CN109450574A - Radio channel multi-path cluster-dividing method and device in high-speed rail communication network - Google Patents
Radio channel multi-path cluster-dividing method and device in high-speed rail communication network Download PDFInfo
- Publication number
- CN109450574A CN109450574A CN201910015950.4A CN201910015950A CN109450574A CN 109450574 A CN109450574 A CN 109450574A CN 201910015950 A CN201910015950 A CN 201910015950A CN 109450574 A CN109450574 A CN 109450574A
- Authority
- CN
- China
- Prior art keywords
- sample
- multipath
- multilink
- clusters
- cluster
- 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
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000004891 communication Methods 0.000 title claims abstract description 34
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 43
- 230000004044 response Effects 0.000 claims abstract description 17
- 239000000284 extract Substances 0.000 claims abstract description 11
- 238000000605 extraction Methods 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 3
- 230000035939 shock Effects 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 8
- 230000008859 change Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 239000004744 fabric Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 238000012567 pattern recognition method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Networks & Wireless Communication (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The present invention provides radio channel multi-path cluster-dividing method and devices in a kind of high-speed rail communication network, comprising: determines the steady interval of the channel impulse response measured in high-speed rail communication network, extracts the power delay profile in steady interval；Noise gate is set, effective multipath component of noise gate or more is extracted, using the tap position of effective multipath component as original sample, weight of the power of multipath component as original sample；The neighborhood distance threshold of neighborhood sample number threshold value and sample is set, original sample and original sample weight are input to DBSCAN algorithm, obtains multiple multilink clusters, and filter out the sample for being determined as noise by DBSCAN algorithm；The sample of obtained multiple multilink clusters and sample weights are separately input into K mean algorithm, determine the multipath number of clusters amount in each multilink cluster, obtains multipath sub-clustering result.The accuracy of multipath sub-clustering can be improved in method of the invention.
Description
Technical field
The present invention relates to radio channel multi-path sub-clusterings in wireless communication technology field more particularly to a kind of high-speed rail communication network
Method and apparatus.
Background technique
In broadband wireless channel, signal reaches receiver by a certain group of scatterer, and receiving signal is by with phase
It like time delay, angle of arrival and leaves the multipath signal at angle and is formed by stacking, these multipath signals with similar parameter are known as multipath cluster.
Broad-band channel model is usually to be constructed by the way of cluster, for example, the Saleh- proposed by Saleh and Valenzuela
Valenzuela channel model and some other directionality channel model.Multipath sub-clustering is the basis of broad-band channel modeling, and
Cluster-dividing method will directly determine the reliability of the accuracy and Analysis of channel property of institute's channel model building.
The most direct mode of multipath sub-clustering be by visual identity, that is, naked-eye observation, but this method inefficiency,
It cannot achieve the processing of mass data.Currently, realizing that multipath sub-clustering is most common side using the clustering algorithm in pattern-recognition
Method.K-means algorithm is a kind of clustering algorithm based on distance, and the multipath for being widely used in wireless channel clusters.The algorithm
Advantage is that algorithm is quick and easy, and the problem is that need to pre-estimate the quantity of sub-clustering, and noise data is compared
It is sensitive.DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm
It is a kind of density-based algorithms, the sample set of arbitrary shape can be clustered, noise sample can be effectively treated.
But the algorithm is not suitable for identification at a distance of closer multipath cluster.
Currently, the deployment of high-speed rail communication network is to carry out identical networking by the way of cell combining.In high-speed rail communication network
In network, it is overlapping covered with non-overlap overlay area that there are base stations, wherein the reception signal in overlapping covered is to come from
The superposition of neighbor base station signal is presented as in power delay profile while the multipath component from multilink occurs.Also,
The multipath component of different links can with the movement of train and close to or far from.Therefore, in high-speed rail communication network, wireless channel
Multipath distribution can have the case where apart from each other, there is also close proximities.In such case, traditional K- is directlyed adopt
Means algorithm or DBSCAN algorithm, which carry out multipath sub-clustering, will appear biggish error.Therefore, it is necessary to a kind of high-speed rail communication networks
Middle radio channel multi-path cluster-dividing method improves the accuracy of multipath sub-clustering.
