 3-4 As shown, this represents the cluster visibility of the communication and sensing channel receivers in an industrial IoT scenario. The horizontal axis represents the antenna number, and the vertical axis represents the cluster number. Figure 3 The time-array axis evolution characteristics of the communication channel. Figure 4 To perceive the time-array axis evolution characteristics of the channel. From Figures 3-4 It can be seen that antennas with different serial numbers have different visibility of clusters in the environment. Furthermore, due to the cluster's birth and death process, the cluster's existence changes at different times. In addition, comparison... Figure 3 and Figure 4 The visibility of clusters in the environment differs between the communication channel and the sensing channel receivers, and the differences in the size and brightness of different points in the two images also reflect the differences in power generation within the clusters.

[0144] like Figure 5 The image shows a comparison of the power delay spectra of communication channels and sensing channels in an industrial IoT scenario. Figure 5 The comparison results show that in the industrial IoT scenario, the energy of the communication link is mainly concentrated in several short-delay clusters, and the delay spread is relatively convergent; while the energy of the sensing link is distributed over a wider delay range, exhibiting a significantly larger delay dispersion, which is consistent with the distance characteristics in the bistatic case and the scattering effect caused by the extended target.

[0145] like Figure 6 The figure shows a comparison of the time correlation functions of communication channels and sensing channels in an industrial IoT scenario. The horizontal axis represents the time interval Δt, and the vertical axis represents the absolute value of the time correlation function |ACF|. From... Figure 6As can be seen, due to the movement of the receivers (receiving end Rx and sensing end Sx) and the cluster, all time autocorrelation functions change with time, and the communication channel has the largest time autocorrelation function TACF. This is because the introduction of a target into the sensing channel increases the complexity of multipath propagation and time variation, thereby reducing the time correlation of the channel. Furthermore, due to the complexity of the Industrial Internet of Things (IIoT) scenario and its rich multipath effects, TACF decays to around 0.1 within 1–2 ms, and then begins to oscillate with time.

[0146] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the above-described integrated sensing channel modeling method for industrial IoT scenarios.

[0147] See Figure 7 The present invention also provides a computer device, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of the above-described integrated sensing channel modeling method for industrial Internet of Things scenarios.

[0148] This invention also provides a computer program product containing instructions that, when run on a computer, causes the computer to perform the steps of the above-described integrated sensing channel modeling method for industrial IoT scenarios.

[0149] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above-mentioned integrated sensing channel modeling method for industrial Internet of Things scenarios.

[0150] It should be noted that those skilled in the art will understand that all or part of the steps implemented in the embodiments of the present invention can be implemented entirely or partially by software, hardware, firmware, or any combination thereof. When implemented in hardware, it can be implemented entirely or partially by purchasing standard parts or modifications. When implemented in software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0151] In summary, this invention addresses the problem in existing technologies that only consider channel modeling or integrated sensing channel modeling for industrial IoT scenarios, without addressing the issue of integrated sensing dual-station sensing channel modeling in industrial IoT scenarios. It provides a method for integrated sensing channel modeling in industrial IoT scenarios. This method models and analyzes the statistical characteristics of integrated sensing channels in industrial IoT. Furthermore, it designs a two-stage cluster birth and death process to address the differences between communication and sensing channels, and considers the movement of different mobile clusters in industrial IoT scenarios, thus providing effective theoretical support for channel design.

