CN101588328A - A kind of combined estimation method of high-precision wireless channel parameterized model - Google Patents
A kind of combined estimation method of high-precision wireless channel parameterized model Download PDFInfo
- Publication number
- CN101588328A CN101588328A CNA2009100546448A CN200910054644A CN101588328A CN 101588328 A CN101588328 A CN 101588328A CN A2009100546448 A CNA2009100546448 A CN A2009100546448A CN 200910054644 A CN200910054644 A CN 200910054644A CN 101588328 A CN101588328 A CN 101588328A
- Authority
- CN
- China
- Prior art keywords
- footpath
- diffuse scattering
- component
- parameter
- estimation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The present invention proposes a kind of combined estimation method of high-precision wireless channel parameterized model, it is mainly formed and may further comprise the steps: reflection footpath component parameter estimation, diffuse scattering footpath component parameter estimation, reflected signal component are rebuild, the diffuse scattering signal component is rebuild.The output of reflection footpath component parameter estimation is rebuild by reflected signal component, and negative feedback is to the input of diffuse scattering footpath component parameter estimation; The output of diffuse scattering footpath component parameter estimation is rebuild by the statistical forecast of diffuse scattering signal component, and negative feedback is to the input of reflection footpath component parameter estimation; By this " butterfly negative feedback " cross-iteration mechanism, the output of two parameter estimators estimates channel model isolated component parameter separately certainly with being in harmony, thereby has improved the resolving accuracy of broadband wireless channel parameterized model.The present invention can be applicable to the high accuracy channel test and the modeling of broadband wireless communication system, digit broadcasting systems etc. such as WIMAX, LTE, B3G, 4G, UWB, DVB.
Description
Technical field
The present invention relates to radio wave propagation, wireless communication technology field.Relate in particular to and be applied to the combined estimation method that radio communication channel is measured a kind of high-precision wireless channel parameterized test model of modeling.
Background technology
The method of Channel Modeling generally is divided into three major types: the one, and desirable statistical model, its general hypothesis fading channel is independent identically distributed multiple Gaussian channel, under the scene of enriching the scattering thing, channel capacity is carried out this model of the general employing of theory analysis, such as the steady irrelevant model of Gauss's broad sense, Saleh-Valenzuela statistical model.The 2nd, by setting up the scattering object geometric distributions in the wireless channel, methods such as employing ray trace are studied modeling, as two Rayleigh vector channel models, Von Mises angular distribution model, dicyclo model, three ring models of annulus distributed model, Gesbert, and typical 3GPP SCM/SCME model.But the scattering environments complexity that the actual wireless communication environments is set up more than simulation many, so the 3rd class modeling method: based on the channel parameter modeling method of actual measurement, can obtain to approach more the wireless channel model of true environment, be with a wide range of applications.This method based on to the wireless channel impulse response the time-frequently-the outage precision measure, utilize channel physical characteristics such as time delay-power spectrum to set up the channel model mathematic(al) representation, adopt method for parameter estimation from a large amount of observation datas, to extract the channel model parameter then.
Method for parameter estimation is based on important key technology in the channel parameter modeling of actual measurement, can be three classes generally: the space is estimated (spectral estimation), is estimated (parametric subspace-based estimation (PSBE)), determines parameter Estimation (deterministic parametric estimation (DPE)) based on the parametrization of subspace.What be worth mentioning in first kind space is estimated is MUSIC (multiple signal classification) algorithm.ESPRIT (estimationof signal parameter via rotational invariance techniques) and UnitaryESPRIT algorithm belong to the parametrization estimation technique of subspace.This in three algorithm estimated as the azimuth in reflection footpath by development at first.In determining method for parameter estimation, what have the representative type is exactly maximization expectation (expectation-maximization (EM)) algorithm, once is used as time delay or azimuthal estimation in reflection footpath.SAGE (space-alternating generalized EM) algorithm is the expansion of EM algorithm, because its algorithm is very flexible, computation complexity is low, fast convergence rate, be not limited to the aerial array and the element number of array of use simultaneously, use so the SAGE algorithm is measured having arrived widely in the modeling in conventional channel.
