CN114124261A - Geometric random modeling method and system for industrial Internet of things channel - Google Patents

Geometric random modeling method and system for industrial Internet of things channel Download PDF

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CN114124261A
CN114124261A CN202111398812.2A CN202111398812A CN114124261A CN 114124261 A CN114124261 A CN 114124261A CN 202111398812 A CN202111398812 A CN 202111398812A CN 114124261 A CN114124261 A CN 114124261A
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effective
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CN114124261B (en
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刘洋
谷稳
郭欣
朱璇
李钢
钱昊
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Jiangnan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention relates to a geometric random modeling method and a geometric random modeling system for an industrial Internet of things channel, which comprise the following steps: establishing a CIR system model representing an industrial Internet of things channel; performing 3D modeling on the parameters of the effective clusters based on the rich scattering characteristics of the millimeter wave industrial channel to obtain a 3D model of the effective clusters; respectively acquiring survival probabilities of effective clusters between different antennas at a transmitter Tx side and a receiver Rx side based on the non-stationary characteristic of an industrial channel space, generating the average number of visible clusters of the antennas at the Tx side and the Rx side according to the survival probabilities, updating the effective clusters which can be observed by the antennas at the Tx side and the Rx side according to the average number of the visible clusters, and acquiring updated effective clusters; and carrying out angle, power and time delay modeling on the updated effective cluster according to the 3D model of the effective cluster. The modeling method is high in accuracy and good in effectiveness.

Description

Geometric random modeling method and system for industrial Internet of things channel
Technical Field
The invention relates to the technical field of Internet of things, in particular to a geometric random modeling method and system for an industrial Internet of things channel.
Background
The industrial internet of things is one of typical application scenarios of the fifth generation mobile communication system. The wireless communication technology is applied to the industrial intelligent production process, and industrial intelligent manufacturing is realized through information interaction and sharing among multi-source heterogeneous devices. Therefore, it is particularly important to construct an industrial internet of things system with Ultra-reliable and Low-latency Communication (urrllc). The study and analysis of channel characteristics is the key to constructing urrllc.
In the past decades, some channel models have been proposed for studying industrial internet of things channels, which can be summarized in two types: the first category is research focused on channel propagation characteristics, and the current research on industrial characteristics is mainly focused on low frequency bands; the second category models CIR of industrial internet of things channels, which basically models scatterers using poisson or gaussian distributions, without considering the distribution characteristics of scatterers in the industrial environment in the millimeter band.
At present, the geometric stochastic model has become a key technology for researching industrial channels due to high precision and universality. In addition, with the increasing shortage of wireless spectrum resources, the development of millimeter wave frequency band becomes one of the research hotspots in the communication field. The large-scale Multi-input Multi-output (MIMO) technology can provide services for a plurality of users in an industrial Internet of things system at the same time. Therefore, the large-scale MIMO technology and the millimeter wave communication technology are combined, and an ultra-reliable low-delay industrial Internet of things communication system is expected to be constructed. However, industrial internet of things channels have abundant scattering properties due to the presence of a large number of metal obstacles, compared to other cases. Poisson distribution and gaussian distribution have been unable to accurately simulate the distribution of millimeter wave frequency band clusters. Furthermore, the introduction of massive MIMO technology makes the spatially non-stationary nature of the clusters in the industrial channel non-negligible. Therefore, it is crucial to establish an accurate geometric stochastic model of the massive MIMO industrial internet of things channel in the millimeter wave frequency band.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the prior art.
In order to solve the technical problem, the invention provides a geometric random modeling method of an industrial Internet of things channel, which comprises the following steps:
s1, establishing a CIR system model representing the industrial Internet of things channel, wherein the CIR system model adopts a double-hop propagation mechanism, and the scattering environment between a transmitter and a receiver corresponding to the CIR system model is modeled as an effective cluster;
s2, performing 3D modeling on the parameters of the effective clusters in the S1 based on rich scattering characteristics of the millimeter wave industrial channel to obtain a 3D model of the effective clusters, wherein the 3D modeling comprises geometric distribution, angles, time delay and power of the effective clusters and rays in the effective clusters;
s3, respectively obtaining survival probabilities of effective clusters between different antennas at the Tx side of a transmitter and the Rx side of a receiver based on the non-stationary characteristics of industrial channel space, generating the average number of visible clusters of the Tx side antenna and the Rx side antenna according to the survival probabilities, updating the effective clusters observed by the Tx side antenna and the Rx side antenna according to the average number of the visible clusters, and obtaining updated effective clusters, wherein the effective clusters observed by the Tx side antenna and the Rx side antenna comprise the surviving clusters and the new clusters;
and S4, carrying out angle, power and time delay modeling on the updated effective cluster according to the 3D model of the effective cluster in the S2.
Preferably, the links of the active cluster in S1 are represented as follows:
n path lnFormed by a pair of effective clustersnRepresenting, i.e. from transmitter Tx to first reflection cluster
Figure BDA0003364350180000021
First bounce and last reflection cluster
Figure BDA0003364350180000022
Last bounce to receiver Rx and multiple inversions between first and last bounceAn abstract virtual link composition of missile components.
