CN103117823A - Short wave channel model building method - Google Patents

Abstract

The invention discloses a short wave channel model building method. The short wave channel model building method includes extracting M samples of a link from a database storing short wave channel parameter samples, and acquiring multipath broadening parameters and Doppler broadening parameters of the samples; utilizing the multipath broadening parameters and the Doppler broadening parameters in the samples as column vectors to establish a channel parameter matrix, and subjecting the vectors in the matrix to uniformization; detecting whether the number of array points covered in a neighborhood radius range of various cluster centers meets the cluster requirement or not; if yes, subjecting the cluster centers to de-uniformization and utilizing the multipath broadening parameters and the Doppler broadening parameters corresponding to the cluster centers subjected to de-uniformization as a short wave channel model. The short wave channel model building method solves the problem that a model obtained by existing channel building technology is large in error and cannot accurately describe the channel characteristics of the link.

Description

Short wave channel model modeling method

Technical Field

The invention relates to the field of short wave channel modeling and mathematical statistics, in particular to a short wave channel model modeling method.

Background

The short wave channel is a typical time-varying parameter channel, and the ionosphere is randomly varied in time domain, space domain and frequency domain, so that the short wave channel parameters are randomly varied, and the channel characteristics of a specific time domain, a specific frequency domain and a specific space domain cannot be described by using an accurate channel model. Currently, the international common approach is to use a channel model composed of ITU-r f.1487 based on typical channel parameters at high, medium and low latitudes. However, due to the time-varying characteristic of the short-wave channel parameters, the error between the channel model using the ITU recommendation and the actual channel model is too large, and the real-time channel parameters obtained by using the channel measurement have great randomness, so that the channel characteristics of the link cannot be completely described.

Disclosure of Invention

The invention provides a short wave channel model modeling method, which is used for solving the problems that in the prior art, the model obtained by the existing channel modeling technology has larger error and can not accurately describe the link channel characteristics under the influence of the time-varying characteristics of short wave channel parameters.

In order to solve the problems, the technical scheme adopted by the invention is as follows:

the invention provides a short wave channel model modeling method, which comprises the following steps:

step 1, extracting M samples of a certain link from a database in which short wave channel parameter samples are stored, and acquiring a multipath broadening parameter and a Doppler broadening parameter in each sample;

step 2, taking the multipath broadening parameters and Doppler broadening parameters of each sample as column vectors, constructing a channel parameter matrix of 2 rows and M columns, and carrying out normalization processing on each vector in the matrix;

step 3, defining each column of the channel parameter matrix as an array point, and calculating a clustering center in each array point by using a two-dimensional clustering combination algorithm;

and 4, detecting whether the number of the array points covered in the neighborhood radius range of each clustering center meets the clustering requirement, if so, performing de-normalization processing on the clustering centers, and taking the multipath broadening parameters and the Doppler broadening parameters corresponding to the de-normalized clustering centers as short wave channel models.

Optionally, in step 1 of the method of the present invention, the short wave channel parameter sample in the database is a parameter sample obtained through a short wave channel measurement experiment.

Optionally, in step 1 of the method of the present invention, M samples meeting the conditions of a certain link are extracted from the database according to a preset time condition, a preset signal-to-noise ratio condition, and a preset sun blackness condition.

Optionally, in step 2 of the method of the present invention, the normalizing process is performed on each vector in the matrix, and specifically includes:

comparing the Doppler broadening parameters with each other to obtain f when the condition 1 is met_xMinimum value of (2)

And comparing the multipath broadening parameters with each other to obtain the tau satisfying the condition 2_yMinimum value of (2)

Are respectively provided with

And

normalizing the corresponding parameter vector in the matrix for the normalization cardinality of the Doppler broadening parameter and the multipath broadening parameter;

wherein the condition 1 is: doppler spread parameter

Greater than f_xThe number of the parameters satisfies a preset threshold; the condition 2 is as follows: multipath broadening parameter

Greater than τ_yThe number of parameters of (2) satisfies a set threshold.

