CN110995383A - Rapid simulation method of high-speed mobile communication channel - Google Patents

Rapid simulation method of high-speed mobile communication channel Download PDF

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CN110995383A
CN110995383A CN201911243621.1A CN201911243621A CN110995383A CN 110995383 A CN110995383 A CN 110995383A CN 201911243621 A CN201911243621 A CN 201911243621A CN 110995383 A CN110995383 A CN 110995383A
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channel
state
formula
model
markov
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林思雨
张慧敏
吴宇
高媛
李元轩
刘秋妍
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Beijing Jiaotong 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
    • H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/0082—Monitoring; Testing using service channels; using auxiliary channels
    • H04B17/0087—Monitoring; Testing using service channels; using auxiliary channels using auxiliary channels or channel simulators
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/373—Predicting channel quality or other radio frequency [RF] parameters
    • 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
    • H04B17/3911—Fading models or fading generators
    • 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
    • H04B17/3913—Predictive models, e.g. based on neural network models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/42—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for mass transport vehicles, e.g. buses, trains or aircraft

Abstract

The invention provides a rapid simulation method of a high-speed mobile communication channel, which comprises the steps of firstly, according to the characteristics of a high-speed railway communication system, comprehensively considering three channel fading types of path loss, shadow fading and small-scale fading to obtain corresponding channel parameters; then, establishing a finite state Markov channel model according to the channel parameters, dividing a signal-to-noise ratio interval of the received signal to correspond to a Markov state, and designing a distance interval and state duration to reasonably describe the change of the channel state; then, deriving Markov model parameters, and deriving channel steady-state and transient characteristic expression channels which accord with the high-speed time-varying channel characteristics under the condition of any moving speed by using a variable substitution theorem; then, according to the steady-state and transient-state characteristics of the Markov channel, a rapid time-varying channel simulator capable of accurately describing the channel state variation information is realized; the rapid simulation method provided by the invention can accurately capture the transition of the channel state at both low speed and high speed.

Description

Rapid simulation method of high-speed mobile communication channel
Technical Field
The invention relates to the technical field of wireless communication, in particular to a rapid simulation method of a high-speed mobile communication channel.
Background
High-speed rails have the advantages of high speed, high punctuation rate, comfort, convenience and the like, and are gradually becoming the preferred transportation mode for travelers. However, the existing high-speed mobile communication system has the problems of low spectrum efficiency, poor communication quality and the like, and the requirement of a user on communication and network service on a high-speed train is difficult to guarantee. The essential reason for this situation is that the influence of environmental changes on the propagation of radio waves is aggravated by the high-speed mobility of trains, but the influence mechanism of the moving speed, particularly the high-speed movement, on the channel change is not completely understood at present. Therefore, establishing a channel model that can efficiently describe the fast time-varying is the key to recognizing the channel variation mechanism and designing a new generation of mobile communication system.
The channel modeling is the basis of communication system design, and accurate cognition of a wireless channel is a precondition for designing a communication system, and provides a real reference for system link level simulation and communication system prototype design. However, the channel modeling method adopted in the existing mobile communication system is only suitable for general wireless communication environments, such as a "physical layer channel model" and an "analysis model", and the two models have high computational complexity and long simulation time and are no longer suitable for a high-speed mobile communication environment.
With regard to modeling of high-speed channels, a markov chain-based "packet-level channel model" is now receiving increasing attention from researchers. The Markov chain channel model can evaluate the steady-state statistical characteristics of the channel, accurately track and describe the transient change characteristics of the channel, and has the advantages of simple expression and convenience for quick simulation in a mathematical form.
Disclosure of Invention
The embodiment of the invention provides a rapid simulation method of a high-speed mobile communication channel, which is used for solving the technical problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A rapid simulation method of a high-speed mobile communication channel comprises the following steps:
establishing a system characteristic model according to the characteristics of the high-speed railway communication system, and obtaining system characteristic parameters by solving the system characteristic model;
establishing a Markov channel model of a finite state based on the system characteristic parameters;
solving the stable state probability and the state transition probability of the Markov channel model;
and carrying out Markov channel simulation based on the stable state probability and the state transition probability of the Markov channel model.