Summary of the invention
The present invention provides radio channel multi-path cluster-dividing method and devices in a kind of high-speed rail communication network, to improve multipath point
The accuracy of cluster.
To achieve the goals above, this invention takes following technical solutions.
The present invention provides radio channel multi-path cluster-dividing methods in a kind of high-speed rail communication network, comprising:
S1 determines the steady interval of the channel impulse response measured in high-speed rail communication network, extract it is described it is steady between
Every interior power delay profile；
The noise gate of the power delay profile is arranged in S2, extracts effective multipath component of the noise gate or more,
Using the tap position of effective multipath component as original sample, using the power of the multipath component as the power of original sample
Weight；
The neighborhood distance threshold of neighborhood sample number threshold value and sample is arranged in S3, by the original sample and described original
Sample weights are input to DBSCAN algorithm, obtain multilink cluster, and filter out the sample for being determined as noise by DBSCAN algorithm；
S4 by the different multilink clusters sample and corresponding weight be separately input into K mean algorithm, determine every
Multipath number of clusters amount in a multilink cluster obtains multipath sub-clustering result.
Further, it is determined that the steady interval of the channel impulse response measured in high-speed rail communication network, comprising: pass through
Local steady interval method determines.
Further, the power delay profile in the steady interval is extracted, comprising: by the letter in the steady interval
The channel shock response progress time is average, obtains power delay profile.
Further, the noise gate of the power delay profile is set, comprising: according to peak atenuation method or base standard of making an uproar
The noise gate of the power delay profile is arranged in method.
Further, neighborhood sample number threshold value is more than or equal to 3 multipath taps, and the neighborhood of the sample is apart from threshold
Value is greater than the neighborhood sample number threshold value.
Further, by multiple multilink clusters sample and sample weights be separately input into K mean algorithm, really
Multipath number of clusters amount in fixed each multilink cluster, the value including determining K according to ancon rule.
Another aspect provides radio channel multi-path sub-clustering devices in a kind of high-speed rail communication network, comprising:
Extraction module is extracted for determining the steady interval of the channel impulse response measured in high-speed rail communication network
Power delay profile in the steady interval；
First limitation module extracts the noise gate or more for the noise gate of the power delay profile to be arranged
Effective multipath component, using the tap position of effective multipath component as original sample, by the power of the multipath component
Weight as original sample；
Second limitation module will be described original for the neighborhood distance threshold of neighborhood sample number threshold value and sample to be arranged
Sample and the original sample weight are input to DBSCAN algorithm, obtain multiple multilink clusters, and filter out noise sample；
Sub-clustering module, for by the sample in multiple multilink clusters and being separately input into K mean value to sample weights
Algorithm determines the multipath number of clusters amount in each multilink cluster, obtains multipath sub-clustering result.
As seen from the above technical solution provided by the invention, the application method can be improved traditional clustering algorithm and be applied to
The performance of radio channel multi-path sub-clustering in high-speed rail communication network is built for the tracking of subsequent multipath cluster and the high-speed rail channel based on multipath cluster
Mould provides support.
The additional aspect of the present invention and advantage will be set forth in part in the description, these will become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, required use in being described below to embodiment
Attached drawing be briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this
For the those of ordinary skill of field, without creative efforts, it can also be obtained according to these attached drawings others
Attached drawing.
Fig. 1 is radio channel multi-path cluster-dividing method flow chart in a kind of high-speed rail communication network of embodiment 1；
Fig. 2 is that DBSCN algorithm extracts multilink cluster and determines the result schematic diagram of noise data；
Fig. 3 be in the prior art using K-means algorithm directly to whole sample carry out the obtained mean square error of sub-clustering with
Multipath number of clusters changes schematic diagram；
The multipath sub-clustering result schematic diagram that Fig. 4 is obtained after the K value to determine whole sample according to ancon rule；
Fig. 5 is to carry out the mean square error and multipath that sub-clustering obtains to first multilink cluster sample using K-means algorithm
Number of clusters changes schematic diagram；
Fig. 6 is to carry out the mean square error and multipath that sub-clustering obtains to second multilink cluster sample using K-means algorithm
Number of clusters changes schematic diagram；
Fig. 7 is that the multipath sub-clustering result obtained after the K value for determining first and second multilink clusters according to ancon rule is shown
It is intended to；
Radio channel multi-path sub-clustering schematic device in a kind of high-speed rail communication network that Fig. 8 provides for embodiment 2.