[0152] It should be understood that the examples and embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Those skilled in the art can make various modifications or changes based on them. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for integrated sensing channel modeling in industrial IoT scenarios, characterized in that, Includes the following steps: S1. Set up an industrial IoT scenario. The scenario content should include at least model parameters, antenna array configuration and signal propagation conditions, and calculate the propagation path loss under the current antenna array configuration. S2. Based on the current industrial IoT scenario, generate large-scale channel parameters with spatial consistency. These large-scale channel parameters include at least shadowing fading, Rice factor, delay spread, and angle spread. S3. For the communication channel and the sensing channel, initialize the number of clusters and the number of rays in the clusters, construct multiple scattering points for the target for the sensing channel, and generate the radar cross-section values ​​of the target scatterer and the environmental scatterer, while determining the visibility of each antenna array to the cluster. S4. Generate the initial cluster and ray's related angles, delays, and power after initialization, and perform random coupling matching of the ray to generate the cross-polarization power ratio and the small-scale channel parameters of the antenna array; S5. Update the positions of the three ends and the large-scale parameters of the channel based on the motion trajectories of the transmitting end, receiving end and sensing receiver in the antenna array. S6. Considering the similarities and differences between communication channels and sensing channels, as well as the movement of different mobile clusters in the industrial IoT scenario, design a two-stage cluster birth and death process, and apply the birth and death of different clusters to simulate the time and space non-stationary characteristics of the integrated communication and sensing channel. S7. Initialize the new cluster, allocate the relevant channel coefficients of the new cluster, and update the relevant angle, delay and power of the surviving cluster. Generate the total channel coefficients based on propagation path loss and shadow fading. S8. Return to S5 until the motion trajectories of the transmitter, receiver, and sensing receiver have been traversed, and the statistical characteristics of the communication channel model and sensing channel model in the industrial Internet of Things scenario are calculated and analyzed. The two-stage cluster birth and death process in S6 is divided into two stages: communication channel and sensing channel. The clusters in the communication channel and sensing channel are the superposition of surviving cluster components and newly formed cluster components. When in a communication channel, the cluster's birth and death process includes: S6.a1, after a time interval and antenna spacing Then, the survival probability of a cluster is: in, The survival probability of the communication channel cluster. The recombination rate of the cluster. and These are the spacing between the unit antennas at the transmitting and receiving ends, respectively. This represents the percentage of mobile clusters in an Industrial Internet of Things (IIoT) scenario. and The average relative velocity between the transmitter and receiver and the scatterers in the cluster. and These are the scene correlation coefficients, which describe spatial correlation and temporal correlation, respectively. S6.a2. Based on the birth-death process, newly formed clusters are generated according to a Poisson distribution: in, For mathematical expectation, The number of newly generated scattering clusters is given by t, where t is the current time and r is the current antenna status. The generation rate of the cluster; When in a sensing channel, the cluster birth and death process includes: S6.b1, after a time interval and antenna spacing Then, the survival probability of a cluster is: in, To sense the survival probability of channel clusters, To sense the unit antenna spacing at the receiving end, To determine the percentage of moving target clusters in the sensing channel, To determine the percentage of mobile environment clusters in the sensing channel, satisfying , To sense the average relative velocity between the receiver and the scatterers in the environmental cluster, and These are the average relative velocities between the transmitter and the sensing receiver and the scatterers in the target cluster, respectively. S6.b2. Based on the birth-death process, newly formed clusters are generated according to a Poisson distribution: 。 2. The integrated sensing channel modeling method for industrial IoT scenarios as described in claim 1, characterized in that, In S1, the industrial IoT scenario is M under millimeter wave conditions. T ×M R ×M S The industrial IoT integrated sensing dual-station sensing scenario, where M T M R and M S The numbers represent the number of antennas for the transmitter Tx, receiver Rx, and sensing receiver Sx, respectively. All three are selected as large-scale MIMO antenna arrays.

3. The integrated sensing channel modeling method for industrial IoT scenarios as described in claim 1, characterized in that, In step S6, the motion of different mobile clusters is considered, and the influence of different cluster environmental characteristics on the channel non-stationary characteristics is analyzed using different speed weights.

4. The integrated sensing channel modeling method for industrial IoT scenarios as described in claim 1, characterized in that, In S7, the communication channel matrix in the industrial IoT scenario and sensing channel matrix They are respectively: In this model, both PL and SH are large-scale models; the former represents the propagation path loss model, and the latter represents the shadow fading model. and These are the small-scale fading channel matrices for the communication channel and the sensing channel, respectively, with the former having a matrix dimension of M. T ×M R The latter matrix has a dimension of M. T ×M S M T M R and M S These represent the number of antennas at the transmitter (Tx), receiver (Rx), and sensing receiver (Sx), respectively.