Preferably, the system characteristic model comprises a path loss model, a shadow fading model and a small-scale fading model;
the method for establishing the system characteristic model according to the characteristics of the high-speed railway communication system and obtaining the system characteristic parameters by solving the system characteristic model comprises the following steps:
carrying out average processing on the received signals to obtain a path loss fading characteristic, a small-scale fading characteristic and a shadow fading characteristic;
modeling the small-scale fading characteristics by a Nakagami channel model method to obtain a small-scale fading model
Figure BDA0002306930140000021
Wherein the fitting m is a Nakagami fading parameter of the small-scale fading model, and in the formula,
Figure BDA0002306930140000022
for average received power, Γ (·) is the gamma function;
performing linear fitting on path loss fading characteristics to construct a path loss model
Figure BDA0002306930140000023
In the formula, Pr(d) To connect toReceived power, α1And α2Is a path loss exponent, d0Denotes a reference distance, dcDenotes the critical distance, PtFor signal transmit power, L is a determined path loss factor related to the environment and antenna configuration, obtained by the following formula,
Figure BDA0002306930140000024
carrying out normal distribution fitting on the shadow fading characteristics to construct a shadow fading model
Figure BDA0002306930140000025
ψ>0, wherein ξ is 10/ln10, μ is the mean of the received signal snr in dB, and σ is the standard deviation of the received signal snr in dB.
Preferably, the establishing of the finite state markov channel model based on the system characteristic parameters comprises:
outputting the shadow fading characteristic and the small-scale fading characteristic through lognormal-Nakagami distribution to obtain the following formula:
Figure BDA0002306930140000031
wherein the content of the first and second substances,
Figure BDA0002306930140000032
represents an average signal received signal-to-noise ratio within a k-th interval;
simplifying the probability density function expression to obtain the following formula:
Figure BDA0002306930140000033
wherein the content of the first and second substances,
Figure BDA0002306930140000034
and
Figure BDA0002306930140000035
respectively passing the mean value and variance of the approximated lognormal distribution function through the following formulas
Figure BDA0002306930140000036
And
Figure BDA0002306930140000037
computationally obtained, where ψ (-) represents an Euler function and ζ (-) represents a Riemann function;
the formula (2) is processed by the following formula
Figure BDA0002306930140000038
And performing output representation.
Preferably, solving the markov channel model for a steady state probability comprises:
integral operation is carried out on the received signal-to-noise ratio amplitude value in a certain signal-to-noise ratio interval, and the average signal receiving signal-to-noise ratio in each signal-to-noise ratio interval is set
Figure BDA0002306930140000039
Keeping the same, constructing the following formula
Figure BDA00023069301400000310
(6) Calculating a steady state probability of the Markov channel model;
solving the state transition probabilities of the markov channel model comprises:
constructing a formula
Figure BDA00023069301400000311
Wherein p isn,jFor channel state from SnTo SjProbability of transition, f (γ)1,γ2) Is a joint probability density function of a two-dimensional lognormal variable;
setting the c-dimension normal distribution vector X ═ X1,x2,…,xc)T，
Figure BDA00023069301400000312
(·)TRepresenting a transpose of a vector, constructing a formula based on this formula (7)
Figure BDA00023069301400000313
Where μ is a mean vector of dimension c, Σ is a covariance matrix of cxc;
setting a c-dimensional vector following a lognormal distribution
Figure BDA00023069301400000314
And is
Figure BDA00023069301400000316
Using the formula y ═ Φ (x) (9),
Figure BDA00023069301400000315
Denotes Y and YiThe joint probability density function f (y) of the lognormal distribution is solved by using a multivariate element-changing method to construct a formula fY(y)＝f(x)|detJy→xL (| (11); wherein, Jy→xExpressing the Jacobian matrix by the formula
Figure BDA0002306930140000041
Defining;
formula (11) and formula (12) are based on to construct formula
Figure BDA0002306930140000042
The joint probability density function f (y) of the lognormal distribution is constructed by constructing the following formula
Figure BDA0002306930140000043
Wherein u is represented by the formula
Figure BDA0002306930140000044
Obtaining, sigma by formula
Figure BDA0002306930140000045
Obtaining;
construction of bivariate lognormal joint probability density function formula
Figure BDA0002306930140000046
Wherein q is represented by the following formula
Figure BDA0002306930140000047
Obtaining;
the state transition probability between arbitrary states is calculated based on the formula (7) and the formula (17).