Preferably, in S1, the channel impulse response of the industrial internet of things is represented by MR×MTMatrix array
Figure BDA0003364350180000031
Represents; wherein h isqp(τ) is
Figure BDA0003364350180000032
And
Figure BDA00033643501800000310
the impulse response between the first and second frequency bands,
Figure BDA0003364350180000033
which is the antenna q of the receiver, is,
Figure BDA0003364350180000034
for the antenna p of the transmitter, the impulse response of the proposed system model can be calculated as:
Figure BDA0003364350180000035
wherein, taun、
Figure BDA0003364350180000036
τLOSAre respectively effective ClusternDelay of (m) thnDelay of the line ray, delay of LOS component, K is the Rice factor, N, MnRespectively Cluster and valid ClusternThe number of internal rays is such that,
Figure BDA0003364350180000037
LOS and NLOS components of the channel impulse response are shown as follows:
Figure BDA0003364350180000038
Figure BDA0003364350180000039
wherein superscripts V and H denote vertical and horizontal polarization, respectively;
Figure BDA0003364350180000041
respectively representing the azimuth and elevation of the receive antenna array,
Figure BDA0003364350180000042
respectively representing the azimuth and elevation of the transmit antenna array,
Figure BDA0003364350180000043
respectively represent valid clustersnAnd the azimuth and elevation angles between the centers of the receive antenna arrays,
Figure BDA0003364350180000044
respectively represent valid clustersnAnd azimuth and elevation angles between the transmit antenna array centers; fT(. and F)R() is the antenna pattern of the transmitter Tx and receiver Rx in the global coordinate system; LOS and NLOS phases
Figure BDA0003364350180000045
Is uniformly distributed in (0,2 pi)]And kappa is cross polarization ratio;
Figure BDA0003364350180000046
the normalized average power of the rays within the effective cluster in the representation; (.)TRepresents a matrix transposition operation, | | | · | | | represents a Frobenius norm operation, rrx,LOSRepresentation and azimuth
Figure BDA0003364350180000047
And elevation angle
Figure BDA0003364350180000048
Associated spherical unit vector, rtx,LOSRepresentation and azimuth
Figure BDA0003364350180000049
And elevation angle
Figure BDA00033643501800000410
The unit vector of the sphere of interest,
Figure BDA00033643501800000411
representation and azimuth
Figure BDA00033643501800000412
And elevation angle
Figure BDA00033643501800000413
The unit vector of the sphere of interest,
Figure BDA00033643501800000414
representation and azimuth
Figure BDA00033643501800000415
And elevation angle
Figure BDA00033643501800000416
The associated spherical unit vector.
Preferably, the S2 includes:
s21, modeling the geometric distribution of the effective clusters based on the number of the effective clusters and the generalized extreme value distribution; modeling the geometric distribution of the rays in the effective cluster based on the quantity of the rays in the effective cluster obeying the generalized pareto distribution;
s22, modeling the angle of the effective cluster based on the angle of the effective cluster obeying package Gaussian distribution;
s23, obtaining distance vectors of the effective clusters at the sides of a transmitter Tx and a receiver Rx according to the angle parameters of the effective clusters, and modeling the delay of the effective clusters based on the distance vectors;
and S24, modeling the power of the effective cluster according to the time delay obtained from S23.
Preferably, the S21 specifically includes:
making the number N of the observed effective clusters obey the generalized extreme value distribution N-GEV (k)e,σe,μe) The geometric distribution of the effective clusters:
Figure BDA0003364350180000051
wherein k ise,σe,μeA shape parameter, a scale parameter related to the standard deviation, and a location parameter related to the expectation, respectively, of the generalized extremum distribution;
for the number M of active intra-cluster raysnSubject it to a generalized pareto distribution Mn～GP(kp,σp,μp) Which is defined as
Figure BDA0003364350180000052
Wherein k isp,σp,μpRespectively a shape parameter, a scale parameter and a position parameter of the generalized pareto distribution.
Preferably, the S22 specifically includes:
efficient ClusternAngle of (2)
Figure BDA0003364350180000053
Obeying a wrapped gaussian distribution, wherein,
Figure BDA0003364350180000054
is an effective ClusternAnd the azimuth and elevation angles between the centers of the receive antenna arrays,
Figure BDA0003364350180000055
is effective ClusternAnd azimuth and elevation angles between the transmit antenna array centers;
m thnThe angle parameter of the strip ray passes through the effective Cluster ClusternThe angle of (d) plus the angular deviation can be obtained:
Figure BDA0003364350180000056
wherein the content of the first and second substances,
Figure BDA0003364350180000061
respectively, the angular deviation of the ray, obeying a Laplace distribution with a mean value of zero and a standard deviation of 1 deg.,
Figure BDA0003364350180000062
respectively as an effective ClusternInner mthAzimuth and elevation angles between the ray and the center of the receive antenna array,
Figure BDA0003364350180000063
respectively as an effective ClusternInner mthAzimuth and elevation angles between the ray and the center of the transmit antenna array.