Specific embodiment
Embodiments of the present invention are described below in detail, the example of the embodiment is shown in the accompanying drawings, wherein from beginning
Same or similar element or element with the same or similar functions are indicated to same or similar label eventually.Below by ginseng
The embodiment for examining attached drawing description is exemplary, and for explaining only the invention, and is not construed as limiting the claims.
Those skilled in the art of the present technique are appreciated that unless expressly stated, singular " one " used herein, " one
It is a ", " described " and "the" may also comprise plural form.It is to be further understood that being arranged used in specification of the invention
Diction " comprising " refer to that there are the feature, integer, step, operation, element and/or component, but it is not excluded that in the presence of or addition
Other one or more features, integer, step, operation, element, component and/or their group.It should be understood that when we claim member
Part is " connected " or when " coupled " to another element, it can be directly connected or coupled to other elements, or there may also be
Intermediary element.In addition, " connection " used herein or " coupling " may include being wirelessly connected or coupling.Wording used herein
"and/or" includes one or more associated any cells for listing item and all combinations.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein (including technology art
Language and scientific term) there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.Should also
Understand, those terms such as defined in the general dictionary, which should be understood that, to be had and the meaning in the context of the prior art
The consistent meaning of justice, and unless defined as here, it will not be explained in an idealized or overly formal meaning.
In order to facilitate understanding of embodiments of the present invention, it is done by taking several specific embodiments as an example below in conjunction with attached drawing further
Explanation, and each embodiment does not constitute the restriction to the embodiment of the present invention.
Embodiment 1
Fig. 1 is radio channel multi-path cluster-dividing method flow chart in a kind of high-speed rail communication network of the invention, referring to Fig.1, should
Method includes the following steps:
S1 determines the steady interval of the channel impulse response measured in high-speed rail communication network, extract it is described it is steady between
Every interior power delay profile.
The noise gate of the power delay profile is arranged in S2, extracts effective multipath component of the noise gate or more,
Using the tap position of effective multipath component as original sample, using the power of the multipath component as the power of original sample
Weight.
The neighborhood distance threshold Eps of neighborhood sample number threshold value MinPts and sample is arranged in S3, by the original sample and
The original sample weight is input to noisy density clustering (the Density-Based Spatial of tool
Clustering of Applications with Noise, DBSCAN) algorithm, obtain multiple multilink clusters, and filter out by
DBSCAN algorithm is determined as the sample of noise.
S4 by multiple multilink clusters sample and sample weights be separately input into K mean value (K-means) algorithm,
It determines the multipath number of clusters amount in each multilink cluster, obtains multipath sub-clustering result.
For the channel impulse response measured in high-speed rail communication network, the power-delay point in steady interval is extracted
Cloth.High-speed rail communication network includes that base station is overlapping covered with non-overlap overlay area.Carry out channel in high-speed rail communication network
Measurement, obtained channel impulse response the case where there are single-link and multilinks.Preferably, it determines and is measured in high-speed rail communication network
The steady interval of obtained channel impulse response, comprising: pass through local steady section (LRS, local region of
Stationarity) or the methods of RUN Test is determined.
Preferably, the channel impulse response progress time in the steady interval is averaged, obtains power delay profile.
The noise gate of power delay profile is set, effective multipath component of thresholding or more is extracted, by effective multipath component
Tap position as original sample, using the power of multipath component as the weight of original sample.Preferably, according to peak atenuation
The noise gate of the power delay profile is arranged in method or bottom basic taper method of making an uproar.
Peak atenuation method is to set noise gate to the peak value of power delay profile to subtract a threshold value, usually takes 20dB
Or 25dB.Bottom basic taper method of making an uproar is to set noise gate to add a threshold value in the average value at the bottom of making an uproar of power delay profile,
Usually take 5dB or 10dB.