Preferably, the performing markov channel simulation based on the stable state probabilities and the state transition probabilities of the markov channel model comprises: and repeatedly executing the substeps for obtaining the stable state probability and the state transition probability of the Markov channel model for multiple times, obtaining the stable state probability and the state transition probability corresponding to multiple intervals and connecting the stable state probability and the state transition probability to obtain the Markov simulation channel.
It can be seen from the above technical solutions provided by the embodiments of the present invention that, the rapid simulation method for a high-speed mobile communication channel provided by the present invention comprehensively considers the influence of factors such as deterministic path loss, shadow fading, small-scale fading, etc. on the channel state, describes the channel steady state and transient state characteristics under different vehicle speed conditions by using a finite state markov chain, describes the influence of factors such as different vehicle speed conditions, different channel state quantities, etc. on the channel change mechanism, and establishes a channel simulator based on the finite state markov chain; the rapid simulation method provided by the invention can accurately capture the transition of the channel state at low speed and high speed, and can more accurately predict the change trend of the channel state.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a process flow diagram of a method for rapid simulation of a high speed mobile communication channel according to the present invention;
FIG. 2 is a flow chart illustrating a preferred embodiment of a method for rapid simulation of a high speed mobile communication channel according to the present invention;
fig. 3 is a chain-like coverage mobile communication system of a fast simulation method of a high-speed mobile communication channel according to the present invention;
FIG. 4 is a finite state Markov model for a fast simulation method for a high speed mobile communication channel according to the present invention;
FIG. 5 is a diagram illustrating a Markov channel model state transition for a fast simulation method for a high speed mobile communication channel in accordance with the present invention;
FIG. 6 is a Markov model probability solution of a fast simulation method for a high speed mobile communication channel according to the present invention;
fig. 7 shows the state stability probability of an eight-state markov channel model in a fast simulation method for a high-speed mobile communication channel according to the present invention. The solid line represents the simulation result of the model, and the dotted line represents the statistical result of the measured data;
FIG. 8 is a state transition probability of an eight-state Markov channel model for a fast simulation method of a high speed mobile communication channel according to the present invention at a vehicle speed v of 40 m/s;
fig. 9 is a state transition probability of an eight-state markov channel model of a fast simulation method for a high-speed mobile communication channel according to the present invention at a vehicle speed v of 100 m/s;
fig. 10 shows the crossing rate of the markov channel level and the average fading time result of the fast simulation method for the high-speed mobile communication channel according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Referring to fig. 1, the present invention provides a fast simulation method for a high-speed mobile communication channel, which is a method for a finite state markov chain-based channel model simulator for a fast time-varying channel, and comprises the following steps:
establishing a system characteristic model according to the characteristics of the high-speed railway communication system, and obtaining system characteristic parameters by solving the system characteristic model;
establishing a finite state Markov channel model based on the system characteristic parameters;
solving the Markov channel model to obtain a stable state probability and a state transition probability;
markov channel simulation is performed based on the steady state probability and the state transition probability.
The invention provides a rapid simulation method of a high-speed mobile communication channel, which comprehensively considers the influence of factors such as deterministic path loss, shadow fading, small-scale fading and the like on the channel state, describes the steady-state and transient-state characteristics of the channel under different vehicle speed conditions, describes the influence mechanism of the speed on the channel change, and establishes a channel simulator based on a finite state Markov chain; the rapid simulation method provided by the invention can accurately capture the transition of the channel state at both low speed and high speed.