But, when above-mentioned channel measurement modeling method is applied to the broad-band channel test model, particularly have under the city hot zones scene of enriching the scattering condition, owing to ignored the influence of diffuse scattering radio wave propagation component effect, there is certain error in estimation for the reflection wave propagation component, this will cause broad-band channel impulse response parameter Estimation inaccurate, perhaps lose efficacy because of the correlation between the high density footpath of diffuse scattering component initiation.
Please refer to shown in Figure 1ly, that curve description is the city hot zones 100MHz bandwidth channel impulse response result of actual measurement among the figure, the impulse response that discrete straight line only represents to adopt the channel model of considering the reflection footpath to estimate, and wherein the effect in reflection footpath is obvious.Though the reflection of estimating footpath is with nearly tens, in still can not description figure because the continuous Energy distribution phenomenon of actual measurement channel impulse response that diffuse scattering causes, and in the impulse response in the reflection footpath of estimating, also the superposeed influence of the effect of dispersing is so amplitude and the actual margin estimated differ bigger.This shows, only consider that the impulse response channel model in discrete reflection footpath not only has been not enough to describe the characteristic of broad-band channel.
Existing to the wireless channel model method for parameter estimation, for example MUSIC algorithm, ESPRIT algorithm, EM algorithm, SAGE algorithm or the like, ignore the influence of having ignored diffuse scattering radio wave propagation component in the estimation procedure disastrously in wireless channel model, cause WiMAX Channel Modeling accuracy to be affected, algorithm itself can lose efficacy under the serious situation, therefore, the broad-band channel test that not too is fit to the abundant city hot zones of scattering is used with modeling.
Given this, now propose a kind of method of estimation, utilize " negative feedback " structure that the radio wave propagation component that is aliasing in the radio communication channel impulse response is together analyzed estimation based on the radio communication channel parameterized model of surveying.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of combined estimation method of high-precision wireless channel parameterized model, be used for solving two kinds of component associating estimation problems of radio communication channel impulse response aliasing, make that the estimation of each component is more accurate.
For solving the problems of the technologies described above, the present invention adopts following technical scheme: a kind of combined estimation method of high-precision wireless channel parameterized model, this method comprise following loop iteration step:
1) original observation signal (a) is superimposed with the negative feedback (g) of the diffuse scattering signal component of reconstruction, obtains reflected signal component to be estimated (b);
2) reflection footpath component parameter estimation;
Reflected signal component (b) utilization parameter estimation algorithm to input reflects footpath impulse response parameter Estimation, the reflection footpath component parameter set (c) that output is estimated;
3) reflected signal component is rebuild;
The reflection footpath component parameter set (c) that utilizes input to estimate is rebuild reflected signal component, the reflected signal component (d) that output is rebuild;
4) original observation signal (a) is superimposed with the negative feedback (d) of the reflected signal component of reconstruction, obtains diffuse scattering signal component (e) to be estimated;
5) diffuse scattering footpath component parameter estimation;
To diffuse scattering signal component (e) the utilization parameter estimation algorithm of input, estimate diffuse scattering footpath impulse response parameter, the amplitude statistics of adding up its time domain distributes, the time domain statistical parameter collection (f) that output diffuse scattering footpath impulse response component is estimated;
6) diffuse scattering signal component is rebuild;
Utilize the time domain statistical parameter collection (f) of the diffuse scattering footpath impulse response component estimation of input, prediction time domain diffuse scattering signal component is output as (g);
7) repeating step 1) to step 6), converge to ideal value, output wireless channel model parameter (p) until the radio communication channel model parameter of estimating;
Adopt the output of reflection footpath component parameter estimation among the present invention, rebuild by reflected signal component, negative feedback is to the input of diffuse scattering footpath component parameter estimation; The output of diffuse scattering footpath component parameter estimation is rebuild by the statistical forecast of diffuse scattering signal component, and negative feedback is to the input of reflection footpath component parameter estimation; By this " butterfly negative feedback " cross-iteration mechanism, the output of two parameter estimators estimates channel model isolated component parameter separately certainly with being in harmony, thereby has improved the resolving accuracy of broadband wireless channel parameterized model.The present invention can be applicable to the high accuracy channel test and the modeling of broadband wireless communication system, digit broadcasting systems etc. such as WIMAX, LTE, B3G, 4G, UWB, DVB.