The neighborhood distance threshold Eps of neighborhood sample number threshold value MinPts and sample is set, original sample and its weight is defeated
Enter to DBSCAN algorithm, obtains multiple multilink clusters, and filter out the sample for being determined as noise by DBSCAN algorithm.Preferably, adjacent
Domain sample number threshold value MinPts is more than or equal to 3 multipath taps, and the neighborhood distance threshold Eps of the sample is greater than described
MinPts value.Because including at least 3 multipaths in multipath cluster in actual channel measurement.Multilink cluster includes K multipath
Cluster, its spacing are greater than Eps.
Preferably, by multiple multilink clusters sample and sample weights be separately input into K-means algorithm, really
Multipath number of clusters amount in fixed each multilink cluster, the value including determining K according to ancon rule.
By in multiple multilink clusters sample and its weight be separately input into K-means algorithm, determine each multilink cluster
Interior multipath number of clusters amount, obtains final multipath sub-clustering result.Determine that the multipath number of clusters amount in each multilink cluster is according to ancon
The assessment result of rule chooses the optimal value of multipath number of clusters amount in multilink cluster.The principle of ancon rule (Elbow method) is
It is continuous to change K value, K-means algorithm is executed, then according to the change curve of cost function and K value, selects " at elbow point "
It is worth the value as K.Cost function usually chooses error sum of squares (sum of the squared errors, SSE), according to
Shown in following formula (1):
Wherein, CkFor k-th of cluster；X is the sample in cluster；xkFor the mass center of cluster.
The present embodiment chooses MinPts=3, Eps=8.Fig. 2 is that DBSCN algorithm extracts multilink cluster and determines noise data
Result schematic diagram, referring to Fig. 2, whole sample is divided into two multilink clusters and two noise samples.Noise sample is filtered
After removing, sub-clustering is directly carried out to whole sample using K-means algorithm in the prior art and obtains mean square error and the change of multipath number of clusters
It is as shown in Figure 3 to change schematic diagram.Obviously, the value " at elbow point " is 3.K=3 is set, it is as shown in Figure 4 to obtain multipath sub-clustering result.From
It can be seen that, there is large error using the sub-clustering result of conventional method, the multipath cluster for being originally used for two clusters is identified as by it in Fig. 4
Cluster.According to the application method, the SSE mean square error and multipath cluster that sub-clustering obtains are carried out to the first and second multilink clusters respectively
The result of variations of number K is as illustrated in Figures 5 and 6.Value for first multilink cluster, " at elbow point " is 4, for second multilink
The value of cluster, " at elbow point " is 2.Setting K=4 and K=2 respectively obtains the multipath sub-clustering of the first and second multilink clusters as a result, so
After merge as shown in Figure 7.From the figure, it can be seen that the method for the present invention would be more accurately sample be divided into six clusters, this point
The case where power of cluster and multipath in cluster is exponentially decayed with time delay in cluster result and Saleh-Valenzuela channel model one
It causes.
Those skilled in the art will be understood that it is above-mentioned it is lifted according to user information determine regulative strategy only preferably say
The technical solution of the bright embodiment of the present invention, rather than to the restriction that the embodiment of the present invention is made.It is any to be determined according to user property
The method of regulative strategy, is all contained in the range of the embodiment of the present invention.
Embodiment 2
Radio channel multi-path sub-clustering schematic device in a kind of high-speed rail communication network that Fig. 8 provides for the present embodiment 2, reference
Fig. 8, the device include:
Extraction module is extracted for determining the steady interval of the channel impulse response measured in high-speed rail communication network
Power delay profile in the steady interval；
First limitation module extracts the noise gate or more for the noise gate of the power delay profile to be arranged
Effective multipath component, using the tap position of effective multipath component as original sample, by the power of the multipath component
Weight as original sample；
Second limitation module, for the neighborhood distance threshold Eps of neighborhood sample number threshold value MinPts and sample to be arranged, by institute
The original sample and the original sample weight stated are input to DBSCAN algorithm, obtain multiple multilink clusters, and filter out noise
Sample；
Sub-clustering module, for by multiple multilink clusters sample and sample weights be separately input into K-means
Algorithm determines the multipath number of clusters amount in each multilink cluster, obtains multipath sub-clustering result.