Preferably, the S23 specifically includes:
respectively obtaining effective clusters according to the angle parametersnDistance vector to transmitter Tx and receiver Rx array center
Figure BDA0003364350180000064
Figure BDA0003364350180000065
Where D is the initial position vector of the receiver Rx,
Figure BDA0003364350180000066
respectively, subject to exponential distribution
Figure BDA0003364350180000067
The Frobenius norm of (a);
delay of LOS component
Figure BDA0003364350180000068
Wherein the content of the first and second substances,
Figure BDA0003364350180000069
is that
Figure BDA00033643501800000610
And
Figure BDA00033643501800000611
the LOS distance vector in between,
Figure BDA00033643501800000612
which is the antenna q of the receiver, is,
Figure BDA00033643501800000613
is the antenna p of the transmitter and,
Figure BDA00033643501800000614
are respectively
Figure BDA00033643501800000615
And
Figure BDA00033643501800000616
a 3D position vector of (a);
delay of NLOS component:
Figure BDA00033643501800000617
wherein the content of the first and second substances,
Figure BDA00033643501800000618
represents a virtual delay, where rτIs the delay ratio, στIs a delay spread factor, munIs a random variable mu subject to uniform distributionnU (0,1), mnDelay of strip ray
Figure BDA00033643501800000619
Following a mean value of
Figure BDA00033643501800000620
The distribution of the indices of (a) to (b),
Figure BDA00033643501800000621
is determined by parameter estimation.
Preferably, the S24 specifically includes:
efficient ClusternThe average power of (d) is:
Figure BDA0003364350180000071
wherein Z isnObeying Gaussian distribution Zn～N(0,σn)，σnIs the standard deviation of the shading for each valid cluster;
m thnThe average power of the bar ray may be calculated as:
Figure BDA0003364350180000072
to ray mnAverage power of (2) at ClusternScaling at the average power of (a) to obtain:
Figure BDA0003364350180000073
normalized to obtain
Figure BDA0003364350180000074
Preferably, the S3 specifically includes:
let recombination rate of effective clusters be lambdaROn the receiver Rx side, the probability of survival of the active cluster at the receive antenna q' is
Figure BDA0003364350180000075
Wherein the content of the first and second substances,
Figure BDA0003364350180000076
is the spacing between the reference antenna q of the receiver and the antenna q' in the receiver Rx that is different from q,
Figure BDA0003364350180000077
respectively the 3D position vectors of the receive antenna q and the receive antenna q',
Figure BDA0003364350180000078
is a scene correlation coefficient describing spatial correlation;
the average number of visible clusters of antenna q' is E N based on the on-off process of the active clusters on the array axisnew]＝N0(1-Psurvival(ΔR))，
Wherein, E [. C]Indicates expectation, N0Indicates the number of initial clusters; the random number of visible clusters of antenna q' is E N according to the meannew]Is randomly generated.
The same theory as above can be used to obtain the survival probability of the effective cluster among different antennas at the Tx side of the transmitter, and the effective cluster at the Tx side is updated and modeled by the same principle.
The invention also discloses a geometric random modeling system of the industrial Internet of things channel, which comprises the following components:
the CIR building module is used for building a CIR system model for representing an industrial Internet of things channel, wherein the CIR system model adopts a double-hop propagation mechanism, and a scattering environment between a transmitter and a receiver corresponding to the CIR system model is modeled as an effective cluster;
the effective cluster modeling module is used for carrying out 3D modeling on the parameters of the effective cluster in S1 based on rich scattering characteristics of the millimeter wave industrial channel to obtain a 3D model of the effective cluster;
the effective cluster updating module is used for respectively acquiring survival probabilities of effective clusters between different antennas at the Tx side of a transmitter and the Rx side of a receiver based on the non-stationary characteristic of an industrial channel space, generating the average number of visible clusters of the Tx side antenna and the Rx side antenna according to the survival probabilities, updating the effective clusters which can be observed by the Tx side antenna and the Rx side antenna according to the average number of the visible clusters, and acquiring updated effective clusters, wherein the effective clusters which can be observed by the Tx side antenna and the Rx side antenna comprise the survived clusters and new clusters;
and the cluster model updating module is used for carrying out angle, power and time delay modeling on the updated effective cluster according to the 3D model of the effective cluster.
Compared with the prior art, the technical scheme of the invention has the following advantages:
the invention provides an industrial Internet of things channel modeling method in a millimeter wave frequency band based on a geometric random model, which considers the rich scattering characteristic of an industrial Internet of things channel, utilizes generalized extreme value distribution and generalized pareto distribution to model the distribution of clusters in a millimeter wave frequency band industrial environment, describes the generation-extinction process of the clusters on an array axis, and simulates the spatial non-stationary characteristic of a large-scale MIMO channel, and simulation results show that the modeling method is high in accuracy and has good effectiveness.