Description of drawings
Fig. 1 is the existing 100MHz bandwidth channel impulse response of once surveying in the impulse response channel model in discrete reflection footpath of only considering;
Fig. 2 is that the present invention's " butterfly negative feedback " isolated component parametric joint is estimated structure chart;
Fig. 3 estimates structure chart for " butterfly negative feedback " isolated component parametric joint of a plurality of isolated components of the present invention;
Fig. 4 is a specific embodiment of the invention structured flowchart;
Fig. 5 is actual measurement channel impulse response in 100MHz broadband among the present invention;
Fig. 6 is the impulse response component in diffuse scattering footpath among the present invention;
Fig. 7 is the impulse response component in reflection footpath among the present invention;
Fig. 8 is the impulse response component in diffuse scattering footpath among the present invention.
The main element symbol description
The original observation signal b of a reflected signal component
The reflected signal component that the parameter set d that c reflection footpath estimates rebuilds
The time domain that e diffuse scattering signal component f diffuse scattering is estimated through the impulse response component
The statistical parameter collection
The diffuse scattering signal component p wireless channel model parameter that g rebuilds
Embodiment
Further specify concrete implementation step of the present invention below in conjunction with accompanying drawing.
A kind of combined estimation method of high-precision wireless channel parameterized model is used for solving two kinds of components associating estimation problems of radio communication channel impulse response aliasing.Comprise following concrete steps:
1) original observation signal (a) is superimposed with the negative feedback (g) of the diffuse scattering signal component of reconstruction, obtains reflected signal component to be estimated (b);
2) reflection footpath component parameter estimation;
Reflected signal component (b) utilization parameter estimation algorithm to input reflects footpath impulse response parameter Estimation, the reflection footpath component parameter set (c) that output is estimated;
3) reflected signal component is rebuild;
The reflection footpath component parameter set (c) that utilizes input to estimate is rebuild reflected signal component, the reflected signal component (d) that output is rebuild;
4) original observation signal (a) is superimposed with the negative feedback (d) of the reflected signal component of reconstruction, obtains diffuse scattering signal component (e) to be estimated;
5) diffuse scattering footpath component parameter estimation;
To diffuse scattering signal component (e) the utilization parameter estimation algorithm of input, estimate diffuse scattering footpath impulse response parameter, the amplitude statistics of adding up its time domain distributes, the time domain statistical parameter collection (f) that output diffuse scattering footpath impulse response component is estimated;
6) diffuse scattering signal component is rebuild;
Utilize the time domain statistical parameter collection (f) of the diffuse scattering footpath impulse response component estimation of input, prediction time domain diffuse scattering signal component is output as (g);
7) repeating step 1) to step 6), converge to ideal value, output wireless channel model parameter (p) until the radio communication channel model parameter of estimating;
In the inventive method step 1) and the step 4), utilize last iteration to estimate the other side's the negative feedback each other of two components, form " butterfly negative feedback " structure, solve the associating estimation problem of two isolated components of aliasing in the raw observation signal, made two component results estimated all more accurate.
The inventive method step 2) utilize reflected signal component that parameter Estimation is carried out in the reflection footpath in, utilization is estimated to obtain effect preferably based on the iteration of maximal possibility estimation algorithm, in order to reduce computation complexity, adopt the SAGE algorithm usually, introduce the disappearance space and take turns iteration and realize.
Utilize the diffuse scattering signal component that diffuse scattering is estimated through parameter in the inventive method step 5), use the SAGE algorithm equally this parameter of amplitude in its each time resolution is at interval estimated.Finish after the complete estimation of once sample, at the distribution relation of its time delay-amplitude of time domain statistical estimate, obtain its time domain statistical distribution parameter, when ensureing estimated accuracy, amount of calculation reduces greatly in step 1) and the step 6) like this.
The inventive method can make that the negative feedback component is zero when the initialization of iteration, utilize primary signal directly carry out step 2) estimation.
Among the present invention, separate the independent element of estimating and not only be confined to reflection footpath and diffuse scattering footpath, can be applied to other radio wave propagation mechanism.
Among the present invention, step (2): ", reflect footpath impulse response parameter Estimation to input reflection footpath signal component utilization parameter estimation algorithm." adopted SAGE algorithm specific implementation in an embodiment.This step can be by other parameter estimation algorithms: MUSIC algorithm, ESPRIT algorithm, Unitary ESPRIT algorithm, EM algorithm replace realizing this step function.
Among the present invention, step (6): " utilize the time domain statistical parameter collection of the diffuse scattering of input, prediction time domain diffuse scattering signal component through the estimation of impulse response component." adopt the method for mean prediction to realize in an embodiment.This step can adopt other Forecasting Methodologies, for example quadratic power prediction, cube prediction.
Among the present invention, be example, can also expand to the estimation of uniting of a plurality of independent elements, as shown in Figure 3 to separate two independent elements.
The present invention is based on isolated component and unite the high-precision broadband wireless channel parametric modeling of estimation realization, and the cross-iteration mechanism by " butterfly negative feedback " structure, at two parallel parameter estimator outputs from the channel model parameter separately that estimates two kinds of isolated components with being in harmony; Reflection footpath component parameter estimation comprises that calculating each reflection footpath lacks the space, estimates reflection footpath parameter, and iteration is upgraded until convergence; Diffuse scattering footpath component parameter estimation comprises that calculating each diffuse scattering directly lacks the space, estimates diffuse scattering footpath parameter, and iteration is upgraded until convergence; With the output signal of reflection footpath component parameter estimation, by input convolution algorithm reconstruct reflection footpath signal, and with the input of its negative feedback to diffuse scattering footpath parameter Estimation; With the output signal of diffuse scattering footpath component parameter estimation, by average statistical prediction reconstruct diffuse scattering signal, and with the input of its negative feedback to reflection footpath parameter Estimation; During the initialization of iteration, make that the negative feedback component is zero, utilize the initialization calculating and the parameter Estimation that lack the space through pretreated raw measurement data.
Concrete performing step
1) initialization
Primary signal is carried out preliminary treatment, tentatively remove noise, unwanted signals such as mistake are measured in filtering.
A. reflect the footpath component parameter estimation
1) the diffuse scattering footpath component average of utilizing known original observed quantity and prediction to rebuild, estimation reflection footpath component sample, that is: original observed quantity is superimposed with the directly negative feedback of component average of diffuse scattering that prediction is rebuild.
2) the reflection footpath parameter vector collection of known last iteration estimation
Estimate the disappearance space sample in l bar reflection footpath according to the SAGE algorithm principle.
3) adopt the maximal possibility estimation algorithm in the disappearance space, to unite and estimate l bar reflection footpath parameter.Repeating step 2) to 3), finish the estimation in L bar reflection footpath, obtain the reflection footpath parameter set of this iteration
Wherein, subscript fs represents the reflection footpath, the i time iteration of subscript [i] expression, and l is the sequence number in discrete reflection footpath, L is discrete reflection footpath sum.
4)
B. reflection footpath signal component is rebuild
5) utilize the parameter of estimating acquisition
Make up the impulse response of reflection footpath signal, again by and the convolution algorithm of original transmitted signal, calculate and rebuild the reflection footpath signal component that receives.
C. diffuse scattering footpath component parameter estimation
6) utilize in the reception reflection footpath of known original observed quantity and reconstruction signal component, estimate that diffuse scattering footpath component sample is, that is: original observed quantity is superimposed with the negative feedback of the reception reflection footpath signal component of reconstruction.
7) the diffuse scattering footpath parameter vector collection of known last iteration estimation
Former according to the SAGE algorithm
Reason is calculated the corresponding diffuse scattering in each time interval Δ τ of diffuse scattering footpath and is directly lacked space sample.
8) adopt the maximal possibility estimation algorithm directly to lack and estimate diffuse scattering footpath parameter in the space sample in diffuse scattering:
9) repeating step 7) to 8) circulate and estimated the diffuse scattering footpath of adding up that N Δ τ is interior, obtain the diffuse scattering footpath parameter vector collection of this iteration
Obtain diffuse scattering footpath impulse response simultaneously.
Wherein, subscript ss represents diffuse scattering footpath, the i time iteration of subscript [i] expression, and Δ τ is that minimum time is differentiated the interval, and N Δ τ is the impulse response amplitude greater than in the duration of noise gate, and N is subdivided into the number of Δ τ for this duration.
D. diffuse scattering signal component mean prediction
10) utilize least square method to estimate the statistical parameter of diffuse scattering footpath amplitude, utilize the parameter of estimating acquisition
Make up the impulse response component mean prediction of prediction diffuse scattering footpath, again by and the convolution algorithm of original transmitted signal, calculate the mean prediction that receives the diffuse scattering signal component.
E. two iteration
11) iteration 1) to 10) step, diffuse scattering footpath component and reflection footpath component are calculated, until convergence.
Be illustrated in figure 5 as 4*10
6The 100MHz broadband wireless channel of individual sample point is measured measured data, and from 1000 impulse response snapshots, can observe in relative time delay is that 3000ns, 5500ns and 6500ns place have tangible reflection footpath to exist respectively.Fig. 6 can observe out its amplitude and present the statistics decline with time delay for adopting method of the present invention, remove the impulse response in diffuse scattering footpath in the noise circumstance after reflecting directly.Fig. 7, Fig. 8 are respectively and adopt the reflection footpath component that method of the present invention estimates and the parameterized model of diffuse scattering footpath component, and the complete impulse response of broadband wireless channel has been described in the two stack exactly.
The present invention can be used for the wireless channel modeling and estimate the field, to obtain high-precision channel model from the channel measurement data of actual measurement.The present invention has solved the isolated component parameter problem that estimates aliasing in the radio communication channel impulse response the how observed quantity under the noise circumstance that receives by " butterfly negative feedback " combined estimation method, make that the estimated result of each component is more accurate, thereby improved the precision of wireless channel parameterized model.The present invention is specially adapted to the accurate estimation of broadband wireless communications channel impulse response parameter model, is applied to the high accuracy Channel Modeling of wireless applications such as WIMAX, LTE, B3G, 4G, UWB, DVB.
The foregoing description is the unrestricted technical scheme of the present invention in order to explanation only.Any technical scheme that does not break away from spirit and scope of the invention all should be encompassed in the middle of the patent claim of the present invention.
Claims (7)
1. the combined estimation method of a high-precision wireless channel parameterized model is characterized in that, this method comprises following loop iteration step:
1) original observation signal (a) is superimposed with the negative feedback (g) of the diffuse scattering signal component of reconstruction, obtains reflected signal component to be estimated (b);
2) reflection footpath component parameter estimation;
Reflected signal component (b) utilization parameter estimation algorithm to input reflects footpath impulse response parameter Estimation, the reflection footpath component parameter set (c) that output is estimated;
3) reflected signal component is rebuild;
The reflection footpath component parameter set (c) that utilizes input to estimate is rebuild reflected signal component, the reflected signal component (d) that output is rebuild;
4) original observation signal (a) is superimposed with the negative feedback (d) of the reflected signal component of reconstruction, obtains diffuse scattering signal component (e) to be estimated;
5) diffuse scattering footpath component parameter estimation;
To diffuse scattering signal component (e) the utilization parameter estimation algorithm of input, estimate diffuse scattering footpath impulse response parameter, the amplitude statistics of adding up its time domain distributes, the time domain statistical parameter collection (f) that output diffuse scattering footpath impulse response component is estimated;
6) diffuse scattering signal component is rebuild;
Utilize the time domain statistical parameter collection (f) of the diffuse scattering footpath impulse response component estimation of input, prediction time domain diffuse scattering signal component is output as (g);
7) repeating step 1) to step 6), converge to ideal value, output wireless channel model parameter (p) until the radio communication channel model parameter of estimating;
2. the combined estimation method of a kind of high-precision wireless channel parameterized model as claimed in claim 1 is characterized in that, described step 2) in reflection footpath component parameter estimation comprise:
A. utilize the diffuse scattering footpath component of known original observation signal and reconstruction, estimate reflection footpath component sample;
B. the reflection footpath parameter vector collection that known last iteration is estimated
Estimate the disappearance space sample in l bar reflection footpath according to the SAGE algorithm principle;
C. adopt the maximal possibility estimation algorithm in the disappearance space, to unite and estimate l bar reflection footpath parameter;
D. repeating step b) to c), finish the estimation in L bar reflection footpath, obtain the reflection footpath parameter set of this iteration
Wherein, subscript fs represents the reflection footpath, the i time iteration of subscript [i] expression, and l is the sequence number in discrete reflection footpath, L is discrete reflection footpath sum.