Optionally, in step 3 of the method of the present invention, calculating a cluster center in each array point by using a two-dimensional cluster combination algorithm specifically includes:

step 31, calculating the density index of each array point, acquiring the array point corresponding to the highest density index in each density index, and judging that the array point is a first clustering center;

step 32, correcting the density index of each array point by using the density index of the kth clustering center, and acquiring the array point corresponding to the highest value in the corrected density index;

and step 33, judging the clustering centers of the acquired array points according to the set clustering judgment threshold, obtaining the (k + 1) th clustering center when the array points are judged to be the clustering centers, and enabling k = k +1, and returning to the step 32.

Wherein, the step 33 specifically includes:

step 331, determining the array point correspondences

Is greater than

If yes, judging the array point as a clustering center; otherwise, go to step 332;

step 332, determining the array point correspondences

Is less than

If yes, judging that the array point is not a clustering center, and terminating the clustering process; otherwise, go to step 333;

step 333, in

Is greater thanIs less than

Time, judge

If yes, judging the array point as a clustering center; otherwise, judging theIf the array point is not the clustering center, setting the density index corresponding to the array point to zero, and selecting the array point with the highest density index in the rest array points as the point to be confirmed, and returning to the step 331;

wherein,

εrespectively as the upper limit and the lower limit of a preset judgment threshold,

the density index of the first clustering center is obtained; d_minFor the distance, r, of the current group point to be confirmed from the first cluster center_aIs the neighborhood radius of the set array point.

Further, in the step 32, a formula is used

The density index of each array point is corrected; in the formula,

is an index of the density of the k-th cluster center,

and

respectively are the Doppler broadening parameter and the multipath broadening parameter of the ith sample after normalization processing,

and

respectively are Doppler broadening parameter and multipath broadening parameter r of the k-th clustering center after normalization processing_bIs positive and satisfies r_bGreater than the neighborhood radius of the array point.

Optionally, step 4 of the method of the present invention specifically includes:

obtaining the number N of array points falling in the neighborhood radius range of each cluster center_ckDetecting

Whether or not it is greater than or equal to a set clustering threshold, an

Whether the average clustering threshold is larger than or equal to a set average clustering threshold or not, when both are larger than or equal to corresponding thresholds, the clustering center is subjected to normalization processing, and a multipath broadening parameter and a Doppler broadening parameter corresponding to the clustering center after the normalization processing are used as short wave channel models; wherein K is the total number of the clustering centers,

any given is meant.

The array points falling into the neighborhood radius range of the cluster center are array points meeting the following conditions: ${(f_{{\mathrm{\σ}}_i}^{\′}-f_{{\mathrm{\σ}}_k}^{\′})}^2+{({\mathrm{\τ}}_{{\mathrm{\σ}}_i}^{\′}-{\mathrm{\τ}}_{{\mathrm{\σ}}_k}^{\′})}^2\≤{\left(r_a\right)}^2;$

in the formula,

and

respectively are Doppler broadening parameters and multipath broadening parameters of the ith array point after normalization processing,

and

respectively a Doppler broadening parameter and a multipath broadening parameter r of the k-th clustering center after normalization processing_aIs the neighborhood radius of the set array point.

Optionally, step 4 of the method further comprises:

when detecting that the number of the array points covered in the radius range of each cluster center does not meet the cluster requirement, ending the modeling process, or adjusting variable parameters used by the two-dimensional cluster combination algorithm, and executing the step 3 again; wherein the variable parameters include: neighborhood radius r of set array points_aNeighborhood r with significantly reduced set density index function_bAnd the upper and lower limits of the set clustering judgment threshold

Andε。

the invention has the following beneficial effects:

the invention establishes a method for modeling a short wave channel model by effectively classifying discrete channel parameters, can establish the short wave channel model under specific time domain, airspace and specific solar blackness number, is closer to the actual channel characteristic compared with the channel model provided by ITU-RF.1487, and can provide effective support for short wave communication and short wave frequency scoring according to the channel model established by the method.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described 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 that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of a short-wave channel model modeling method according to an embodiment of the present invention;

fig. 2 is a flowchart of an algorithm for performing two-dimensional clustering combination on channel parameters according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In order to solve the problems that the model error obtained by the existing channel modeling technology is large and the link channel characteristics cannot be accurately described in the prior art, the embodiment of the invention provides a short wave channel model modeling method. The technical solution of the present invention is described in detail by several specific examples.