Preferably, extraction module, for being determined in high-speed rail communication network by local steady section or RUN Test method
Measure the steady interval of obtained channel impulse response.
Preferably, extraction module obtains function for the channel impulse response progress time in the steady interval to be averaged
Rate delay distribution.
Preferably, the first limitation module, for the power-delay point to be arranged according to peak atenuation method or bottom basic taper method of making an uproar
The noise gate of cloth.
Preferably, the second limitation module is that more than or equal to 3 multipaths are taken out for neighborhood sample number threshold value MinPts to be arranged
Head, the neighborhood distance threshold Eps of sample are greater than the value of the MinPts.
Preferably, sub-clustering module, for by multiple multilink clusters sample and sample weights be separately input into
K-means algorithm determines the value of K according to ancon rule, determines the multipath number of clusters amount in each multilink cluster.
The detailed process of radio channel multi-path sub-clustering is carried out in high-speed rail communication network with before with the device of the embodiment of the present invention
It is similar to state embodiment of the method, details are not described herein again.
Those of ordinary skill in the art will appreciate that: attached drawing is the schematic diagram of one embodiment, module in attached drawing or
Process is not necessarily implemented necessary to the present invention.
As seen through the above description of the embodiments, those skilled in the art can be understood that the present invention can
It realizes by means of software and necessary general hardware platform.Based on this understanding, technical solution of the present invention essence
On in other words the part that contributes to existing technology can be embodied in the form of software products, the computer software product
It can store in storage medium, such as ROM/RAM, magnetic disk, CD, including some instructions are used so that a computer equipment
(can be personal computer, server or the network equipment etc.) executes the certain of each embodiment or embodiment of the invention
Method described in part.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for device or
For system embodiment, since it is substantially similar to the method embodiment, so describing fairly simple, related place is referring to method
The part of embodiment illustrates.Apparatus and system embodiment described above is only schematical, wherein the conduct
The unit of separate part description may or may not be physically separated, component shown as a unit can be or
Person may not be physical unit, it can and it is in one place, or may be distributed over multiple network units.It can root
According to actual need that some or all of the modules therein is selected to achieve the purpose of the solution of this embodiment.Ordinary skill
Personnel can understand and implement without creative efforts.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of protection of the claims
Subject to.
Claims (7)
1. radio channel multi-path cluster-dividing method in a kind of high-speed rail communication network characterized by comprising
It determines the steady interval of the channel impulse response measured in high-speed rail communication network, extracts in the steady interval
Power delay profile；
The noise gate of the power delay profile is set, effective multipath component of the noise gate or more is extracted, it will be described
The tap position of effective multipath component is as original sample, using the power of the multipath component as the weight of original sample；
The neighborhood distance threshold of neighborhood sample number threshold value and sample is set, the original sample and the original sample are weighed
It is input to DBSCAN algorithm again, obtains multilink cluster, and filter out the sample for being determined as noise by DBSCAN algorithm；
By in the different multilink clusters sample and corresponding weight be separately input into K mean algorithm, determine each described
Multipath number of clusters amount in multilink cluster obtains multipath sub-clustering result.
2. the method according to claim 1, wherein the letter measured in the determination high-speed rail communication network
The steady interval of channel shock response, comprising: determined by local steady interval method.
3. the method according to claim 1, wherein power-delay of the extraction in the steady interval
Distribution, comprising: the channel impulse response progress time in the steady interval is averaged, obtains power delay profile.
4. the method according to claim 1, wherein the Noise gate of the setting power delay profile
Limit, comprising: the noise gate of the power delay profile is set according to peak atenuation method or bottom basic taper method of making an uproar.
5. the method according to claim 1, wherein the neighborhood sample number threshold value is more than or equal to 3
The neighborhood distance threshold of multipath tap, the sample is greater than the neighborhood sample number threshold value.
6. the method according to claim 1, wherein the sample by multiple multilink clusters and
Sample weights are separately input into K mean algorithm, determine the multipath number of clusters amount in each multilink cluster, including according to ancon method
Then determine the value of K.