3. the combined estimation method of a kind of high-precision wireless channel parameterized model as claimed in claim 1 is characterized in that, the reflected signal component in the described step 3) is rebuild and comprised: utilize the parameter of estimating acquisition
Make up the impulse response of reflection footpath signal, again by and the convolution algorithm of original transmitted signal, calculate and rebuild the reflected signal component that receives;
Wherein, subscript fs represents the reflection footpath, the i time iteration of subscript [i] expression.
4. the combined estimation method of a kind of high-precision wireless channel parameterized model as claimed in claim 1 is characterized in that, the diffuse scattering footpath component parameter estimation in the described step 5) comprises:
A. utilize reflected signal component, estimate diffuse scattering footpath component sample in known original observation signal and reconstruction;
B. the diffuse scattering footpath parameter vector collection that known last iteration is estimated
According to the SAGE algorithm principle,
Calculate the corresponding diffuse scattering in each time interval Δ τ of diffuse scattering footpath and directly lack space sample;
C. adopt the maximal possibility estimation algorithm directly to lack and estimate diffuse scattering footpath parameter in the space sample in diffuse scattering;
D. repeating step B) to C) circulation has estimated N Δ τ interior add up diffuse scattering directly, the diffuse scattering that obtains this iteration is the parameter vector collection directly
Obtain diffuse scattering footpath impulse response simultaneously;
Wherein, subscript ss represents diffuse scattering footpath, the i time iteration of subscript [i] expression, and Δ τ is that minimum time is differentiated the interval, and N Δ τ is the impulse response amplitude greater than in the duration of noise gate, and N is subdivided into the number of Δ τ for this duration.
5. the combined estimation method of a kind of high-precision wireless channel parameterized model as claimed in claim 1, it is characterized in that, diffuse scattering signal component in the described step 6) is rebuild and is comprised: utilize least square method to estimate the statistical parameter of diffuse scattering footpath amplitude, the parameter of utilizing estimation to obtain
Make up the impulse response component mean prediction of diffuse scattering footpath, again by and the convolution algorithm of original transmitted signal, calculate the mean prediction that receives the diffuse scattering signal component; Wherein, subscript ss represents the diffuse scattering footpath, the i time iteration of subscript [i] expression.
6. the combined estimation method of a kind of high-precision wireless channel parameterized model as claimed in claim 1 is characterized in that, described step 2) in parameter estimation algorithm comprise MUSIC algorithm, ESPRIT algorithm, UnitaryESPRIT algorithm or EM algorithm.
