CN109150775A - A kind of online channel state information estimation method of the robustness of environment adaptive noise dynamic change - Google Patents
A kind of online channel state information estimation method of the robustness of environment adaptive noise dynamic change Download PDFInfo
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Abstract
The present invention relates to a kind of online channel state information estimation methods of the robustness of environment adaptive noise dynamic change, comprising: the online machine learning model of building dynamic noise estimation the characteristics of based on communication noise environment real-time change；It is embedded in channel variation canonical in a model based on the characteristics of channel consecutive variations, constructs complete statistical model, and according to MAP estimation method, obtains complete online channel estimation machine learning model；Using base station stored equipment, the ambient noise distribution parameter and channel state information of a upper period were saved, in conjunction with online channel estimation model, obtains high-precision channel state information estimation in real time.The present invention is based on machine learning principles, a kind of fast speed, high-precision, the online channel state information estimation method for capableing of environment adaptive noise are realized, in practical applications to reducing communication delay, reduce that pilot signal uses, to improve the rate of information throughput significant.
Description
Technical field
The present invention relates to a kind of radio communication channel estimation methods, and in particular to a kind of environment adaptive noise dynamic change
The online channel state information estimation method of robustness.
Background technique
Wireless communication is one of one of most active field of scientific technological advance and field of wireless communications rapid development
Branch.Due to transmission signal in very complicated way and environmental interaction, the variation of radio communication channel status information
Certain adverse effect is caused to transmission signal.Wireless communication system will reach optimum performance, one of significant challenge be exactly
The receiving end of system provides accurate channel state information, that is, carries out accurate channel state information estimation.
The accurate estimation of channel state information is to improve signal reconstruction, signal source detection, and the technologies such as sources number detection are quasi-
The basis of exactness.Guarantee high-precision and it is efficient under the premise of, accurate estimation is carried out to channel state information in real time and is remained unchanged
It is a huge challenge.
In the field of wireless communication, has the technology of many channel state information estimations.Common technology includes being based on
The channel estimation methods of least square are based on least mean-square error channel estimation methods, the channel estimation based on singular value decomposition
Method etc..
Channel estimation methods based on least square and least mean-square error channel estimation methods are based on, to signal system
Noise profile is assumed to white Gaussian noise, is not inconsistent with the distribution of actual Complex Noise.In addition, MoG and MoPE method is for communication
The finer and smoother statistical distribution of environmental consideration it is assumed that using some mixed distributions (such as mixed Gaussian) go fitting ambient noise,
Better channel estimation effect is obtained.
Although existing method has been achieved for the significant effect of comparison, apart from real practical application, there are also certain differences
Away from.Today's society, the signal data of wireless communication are all increasing rapidly all the time, it is desirable that channel state information estimation technique exists
Guarantee it is high-precision under the premise of also to have high efficiency；On the other hand, in face of the signal data at every moment continued to bring out, Wo Menxu
A kind of channel state information estimation technique online in real time is provided.In addition, existing at any time constantly in true communication environment
The Complex Noise of real-time dynamic change, promoting channel estimation methods is also to need to study to the robustness of this real noise environment
Major issue.Now still without a kind of online channel state information estimation method, can reach simultaneously high-precision, high efficiency,
The multiple requesting of robustness.
Summary of the invention
In view of the deficiencies of the prior art, the purpose of the present invention is to provide a kind of Shandongs of environment adaptive noise dynamic change
The online channel state information estimation method of stick can more sufficiently accurately utilize communicating knowledge prior information and its noise ring
Border change information carries out abundant statistical modeling, thus reach higher precision and more robust channel state information estimation effect, and
And can guarantee the high efficiency of processing, it is capable of the variation of effective adaptive communications environment and channel.