5. The integrated sensing channel modeling method for industrial IoT scenarios as described in claim 4, characterized in that, The communication channel propagates from the signal transmitted at the transmitter Tx to the receiver Rx via a line-of-sight (LOS) path and a non-line-of-sight (NLOS) path. The channel impulse response (CIR) of the communication channel model is determined by... The expression is: in, This represents the channel impulse response of the communication channel model. For the line-of-sight (LOS) path component, For non-line-of-sight (NLOS) path components, t and τ represent the current time and delay, respectively, K is the Rice factor of the channel, N and M are the number of clusters and the total number of rays in the clusters, respectively, and n and m are the cluster and the number of rays in the clusters, respectively. , These represent the vertical and horizontal polarizations of the transmitting antenna, respectively. , These represent the vertical and horizontal polarizations of the receiving antenna, respectively. , These represent the azimuth and elevation angles of the transmitting antenna, respectively. , These represent the azimuth and elevation angles of the receiving antenna, respectively. Indicates the distance between the transmitting and receiving ends, (·) T Indicates matrix transpose. , Let Tx and Rx be the unit spherical vectors representing the line-of-sight (LOS) components of the transmitter (Tx) and receiver (Rx), respectively. , Let represent the position vectors of the q-th receiving antenna and the p-th transmitting antenna, respectively. The Doppler frequency shift represents the line-of-sight (LOS) component. Let represent the path delay of the line-of-sight (LOS) component of the communication channel, j represent the imaginary unit, λ0 represent the wavelength, and δ(·) represent the Dirac function; This represents the average power of the m-th ray in the n-th cluster. , Representing the nth cluster C n The m-th ray in the middle has an azimuth and elevation angle relative to the center of the transmitting antenna. , , and They represent The initial phase within the range follows a uniform distribution. Indicates the cross-polarization ratio, , Representing the nth cluster C n The azimuth and elevation angles of the m-th ray relative to the center of the receiving antenna. , Let Tx be the unit spherical vectors of the non-line-of-sight components of the transmitter Tx and the receiver Rx, respectively. Indicates the Doppler frequency shift of the non-line-of-sight NLOS component. The path delay represents the non-line-of-sight (NLOS) component of the communication channel; The sensing channel propagates the signal transmitted by the transmitter Tx to the sensing receiver Sx through the target Tar path and the environment Env path. The channel impulse response (CIR) of the sensing channel model is determined by... The expression is: in, The channel impulse response of the sensing channel model. For the target Tar path component, For the Environment Env path component, n' and m' are the cluster and ray number indices in the sensing channel, respectively; K1 and K2 are Rice factors for the path from the transmitter to the target Tar and the path from the target to the sensing receiver, respectively. , , and These represent the channel impulse responses of the four sub-paths in the target Tar path: LOS-Target-LOS, NLOS-Target-LOS, LOS-Target-NLOS, and NLOS-Target-NLOS, respectively. Indicates the power of the environmental Env path. , Representing the n'th cluster C n' The m'-th ray's azimuth and elevation angles relative to the center of the transmitting antenna. , Representing the n'th cluster C n' The m'-th ray and the azimuth and elevation angles of the center of the sensing antenna are... , , and These represent the initial phases, Indicates the cross-polarization ratio of the sensing channel. , Let Sx and Tx represent the unit spherical vectors of the non-line-of-sight (NLOS) components of the sensing receiver and transmitter, respectively. This represents the position vector of the s-th sensing antenna. This indicates the Doppler frequency shift of the environment's Env path. This represents the delay component of the environment's Env path.

6. The integrated sensing channel modeling method for industrial IoT scenarios as described in claim 1, characterized in that, In S8, the statistical characteristics of the communication channel model and the sensing channel model include the cluster visibility of the communication channel receiver and the sensing channel receiver, the power delay spectrum of the communication channel and the sensing channel, and the spatiotemporal frequency correlation function of the communication channel and the sensing channel.

7. A computer-readable storage medium, characterized in that, The system stores a computer program, which, when executed by a processor, causes the processor to perform the steps of the integrated sensing channel modeling method for industrial IoT scenarios as described in any one of claims 1-6.

8. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the integrated sensing channel modeling method for industrial IoT scenarios as described in any one of claims 1-6.

Images