7. radio channel multi-path sub-clustering device in a kind of high-speed rail communication network characterized by comprising
Extraction module is extracted for determining the steady interval of the channel impulse response measured in high-speed rail communication network in institute
State the power delay profile in steady interval；
First limitation module extracts having for the noise gate or more for the noise gate of the power delay profile to be arranged
Imitate multipath component, using the tap position of effective multipath component as original sample, using the power of the multipath component as
The weight of original sample；
Second limitation module, for the neighborhood distance threshold of neighborhood sample number threshold value and sample to be arranged, by the original sample
And the original sample weight is input to DBSCAN algorithm, obtains multiple multilink clusters, and filter out noise sample；
Sub-clustering module, for by the sample in multiple multilink clusters and being separately input into K mean algorithm to sample weights,
It determines the multipath number of clusters amount in each multilink cluster, obtains multipath sub-clustering result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910015950.4A CN109450574B (en) | 2019-01-08 | 2019-01-08 | Wireless channel multipath clustering method and device in high-speed rail communication network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910015950.4A CN109450574B (en) | 2019-01-08 | 2019-01-08 | Wireless channel multipath clustering method and device in high-speed rail communication network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109450574A true CN109450574A (en) | 2019-03-08 |
| CN109450574B CN109450574B (en) | 2020-09-15 |
Family
ID=65540025
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910015950.4A Expired - Fee Related CN109450574B (en) | 2019-01-08 | 2019-01-08 | Wireless channel multipath clustering method and device in high-speed rail communication network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109450574B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110071882A (en) * | 2019-05-08 | 2019-07-30 | 武汉轻工大学 | Cluster information acquisition method, device, equipment and the storage medium of signal shock response |
| CN110988856A (en) * | 2019-12-19 | 2020-04-10 | 电子科技大学 | Target detection trace agglomeration algorithm based on density clustering |
| CN114268523A (en) * | 2021-12-21 | 2022-04-01 | 哲库科技(北京)有限公司 | Method and device for determining time domain correlation, signal receiving end and storage medium |
| CN114422054A (en) * | 2022-03-30 | 2022-04-29 | 山东交通学院 | Wireless channel quasi-stationary interval calculation device and method based on angle information |
| CN114448531A (en) * | 2021-12-06 | 2022-05-06 | 西安电子科技大学 | Channel characteristic analysis method, system, medium, equipment and processing terminal |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103401621A (en) * | 2013-07-30 | 2013-11-20 | 北京交通大学 | Partitioned mixed channel modeling method based on cluster delay line in high-speed railway viaduct scene |
| CN105610528A (en) * | 2015-12-17 | 2016-05-25 | 北京交通大学 | Clustering and tracking method for multipath component of time-varying channel |
| CN106126807A (en) * | 2016-06-22 | 2016-11-16 | 北京邮电大学 | A kind of Wireless Channel Modeling method based on big data mining |
| US10122829B2 (en) * | 2008-11-12 | 2018-11-06 | Teloip Inc. | System and method for providing a control plane for quality of service |
-
2019
- 2019-01-08 CN CN201910015950.4A patent/CN109450574B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10122829B2 (en) * | 2008-11-12 | 2018-11-06 | Teloip Inc. | System and method for providing a control plane for quality of service |
| CN103401621A (en) * | 2013-07-30 | 2013-11-20 | 北京交通大学 | Partitioned mixed channel modeling method based on cluster delay line in high-speed railway viaduct scene |
| CN105610528A (en) * | 2015-12-17 | 2016-05-25 | 北京交通大学 | Clustering and tracking method for multipath component of time-varying channel |
| CN106126807A (en) * | 2016-06-22 | 2016-11-16 | 北京邮电大学 | A kind of Wireless Channel Modeling method based on big data mining |
Non-Patent Citations (4)
| Title |
|---|
| JIAN WANG等: "Unsupervised Clustering for Millimeter-Wave Channel", 《2017 IEEE 86TH VEHICULAR TECHNOLOGY CONFERENCE (VTC-FALL)》 * |
| RUISI HE,: "A Kernel-Power-Density-Based Algorithm for Channel Multipath Components Clustering", 《IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS》 * |