Drawings
FIG. 1 is a schematic diagram of an industrial Internet of things channel scattering environment according to the present invention;
FIG. 2 is a graph comparing inter-cluster delay cumulative distribution functions for simulation, measurement, and reference models;
FIG. 3 is a graph comparing simulated and theoretical normalized spatial cross-correlation function curves;
fig. 4 is a diagram of channel capacity analysis for different antenna arrays.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Some terms in the present invention explain: impulse response (Channel Impulse Model, CIR); three-dimensional (3D).
Referring to fig. 1, the invention discloses a geometric stochastic modeling method for an industrial internet of things channel, comprising the following steps:
step one, establishing a CIR system model representing an industrial Internet of things channel based on a 3GPP standard model, and providing MR×MTThe industrial internet of things communication system is characterized in that the CIR system model adopts a double-hop propagation mechanism, and the scattering environment between a transmitter and a receiver corresponding to the CIR system model is modeled as an effective cluster.
Wherein, the links of the active cluster in the step one are represented as follows: n path lnFormed by a pair of effective clustersnRepresenting, i.e. from transmitter Tx to first reflection cluster
Figure BDA0003364350180000091
First bounce and last reflection cluster
Figure BDA0003364350180000092
The last bounce to the receiver Rx and the multiple bounces between the first and last bounce constitute an abstract virtual link.
The invention assumes that a typical industrial environment is in a static state, and the channel impulse response of the industrial Internet of things is MR×MTMatrix array
Figure BDA0003364350180000101
Represents; wherein h isqp(τ) is
Figure BDA0003364350180000102
And
Figure BDA0003364350180000103
the impulse response between the first and second frequency bands,
Figure BDA0003364350180000104
which is the antenna q of the receiver, is,
Figure BDA0003364350180000105
for the antenna p of the transmitter, the impulse response of the proposed system model can be calculated as:
Figure BDA0003364350180000106
wherein, taun、
Figure BDA00033643501800001011
τLOSAre respectively effective ClusternDelay of (m) thnDelay of the line ray, delay of LOS component, K is the Rice factor, N, MnRespectively Cluster and valid ClusternThe number of internal rays is such that,
Figure BDA0003364350180000107
LOS and NLOS components of the channel impulse response are shown as follows:
Figure BDA0003364350180000108
Figure BDA0003364350180000109
wherein superscripts V and H denote vertical and horizontal polarization, respectively;
Figure BDA00033643501800001010
respectively representing the azimuth and elevation of the receive antenna array,
Figure BDA0003364350180000111
respectively representing the azimuth and elevation of the transmit antenna array,
Figure BDA0003364350180000112
respectively represent valid clustersnAnd the azimuth and elevation angles between the centers of the receive antenna arrays,
Figure BDA0003364350180000113
respectively represent valid clustersnAzimuth and elevation angles from the center of the transmit antenna array；FT(. and F)R() is the antenna pattern of the transmitter Tx and receiver Rx in the global coordinate system; LOS and NLOS phases
Figure BDA0003364350180000114
Is uniformly distributed in (0,2 pi)]And kappa is cross polarization ratio;
Figure BDA0003364350180000115
the normalized average power of the rays within the effective cluster in the representation; (.)TRepresents a matrix transposition operation, | | | · | | | represents a Frobenius norm operation, rrx,LOSRepresentation and azimuth
Figure BDA0003364350180000116
And elevation angle
Figure BDA0003364350180000117
Associated spherical unit vector, rtx,LOSRepresentation and azimuth
Figure BDA0003364350180000118
And elevation angle
Figure BDA0003364350180000119
The unit vector of the sphere of interest,
Figure BDA00033643501800001110
representation and azimuth
Figure BDA00033643501800001111
And elevation angle
Figure BDA00033643501800001112
The unit vector of the sphere of interest,
Figure BDA00033643501800001113
representation and azimuth
Figure BDA00033643501800001114
And elevation angle
Figure BDA00033643501800001115
The associated spherical unit vector.
And step two, performing 3D modeling on the parameters of the effective clusters in the step one based on rich scattering characteristics of the millimeter wave industrial channel to obtain a 3D model of the effective clusters, wherein the 3D modeling comprises the geometric distribution, the angle, the time delay and the power of the effective clusters and rays in the effective clusters. It is worth mentioning that the distribution of clusters in the millimeter wave channel of the industrial internet of things cannot be accurately modeled by the poisson distribution and the gaussian distribution, so that the invention respectively simulates the geometric distribution of the clusters and the rays in the clusters by utilizing the generalized extreme value distribution and the generalized pareto distribution.