Example one

As shown in fig. 1, an embodiment of the present invention provides a short-wave channel model modeling method, including:

step S101, extracting M samples of a certain link from a database in which short wave channel parameter samples are stored, and acquiring a multipath broadening parameter and a Doppler broadening parameter in each sample;

in this step, the short-wave channel parameter sample in the database is a parameter sample obtained through a short-wave channel measurement experiment.

Further, in this step, M samples that satisfy the conditions of a certain link are extracted from the database according to a preset time condition, a signal-to-noise ratio condition, and a sun blackness condition. Wherein, each condition can be flexibly set according to the specific modeling requirement.

Step S102, taking the multipath broadening parameters and Doppler broadening parameters of each sample as column vectors, constructing a channel parameter matrix with 2 rows and M columns, and carrying out normalization processing on each vector in the matrix;

in this step, the method of normalizing the quantities in the matrix includes:

the first method is as follows:

(1) comparing the Doppler broadening parameters with each other to obtain f when the condition 1 is met_xMinimum value of (2)And comparing the multipath broadening parameters with each other to obtain the tau satisfying the condition 2_yMinimum value of (2)

(2) Are respectively provided with

And

normalizing the corresponding parameter vector in the matrix for the normalization cardinality of the Doppler broadening parameter and the multipath broadening parameter;

in the above embodiment, condition 1 is: doppler spread parameterGreater than f_xThe number of the parameters satisfies a preset threshold; the condition 2 is: multipath broadening parameter

Greater than τ_yThe number of parameters of (2) satisfies a set threshold.

The second method comprises the following steps:

setting a Doppler spread parameter decision threshold value according to a specific step length

N, calculated to satisfy the condition

The number L of the time-domain Doppler spread parameters_fAnd find L_fEach f satisfying a predetermined threshold value or more_xMinimum value of

With the said

Normalizing the Doppler broadening parameters in the matrix for the normalization cardinal number of the Doppler broadening parameters;

setting multi-path broadening parameter decision threshold according to specific step lengthN, calculated to satisfy the condition

Number of time, Doppler spread parameters tau_lAnd find τ_lSatisfy each of τ greater than a set threshold_yMinimum value of

With the saidAnd normalizing the multipath broadening parameters in the matrix for the normalization cardinality of the multipath broadening parameters.

In the present invention, the threshold is proposed to be equal to or greater than M · 0.95 for setting the above threshold, but those skilled in the art can flexibly set the threshold according to the specific modeling requirements.

Step S103, defining each column of the channel parameter matrix as an array point, and calculating a clustering center in each array point by using a two-dimensional clustering combination algorithm;

and step S104, detecting whether the number of the array points covered in the neighborhood radius range of each clustering center meets the clustering requirement, if so, performing de-normalization processing on the clustering centers, and taking the multipath broadening parameters and the Doppler broadening parameters corresponding to the de-normalized clustering centers as short wave channel models.

The method comprises the following steps: obtaining the number of array points falling in the neighborhood radius range of each cluster centerN_ckDetectingWhether or not it is greater than or equal to a set clustering threshold, an

Whether the average clustering threshold is larger than or equal to a set average clustering threshold or not, when both are larger than or equal to corresponding thresholds, the clustering center is subjected to normalization processing, and a multipath broadening parameter and a Doppler broadening parameter corresponding to the clustering center after the normalization processing are used as short wave channel models; wherein K is the total number of the clustering centers,

any given is meant.

The setting of the clustering threshold and the average clustering threshold indicates whether the calculated clustering center has typicality, the specific values of the clustering center and the average clustering threshold are not unique, the average clustering threshold is recommended to be greater than or equal to 60% in the embodiment, and the clustering threshold is recommended to be greater than or equal to 12%.

Further, in step S104, when it is detected that the number of array points covered within the radius range of each cluster center does not meet the cluster requirement, the modeling process is ended, or a variable parameter used by the two-dimensional cluster combination algorithm is adjusted, and step S103 is executed again; wherein the variable parameters include, but are not limited to: neighborhood radius r of set array points_aNeighborhood r with significantly reduced set density index function_bAnd the upper and lower limits of the set clustering judgment threshold

Andε。

example two

The embodiment of the invention provides a short wave channel model modeling method, which has the same principle as the method of the first embodiment, and further elaborates the method of the first embodiment by combining specific implementation details, wherein the method comprises the following steps:

step A: obtaining a channel parameter sample;

and B: performing two-dimensional clustering combination on the channel parameters;

and C: the clustering center is corresponding to the channel model;

the specific implementation process of the step A is as follows:

according to modeling requirements, two irrelevant channel parameters which can completely reflect channel characteristics are obtained: multipath broadening and doppler broadening. Wherein, the channel parameter sources are: after a channel measurement experiment is completed on a certain specific link, the calculated channel parameters are analyzed from the measured data, each measured data sample corresponds to a group of channel parameters, and the channel parameters are shown in table 1 as a channel parameter table.

TABLE 1

Step A1: setting channel parameter extraction conditions according to the short wave channel characteristics;

(1) setting a measurement time condition: t is_min<t<T_maxIn general, the time interval is preferably 1 month<T_max-T_min<3 months old

(2) Setting a signal-to-noise ratio condition: snr > xdB, typically x is chosen to be 5 dB.

(3) Setting the sun black seed number condition: n is a radical of_min<n<N_maxIn general, the interval of the number of sun black is N_max-N_minAnd 25, selecting.

Step A2: extracting channel parameters;

using the three conditions of step A1, finding the intersection of the data samples satisfying the conditions, assuming that the intersection of the data samples is M, and setting the corresponding Doppler spread parameter f_σAnd multipath spread channel parameter tau_σRespectively put into the channel parameter matrixes.

$\mathrm{Channel}-\mathrm{Paramet}=\left[\begin{array}{c}{f}_{{\mathrm{\&sigma;}}_1},f_{{\mathrm{\&sigma;}}_2},...,f_{{\mathrm{\&sigma;}}_M}\\ {\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_1},{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_2},...,{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_M}\end{array}\right]$

Step A3: the channel parameters are normalized, and the method comprises the following specific steps:

step A31 of calculating satisfaction of the condition

τ_σ>τ_yNumber L of parameters of time-Doppler broadening and multipath broadening_fAnd τ_lA correspondence table of parameter values and the number of parameters is generated as shown in table 2.

TABLE 2

Step A32 obtaining a composition satisfying L from Table 2_fF is not less than M.0.95_xMinimum value of

Simultaneous determination of the satisfying of_lTau of not less than M.0.95_yMinimum value of

Step A33: using the 1 st and 2 nd row numbers in the channel parameter matrix respectively

And

carrying out normalization processing to obtain:

$\mathrm{Channel}-\mathrm{Paramet}-\mathrm{Unitary}=\left[\begin{array}{c}{f}_{{\mathrm{\&sigma;}}_1}/f_{x_L},f_{{\mathrm{\&sigma;}}_2}/f_{x_L},...,f_{{\mathrm{\&sigma;}}_M}/f_{x_L}\\ {\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_1}/{\mathrm{\&tau;}}_{y_L},{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_2}/{\mathrm{\&tau;}}_{y_L},...,{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_M}/{\mathrm{\&tau;}}_{y_L}\end{array}\right]$

for step B, the specific implementation process is shown in fig. 2, and includes:

first, the density index of each column of the data set in the Channel-parameter-Unit matrix is calculated.

Grouping each column of data in the Channel-parameter-Unit matrix

All as candidate array points for the cluster center. Calculate each column

The density index of (2):

$D_i=\underset{j=1}{\overset{M}{\mathrm{\&Sigma;}}}\exp [-\frac{{(f_{{\mathrm{\&sigma;}}_i}/f_{x_L}-f_{{\mathrm{\&sigma;}}_j}/f_{x_L})}^2+{({\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_i}/{\mathrm{\&tau;}}_{y_L}-{\mathrm{\&tau;}}_{\mathrm{\&sigma;j}}/{\mathrm{\&tau;}}_{y_L})}^2}{{\left(r_a\right)}^2}]$

wherein r is_aIs a positive number, defines the neighborhood radius of the point, if the array

The surrounding array points have high density values, and the array points outside the radius have very small contribution to the density index of the point. r is_aIs related to the number of desired cluster centers, when r is_aThe larger the cluster center is, the smaller the number of the obtained cluster centers is; on the contrary, when r_aThe smaller the number of cluster centers obtained. Therefore, r_aCan be flexibly set according to requirements.