| SUCHETA BHAUMIK等: "Combined K-Means and Amplitude Clustering of Impulse Response for 60 GHz Vehicular Channels", 《2018 15TH INTERNATIONAL SYMPOSIUM ON WIRELESS COMMUNICATION SYSTEMS (ISWCS)》 * |
| 杨晋生等: "WKPowerMeans 多径簇识别算法", 《天津大学学报(自然科学与工程技术版)》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110071882A (en) * | 2019-05-08 | 2019-07-30 | 武汉轻工大学 | Cluster information acquisition method, device, equipment and the storage medium of signal shock response |
| CN110988856A (en) * | 2019-12-19 | 2020-04-10 | 电子科技大学 | Target detection trace agglomeration algorithm based on density clustering |
| CN110988856B (en) * | 2019-12-19 | 2021-08-03 | 电子科技大学 | Target detection trace agglomeration algorithm based on density clustering |
| CN114448531A (en) * | 2021-12-06 | 2022-05-06 | 西安电子科技大学 | Channel characteristic analysis method, system, medium, equipment and processing terminal |
| CN114268523A (en) * | 2021-12-21 | 2022-04-01 | 哲库科技(北京)有限公司 | Method and device for determining time domain correlation, signal receiving end and storage medium |
| CN114268523B (en) * | 2021-12-21 | 2024-01-12 | 哲库科技(北京)有限公司 | Method, device, signal receiving end and storage medium for determining time domain correlation |
| CN114422054A (en) * | 2022-03-30 | 2022-04-29 | 山东交通学院 | Wireless channel quasi-stationary interval calculation device and method based on angle information |
| CN114422054B (en) * | 2022-03-30 | 2022-06-17 | 山东交通学院 | Wireless channel quasi-stationary interval calculation device and method based on angle information |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109450574B (en) | 2020-09-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109450574A (en) | Radio channel multi-path cluster-dividing method and device in high-speed rail communication network | |
| CN107769828B (en) | CSI-MIMO indoor positioning method and system based on characteristic value extraction | |
| US10374902B2 (en) | Method for clustering wireless channel MPCs based on a KPD doctrine | |
| CN108924736B (en) | PCA-Kalman-based passive indoor personnel state detection method | |
| CN108038419B (en) | Wi-Fi-based indoor personnel passive detection method | |
| CN112153736A (en) | Personnel action identification and position estimation method based on channel state information | |
| CN110072186B (en) | Weighted naive Bayes indoor positioning method based on independent attributes | |
| CN106412839A (en) | Indoor positioning and tracking method based on secondary partition and gradient fingerprint match | |
| CN112040397B (en) | CSI indoor fingerprint positioning method based on adaptive Kalman filtering | |
| CN107451605A (en) | A kind of simple target recognition methods based on channel condition information and SVMs | |
| CN112333653A (en) | Identity intelligent identification method and system based on WiFi channel state information | |
| CN108122310A (en) | A kind of people flow rate statistical method based on WiFi channel state informations and dynamic time warping | |
| CN109884591A (en) | A kind of multi-rotor unmanned aerial vehicle acoustical signal Enhancement Method based on microphone array | |
| CN108199987A (en) | A kind of Downlink channel estimation method in the extensive mimo systems of FDD | |
| CN108810910A (en) | Intrusion detection method for constructing visible network based on channel state information | |
| CN113438731B (en) | Positioning method and system based on signal quality control and characteristic fingerprint enhancement | |
| CN110730473A (en) | Signal feature extraction method for WiFi activity recognition | |
| Bahadori et al. | ReWiS: Reliable Wi-Fi sensing through few-shot multi-antenna multi-receiver CSI learning | |
| CN106548136A (en) | A kind of wireless channel scene classification method | |
| CN103826241A (en) | Method used for judging whether client side is located inside room or outside room and applied to wireless network | |
| CN110290466B (en) | Floor distinguishing method, device, equipment and computer storage medium | |
| CN111817803A (en) | Frequency spectrum sensing method and system based on correlation coefficient and K-means clustering algorithm and computer readable storage medium | |
| CN113453180B (en) | Intelligent detection method and system for human body tumble and information data processing terminal | |
| CN114639169A (en) | Human body action recognition system based on attention mechanism feature fusion and position independence | |
| CN114362851A (en) | Wireless channel data denoising method based on machine learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200915 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |