CN107317607A - A kind of multilink multi-antenna channel combines statistical property modeling method - Google Patents

A kind of multilink multi-antenna channel combines statistical property modeling method Download PDF

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CN107317607A
CN107317607A CN201710355425.8A CN201710355425A CN107317607A CN 107317607 A CN107317607 A CN 107317607A CN 201710355425 A CN201710355425 A CN 201710355425A CN 107317607 A CN107317607 A CN 107317607A
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correlation
cross
random variable
gaussian random
link
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CN107317607B (en
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周涛
陶成
刘留
张楠
文辉
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Beijing Jiaotong University
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Beijing Jiaotong University
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    • 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
    • H04B7/022—Site diversity; Macro-diversity
    • H04B7/024—Co-operative use of antennas of several sites, e.g. in co-ordinated multipoint or co-operative multiple-input multiple-output [MIMO] systems
    • 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
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Monitoring And Testing Of Transmission In General (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The present invention discloses a kind of multilink multi-antenna channel joint statistical property modeling method, including：Generate independent identically distributed Gaussian random variable and be respectively divided to the set of correspondence distribution link；By filtering method, corresponding autocorrelation is respectively loaded on the Gaussian random variable in set；Cholesky decomposition is carried out to cross correlation matrix number in set to obtain gathering interior cross-correlation transition matrix, and cross correlation in corresponding set is respectively loaded on to the Gaussian random variable in set；Similarly, cross-correlation transition matrix between being gathered, cross correlation between corresponding set is loaded onto the new set being made up of the same Gaussian random variable of different sets；By with the Gaussian random variable of cross correlation includes the emulation data of shadow fading, K factor, delay spread and angle spread by logarithm normal distribution generation between cross correlation and link in autocorrelation, link.The present invention can relatively accurately be described to the joint statistical property of multilink multi-antenna channel large scale parameter.