Then, selecting the array point with the highest density index, judging the clustering center, and correcting the density index of each array point by using the density index of the array point when the array point is the clustering center, wherein the method specifically comprises the following steps:

after the density index of each array point is calculated, the array point with the highest density index is selected

As a first cluster center, the center of the cluster,

is an index of the density. Let us currently select the kth cluster center

Has a density index of

Then use

Correcting the density index of each array point:

$D_i=D_i-D_{c_k}\exp [-\frac{{(f_{{\mathrm{\&sigma;}}_i}/f_{x_L}-f_{{\mathrm{\&sigma;}}_k}/f_{x_L})}^2+{({\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_i}/{\mathrm{\&tau;}}_{y_L}-{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_k}/{\mathrm{\&tau;}}_{y_L})}^2}{{\left(r_b\right)}^2}]$

wherein r is_bIs a positive number defining a neighborhood of significantly reduced density index function, usually r_b>r_aTo avoid the occurrence of cluster centers with very close distance, in this embodiment, r is taken_b＝1.5r_a. The density index of the array of points that are apparently close to the previous cluster center will be significantly reduced by the correction, so that these points are less likely to be selected as the next cluster center.

Selecting the array point of the highest density index from the corrected density indexes, and calculating to obtain the density index

Then, judging whether the array points meet the requirement of a clustering center or not by using the following judgment mode, and judging whether clustering is terminated or not; setting the upper limit of the judgment threshold as

The lower limit isε＝0.15。

(a) When in use

Consider that

Is a cluster center, to

And after the density indexes of each group are corrected, continuing the next judgment process.

(b) When in use

Consider that

Not the cluster center, the clustering process is terminated.

(c) When in use

Time, judgement formula

If yes, then the method is considered to be true

Is a cluster center, to

After the density indexes of each group are corrected, continuing the next judgment process; if not, consider that

And if not, setting the density index of the array point to be 0, selecting the point with the highest density index in the rest array points as the point to be confirmed, and judging again.

For step C, the specific implementation process is as follows:

step C1: calculating all array points in the Channel-parameter-Unit matrix

The proportion that falls within the radius of the center of each cluster. The number of array points falling within the radius range of the kth cluster center is: satisfy the requirement of ${(f_{{\mathrm{\&sigma;}}_i}/f_{x_L}-f_{{\mathrm{\&sigma;}}_{\mathrm{ck}}}/f_{x_L})}^2+{({\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_i}/{\mathrm{\&tau;}}_{y_L}-{\mathrm{\&tau;}}_{\mathrm{\&sigma;ck}}/{\mathrm{\&tau;}}_{y_L})}^2\&le;{\left(r_a\right)}^2$ The number of array points is recorded as N_ck。

Step C2: checking N within each cluster center radius_ckValue when

And is $\frac{\&ForAll;N_{\mathrm{ck}}}{M}\&times;100\%\&GreaterEqual;15\%$ When, consider the cluster center

A channel model can be represented.

Step C3: and denormalizing the clustering centers meeting the requirements to obtain a channel model. The channel model is expressed as $(f_{{\mathrm{\&sigma;}}_{c_1}},{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_{c_1}})...(f_{{\mathrm{\&sigma;}}_{c_k}},{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_{c_k}}).$

To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined purpose, a specific application example is given below to further explain the technical solution proposed by the present invention.

As shown in table 3, the channel parameter table is a channel parameter table extracted from measurement data transmitted from the Qingdao in 2012 and received in 2012 and 1/12 th to 2012 and 1/17 th, Beijing.

TABLE 3

The short wave channel model modeling method based on the measured data comprises the following specific steps:

step A: channel parameter samples are obtained. The doppler spread and multipath spread parameter samples for channel modeling are obtained from table 3, and the specific steps are as follows:

a1: setting channel parameter extraction conditions according to the short wave channel characteristics; because the time span of the measured data is small, the channel parameters extracted by all the measured data can be used. And setting the signal-to-noise ratio condition to be Snr >5 dB. The measurement experiment is in winter, the time span is short, the change of the number of the solar black seeds is not large (the difference between the maximum value and the minimum value is not more than 25), and the solar black seed number interval is not divided.

A2: extracting channel parameters; from the conditions of using step a1, a data sample set of 1135 samples can be obtained, and the doppler spread parameters f corresponding to the data sample set are calculated_σAnd multipath spread channel parameter tau_σRespectively put into the channel parameter matrixes.

$\mathrm{Channel}-\mathrm{Paramet}=\left[\begin{array}{c}{f}_{{\mathrm{\&sigma;}}_1},f_{{\mathrm{\&sigma;}}_2},...,f_{{\mathrm{\&sigma;}}_M}\\ {\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_1},{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_2},...,{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_M}\end{array}\right]=\left[\begin{array}{cccc}2.52& 2.41& 3.23& ...\\ 0.25& 0.36& 0.21& ...\end{array}\right]$

A3: carrying out normalization processing on the channel parameters; respectively finding out a numerical value for normalization according to the characteristics of Doppler broadening and multipath broadening parameters, and respectively normalizing the two parameters, wherein the method specifically comprises the following steps:

a31: calculating satisfaction condition

τ_σ>τ_yNumber L of parameters of time-Doppler broadening and multipath broadening_fAnd τ_l. Generating a corresponding table of the parameter values and the parameter numbers, as shown in table 4;

TABLE 4

A32: the L is obtained from Table 4_fF is not less than M.0.98_xMinimum value of 0.83Hz, and satisfying τ_lTau of not less than M.0.98_yThe minimum value is 79 ms.

A33: respectively normalizing the first row of data and the second row of data in the channel parameter matrix by 0.83 and 79 to obtain:

$\mathrm{Channel}-\mathrm{Paramet}-\mathrm{Unitary}=\left[\begin{array}{c}{f}_{{\mathrm{\&sigma;}}_1}/f_{x_L},f_{{\mathrm{\&sigma;}}_2}/f_{x_L},...,f_{{\mathrm{\&sigma;}}_M}/f_{x_L}\\ {\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_1}/{\mathrm{\&tau;}}_{y_L},{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_2}/{\mathrm{\&tau;}}_{y_L},...,{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_M}/{\mathrm{\&tau;}}_{y_L}\end{array}\right]=\left[\begin{array}{ccccc}0.3190& & 0.3051& 0.4089& ...\\ 0.3012& & 0.4337& 0.2530& ...\end{array}\right]$

step B, performing two-dimensional clustering combination on the matrix Channel-parameter-Unit according to the following flow:

first, the density index of each column of the data set in the Channel-parameter-Unit matrix is calculated.

Grouping each column of data in the Channel-parameter-Unit matrix

All as candidate array points for the cluster center. Calculate each column

The density index of (2):

$D_i=\underset{j=1}{\overset{M}{\mathrm{\&Sigma;}}}\exp [-\frac{{(f_{{\mathrm{\&sigma;}}_i}/f_{x_L}-f_{{\mathrm{\&sigma;}}_j}/f_{x_L})}^2+{({\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_i}/{\mathrm{\&tau;}}_{y_L}-{\mathrm{\&tau;}}_{\mathrm{\&sigma;j}}/{\mathrm{\&tau;}}_{y_L})}^2}{{\left(r_a\right)}^2}]$

wherein r is_a＝0.17。

Then, the array point of the highest density index is selected, and the density index of each data point is updated.

After the density index of each array point is calculated, the array point with the highest density index is selected

As a first cluster center, the center of the cluster,is an index of the density. Let us currently select the kth cluster center

Has a density index of

Then use

Correcting density index for each array of points

$D_i=D_i-D_{c_k}\exp [-\frac{{(f_{{\mathrm{\&sigma;}}_i}/f_{x_L}-f_{{\mathrm{\&sigma;}}_{k+1}}/f_{x_L})}^2+{({\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_i}/{\mathrm{\&tau;}}_{y_L}-{\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_{k+1}}/{\mathrm{\&tau;}}_{y_L})}^2}{{\left(r_b\right)}^2}]$

Wherein r is_b＝1.5r_a。

Selecting the array point of the highest density index from the corrected density indexes, and calculating to obtain the density index

Then theJudging whether the array points meet the requirement of a clustering center or not by using the following judgment mode, and judging whether clustering is terminated or not; setting a decision threshold as

ε=0.15；

(a) When in useConsider that

A cluster center is formed, and the correction process of the density index is continued.

(b) When in useConsider that

Not the cluster center, the clustering process is terminated.

(c) When in use

Time, judgement formula

Whether it is true or not, if not, considering that

And if not, setting the density index of the array point to be 0, selecting the point with the highest density index in the rest array points as the point to be confirmed, and judging again.

Three cluster centers are obtained by the clustering: (0.2530,0.0759),(0.2651,0.6329),(0.2410,0.3418).

And step C, corresponding the clustering center with the channel model according to the following steps.

Step C1: and calculating the proportion of all array points in the Channel-parameter-Unit matrix within the radius range of the center of each cluster. According to a constraint formula ${(f_{{\mathrm{\&sigma;}}_i}/f_{x_L}-f_{{\mathrm{\&sigma;}}_{\mathrm{ck}}}/f_{x_L})}^2+{({\mathrm{\&tau;}}_{{\mathrm{\&sigma;}}_i}/{\mathrm{\&tau;}}_{y_L}-{\mathrm{\&tau;}}_{\mathrm{\&sigma;ck}}/{\mathrm{\&tau;}}_{y_L})}^2\&le;{\left(r_a\right)}^2$ The number of array points falling in the center of the three clusters is calculated as 454, 219 and 170 respectively.

Step C2: after inspection, all cluster center radiiThe ratio of the number of array points in the range to the total number of array points is $\frac{\underset{k=1}{\overset{K}{\mathrm{\&Sigma;}}}{N}_{\mathrm{ck}}}{M}\&times;100\%=\frac{839}{1135}\&times;100\%=73.9\%\&GreaterEqual;70\%,$ And is $\frac{\&ForAll;N_{\mathrm{ck}}}{M}\&times;100\%\&GreaterEqual;15\%,$ The cluster centers (0.2530, 0.0759), (0.2651, 0.6329) and (0.2410, 0.3418) are considered to represent the channel model.

Step C3: and denormalizing the clustering centers meeting the requirements to obtain a channel model. The channel model of the link under the conditions set in step a1 can be obtained as shown in table 5.

TABLE 5

Channel parameters	Doppler broadening (Hz)	Multipath spread (ms)
			Channel model 1	0.21	0.6
Channel model 2	0.22	5.0
			Channel model 3	0.2	2.7

In summary, the method for modeling the short-wave channel model by effectively classifying the discrete channel parameters is established in the embodiment of the present invention, and by using the method provided in the embodiment of the present invention, the short-wave channel model under a specific time domain, a specific space domain and a specific number of solar blacks can be established, which is closer to the actual channel characteristic than the channel model provided by ITU-rf.1487, and the channel model established according to the method can provide effective support for short-wave communication and short-wave frequency scoring.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A short wave channel model modeling method is characterized by comprising the following steps:

step 1, extracting M samples of a certain link from a database in which short wave channel parameter samples are stored, and acquiring a multipath broadening parameter and a Doppler broadening parameter in each sample;

step 2, taking the multipath broadening parameters and Doppler broadening parameters of each sample as column vectors, constructing a channel parameter matrix of 2 rows and M columns, and carrying out normalization processing on each vector in the matrix;

step 3, defining each column of the channel parameter matrix as an array point, and calculating a clustering center in each array point by using a two-dimensional clustering combination algorithm;

and 4, detecting whether the number of the array points covered in the neighborhood radius range of each clustering center meets the clustering requirement, if so, performing de-normalization processing on the clustering centers, and taking the multipath broadening parameters and the Doppler broadening parameters corresponding to the de-normalized clustering centers as short wave channel models.

2. The method of claim 1, wherein in the step 1, the short wave channel parameter samples in the database are parameter samples obtained through a short wave channel measurement experiment.

3. The method according to claim 1 or 2, wherein in the step 1, M samples meeting the conditions of a certain link are extracted from the database according to preset time conditions, signal-to-noise ratio conditions and sun blackness conditions.

4. The method according to claim 1, wherein in the step 2, normalizing the vector quantities in the matrix specifically includes:

comparing the Doppler broadening parameters with each other to obtain f when the condition 1 is met_xMinimum value of (2)

And comparing the multipath broadening parameters with each other to obtain the tau satisfying the condition 2_yMinimum value of (2)

Are respectively provided with

Andnormalizing the corresponding parameter vector in the matrix for the normalization cardinality of the Doppler broadening parameter and the multipath broadening parameter;

wherein the condition 1 is: doppler spread parameter

Greater than f_xThe number of the parameters satisfies a preset threshold; the condition 2 is as follows: multipath broadening parameter

Greater than τ_yThe number of parameters of (2) satisfies a set threshold.

5. The method according to claim 1 or 4, wherein in the step 3, calculating the cluster center in each array point by using a two-dimensional cluster combination algorithm specifically comprises:

step 31, calculating the density index of each array point, acquiring the array point corresponding to the highest density index in each density index, and judging that the array point is a first clustering center;

step 32, correcting the density index of each array point by using the density index of the kth clustering center, and acquiring the array point corresponding to the highest value in the corrected density index;

and step 33, judging the clustering centers of the acquired array points according to the set clustering judgment threshold, obtaining the (k + 1) th clustering center when the array points are judged to be the clustering centers, and enabling k = k +1, and returning to the step 32.

6. The method according to claim 5, wherein the step 33 specifically comprises:

step 331, determining the array point correspondences

Is greater than

If yes, judging the array point as a clustering center; otherwise, go to step 332;

step 332, determining the array point correspondences

Is less than

If yes, judging that the array point is not a clustering center, and terminating the clustering process; otherwise, go to step 333;

step 333, in

Is greater thanIs less than

Time, judge

If yes, judging the array point as a clustering center; otherwise, it is determined that the array point is not the clustering center, the density index corresponding to the array point is set to zero, and the array point corresponding to the highest density index in the remaining array points is selected as the point to be confirmed, and the procedure returns to step 331;

wherein, εrespectively as the upper limit and the lower limit of a preset judgment threshold,

the density index of the first clustering center is obtained; d_minFor the array point currently to be confirmedDistance from the first cluster center, r_aIs the neighborhood radius of the set array point.

7. The method of claim 5, wherein in step 32, a formula is used

The density index of each array point is corrected; in the formula,is an index of the density of the k-th cluster center,

andrespectively are the Doppler broadening parameter and the multipath broadening parameter of the ith sample after normalization processing,

and

respectively are Doppler broadening parameter and multipath broadening parameter r of the k-th clustering center after normalization processing_bIs positive and satisfies r_bGreater than the neighborhood radius of the array point.

8. The method according to claim 1, wherein the step 4 specifically comprises:

obtaining the number N of array points falling in the neighborhood radius range of each cluster center_ckDetectingWhether or not it is greater than or equal to a set clustering threshold, an

Whether the average clustering threshold is larger than or equal to a set average clustering threshold or not, when both are larger than or equal to corresponding thresholds, the clustering center is subjected to normalization processing, and a multipath broadening parameter and a Doppler broadening parameter corresponding to the clustering center after the normalization processing are used as short wave channel models; wherein K is the total number of the clustering centers,any given is meant.

9. The method of claim 8, wherein the array points falling within the neighborhood radius of the cluster center are array points satisfying the following condition:

in the formula,

and

respectively are Doppler broadening parameters and multipath broadening parameters of the ith array point after normalization processing,

and

respectively a Doppler broadening parameter and a multipath broadening parameter r of the k-th clustering center after normalization processing_aIs the neighborhood radius of the set array point.

10. The method of claim 1, 8 or 9, wherein the step 4 further comprises:

when detecting that the number of the array points covered in the radius range of each cluster center does not meet the cluster requirement, ending the modeling process, or adjusting variable parameters used by the two-dimensional cluster combination algorithm, and executing the step 3 again; wherein the variable parameters include: neighborhood radius r of set array points_aNeighborhood r with significantly reduced set density index function_bAnd the upper and lower limits of the set clustering judgment thresholdAndε。

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