A system model is established according to the characteristic that the existing high-speed railway communication system generally adopts a linear base station coverage mode, as shown in figure 3. The signal in each cell experiences three channel fading effects, path loss, shadowing and small scale fading. Under the influence of three kinds of channel fading, the receiving signal-to-noise ratio of the train in operation shows periodic change, namely in the process of driving to a target base station, the signal-to-noise ratio of the receiving signal is gradually increased; in the process of being far away from the target base station, the signal-to-noise ratio of the received signal is gradually reduced until the next service base station is switched;
in the invention, three fading types are comprehensively considered in the channel modeling process, and the influence is reflected in the difference of the steady-state characteristics of the channel when the train is positioned at different positions of a cell and the difference of the transient characteristics of the channel when the train runs at different speeds;
further, in some preferred embodiments, as shown in fig. 2, the system characteristic model includes a path loss model, a shadow fading model, and a small-scale fading model;
the method comprises the following steps of establishing a system characteristic model according to the characteristics of the high-speed railway communication system, and obtaining system characteristic parameters by solving the system characteristic model:
using signals of length 40 timesThe sliding window of wavelength averages the received signal, separating the size scale fading. Modeling the obtained small-scale fading by using Nakagami distribution to obtain the small-scale fading model
Figure BDA0002306930140000071
Through data fitting, a fading parameter m of the small-scale fading Nakagami can be obtained;
Figure BDA0002306930140000072
for average received power, Γ (·) is the gamma function;
performing linear fitting on the path loss fading characteristics, and constructing the path loss model by combining with the measured environment
Figure BDA0002306930140000073
In the formula, Pr(d) To receive power, α1And α2Is a path loss exponent, d0Denotes a reference distance, dcDenotes the critical distance, PtFor signal transmit power, L is a determined path loss factor related to the environment and antenna configuration, obtained by the following formula,
Figure BDA0002306930140000074
carrying out normal distribution fitting on the shadow fading characteristics to construct the shadow fading model
Figure BDA0002306930140000075
ψ>0, wherein ξ is 10/ln10, μ is the mean of the received signal-to-noise ratios in dB, and σ is the standard deviation of the received signal-to-noise ratios in dB;
the parameters of the channel fading model obtained from the above substeps will be used as input parameters for this markov chain channel simulator.
The state transition diagram of the channel model based on markov is shown in fig. 4, and is based on the signal-to-noise ratio periodic variation characteristic of the received signal in the high-speed rail communication system, so that a finite state markov channel simulation model is established by taking a half cell distance range as an example; the step of establishing a finite state Markov channel model based on the system characteristic parameters comprises the following steps:
dividing half cell into K equal-length intervals, wherein the distance of each interval is d, and assuming that the average signal-to-noise ratio of each interval is unchanged, making
Figure BDA0002306930140000081
K is 1,2, …, K denotes the average signal-to-noise ratio of each interval;
each interval is further subdivided into equal time slots TF. In each time slot, the instantaneous signal-to-noise ratio of the received signal is assumed to be constant, i.e., the state of the channel remains constant. The length of the time slot should be selected according to the actual system design or the service characteristics carried by the system, and is less than the coherence time of the channel, so as to ensure that the channel state remains unchanged in one time slot. When the received signal-to-noise ratio gamma of a certain time slotnFalls within the interval [ gamma ]n,,Γn+1) Internal time, let the channel state be denoted as Sn. When the channel status is from SnChange to SjThe corresponding channel state transition probability is denoted as pn,j；
The signal receiving signal-to-noise ratio is divided into equal-length non-overlapping signal-to-noise ratio intervals, and the division threshold value of each signal-to-noise ratio interval is expressed as gamman,Γ1＝-∞,ΓN+1Infinity (N ═ 1,2, …, N + 1); the division number of the signal-to-noise ratio interval is selected according to the service characteristics borne by the actual system and the system design;
outputting the shadow fading characteristics and the small-scale fading characteristics through lognormal-Nakagami distribution to obtain a formula of a probability density function as follows:
Figure BDA0002306930140000082
wherein the content of the first and second substances,
Figure BDA0002306930140000083
represents an average signal received signal-to-noise ratio within a k-th interval;
for simplifying integral operation, the probability density function is simplifiedIn other words, the following formula for lognormal distribution is obtained:
Figure BDA0002306930140000084
wherein the content of the first and second substances,
Figure BDA0002306930140000085
and
Figure BDA0002306930140000086
respectively passing the mean value and variance of the approximated lognormal distribution function through the following formulas
Figure BDA0002306930140000087
And
Figure BDA0002306930140000088
computationally obtained, where ψ (-) represents an Euler function and ζ (-) represents a Riemann function;
then, the formula (2) of the lognormal distribution is passed through the formula of the cumulative distribution function as follows
Figure BDA0002306930140000089
Carrying out output representation;
and then, deriving the stable state probability and the state transition probability of the Markov model based on the accumulative distribution function of the approximately lognormal distribution function.
Further, solving the markov channel model for the steady state probabilities includes:
the solution principle of the markov model for the steady state probability is shown in fig. 5, i.e. the snr amplitude of the received signal is integrated within the corresponding snr interval. When a system model is established, the average received signal-to-noise ratio in each interval is assumed to be kept unchanged, so that the channel state stability probability in each interval is kept unchanged; the probability of state stability in each interval can be determined by constructing the following equation
Figure BDA0002306930140000091
Calculating;
further, solving the markov channel model to obtain the state transition probabilities comprises:
in a high-speed environment, the channel changes violently, so that the channel state only shifts to the same state or an adjacent state at the next moment, and the high-speed time-varying channel is more prone to the condition of cross-state shift; the invention utilizes the joint probability density function of the lognormal distribution function to calculate the channel state transition probability between any states, in particular to construct a formula
Figure BDA0002306930140000092
Wherein p isn,jFor channel state from SnTo SjProbability of transition, f (γ)1,γ2) Is a joint probability density function of a two-dimensional lognormal variable;
the joint probability density function of the lognormal distribution may be derived from the joint probability density function of the normal distribution. First, consider a c-dimensional normal distribution vector X ═ X (X)1,x2,…,xc)T，
Figure BDA0002306930140000093
(·)TRepresenting the transpose of the vector, a multivariate normal distribution joint probability density function can be constructed by constructing a formula based on the formula (7)
Figure BDA0002306930140000094
Where μ is a mean vector of dimension c, Σ is a covariance matrix of cxc;
continuing to define a c-dimensional vector obeying a lognormal distribution
Figure BDA0002306930140000095
And is
Figure BDA00023069301400000910
i 1, 2.. c, the relationship y ═ Φ (x) (9) between them can be expressed in the form of a function, then
Figure BDA0002306930140000096
Then, a joint probability density function f (y) of lognormal distribution is solved by using a multivariate infinitesimal transformation method to construct a formula fY(y)＝f(x)|detJy→xL (| (11); wherein, Jy→xExpressing the Jacobian matrix by the formula
Figure BDA0002306930140000097
Defining;
then, a formula can be constructed based on the formula (11) and the formula (12)
Figure BDA0002306930140000098
Figure BDA0002306930140000099
Therefore, the c-dimensional lognormal distribution probability density function can be further constructed by constructing the following formula
Figure BDA0002306930140000101
Represents;
the present invention is a channel model based on a first order Markov chain, so that only one bivariate lognormal joint probability density function is needed, and then the 2-dimensional mean and covariance matrix can be represented by
Figure BDA0002306930140000102
Figure BDA0002306930140000103
Thereby obtaining a bivariate lognormal joint probability density function
Figure BDA0002306930140000104
Wherein q is represented by the following formula
Figure BDA0002306930140000105
Obtaining;
the state transition probability between arbitrary states is calculated based on the formula (7) and the formula (17).
Further, performing markov channel simulation based on the above steady state probabilities and state transition probabilities includes:
the parameters corresponding to the three channel fading models are activated in the steps, and the channel parameters corresponding to the path loss, the shadow fading and the small-scale fading are input into a Markov model to be used as the input of a Markov model channel simulator;
in the second and third steps, the Markov model channel simulator divides the half-cell distance into K equal-length intervals, the distance of each interval is d, and the average signal-to-noise ratio of each interval is assumed
Figure BDA0002306930140000106
And respectively establishing a Markov chain for each interval without changing. The interval is further subdivided into equal-length time slots TFThe received snr for each time slot corresponds to a markov state, and the channel state shifts with the time slot. The simulator correspondingly calculates the steady-state and transient-state characteristics of the channel in each interval to obtain the steady-state probability and the state transition probability corresponding to each interval, and all the intervals are connected to finally obtain the Markov simulation channel with the half cell and the longer distance.
The present invention also provides an embodiment for verifying the method provided by the present invention, as shown in fig. 7 to 10;
fig. 7 shows the state stability probability of the eight-state markov channel model at a vehicle speed v of 40 m/s. The probability of state 8 representing the best channel quality decreases as the interval index increases, and the probability of state 1 representing the worst channel quality gradually increases.
Fig. 8 shows the state transition probability of the eight-state markov channel model at a vehicle speed v of 40 m/s. For the eight state markov channel model, there are 8 transition cases per state in the next slot, which means that the eight state markov state transition matrix has 64 elements in total; to more clearly show the results of the state transitions, the 25 th, 44 th and 90 th intervals were chosen for observation. These three intervals are three typical positions, which correspond to a position near the base station, a middle position of a half cell, and a cell boundary position, respectively. It can be seen from fig. 8 that the proposed model state transition probability result matches the measurement result well.
To demonstrate that the channel simulator proposed in the present invention can accurately model the channel at any vehicle speed, the state transition probability of the eight-state markov channel model at a vehicle speed v of 100m/s is shown in fig. 9.
Simulation results show that the proposed finite state markov channel model can accurately capture the transition of the channel state at both low and high speeds.
Finally, fig. 10 shows the comparison result of the level crossing rate and the average fading time parameter of the channel simulation and measurement data. It can be seen that the second order characteristic parameters of the simulation output and the measured data match well in most cases.
In summary, according to the rapid simulation method for the high-speed mobile communication channel provided by the invention, firstly, according to the characteristics of the high-speed railway communication system, path loss, shadow fading and small-scale fading of three channel fading types are comprehensively considered, so as to obtain corresponding channel parameters; then, a finite state Markov channel model is established according to the channel parameters, the signal-to-noise ratio interval of the received signal is divided to correspond to the Markov state, and the distance interval and the state duration are designed to reasonably describe the change of the channel state. Then, deriving Markov model parameters, and deriving channel steady-state and transient characteristic expression channels which accord with the high-speed time-varying channel characteristics under the condition of any moving speed by using a variable substitution theorem; then, according to the steady-state and transient-state characteristics of the Markov channel, a rapid time-varying channel simulator capable of accurately describing the channel state variation information is realized;
the rapid simulation method provided by the invention can accurately capture the transition of the channel state at low speed and high speed, and can more accurately predict the change trend of the channel state.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. A fast simulation method of a high-speed mobile communication channel is characterized by comprising the following steps:
establishing a system characteristic model according to the characteristics of the high-speed railway communication system, and obtaining system characteristic parameters by solving the system characteristic model;
establishing a Markov channel model of a finite state based on the system characteristic parameters;
solving the stable state probability and the state transition probability of the Markov channel model;
and carrying out Markov channel simulation based on the stable state probability and the state transition probability of the Markov channel model.
2. The method of claim 1, wherein the system characteristic model comprises a path loss model, a shadow fading model, and a small-scale fading model;
the establishing of the system characteristic model according to the characteristics of the high-speed railway communication system, and the obtaining of the system characteristic parameters by solving the system characteristic model comprises the following steps:
carrying out average processing on the received signals to obtain a path loss fading characteristic, a small-scale fading characteristic and a shadow fading characteristic;
modeling the small-scale fading characteristics by a Nakagami channel model method to obtain the small-scale fading model
Figure FDA0002306930130000011
Wherein the fitting m is a Nakagami fading parameter of the small-scale fading model, and in the formula,
Figure FDA0002306930130000012
for average received power, Γ (·) is the gamma function;
performing linear fitting on the path loss fading characteristics to construct the path loss model
Figure FDA0002306930130000013
In the formula, Pr(d) To receive power, α1And α2Is a path loss exponent, d0Denotes a reference distance, dcDenotes the critical distance, PtFor signal transmit power, L is a determined path loss factor related to the environment and antenna configuration, obtained by the following formula,
Figure FDA0002306930130000014
carrying out normal distribution fitting on the shadow fading characteristics to construct the shadow fading model
Figure FDA0002306930130000015
Where ξ is 10/ln10, μ is the mean snr of the received signal in dB, and σ is the standard deviation of the snr of the received signal in dB.
3. The method of claim 2, wherein the establishing a finite state markov channel model based on the system characteristic parameters comprises:
outputting the shadow fading characteristic and the small-scale fading characteristic through lognormal-Nakagami distribution to obtain the following formula:
Figure FDA0002306930130000021
wherein the content of the first and second substances,
Figure FDA0002306930130000022
represents an average signal received signal-to-noise ratio within a k-th interval;
simplifying the probability density function expression to obtain the following formula:
Figure FDA0002306930130000023
wherein the content of the first and second substances,
Figure FDA0002306930130000024
and
Figure FDA0002306930130000025
respectively passing the mean value and variance of the approximated lognormal distribution function through the following formulas
Figure FDA0002306930130000026
And
Figure FDA0002306930130000027
computationally obtained, where ψ (-) represents an Euler function and ζ (-) represents a Riemann function;
passing the formula (2) through the following formula
Figure FDA0002306930130000028
And performing output representation.
4. The method of claim 3, wherein solving the markov channel model for a steady state probability comprises:
integrating the received signal-to-noise ratio amplitude in a certain signal-to-noise ratio interval, and setting the average signal received signal-to-noise ratio in each signal-to-noise ratio interval
Figure FDA0002306930130000029
Keeping the same, constructing the following formula
Figure FDA00023069301300000210
Calculating a steady state probability of the Markov channel model;
said solving state transition probabilities of the markov channel model comprises:
constructing a formula
Figure FDA00023069301300000211
Wherein p isn,jFor channel state from SnTo SjProbability of transition, f (γ)1,γ2) Is a joint probability density function of a two-dimensional lognormal variable;
setting the c-dimension normal distribution vector X ═ X1,x2,…,xc)T，
Figure FDA0002306930130000031
(·)TRepresenting a transpose of a vector, constructing a formula based on this formula (7)
Figure FDA0002306930130000032
Where μ is a mean vector of dimension c, Σ is a covariance matrix of cxc;
setting a c-dimensional vector following a lognormal distribution
Figure FDA0002306930130000033
And is
Figure FDA0002306930130000034
Using the formula y ═ Φ (x) (9),
Figure FDA0002306930130000035
Represents said Y and YiThe joint probability density function f (y) of the lognormal distribution is solved by using a multivariate element-changing method to construct a formula fY(y)＝f(x)|detJy→xL (| (11); wherein, Jy→xExpressing the Jacobian matrix by the formula
Figure FDA0002306930130000036
Defining;
constructing a formula based on the formula (11) and the formula (12)
Figure FDA0002306930130000037
Constructing the joint probability density function f (y) of the lognormal distribution by constructing the following formula
Figure FDA0002306930130000038
Represents; wherein u is represented by the formula
Figure FDA0002306930130000039
Obtaining, sigma by formula
Figure FDA00023069301300000310
Obtaining;
construction of bivariate lognormal joint probability density function formula
Figure FDA00023069301300000311
Wherein q is represented by the following formula
Figure FDA00023069301300000312
Obtaining;
calculating the state transition probability between arbitrary states based on the formula (7) and the formula (17).
5. The method of claim 4, wherein performing Markov channel simulation based on the Markov channel model's steady state probabilities and state transition probabilities comprises: and repeatedly executing the substeps for obtaining the stable state probability and the state transition probability of the Markov channel model for multiple times, obtaining the stable state probability and the state transition probability corresponding to multiple intervals and connecting the stable state probability and the state transition probability to obtain a Markov simulation channel.
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