Description

A kind of multilink multi-antenna channel combines statistical property modeling method
Technical field
The present invention relates to wireless communication technology field.Combine more particularly, to a kind of multilink multi-antenna channel and count Characteristics modeling method.
Background technology
Coordinate multipoint (Coordinated Multiple Points, CoMP) is a kind of for improving the new of efficiency of transmission Technology, the interference signal from other cells is changed into useful signal by it by way of can launching or receive cooperating.It is right In the application of new technology, it is necessary first to be fully understood by the channel characteristics of its application scenarios, suitable channel model pair is then built Its performance is estimated.Channel model is that one of wireless propagation environment and its channel characteristics is abstractively described.Channel model Assessed in wireless communication system design to standardization so that into the links finally disposed, playing the role of important.
In order to assess the performance of CoMP technologies, it is necessary to using multi-link channel model, that is, consider from multiple websites to one Or the multi-link channel feature of multiple mobile stations.Multi-link channel correlation determines that the diversity gain and channel of CoMP system hold Amount：Shadow fading correlation is weaker between link, then the macro diversity of CoMP system is bigger；Multipath fading correlation between link Weaker, then the differential diversity gain and channel capacity of CoMP system are bigger.Ideally, when being orthogonal between different links, CoMP System has optimum performance.However, in actual communication environments, orthogonality between link it is difficult to ensure that, reason is：Firstth, Different links there may be stable propagation path composition such as sighting distance (Line of Sight, LOS) path etc.；Secondth, due to passing The symmetry of environment is broadcast, different links there may be common scattering object or reflector, the propagation path that they are produced has phase Like property.
Existing most channel model assumes that the channel of different links is independent, belongs to single-link channel model, And the research of multi-link channel model is very deficient.Accordingly, it is desirable to provide a kind of multilink multi-antenna channel joint statistical property Modeling method.
The content of the invention
Combine statistical property modeling method it is an object of the invention to provide a kind of multilink multi-antenna channel.
To reach above-mentioned purpose, the present invention uses following technical proposals：
A kind of multilink multi-antenna channel combines statistical property modeling method, comprises the following steps：
Independent identically distributed Gaussian random variable is generated, each Gaussian random variable is respectively divided to correspondence distribution link Set, the different distribution link of different set correspondences, set and distribution link are one-to-one relations；
By filtering method, corresponding autocorrelation is respectively loaded on the Gaussian random variable in set, a kind of theory is changed Method is：By filtering method, make the Gaussian random variable in set that there is corresponding autocorrelation；
Cholesky decomposition is carried out to cross correlation matrix number in set to obtain gathering interior cross-correlation transition matrix, passes through collection Cross-correlation transition matrix will represent in link cross correlation in the set of cross correlation accordingly and be respectively loaded in set in closing Gaussian random variable；
Cross-correlation transition matrix between being gathered is decomposed cross correlation matrix number progress cholesky set, passes through collection Cross-correlation transition matrix will represent between link that cross correlation is loaded onto by different sets between the set of cross correlation accordingly between conjunction Same Gaussian random variable composition new set；
By with the Gaussian random variable of cross correlation presses lognormal between cross correlation and link in autocorrelation, link Distribution generation includes the multilink multi-antenna channel joint statistical property mould of shadow fading, K factor, delay spread and angle spread Type emulates data.
Beneficial effects of the present invention are as follows：
Technical scheme of the present invention can be carried out to the joint statistical property of multilink multi-antenna channel large scale parameter Relatively accurately describe, reference can be provided with assessing for the design for the multiple antennas GSM that cooperates.
Brief description of the drawings
The embodiment to the present invention is described in further detail below in conjunction with the accompanying drawings；
Fig. 1 shows that multilink multi-antenna channel combines the flow chart of statistical property modeling method.
Fig. 2 shows that multilink multi-antenna channel combines the schematic diagram data of statistical property modeling method.
Fig. 3 shows testing for the shadow fading modeled applied to multilink multi-antenna channel under the scene of actual high-speed railway Plain Demonstrate,prove result schematic diagram.
Fig. 4 shows testing for the angle spread modeled applied to multilink multi-antenna channel under the scene of actual high-speed railway Plain Demonstrate,prove result schematic diagram.
Embodiment
In order to illustrate more clearly of the present invention, the present invention is done further with reference to preferred embodiments and drawings It is bright.Similar part is indicated with identical reference in accompanying drawing.It will be appreciated by those skilled in the art that institute is specific below The content of description is illustrative and be not restrictive, and should not be limited the scope of the invention with this.
As shown in figure 1, multilink multi-antenna channel joint statistical property modeling method disclosed by the invention includes following step Suddenly：
Independent identically distributed Gaussian random variable is generated, each Gaussian random variable is respectively divided to correspondence distribution link Set；
By filtering method, corresponding autocorrelation is respectively loaded on the Gaussian random variable in set；
Cholesky decomposition is carried out to cross correlation matrix number in set to obtain gathering interior cross-correlation transition matrix, passes through collection Cross-correlation transition matrix will represent in link cross correlation in the set of cross correlation accordingly and be respectively loaded in set in closing Gaussian random variable；
Cross-correlation transition matrix between being gathered is decomposed cross correlation matrix number progress cholesky set, passes through collection Cross-correlation transition matrix will represent between link that cross correlation is loaded onto by different sets between the set of cross correlation accordingly between conjunction Same Gaussian random variable composition new set；
By with the Gaussian random variable of cross correlation presses lognormal between cross correlation and link in autocorrelation, link Distribution generation includes the multilink multi-antenna channel joint statistical property mould of shadow fading, K factor, delay spread and angle spread Type emulates data.
As shown in Fig. 2 substituting into the multilink multi-antenna channel joint statistical property modeling method of specific variable and data Comprise the following steps：
Generate independent identically distributed Gaussian random variable x1′,1,x1′,2,x1′,3,x1′,4,x′2,1,x2′,2,x2′,3,x2′,4, Each Gaussian random variable is respectively divided to the set X of correspondence distribution link1'={ x1′,1,x1′,2,x1′,3,x1′,4And X2'= {x2′,1,x2′,2,x2′,3,x2′,4}；
By filtering method, corresponding autocorrelation is respectively loaded on set X1' and X2' interior Gaussian random variable makes Variable in set, obtains X1"={ x "1,1,x″1,2,x″1,3,x″1,4And X2"={ x "2,1,x″2,2,x″2,3,x″2,4, here Autocorrelation is the result obtained by actual measurement data, is obtained by following formula：
Wherein, d represents distance；Δ d represents distance interval；Represent the average of sample；L represents the number of sample；
Cholesky decomposition is carried out to cross correlation matrix number in set to obtain gathering interior cross-correlation transition matrix Cm, pass through Cross-correlation transition matrix C in setmIt will represent in link that cross correlation is respectively loaded on collection in the set of cross correlation accordingly Close X1" and X2" interior Gaussian random variable, i.e.,
[x′″m,1x′″m,2x′″m,3x′″m,4]=[x "m,1x″m,2x″m,3x″m,4]·Cm
Wherein, m=1,2；Cross-correlation transition matrix C in setmCross correlation matrix number it can be carried out in set Cholesky is decomposed and obtained：
Wherein,For CmAssociate matrix；The cross-correlation coefficient of different variables in set is represented,
I=1,2,3,4, j=1,2,3,4；
Cross-correlation transition matrix D between being gathered is decomposed cross correlation matrix number progress cholesky setn, pass through Cross-correlation transition matrix D between setnIt will represent between link that cross correlation is loaded onto by difference between the set of cross correlation accordingly New set { the x ' " of the same Gaussian random variable composition of set1,1,x′″2,1}、{x′″1,2,x′″2,2}、{x′″1,3,x′″2,3And {x′″1,4,x′″2,4, i.e.,
[x1,n x2,n]=[x ' "1,n x′″2,n]·Dn
Wherein, n=1,2,3,4；Cross-correlation transition matrix D between transition matrix setnIt can be obtained by following formula
Wherein,Represent the cross-correlation coefficient of same variable between gathering, p=1,2, q=1,2.
Cross correlation (gathering cross correlation between interior or set in other words) is by actual measurement data in link or between link The result of acquisition, is obtained by following formula
By with the Gaussian random variable of cross correlation presses lognormal between cross correlation and link in autocorrelation, link Distribution generation includes shadow fading (SFm), K factor (Km), delay spread (DSm) and angle spread (ASm) many days of multilink Line channel combines statistical property model emulation data：
Wherein,For the standard deviation of the shadow fading of actual measurement；μ represents K factor, delay spread and the angle spread of actual measurement Average, ε represents the K factor of actual measurement, the standard deviation of delay spread and angle spread, for exampleFor actual measurement K factor it is equal Value,For the standard deviation of the K factor of actual measurement.
Apply the present invention under the scene of actual high-speed railway Plain multilink multi-antenna channel to model, its shadow fading and The result of angle spread as shown in Figures 3 and 4, from first order statistic Cumulative Distribution Function (Cumulative Distribution Function, CDF) with pair of second-order statistic level crossing rate (lcr) (Level Crossing Rate, LCR) There is preferable uniformity than can be seen that actual measured results and model emulation result, so as to demonstrate the feasible of the present invention Property.
Obviously, the above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not pair The restriction of embodiments of the present invention, for those of ordinary skill in the field, may be used also on the basis of the above description To make other changes in different forms, all embodiments can not be exhaustive here, it is every to belong to this hair Row of the obvious changes or variations that bright technical scheme is extended out still in protection scope of the present invention.

Claims (1)

1. a kind of multilink multi-antenna channel combines statistical property modeling method, it is characterised in that this method comprises the following steps：
Independent identically distributed Gaussian random variable is generated, each Gaussian random variable is respectively divided to the collection of correspondence distribution link Close；
By filtering method, corresponding autocorrelation is respectively loaded on the Gaussian random variable in set；
Cholesky decomposition is carried out to cross correlation matrix number in set to obtain gathering interior cross-correlation transition matrix, by set Cross-correlation transition matrix will represent in link cross correlation in the set of cross correlation accordingly and be respectively loaded on the height in set This stochastic variable；
Cross-correlation transition matrix between being gathered is decomposed cross correlation matrix number progress cholesky set, by between set Cross-correlation transition matrix will represent between link that cross correlation is loaded onto by the same of different sets between the set of cross correlation accordingly The new set of one Gaussian random variable composition；
By with the Gaussian random variable of cross correlation presses logarithm normal distribution between cross correlation and link in autocorrelation, link The multilink multi-antenna channel joint statistical property model that generation includes shadow fading, K factor, delay spread and angle spread is imitated True data.
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