In order to achieve the above objectives, the technical solution adopted by the present invention are as follows:
A kind of online channel state information estimation method of the robustness of environment adaptive noise dynamic change, specific steps packet
It includes:
1) randomness based on communication noise environmental change carries out parametrization distribution modeling, and channel state information is made to estimate mould
Type can be adaptive to communication noise environment dynamic change under different time different scenes, further estimate channel state information
The being associated property of noise information of a period communication noise environmental information constrains coding on model insertion, realizes dynamic communication noise
The adaptive modeling of environment；
2) the characteristics of being based on channel consecutive variations is modeled, in 1) on channel state information estimation model insertion for the moment
The relationship information coding of section channel state information, realizes the adaptive modeling of channel；Complete statistics is constructed in conjunction with step 1)
Model, and according to MAP estimation method, it establishes complete online channel state information and estimates machine learning model；
3) base station stored equipment is utilized, upper period noise circumstance distribution parameter and channel state information are saved, in conjunction with step
Rapid online channel state information 2) estimates machine learning model, obtains high-precision channel state information estimation.
The present invention establishes the inherent statistics priori based on wireless communication noise circumstance and channel itself, carries out specific aim respectively
Analysis is with coding, it can be achieved that speed is fast, precision is high, the online channel state information of robustness is estimated.And the channel of precise and high efficiency
Status information estimation has important application meaning for improving signal reconstruction, signal source detection, sources number detection etc..
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is under dynamic noise environment and channel state information, using LS channel estimation method, based on mixing
The offline channel estimation methods of Gauss and the precision of channel estimation comparison diagram of the online channel estimation methods of mentioned machine learning；It can be with
Find out under dynamic noise environment and channel state information, the mentioned obtained channel precision of method is apparently higher than classical LS algorithm
With the off-line algorithm based on mixed Gaussian, illustrate that proposed method can be with adaptive learning dynamic noise environment and channel shape
State.
Fig. 3 is that the offline channel estimation methods and mentioned machine learning in different sample sizes, based on mixed Gaussian exist
Comparison diagram the time required to the channel estimation of line channel estimation methods；As can be seen that the method proposed can significantly reduce channel
Time needed for estimation, with the increase of training sample amount, time loss at magnitude reduction.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawings and embodiments.
With reference to Fig. 1, a kind of online channel state information estimation of robustness of environment adaptive noise dynamic change of the present invention
Method, specific steps include:
1) randomness based on communication noise environmental change carries out parametrization distribution modeling, and channel state information is made to estimate mould
Type can be adaptive to communication noise environment dynamic change under different time different scenes, further estimate channel state information
The being associated property of noise information of a period communication noise environmental information constrains coding on model insertion, realizes dynamic communication noise
The adaptive modeling of environment；Specifically include following sub-step:
A) it obtains pilot signal and its receives signal data；
B) according to Principle of Communication, for s transmitting antenna, multiple-input and multiple-output (MIMO) system of r receiving antenna, letter
Road model may be expressed as:
Yt=HtXt+Et (18)
WhereinIndicate the pilot signal matrix that the t period emits,For the period receiving end t
Received signal matrix,For the channel matrix that the t period is to be estimated,Indicate ambient noise square
Battle array, wherein d indicates the hits of t period signal；In order to which algorithm designs conveniently, model is changed into real number field from complex field:
Wherein
Wherein Re (), Im () respectively represent real and imaginary parts.Result in this way in real number field can transform to plural number
Domain.It uses belowIt indicates.
C) according to the Gaussian mixtures to noise it is assumed that having
WhereinFor YtI-th, j element,For HtThe i-th row,For XtJth column,Indicate hidden variable,The i-th of t period is represented, j is a
Element is not belonging to k-th of blending constituent in Gaussian mixtures,The i-th of t period is represented, j element belongs to
K-th of blending constituent in Gaussian mixtures, and meetThe distribution of Multi representative polynomial,For
The variance of k-th of blending constituent,For the mean value of k-th of blending constituent, K represents the number of blending constituent, and N () is represented just
State distribution, T represent matrix transposition；
For the regularization noise information coding for being embedded in upper period communication noise environmental information, realize dynamic for actual communication
The adaptive modeling of state noise circumstance carries out conjugate prior form hypothesis to noise profile variable in model (19) respectively:
HereIndicate the inverse Gamma Joint Distribution of Gauss-, Dir indicates Dirichlet
Distribution,Wherein Nt -1,It is the intermediate variable for simplified formula,It is the ratio of k-th of mixed Gaussian ingredient of t-1 period,'s
Expression formula is as follows:
Wherein P () represents probability distribution,Degree of membership is represented, indicates that i-th, the j element of t period belongs to mixing
The degree of k-th of blending constituent in Gaussian Profile,Meaning it is identical with (20).
2) the characteristics of being based on channel consecutive variations is modeled, in 1) on channel state information estimation model insertion for the moment
The relationship information coding of section channel state information, realizes the adaptive modeling of channel；Complete statistics is constructed in conjunction with step 1)
Model, and according to MAP estimation method, it establishes complete online channel state information and estimates machine learning model；Specific packet
Include following sub-step:
A) according to the Gaussian Profile to channel it is assumed that having:
Wherein, ρ is channel variation coefficient, and A is hyper parameter,For positive semidefinite matrix.
B) step 1) and MAP estimation method are combined, channel state information Estimation Optimization problem as follows can be obtained:
Abbreviation are as follows:
Wherein DKL(| |) indicate KL divergence, RE(Πt,∑t,Mt) it is noise regular terms, form are as follows:
RH(Ht) it is channel regular terms, form are as follows:
HereIt is the mixed stocker of Gaussian mixtures belonging to the residual error Et of t period
Number,Indicate the variance of each blending constituent of Gaussian mixtures, Mt={ μ1,μ2,…,μK}
The mean value of each blending constituent of t period Gaussian mixtures,It is for simplified formula institute
The intermediate variable of definition, Πt-1, ∑t-1, Mt-1Indicate the mixed coefficint at corresponding t-1 moment, variance vectors and mean vector,
C is indicated and Πt,∑t,MtUnrelated constant.
3) base station stored equipment is utilized, upper period noise circumstance distribution parameter and channel state information are saved, based on step
The rapid pilot signal data 1) inputted and its reception signal data estimate machine in conjunction with the online channel state information of step 2)
Learning model obtains high-precision channel state information estimation.Specifically include following sub-step:
A) preceding 200 sample datas are used and initialize parameters using following MoG algorithm 1:
Algorithm 1:MoG initialization algorithm
Input: preceding 200 pilot datas and corresponding signal data
Output: H0,M0,Π0,∑0
The random sampling on [0,1] of step 1. initializes Π, ∑, M, random initializtion H
Walk 2.repeat:
E- step:
M- step:
Walk 3.until converge
B) fixed H=Ht-1, Π is updated using EM algorithmt,∑t,Mt, per period primary iteration data are used pre- in base station
The mixed coefficint of the upper period mixed Gaussian first stored, variance vectors and mean vector information ∏t,0,∑t,0,Mt,0, iteration
(subscript s indicates the number of iterations) format is as follows:
E- step:
M- step:
Wherein:
WhereinNt,s,It is in order to intermediate defined in simplified formula
Variable.
C) stopping criterion for iteration:
Wherein Πt,s+1,∑t,s+1,Mt,s+1Indicate the mixed coefficint of corresponding s+1 iterative mixing Gauss of t moment, side
Difference vector and mean vector, Πt,s,∑t,s,Mt,sIndicate the mixed coefficint of corresponding s iterative mixing Gauss of t moment, variance
Vector sum mean vector, L () are the objective function that (25) formula defines.
D) ∏ has been updated according to the above processt,∑t,MtAfterwards, H is updatedt, specific Optimized model is as follows:
Wherein WtThe oriental matrix of t period is represented,⊙ is represented
Dot product.
Above-mentioned model (27) has following explicit solution:
WhereinIndicate WtThe i-th row, Indicate HtThe i-th row, A, b are super
Parameter,WithExpression formula it is as follows:
E) relevant information is updated in a base station, in case subsequent period channel estimation uses；
The information saved includes: present period preferred channels estimation H*Restore metric parameter with suitable environment noise parameter
Π*,∑*,M*, realize the preferred channels status information estimation of subsequent period.
Under dynamic noise environment and channel state information, to LS channel estimation method, based on mixed Gaussian
Offline channel estimation methods and the online channel estimation methods precision of the present invention compare, with reference to Fig. 2, it can be seen that dynamically making an uproar
Under acoustic environment and channel state information, the obtained channel precision of the present invention is apparently higher than classical LS algorithm and based on mixed Gaussian
Off-line algorithm, the online channel estimation methods of the present invention can be with adaptive learning dynamic noise environment and channel status.
In different sample sizes, to offline channel estimation methods and the online channel estimation of the present invention based on mixed Gaussian
It is compared the time required to the channel estimation of method, with reference to Fig. 3, it can be seen that the present invention can significantly reduce channel estimation institute
The time needed, with the increase of training sample amount, time loss at magnitude reduction.
Claims (4)
1. a kind of online channel state information estimation method of the robustness of environment adaptive noise dynamic change, which is characterized in that
Include the following steps:
1) randomness based on communication noise environmental change carries out parametrization distribution modeling, and channel state information is enable to estimate model
It is enough adaptive to communication noise environment dynamic change under different time different scenes, and model insertion is estimated to channel state information
The being associated property of noise information of upper period communication noise environmental information constrains coding, realizes actual communication dynamic communication noise
The adaptive modeling of environment；
2) the characteristics of being based on channel consecutive variations is modeled, to a period believes on channel state information estimation model insertion in 1)
The relationship information of channel state information encodes, and realizes the adaptive modeling of channel；Complete statistical model is constructed in conjunction with step 1),
And according to MAP estimation method, complete online channel estimation machine learning model is established；
3) base station stored equipment is utilized, upper period noise circumstance distribution parameter and channel state information are saved, in conjunction with step 2)
Online channel estimation machine learning model, obtain the estimation of high-precision channel state information.
2. the online channel state information estimation side of the robustness of environment adaptive noise dynamic change according to claim 1
Method, it is characterised in that: the step 1) includes following sub-step:
A) it obtains pilot signal and receives signal data；
B) according to wireless communication principles, for s transmitting antenna, multiple-input and multiple-output (MIMO) system of r receiving antenna, letter
Road model is expressed as:
Yt=HtXt+Et (1)
WhereinIndicate the pilot signal matrix that the t period emits,For the reception of the period receiving end t
Signal matrix,For the channel matrix that the t period is to be estimated,Indicate ambient noise matrix, d table
Show the hits of t period signal；In order to which algorithm designs conveniently, channel model is changed into real number field from complex field:
Wherein
Wherein Re (), Im () respectively represent real and imaginary parts, so that the result in real number field is transformed to complex field, useIt indicates；
C) the parametrization distribution modeling of the step 1) is by the noise variance E in model (2)tGaussian mixtures are encoded to, are made
Its dynamic change for being adaptive to communication environment under different time different scenes, corresponding model are as follows:
WhereinFor YtI-th, j element,For HtThe i-th row,For XtJth column, Indicate hidden variable,The i-th of t period is represented, j element does not belong to
K-th of blending constituent in Gaussian mixtures,The i-th of t period is represented, j element belongs to mixed Gaussian
K-th of blending constituent in distribution, and meetThe distribution of Multi representative polynomial,It is mixed for k-th
The variance of ingredient,For the mean value of k-th of blending constituent, K represents the number of blending constituent, and N () represents normal distribution, T generation
Table matrix transposition；
The noise information to a period communication noise environmental information on channel state information estimation model insertion is associated
Property constraint coding, realize the adaptive modeling of actual communication dynamic noise environment, process are as follows: respectively to noise in model (3) point
Cloth variable carries out conjugate prior form hypothesis:
WhereinIndicating the inverse Gamma Joint Distribution of Gauss-, Dir indicates Dirichlet distribution,Wherein Nt-1,It is the intermediate variable for simplified formula,It is the ratio of k-th of mixed Gaussian ingredient of t-1 period,Expression
Formula is as follows:
Wherein P () represents probability distribution,Degree of membership is represented, indicates that i-th, the j element of t period belongs to mixed Gaussian
The degree of k-th of blending constituent in distribution,Meaning it is identical with formula (3).
3. the online channel state information estimation side of the robustness of environment adaptive noise dynamic change according to claim 2
Method, which is characterized in that the step 2) includes following sub-step:
A) in order to model (3) are embedded in the regularization coding of upper period channel state information, make its be adaptive to channel due to
The dynamic change of Small-scale fading, corresponding model modeling are as follows:
Wherein, ρ is channel variation coefficient, and A is hyper parameter,For positive semidefinite matrix；
B) statistical model is constructed based on step 1):
Wherein P () represents probability distribution,It is the residual error E of t periodtAffiliated mixed Gaussian
The mixed coefficint of distribution,Indicate the side of each blending constituent of t period Gaussian mixtures
Difference, Mt={ μ1,μ2,…,μKThe each blending constituent of t period Gaussian mixtures mean value,Be for intermediate variable defined in simplified formula,As (6) formula defines；
According to MAP estimation principle, can be converted by the channel state information estimation model of statistical model conversion following excellent
Change problem:
Abbreviation are as follows:
Wherein PE(Пt,∑t,Mt) it is noise regular terms, form are as follows:,
DKL(| |) indicating KL divergence, C is indicated and Πt,∑t,MtUnrelated constant
RH(Ht) it is channel regular terms, form are as follows:
HereIt is the residual error E of t periodtThe mixed coefficint of affiliated Gaussian mixtures,Indicate the variance of each blending constituent of Gaussian mixtures, Mt={ μ1,μ2,…,μKT
The mean value of each blending constituent of period Gaussian mixtures,It is in order to which simplified formula is defined
Intermediate variable, Πt-1, ∑t-1, Mt-1Indicate the mixed coefficint at corresponding t-1 moment, variance vectors and mean vector, C table
Show and ∏t,∑t,MtUnrelated constant.
4. the online channel state information estimation side of the robustness of environment adaptive noise dynamic change according to claim 3
Method, it is characterised in that: the step 3) utilizes base station stored equipment, saves upper period noise circumstance distribution parameter and channel shape
State information, the online channel state information estimation problem Optimized model in conjunction with obtained in step 2) are updated online using EM algorithm
Channel state information estimates the parameter Π in modelt,∑t,Mt, specifically include following sub-step:
A) it provides E in EM algorithm and walks degree of membershipMore new formula:
Subscript s indicates the s times iteration in formula；
B) Iteration and termination condition that M is walked in EM algorithm are provided:
Iteration are as follows:
Wherein:
WhereinIt is to become in order to intermediate defined in simplified formula
Amount.
Stopping criterion for iteration are as follows:
Wherein Πt,s+1,∑t,s+1,Mt,s+1Indicate the mixed coefficint of corresponding s+1 iterative mixing Gauss of t moment, variance to
Amount and mean vector, Πt,s,∑t,s,Mt,sIndicate the mixed coefficint of corresponding s iterative mixing Gauss of t moment, variance vectors
And mean vector, L () are the objective function that (8) formula defines.
C) iteration initial value is set:
To initial time period data using the mixed coefficint of pre-stored upper period mixed Gaussian in base station, variance vectors and equal
It is worth vector information Πt,0,∑t,0,Mt,0；
D) interative computation for carrying out (9)-(12) formula, until meeting (13) the formula condition of termination.
E) to t period data, in undated parameter Πt,∑t,MtOn the basis of, by such as drag to channel Ht-1It is finely adjusted
The H updatedt:
Wherein WtThe oriental matrix of t period is represented,⊙ represents dot product；
Model (14) has following solution:
WhereinIndicate HtThe i-th row, A, b are hyper parameter,WithExpression formula it is as follows:
F) relevant information is updated in a base station, in case subsequent period channel state information estimated service life；
The information saved includes: present period preferred channels status information estimation H*Restore to measure with suitable environment noise parameter
Parameter Π*,∑*,M*。
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| CN116193504A (en) * | 2023-04-18 | 2023-05-30 | 南京云程半导体有限公司 | Channel state information reporting method, electronic equipment and storage medium |
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