7. the combined estimation method of a kind of high-precision wireless channel parameterized model as claimed in claim 1 is characterized in that, prediction time domain diffuse scattering signal component comprises mean prediction, quadratic power prediction or cube prediction in the described step 6).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009100546448A CN101588328B (en) | 2009-07-10 | 2009-07-10 | Joint estimation method of high-precision wireless channel parameterized model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009100546448A CN101588328B (en) | 2009-07-10 | 2009-07-10 | Joint estimation method of high-precision wireless channel parameterized model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101588328A true CN101588328A (en) | 2009-11-25 |
| CN101588328B CN101588328B (en) | 2012-05-09 |
Family
ID=41372396
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2009100546448A Expired - Fee Related CN101588328B (en) | 2009-07-10 | 2009-07-10 | Joint estimation method of high-precision wireless channel parameterized model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101588328B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101982953A (en) * | 2010-11-04 | 2011-03-02 | 中国科学院上海微系统与信息技术研究所 | Frequency domain multi-dimensional parameterized model of broadband wireless communication channel and modeling method |
| CN102223661A (en) * | 2011-07-04 | 2011-10-19 | 北京理工大学 | Multilayer time delay-capacity model for LTE (Long Term Evolution) |
| CN102739329A (en) * | 2012-06-25 | 2012-10-17 | 中国科学院上海微系统与信息技术研究所 | Method for establishing path loss model of light rail line road sensor network |
| CN101789812B (en) * | 2010-01-15 | 2013-02-27 | 重庆邮电大学 | LTE MIMO communication transmission channel modeling method based on dual polarized antenna |
| CN103684640A (en) * | 2013-12-27 | 2014-03-26 | 南京理工大学 | Simulation method of large-scale complex UWB (Ultra Wide Band) channel |
| CN106680797A (en) * | 2016-06-21 | 2017-05-17 | 大连大学 | Novel target parameter estimation based on wideband ambiguity function |
| CN106713191A (en) * | 2017-02-28 | 2017-05-24 | 西安电子科技大学 | Multistage searching SAGE method |
| CN110186415A (en) * | 2019-05-21 | 2019-08-30 | 北京交通大学 | The method for obtaining channel information for anti-or beyond-the-horizon communication ray tracing |
| CN110212999A (en) * | 2019-05-24 | 2019-09-06 | 谭毅 | A method of predicting different frequency communication channel parameters under same environment |
| CN111431638A (en) * | 2019-01-10 | 2020-07-17 | 电信科学技术研究院有限公司 | Channel model parameter method and device |
| CN113411142A (en) * | 2021-06-29 | 2021-09-17 | 齐鲁工业大学 | Broadband certainty maximum likelihood direction finding method, system, terminal and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7139336B2 (en) * | 2002-04-05 | 2006-11-21 | Nokia Corporation | Method and system for channel estimation using iterative estimation and detection |
| CN101060505B (en) * | 2006-04-19 | 2011-06-01 | 鼎桥通信技术有限公司 | Joint channel estimation method and estimation device in a wireless mobile communication system |
-
2009
- 2009-07-10 CN CN2009100546448A patent/CN101588328B/en not_active Expired - Fee Related
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101789812B (en) * | 2010-01-15 | 2013-02-27 | 重庆邮电大学 | LTE MIMO communication transmission channel modeling method based on dual polarized antenna |
| CN101982953B (en) * | 2010-11-04 | 2013-06-26 | 中国科学院上海微系统与信息技术研究所 | Frequency domain multi-dimensional parameterized model of broadband wireless communication channel and modeling method |
| CN101982953A (en) * | 2010-11-04 | 2011-03-02 | 中国科学院上海微系统与信息技术研究所 | Frequency domain multi-dimensional parameterized model of broadband wireless communication channel and modeling method |
| CN102223661A (en) * | 2011-07-04 | 2011-10-19 | 北京理工大学 | Multilayer time delay-capacity model for LTE (Long Term Evolution) |
| CN102223661B (en) * | 2011-07-04 | 2013-09-18 | 北京理工大学 | Multilayer time delay-capacity model for LTE (Long Term Evolution) |
| CN102739329B (en) * | 2012-06-25 | 2014-08-20 | 中国科学院上海微系统与信息技术研究所 | Method for establishing path loss model of light rail line road sensor network |
| CN102739329A (en) * | 2012-06-25 | 2012-10-17 | 中国科学院上海微系统与信息技术研究所 | Method for establishing path loss model of light rail line road sensor network |