S21, modeling the geometric distribution of the effective clusters based on the number of the effective clusters and the generalized extreme value distribution; based on the number of rays in the effective cluster obeying generalized pareto distribution, modeling the geometric distribution of the rays in the effective cluster, specifically comprising:
making the number N of the observed effective clusters obey the generalized extreme value distribution N-GEV (k)e,σe,μe) The geometric distribution of the effective clusters:
Figure BDA0003364350180000121
wherein k ise,σe,μeA shape parameter, a scale parameter related to the standard deviation, and a location parameter related to the expectation, respectively, of the generalized extremum distribution;
for the number M of active intra-cluster raysnSubject it to a generalized pareto distribution Mn～GP(kp,σp,μp) It is defined as:
Figure BDA0003364350180000122
wherein k isp,σp,μpRespectively a shape parameter, a scale parameter and a position parameter of the generalized pareto distribution.
S22, modeling the angle of the effective cluster based on the angle of the effective cluster obeying package Gaussian distribution, and specifically comprising the following steps:
efficient ClusternAngle of (2)
Figure BDA0003364350180000123
Obeying a wrapped gaussian distribution, wherein,
Figure BDA0003364350180000124
is an effective ClusternAnd the azimuth and elevation angles between the centers of the receive antenna arrays,
Figure BDA0003364350180000125
is effective ClusternAnd azimuth and elevation angles between the transmit antenna array centers;
m thnThe angle parameter of the strip ray passes through the effective Cluster ClusternThe angle of (d) plus the angular deviation can be obtained:
Figure BDA0003364350180000126
wherein the content of the first and second substances,
Figure BDA0003364350180000131
respectively, the angular deviation of the ray, obeying a Laplace distribution with a mean value of zero and a standard deviation of 1 deg.,
Figure BDA0003364350180000132
respectively as an effective ClusternInner mthAzimuth and elevation angles between the ray and the center of the receive antenna array,
Figure BDA0003364350180000133
respectively as an effective ClusternInner mthAzimuth and elevation angles between the ray and the center of the transmit antenna array.
S23, obtaining distance vectors of the effective cluster at the Tx and Rx sides of the transmitter according to the angle parameter of the effective cluster, and modeling the delay of the effective cluster based on the distance vectors, which specifically includes:
respectively obtaining effective clusters according to the angle parametersnDistance vector to transmitter Tx and receiver Rx array center
Figure BDA0003364350180000134
Figure BDA0003364350180000135
Where D is the initial position vector of the receiver Rx,
Figure BDA0003364350180000136
respectively, subject to exponential distribution
Figure BDA0003364350180000137
The Frobenius norm of (a);
delay of LOS component
Figure BDA0003364350180000138
Wherein the content of the first and second substances,
Figure BDA0003364350180000139
is that
Figure BDA00033643501800001310
And
Figure BDA00033643501800001311
the LOS distance vector in between,
Figure BDA00033643501800001312
which is the antenna q of the receiver, is,
Figure BDA00033643501800001313
is the antenna p of the transmitter and,
Figure BDA00033643501800001314
are respectively
Figure BDA00033643501800001315
And
Figure BDA00033643501800001316
a 3D position vector of (a);
delay of NLOS component:
Figure BDA00033643501800001317
wherein the content of the first and second substances,
Figure BDA00033643501800001318
represents a virtual delay, where rτIs the delay ratio, στIs a delay spread factor, munIs a random variable mu subject to uniform distributionnU (0,1), mnDelay of strip ray
Figure BDA00033643501800001319
Following a mean value of
Figure BDA00033643501800001320
The distribution of the indices of (a) to (b),
Figure BDA00033643501800001321
is determined by parameter estimation.
S24, modeling the power of the effective cluster according to the time delay obtained from S23, which specifically comprises the following steps:
efficient ClusternThe average power of (d) is:
Figure BDA0003364350180000141
wherein Z isnObeying Gaussian distribution Zn～N(0,σn)，σnIs the standard deviation of the shading for each valid cluster;
m thnThe average power of the bar ray may be calculated as:
Figure BDA0003364350180000142
to ray mnAverage power of (2) at ClusternScaling at the average power of (a) to obtain:
Figure BDA0003364350180000143
normalized to obtain
Figure BDA0003364350180000144
Respectively acquiring survival probabilities of effective clusters between different antennas at the Tx side of a transmitter and the Rx side of a receiver based on the non-stationary characteristic of the industrial channel space, generating the average number of visible clusters of the antennas at the Tx side and the Rx side according to the survival probabilities, updating the effective clusters which can be observed by the antennas at the Tx side and the Rx side according to the average number of the visible clusters, and acquiring updated effective clusters, wherein the effective clusters which can be observed by the antennas at the Tx side and the Rx side include a surviving cluster and a new cluster, and the method specifically comprises the following steps:
the non-stationary characteristics of the industrial channel space are described by using the generation-extinction process of the clusters on the array axis. Let recombination rate of effective clusters be lambdaROn the receiver Rx side, the probability of survival of the active cluster at the receive antenna q' is
Figure BDA0003364350180000145
Wherein the content of the first and second substances,
Figure BDA0003364350180000146
is the spacing between the reference antenna q of the receiver and the antenna q' in the receiver Rx that is different from q,
Figure BDA0003364350180000151
respectively the 3D position vectors of the receive antenna q and the receive antenna q',
Figure BDA0003364350180000152
is a scene correlation coefficient describing spatial correlation;
the average number of visible clusters of antenna q' is E N based on the on-off process of the active clusters on the array axisnew]＝N0(1-Psurvival(ΔR))，
Wherein, E [. C]Indicates expectation, N0Indicates the number of initial clusters; the random number of visible clusters of antenna q' is E N according to the meannew]Is randomly generated.
The same theory as above can be used to obtain the survival probability of the effective cluster among different antennas at the Tx side of the transmitter, and the effective cluster at the Tx side is updated and modeled by the same principle.
And step four, carrying out angle, power and time delay modeling on the updated effective cluster according to the 3D model of the effective cluster in the step two.
And then, the statistical characteristics of the industrial Internet of things channel, including a spatial correlation function, inter-cluster delay and channel capacity, can be analyzed, and fitting between theory and simulation, simulation and measurement is carried out.
The invention also discloses a geometric random modeling system of the industrial Internet of things channel, which comprises a CIR construction module, an effective cluster modeling module, an effective cluster updating module and a cluster model updating module.
The CIR building module is used for building a CIR system model for representing industrial Internet of things channels, wherein the CIR system model adopts a double-hop propagation mechanism, and a scattering environment between a transmitter and a receiver corresponding to the CIR system model is modeled as an effective cluster.
And the effective cluster modeling module performs 3D modeling on the parameters of the effective cluster in S1 based on the rich scattering characteristics of the millimeter wave industrial channel to obtain a 3D model of the effective cluster.
The effective cluster updating module respectively obtains survival probabilities of effective clusters between different antennas on a transmitter Tx side and a receiver Rx side based on the non-stationary characteristic of an industrial channel space, generates the average number of visible clusters of the Tx side antenna and the Rx side antenna according to the survival probabilities, updates the effective clusters observed by the Tx side antenna and the Rx side antenna according to the average number of the visible clusters, and obtains updated effective clusters, wherein the effective clusters observed by the Tx side antenna and the Rx side antenna comprise the surviving clusters and the new clusters.
And the cluster model updating module carries out angle, power and time delay modeling on the updated effective cluster according to the 3D model of the effective cluster.
The technical solution of the present invention is further explained and explained with reference to the specific embodiments.
The present invention selects a mechanical room that can represent most industrial environments as a reference environment. To verify the accuracy of the model of the invention, the simulation result is fitted with the actually measured Cumulative Distribution Function (CDF) result of the inter-cluster delay, and a general geometric stochastic channel model is selected as a reference model for comparison with the model proposed by the present invention, as shown in fig. 2. The result shows that the CDF simulation value of the inter-cluster delay of the model provided by the invention has better consistency with the measured value, the fitting effect is better than that of a reference model, and the accuracy of the model is verified.
Fig. 3 depicts a 3D massive MIMO channel model simulation versus theoretical Rx normalized spatial correlation Function (SCCF) plot. The SCCF decreases as the antenna index decreases. The theoretical result is consistent with the fitting result of the simulation result, and the effectiveness of the industrial Internet of things channel modeling method based on the geometric random model in the millimeter wave frequency band is proved.
Theoretically, the size of the channel capacity is proportional to the number of antennas, and the M × M antenna system has a channel capacity gain M times higher than the 1 × 1 system. The simulation results of fig. 4 are not consistent with the theoretical results, which can be explained by the rich scattering characteristics of the industrial channel. In addition, with different antenna beam widths, the channel capacity and the number of clusters that can be observed by the antenna theoretically increase as the antenna beam increases. However, taking antenna beams 7 ° and 20 ° as examples, the present invention finds that the average channel capacity of the antenna beam at 20 ° is only 1.0342 times larger than that of the antenna beam at 7 °. This is because the number of obstacles in the mechanical room is large and the probability of a long path being observable with a 20 ° antenna is low, and therefore, no more information is displayed using a 20 ° antenna than using a 7 ° antenna.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims (10)

1. A geometric random modeling method for an industrial Internet of things channel is characterized by comprising the following steps:
s1, establishing a CIR system model representing the industrial Internet of things channel, wherein the CIR system model adopts a double-hop propagation mechanism, and the scattering environment between a transmitter and a receiver corresponding to the CIR system model is modeled as an effective cluster;
s2, performing 3D modeling on the parameters of the effective clusters in the S1 based on rich scattering characteristics of the millimeter wave industrial channel to obtain a 3D model of the effective clusters, wherein the 3D modeling comprises geometric distribution, angles, time delay and power of the effective clusters and rays in the effective clusters;
s3, respectively obtaining survival probabilities of effective clusters between different antennas at the Tx side of a transmitter and the Rx side of a receiver based on the non-stationary characteristics of industrial channel space, generating the average number of visible clusters of the Tx side antenna and the Rx side antenna according to the survival probabilities, updating the effective clusters observed by the Tx side antenna and the Rx side antenna according to the average number of the visible clusters, and obtaining updated effective clusters, wherein the effective clusters observed by the Tx side antenna and the Rx side antenna comprise the surviving clusters and the new clusters;
and S4, carrying out angle, power and time delay modeling on the updated effective cluster according to the 3D model of the effective cluster in the S2.
2. The method of claim 1, wherein the links of the active cluster in S1 are represented as follows:
n path lnFormed by a pair of effective clustersnRepresenting, i.e. from transmitter Tx to first reflection cluster
Figure FDA0003364350170000011
First bounce and last reflection cluster
Figure FDA0003364350170000012
The last bounce to the receiver Rx and the multiple bounces between the first and last bounce constitute an abstract virtual link.
3. The method as claimed in claim 2, wherein in S1, the channel impulse response of the industrial internet of things is represented by MR×MTMatrix array
Figure FDA0003364350170000021
Represents; wherein h isqp(τ) is
Figure FDA0003364350170000022
And
Figure FDA0003364350170000023
the impulse response between the first and second frequency bands,
Figure FDA0003364350170000024
which is the antenna q of the receiver, is,
Figure FDA0003364350170000025
for the antenna p of the transmitter, the impulse response of the proposed system model can be calculated as:
Figure FDA0003364350170000026
wherein, taun、
Figure FDA0003364350170000027
τLOSAre respectively effective ClusternDelay of (m) thnDelay of the line ray, delay of LOS component, K is the Rice factor, N, MnRespectively Cluster and valid ClusternThe number of internal rays is such that,
Figure FDA0003364350170000028
LOS and NLOS components of the channel impulse response are shown as follows:
Figure FDA0003364350170000029
Figure FDA00033643501700000210
Figure FDA00033643501700000211
wherein superscripts V and H denote vertical and horizontal polarization, respectively;
Figure FDA00033643501700000212
respectively representing the azimuth and elevation of the receive antenna array,
Figure FDA0003364350170000031
respectively representing the azimuth and elevation of the transmit antenna array,
Figure FDA0003364350170000032
respectively represent valid clustersnAnd the azimuth and elevation angles between the centers of the receive antenna arrays,
Figure FDA0003364350170000033
respectively represent valid clustersnAnd azimuth and elevation angles between the transmit antenna array centers; fT(. and F)R() is the antenna pattern of the transmitter Tx and receiver Rx in the global coordinate system; LOS and NLOS phases
Figure FDA0003364350170000034
Is uniformly distributed in (0,2 pi)]And kappa is cross polarization ratio;
Figure FDA0003364350170000035
the normalized average power of the rays within the effective cluster in the representation; (.)TRepresents a matrix transposition operation, | | | · | | | represents a Frobenius norm operation, rrx,LOSRepresentation and azimuth
Figure FDA0003364350170000036
And elevation angle
Figure FDA0003364350170000037
Associated spherical unit vector, rtx,LOSRepresentation and azimuth
Figure FDA0003364350170000038
And elevation angle
Figure FDA0003364350170000039
The unit vector of the sphere of interest,
Figure FDA00033643501700000310
representation and azimuth
Figure FDA00033643501700000311
And elevation angle
Figure FDA00033643501700000312
The unit vector of the sphere of interest,
Figure FDA00033643501700000313
representation and azimuth
Figure FDA00033643501700000314
And elevation angle
Figure FDA00033643501700000315
The associated spherical unit vector.
4. The method for geometrically stochastic modeling of an industrial internet of things channel according to claim 1, wherein the S2 comprises:
s21, modeling the geometric distribution of the effective clusters based on the number of the effective clusters and the generalized extreme value distribution; modeling the geometric distribution of the rays in the effective cluster based on the quantity of the rays in the effective cluster obeying the generalized pareto distribution;
s22, modeling the angle of the effective cluster based on the angle of the effective cluster obeying package Gaussian distribution;
s23, obtaining distance vectors of the effective clusters at the sides of a transmitter Tx and a receiver Rx according to the angle parameters of the effective clusters, and modeling the delay of the effective clusters based on the distance vectors;
and S24, modeling the power of the effective cluster according to the time delay obtained from S23.
5. The method according to claim 1, wherein the S21 specifically includes:
making the number N of the observed effective clusters obey the generalized extreme value distribution N-GEV (k)e,σe,μe) The geometric distribution of the effective clusters:
Figure FDA0003364350170000041
wherein k ise,σe,μeShape parameters, and criteria, respectively, of a generalized extremum distributionA difference-dependent scale parameter and an expectation-dependent location parameter;
for the number M of active intra-cluster raysnSubject it to a generalized pareto distribution Mn～GP(kp,σp,μp) Which is defined as
Figure FDA0003364350170000042
Wherein k isp,σp,μpRespectively a shape parameter, a scale parameter and a position parameter of the generalized pareto distribution.
6. The method according to claim 4, wherein the S22 specifically comprises:
efficient ClusternAngle of (2)
Figure FDA0003364350170000043
Obeying a wrapped gaussian distribution, wherein,
Figure FDA0003364350170000044
is an effective ClusternAnd the azimuth and elevation angles between the centers of the receive antenna arrays,
Figure FDA0003364350170000045
is effective ClusternAnd azimuth and elevation angles between the transmit antenna array centers;
m thnThe angle parameter of the strip ray passes through the effective Cluster ClusternThe angle of (d) plus the angular deviation can be obtained:
Figure FDA0003364350170000051
wherein, isA,ΔφE,
Figure FDA0003364350170000052
Respectively, the angular deviation of the ray, obeying a Laplace distribution with a mean value of zero and a standard deviation of 1 deg.,
Figure FDA0003364350170000053
respectively as an effective ClusternInner mthAzimuth and elevation angles between the ray and the center of the receive antenna array,
Figure FDA0003364350170000054
respectively as an effective ClusternInner mthAzimuth and elevation angles between the ray and the center of the transmit antenna array.
7. The method according to claim 6, wherein the S23 specifically includes:
respectively obtaining effective clusters according to the angle parametersnDistance vector to transmitter Tx and receiver Rx array center
Figure FDA0003364350170000055
Figure FDA0003364350170000056
Where D is the initial position vector of the receiver Rx,
Figure FDA0003364350170000057
respectively, subject to exponential distribution
Figure FDA0003364350170000058
The Frobenius norm of (a);
delay of LOS component
Figure FDA0003364350170000059
Wherein the content of the first and second substances,
Figure FDA00033643501700000510
is that
Figure FDA00033643501700000511
And
Figure FDA00033643501700000512
the LOS distance vector in between,
Figure FDA00033643501700000513
which is the antenna q of the receiver, is,
Figure FDA00033643501700000514
is the antenna p of the transmitter and,
Figure FDA00033643501700000515
are respectively
Figure FDA00033643501700000516
And
Figure FDA00033643501700000517
a 3D position vector of (a);
delay of NLOS component:
Figure FDA00033643501700000518
wherein the content of the first and second substances,
Figure FDA00033643501700000519
represents a virtual delay, where rτIs the delay ratio, στIs a delay spread factor, munIs a random variable mu subject to uniform distributionnU (0,1), mnDelay of strip ray
Figure FDA00033643501700000520
Following a mean value of
Figure FDA0003364350170000061
The distribution of the indices of (a) to (b),
Figure FDA0003364350170000062
is determined by parameter estimation.
8. The method according to claim 7, wherein the S24 specifically includes:
efficient ClusternThe average power of (d) is:
Figure FDA0003364350170000063
wherein Z isnObeying Gaussian distribution Zn～N(0,σn)，σnIs the standard deviation of the shading for each valid cluster;
m thnThe average power of the bar ray may be calculated as:
Figure FDA0003364350170000064
to ray mnAverage power of (2) at ClusternScaling at the average power of (a) to obtain:
Figure FDA0003364350170000065
normalized to obtain
Figure FDA0003364350170000066
9. The method for geometrically stochastic modeling of an industrial internet of things channel according to claim 1, wherein the S3 comprises:
let recombination rate of effective clusters be lambdaROn the receiver Rx side, the probability of survival of the active cluster at the receive antenna q' is
Figure FDA0003364350170000067
Wherein the content of the first and second substances,
Figure FDA0003364350170000071
is the spacing between the reference antenna q of the receiver and the antenna q' in the receiver Rx that is different from q,
Figure FDA0003364350170000072
respectively the 3D position vectors of the receive antenna q and the receive antenna q',
Figure FDA0003364350170000073
is a scene correlation coefficient describing spatial correlation;
the average number of visible clusters of antenna q' is E N based on the on-off process of the active clusters on the array axisnew]＝N0(1-Psurvival(ΔR))，
Wherein, E [. C]Indicates expectation, N0Indicates the number of initial clusters; the random number of visible clusters of antenna q' is E N according to the meannew]Is randomly generated.
10. A geometric stochastic modeling system for industrial internet of things channels, comprising:
the CIR building module is used for building a CIR system model for representing an industrial Internet of things channel, wherein the CIR system model adopts a double-hop propagation mechanism, and a scattering environment between a transmitter and a receiver corresponding to the CIR system model is modeled as an effective cluster;
the effective cluster modeling module is used for carrying out 3D modeling on the parameters of the effective cluster in S1 based on rich scattering characteristics of the millimeter wave industrial channel to obtain a 3D model of the effective cluster;
the effective cluster updating module is used for respectively acquiring survival probabilities of effective clusters between different antennas at the Tx side and the Rx side of a transmitter and receiving the signals of the effective clusters on the basis of the non-stationary characteristics of industrial channel space, generating the average number of visible clusters of the Tx side and the Rx side according to the survival probabilities, updating the effective clusters which can be observed by the Tx side and the Rx side antennas according to the average number of the visible clusters, and acquiring updated effective clusters, wherein the effective clusters which can be observed by the Tx side and the Rx side antennas comprise the surviving clusters and the new clusters;
and the cluster model updating module is used for carrying out angle, power and time delay modeling on the updated effective cluster according to the 3D model of the effective cluster.
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