| CN103684640B (en) * | 2013-12-27 | 2016-04-20 | 南京理工大学 | A kind of emulation mode of large-scale complex UWB channel |
| CN103684640A (en) * | 2013-12-27 | 2014-03-26 | 南京理工大学 | Simulation method of large-scale complex UWB (Ultra Wide Band) channel |
| CN106680797A (en) * | 2016-06-21 | 2017-05-17 | 大连大学 | Novel target parameter estimation based on wideband ambiguity function |
| CN106713191A (en) * | 2017-02-28 | 2017-05-24 | 西安电子科技大学 | Multistage searching SAGE method |
| CN106713191B (en) * | 2017-02-28 | 2020-09-08 | 西安电子科技大学 | Multi-stage search SAGE method |
| CN111431638A (en) * | 2019-01-10 | 2020-07-17 | 电信科学技术研究院有限公司 | Channel model parameter method and device |
| CN111431638B (en) * | 2019-01-10 | 2022-03-22 | 大唐移动通信设备有限公司 | Channel model parameter method and device |
| CN110186415A (en) * | 2019-05-21 | 2019-08-30 | 北京交通大学 | The method for obtaining channel information for anti-or beyond-the-horizon communication ray tracing |
| CN110212999A (en) * | 2019-05-24 | 2019-09-06 | 谭毅 | A method of predicting different frequency communication channel parameters under same environment |
| CN110212999B (en) * | 2019-05-24 | 2021-11-12 | 谭毅 | Method for predicting communication channel parameters of different frequencies in same environment |
| CN113411142A (en) * | 2021-06-29 | 2021-09-17 | 齐鲁工业大学 | Broadband certainty maximum likelihood direction finding method, system, terminal and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101588328B (en) | 2012-05-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101588328B (en) | Joint estimation method of high-precision wireless channel parameterized model | |
| Asadi et al. | Extremes on river networks | |
| Xue et al. | Empirical likelihood for single-index varying-coefficient models | |
| CN108668358B (en) | Arrival time-based cooperative positioning method applied to wireless sensor network | |
| CN101982953B (en) | Frequency domain multi-dimensional parameterized model of broadband wireless communication channel and modeling method | |
| CN109298383A (en) | A kind of relatively prime battle array direction of arrival angle estimation method based on variational Bayesian | |
| CN110109050A (en) | The DOA estimation method of unknown mutual coupling under nested array based on sparse Bayesian | |
| CN109633538A (en) | The maximum likelihood time difference estimation method of nonuniform sampling system | |
| CN101631318A (en) | Diverse wireless channel analysis platform based on antenna radiation directional diagram and method thereof | |
| CN110098882B (en) | Multi-antenna broadband spectrum detection method based on compressed sensing and entropy | |
| CN103888145A (en) | Method for reconstructing signals | |
| CN102421105A (en) | Method for modeling ultra wide-band (UWB) compressive sensing wireless channel | |
| CN104950297A (en) | Array element error estimation method based on matrix 1-norm fitting | |
| CN105467361A (en) | Radio signal complex envelope and carrier phase information combination BVR (beyond visual range) target direct positioning method | |
| CN105517019A (en) | Method for detecting LTE (Long Term Evolution) network performance by using integrated regression system | |
| CN105259410A (en) | Under-sampling waveform frequency estimation method and device under strong noise interference | |
| CN109752744A (en) | Multi-satellite combined orbit determination method based on model error compensation | |
| CN110247719A (en) | The playback of 5G time varying channel and emulation mode based on machine learning | |
| CN103338024A (en) | Complementation Kalman filtering device and method of time delay in antenna array | |
| CN109783960A (en) | A kind of Wave arrival direction estimating method based on meshing refinement | |
| CN103412974A (en) | Method for calculating channel capacity of sparse microwave imaging radar system | |
| CN104105049A (en) | Room impulse response function measuring method allowing using quantity of microphones to be reduced | |
| CN112436882B (en) | LEO satellite channel modeling method and device based on double Markov models | |
| CN103929256B (en) | A kind of multiframe compressed sensing signal spectrum detection method | |
| CN105656577A (en) | Clustering method and device for channel impulse response |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20120509 Termination date: 20160710 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |