CN109309640A - Fanaticism format identification method based on machine learning - Google Patents
Fanaticism format identification method based on machine learning Download PDFInfo
- Publication number
- CN109309640A CN109309640A CN201811046509.4A CN201811046509A CN109309640A CN 109309640 A CN109309640 A CN 109309640A CN 201811046509 A CN201811046509 A CN 201811046509A CN 109309640 A CN109309640 A CN 109309640A
- Authority
- CN
- China
- Prior art keywords
- amplitude
- signal
- data
- circle
- fanaticism
- 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
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/0012—Modulated-carrier systems arrangements for identifying the type of modulation
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
- H04B10/60—Receivers
- H04B10/61—Coherent receivers
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Electromagnetism (AREA)
- Optical Communication System (AREA)
Abstract
The fanaticism format identification method based on machine learning that the invention discloses a kind of, the following steps are included: (1) obtains signal from coherent optical heterodyne communicatio rear end, signal is after dispersion compensation and clock recovery algorithm compensation, the amplitude circle for obtaining signal is used as identification target, and the circle number of amplitude circle is identification feature parameter；(2) amplitude diagonal line is converted by the amplitude circle mapping that step (1) obtains, realized non-linear to linear mapping；(3) the cornerwise cluster centre of amplitude is found out using clustering algorithm；(4) difference for calculating decision diagram logarithm finds out the circle number of amplitude circle, identification signal modulation format.The present invention is in the treatment process to coherent light communication data, using the MFI method based on amplitude characteristic, in the case where no any other priori knowledge, switch to linear mapping by non-linear, without the number for iteratively quickly identifying the cornerwise cluster centre of amplitude in linear space, therefore, it is determined that format modulation signal.This method has high robust to noise, adapts to the modulation format signal of M-QAM popular in current communication system, provides technical foundation for next generation's cognition optical-fiber network.
Description
Technical field
The present invention relates to intelligent coherent fiber communication methods, and in particular to a kind of physical layer in elastic optical network
Intelligent fanaticism format identification method.
Background technique
It, can be using according to business in order to increase the availability of frequency spectrum of wavelength-division multiplex (WDM) system with fixed band gap
The elastic optical network of demand assignment difference band gap.In order to support the elastic optical network system of this distribution according to need resource, control is flat
Face needs some information of physical layer, such as transmit signal modulation format, wavelength available and optical line by.In addition, modulation format
Identify that (MFI) module is also most important for a coherent light receiving system, some of important Digital Signal Processing (DSP)
Algorithm, such as adaptive equalization, carrier phase recovery and signal decision algorithm require to obtain signal modulation information in advance.Cause
This, blind MFI algorithm is essential for following cognition optical-fiber network.
Intelligent modulation format identification (MFI) method is roughly divided into two classes, decision-theoretic approach and feature extracting method.It is based on
The MFI method of decision theory is a kind of more hypothesis verification methods, and such methods belong to bayesian theory, many priori is needed to know
Know.The method of MFI based on feature extraction is modulated format identification by feature extraction, wherein relatively conventional is to utilize width
Degree and identification feature of the constellation feature as MFI method.
In the prior art, the different location locating in coherent optical heterodyne communicatio according to MFI module, can will be based on feature extraction
MFI method be divided into two classes.The first kind is that MFI module is placed on before carrier phase recovery module, extracts amplitude characteristic as knowledge
It does not refer to.Amplitude recognition feature includes that papr (PAPR), amplitude histogram and the Stokes of signal amplitude are empty
Between in amplitude distribution cumulative distribution function etc..Second class is that MFI module is placed on after carrier phase recovery module, is utilized
To planisphere or eye figure directly distinguished by convolutional neural networks.To realize Classification and Identification, need to the classification in MFI module
Device carries out pre-training, for example, by using artificial neural network (ANN), deep neural network (DNN) and convolutional neural networks network
(CNN) algorithm, such methods realize that MFI needs the training of large sample to carry out Optimized model parameter.
Therefore, how to realize simple, strong noise robustness fanaticism format identification, have for the art important
Meaning.
Summary of the invention
Goal of the invention of the invention is to provide a kind of fanaticism format identification method based on machine learning, is not needing it
In the case where its priori knowledge, realization accurately identifies the modulation format of now widely used multi-signal, guarantees identification essence
Degree and to the high robust of noise.
To achieve the above object of the invention, the technical solution adopted by the present invention is that: a kind of fanaticism lattice based on machine learning
Formula recognition methods, for before carrier phase recovery, comprising the following steps:
(1) signal is obtained from coherent optical heterodyne communicatio rear end, signal obtains after dispersion compensation and clock recovery algorithm compensation
The amplitude circle for obtaining signal is used as identification target, and the circle number of amplitude circle is identification feature parameter；
(2) amplitude diagonal line is converted by the amplitude circle mapping that step (1) obtains, realized non-linear to linear mapping；
(3) the cornerwise cluster centre of amplitude is found out using clustering algorithm；
(4) difference for calculating decision diagram logarithm finds out the circle number of amplitude circle, identification signal modulation format.
In above-mentioned technical proposal, in step (1), the compensated data of clock recovery algorithm are clock recovery data, docking
The clock recovery data of receipts are pre-processed, it is normalized in the average energy based on clock recovery data, are obtained
Data after normalizationp i :
In formula,x i It is clock recovery data,kIt is the total number of data point,iIt is data point.
In step (2), converting the cornerwise method of amplitude for amplitude circle mapping is, by the QAM in cartesian coordinate system
Signal is transformed into polar coordinates, and in polar coordinates, for the variation range of every amplitude level all by noise dominant, noise is bigger, vibration
Width level is wider, extracts the vertical axis ρ under polar coordinates as identification feature, as abscissa and is indulged with the amplitude information of complex signal
Coordinate draws amplitude diagonal line.
In step (3), the clustering algorithm is used based on density-distance clustering algorithm.
It is described based on density-distance clustering algorithm specifically, two elements of algorithm are densityρiAnd distanceδi, densityρi
Using gaussian kernel function, define:
WhereinI s ={ 1,2 ..., N }It is the tally set of data point,d ij It is dataiWith datajDistance,dc=ω* d F(N(N-1)/50) It is off value distance, ω is the weight parameter of dc, whereinF()It is round-off function,NIt is data point number；
δiIt is dataiThe minimum value of distance between the higher other data of density；
The cluster centre of cluster is these with relatively large local densityρiWith it is bigδiData point, using decision diagram, i.e.,ρ iIt is rightδiCurve can be with fast search cluster cluster centre；Pass through draftingγCurve is observed that the quantity of cluster centre, whereinγIt is defined as,。
In above-mentioned technical proposal, for the data with the maximum local density,δiIt is the maximum of all data distances
Value.
Preferably, weight parameter ω is 0.2.
In step (4), obtained γ value is arranged first in descending order, however uses logarithm γ value again,
According to formula, the X-coordinate of the smallest Y value is obtained,
The circle number n as identified.
Due to the above technical solutions, the present invention has the following advantages over the prior art:
The present invention is in the treatment process to coherent light communication data, using the MFI method based on amplitude characteristic, not any
In the case where other priori knowledges, switch to linear mapping by non-linear, without iteratively quick identification width in linear space
The number of cornerwise cluster centre is spent, therefore, it is determined that format modulation signal.This method has high robust to noise, adapts to
The modulation format signal of M-QAM popular in communication system at present provides technical foundation for next generation's cognition optical-fiber network, can be with
Save the hardware cost of future communication systems.
By the validity of experimental verification this method, for the format modulation signal recognition accuracy for reaching 95%, QPSK signal
The optical signal to noise ratio (OSNR) of 13.2 dB of subsistence level, the OSNR of 16 QAM signal subsistence level, 13.3 dB, 64 QAM signals are most
The low OSNR for needing 19.7 dB.Method of the invention simultaneously is to nonlinear fiber robustness with higher, in single mode optical fiber
(SMF) in transmission experiment, have verified that accuracy of identification is unrelated with the incident power of 16/64 QAM signal.
Detailed description of the invention
Fig. 1 is 4/16/64-QAM signal amplitude figure, and optical signal to noise ratio (OSNR) is respectively 17 dB, 21 dB and 26 dB；
The explanatory diagram of the blind MFI algorithm of Fig. 2；
Fig. 3 is the identification situation in embodiment, average circle number (b) recognition accuracy for the identification amplitude circle that (a) is calculated and OSNR
(dB) relationship；
Fig. 4 is the experimental system block diagram that blind MFI method is realized in embodiment；
Fig. 5 is in embodiment, (a) identify amplitude circle average circle number and (b) accuracy of identification of 4/16/64-QAM signal and
The relationship of OSNR (dB), the amplitude image for the 64-QAM signal that insertion figure is 20.8-dB OSNR and the difference of logarithm γ value are bent
Line；
Fig. 6 be the amplitude circle measured in embodiment average identification circle number and accuracy of identification and SMF in emit signal power
(dBm) relational graph.
Specific embodiment
The invention will be further described with reference to the accompanying drawings and embodiments:
Embodiment one: compared with traditional K-means clustering algorithm that n observation is divided into known k cluster, quick clustering is calculated
Method can quickly track cluster centre in the case where no priori knowledge, so that decision diagram is drawn out, final computing cluster
Quantity.Therefore, present invention application quick clustering algorithm handles the amplitude data of signal, by identify the quantity of amplitude leyel come
Judge the modulation format of signal.Fig. 1 shows the map of magnitudes of the 4/16/64-QAM with various OSNR.It is obvious that different tune
The amplitude circle of signal processed has different circle numbers, that is, different amplitude leyel numbers.With the increasing of order of modulation and system noise
Add, the map of magnitudes of signal becomes to obscure very much, it is difficult to distinguish.Therefore, similar and fuzzy map of magnitudes how is distinguished as ability
The difficult point in domain.
A kind of fanaticism format identification method based on machine learning of the invention, comprising the following steps:
(1) signal is obtained from coherent optical heterodyne communicatio rear end, signal obtains after dispersion compensation and clock recovery algorithm compensation
The amplitude circle for obtaining signal is used as identification target, and the circle number of amplitude circle is identification feature parameter；
Purpose is the circle number (that is, amplitude leyel number) by the amplitude circle of identification signal come the modulation format of decision signal；Wherein,
The compensated data of clock recovery algorithm are clock recovery data, are pre-processed to received clock recovery data, when being based on
It is normalized in the average energy that clock restores data, the data after being normalizedp i :
In formula,x i It is clock recovery data,kIt is the total number of data,iIt is data point.
(2) amplitude diagonal line is converted by the amplitude circle mapping that step (1) obtains, realization is non-linear to reflect to linear
It penetrates；
Due to the amplitude circle of signal after clock recovery be it is nonlinear, be difficult to correctly find its cluster centre.The present invention
It is converted.In Fig. 2 (a), the 64-QAM signal in cartesian coordinate system is transformed into polar coordinates.In polar coordinates
In, system noise determines the amplitude variation of each amplitude, and noise is bigger, and amplitude longitudinal direction amplitude is wider.The present invention proposes to extract
Vertical axis amplitude ρ under polar coordinates draws amplitude diagonal line (that is, with the amplitude information of complex signal as cross as identification feature
The curve that coordinate and ordinate are drawn), as shown in Fig. 2 (b).
The problem of obtaining the cluster centre of amplitude circle as a result, it is cornerwise to be converted into the amplitude how obtained in Fig. 2 (b)
Cluster centre.Therefore, non-linear clustering problem can be converted to linear clustering problem by the mapping proposed by the invention.
(3) the cornerwise cluster centre of amplitude is found out using clustering algorithm；
That is densityρiAnd distanceδi.Make density p i used here as gaussian kernel function, define:
I s ={ 1,2 ..., N }It is the tally set of data point,d ij It is dataiWith datajDistance.dc=ω* d F(N(N-1)/50) It is off value distance, whereinF()It is round-off function,NIt is data point number,ωIt is weight parameter.δiIt is several
According toiThe minimum value of distance between the higher other data of density.But for the data with the maximum local density,δi
It is the maximum value of all data distances.Cluster centers are exactly these with relatively large local densityρiWith it is bigδiData
Point.Therefore, it can use decision diagram (i.e.ρiIt is rightδiCurve graph), the cluster centre of fast search cluster.As shown in Figure 2 (c), side
What block indicated is cluster centre, and what other stains indicated is that other cluster points or cluster are dizzy.Generally, pass through draftingγCurve can be seen
The quantity of cluster centre is observed, as shown in Figure 2 (d) shows, whereinγIt is defined as,
In Fig. 2 (d), depict 20 it is biggishγValue, and it is arranged in descending order.As can be seen that the 9th point in Fig. 2 (d)
And there is variation between the 10th point.Since cluster centre is with higherγValue, and other clusters point is with lowerγValue.Cause
This, occursγIt is worth the place of variation, that is, cluster centre excessively arrives the place of cluster point.Therefore, pass through searchingγValue mutation
Position may thereby determine that the number of cluster centre.And it can be determined that the modulation format of signal using the number of cluster centre.
However, in Fig. 2 (d)γValue mutation is not it is obvious that being difficult to differentiate between.It is proposed pair in the present inventionγValue takes logarithm, from
And make log (γ)Value mutation increases, as shown in Fig. 2 (e).In figure, the 9th log (γ)Value and the 10th log (γ)Value is obvious
It is different, so as to be clearly observed cluster centre (have higher log (γ)Value), such as the relatively high hurdle institute in Fig. 2 (e)
Show.
In order to the quantity of cluster centre in automatic computing cluster, the present invention utilizes following formula,
By calculate adjacent log (γ)The difference of value, as shown in Fig. 2 (f).The position of difference maximum point is exactly the number of cluster centre
Amount.
(4) difference for calculating decision diagram logarithm finds out the circle number of amplitude circle, identification signal modulation format.
According to the numerical value of the X-coordinate point under the minimum value of the Y of upper step acquisition, the width of the amplitude circle of QAM signal can be determined
Level number n value is spent, so as to judge the modulation format of signal.
To further understand the present invention, the feasibility of the invention using Simulation, and further have evaluated the hair
The performance of the MFI method of bright proposition.In analogue system, 12.5Gbaud 4/8/16/32/64/256-QAM signal passes through additivity
Gaussian white noise channel.The value of OSNR decides the distribution of amplitudes of these signals, therefore has significant impact to MFI performance.
Fig. 3 depicts the function of the circle number (that is, amplitude leyel number) and recognition accuracy of amplitude circle as OSNR value.Scheming
The amplitude distribution figure of the 256-QAM signal of 30-dB OSNR and the circle number n of identification are inserted in 3(a).To each of each OSNR
A modulated signal has used 100 test datas respectively.Since the map of magnitudes of poor signal is relatively fuzzyyer, it is difficult to it differentiates, because
The circle number of the signal amplitude circle identified in this each test may not be identical.In emulation and experiment, the circle number of amplitude circle is equal
For the average of the circle number of the amplitude circle of all tests.The MFI scheme of the invention can be used to identify within the scope of different OSNR
QAM signal.In addition, QPSK and 8-QAM signal of the OSNR less than 10 dB has the amplitude distribution of diverging, therefore they will be wrong
It accidentally identifies, and obtains biggish amplitude leyel number, such as Fig. 3 (a).For the lower OSNR signal of about 12-dB, it is difficult correct
Identify 32/64/ 56-QAM modulation format.Such as Fig. 3 (b), reach 95% accuracy of identification, QPSK signal subsistence level 7-dB
OSNR, 8-QAM signal subsistence level 10-dB OSNR, 16-QAM signal subsistence level 16-dB OSNR, 32-QAM signal are minimum
Need 21-dB OSNR, 64-QAM signal subsistence level 22-dB OSNR, 256-QAM signal subsistence level 33-dB OSNR.
In addition, testing the performance of the MFI method of invention proposition using Fig. 4 experiment.By adjusting in Fig. 4 experimental provision
First adjustable attenuator (VOA), take away single mode optical fiber (SMF), the signal with different OSNR values can be measured, and calculate
MFI recognition accuracy.
Fig. 5 depicts the relationship of the circle number and accuracy of identification and OSNR value for the amplitude circle that experiment measures.To reach 95%
Accuracy of identification, QPSK signal subsistence level 13.2-dB OSNR, 16-QAM signal subsistence level 13.3-dB OSNR, 64-QAM letter
Number subsistence level 19.7-dB OSNR, wherein each modulation format signal uses 50 test datas.As shown in Figure 3 and Figure 5, should
Experimental measurements are very similar with simulation result, existing OSNR deviation be characteristic due to optical-fibre channel and photoelectric subassembly and
Caused by quantity difference using modulated signal.
To assess proposed MFI to the robustness of nonlinear fiber.The front position of preamplifier in an experiment, insertion
SMF hop (including EDFA, VOA and SMF).Because 16-QAM and 64-QAM signal is more susceptible to the influence of nonlinear fiber,
Therefore experiment measures the accuracy of identification of both signals and the relationship of injection SMF signal power.Fig. 6 depict 16-QAM and
The accuracy of identification of 64-QAM signal and the relationship of injection SMF signal power.Insertion figure is minimum signal power and peak signal function
The map of magnitudes and planisphere of corresponding signal when rate.From the planisphere of insertion, spontaneous radiation (ASE) can clearly be observed that
The influence of noise and nonlinear fiber.In the case of low signal power, ASE noise is bigger, causes the fuzzy of planisphere.High RST
Under power situation, nonlinear fiber causes signal constellation (in digital modulation) phase rotation.Because nonlinear fiber has distorted the planisphere of signal, but
What the map of magnitudes of signal is influenced without, therefore MFI scheme of the invention has very strong robustness to nonlinear fiber.Note
The signal power for entering SMF influences less, as shown in the triangle marked curve in Fig. 6 the accuracy of identification of measurement.
Claims (8)
1. a kind of fanaticism format identification method based on machine learning, for before carrier phase recovery, which is characterized in that packet
Include following steps:
(1) signal is obtained from coherent optical heterodyne communicatio rear end, signal obtains after dispersion compensation and clock recovery algorithm compensation
The amplitude circle for obtaining signal is used as identification target, and the circle number of amplitude circle is identification feature parameter；
(2) amplitude diagonal line is converted by the amplitude circle mapping that step (1) obtains, realized non-linear to linear mapping；
(3) the cornerwise cluster centre of amplitude is found out using clustering algorithm；
(4) difference for calculating decision diagram logarithm finds out the circle number of amplitude circle, identification signal modulation format.
2. the fanaticism format identification method according to claim 1 based on machine learning, it is characterised in that: step (1)
In, the compensated data of clock recovery algorithm are clock recovery data, pre-process, are based on to received clock recovery data
It is normalized in the average energy of clock recovery data, the data after being normalizedp i :
In formula,x i It is clock recovery data,kIt is the total number of data,iIt is data point.
3. the fanaticism format identification method according to claim 1 based on machine learning, it is characterised in that: step (2)
In, converting the cornerwise method of amplitude for amplitude circle mapping is that the QAM signal in cartesian coordinate system is transformed into polar coordinates
In, in polar coordinates, for the variation range of every amplitude level all by noise dominant, noise is bigger, and amplitude level is wider, extracts pole
Vertical axis ρ under coordinate is diagonal as abscissa and ordinate drafting amplitude with the amplitude information of complex signal as identification feature
Line.
4. the fanaticism format identification method according to claim 1 based on machine learning, it is characterised in that: step (3)
In, the clustering algorithm is used based on density-distance clustering algorithm.
5. the fanaticism format identification method according to claim 4 based on machine learning, it is characterised in that: described to be based on
Density-distance clustering algorithm is specifically, two elements of algorithm are densityρiAnd distanceδi, densityρiUsing gaussian kernel function,
Definition:
WhereinI s ={ 1,2 ..., N }It is the tally set of data point,d ij It is dataiWith datajDistance,dc=ω* d F(N(N-1)/50) It is off value distance, ω is the weight parameter of dc, whereinF()It is round-off function,NIt is data point number；
δiIt is dataiThe minimum value of distance between the higher other data of density；
The cluster centre of cluster is these with relatively large local densityρiWith it is bigδiData point, using decision diagram, i.e.,ρ iIt is rightδiCurve can be with fast search cluster cluster centre；Pass through draftingγCurve is observed that the quantity of cluster centre, whereinγIt is defined as,。
6. the fanaticism format identification method according to claim 5 based on machine learning, it is characterised in that: for having
For the data of the maximum local density,δiIt is the maximum value of all data distances.
7. the fanaticism format identification method according to claim 5 based on machine learning, it is characterised in that: weight parameter
ω is 0.2.
8. the fanaticism format identification method according to claim 1 based on machine learning, it is characterised in that: step (4)
In, obtained γ value is arranged in descending order first, however logarithm γ value is used again,
According to formula, the X-coordinate of the smallest Y value is obtained,
The circle number n as identified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811046509.4A CN109309640B (en) | 2018-09-08 | 2018-09-08 | Blind signal format identification method based on machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811046509.4A CN109309640B (en) | 2018-09-08 | 2018-09-08 | Blind signal format identification method based on machine learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109309640A true CN109309640A (en) | 2019-02-05 |
| CN109309640B CN109309640B (en) | 2021-11-05 |
Family
ID=65224974
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811046509.4A Active CN109309640B (en) | 2018-09-08 | 2018-09-08 | Blind signal format identification method based on machine learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109309640B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110071885A (en) * | 2019-04-17 | 2019-07-30 | 成都华日通讯技术有限公司 | A kind of deep learning method of discrimination of PSK digital signal subclass Modulation Identification |
| CN113205000A (en) * | 2021-04-07 | 2021-08-03 | 江苏师范大学 | Modulation mode identification method based on Cauchy Score polar coordinate diagram |
| CN113518049A (en) * | 2021-04-13 | 2021-10-19 | 江苏师范大学 | Modulation identification method based on fractional low-order polar coordinate and deep learning |
| CN113537061A (en) * | 2021-07-16 | 2021-10-22 | 中天通信技术有限公司 | Format identification method, device and storage medium for two-dimensional quadrature amplitude modulation signal |
| CN114500200A (en) * | 2022-02-22 | 2022-05-13 | 苏州大学 | Probability shaping QAM dynamic equalization and digital signal processing method |
| CN114531328A (en) * | 2022-04-24 | 2022-05-24 | 四川轻化工大学 | Modulation format identification method based on signal envelope flatness |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9729362B1 (en) * | 2013-03-20 | 2017-08-08 | Georgia Tech Research Corporation | Systems and methods for autonomous signal modulation format identification |
| CN107342962A (en) * | 2017-07-03 | 2017-11-10 | 北京邮电大学 | Deep learning intelligence Analysis On Constellation Map method based on convolutional neural networks |
| CN108173600A (en) * | 2017-12-26 | 2018-06-15 | 中山大学 | Stokes spatial coherence optical modulation formats recognition methods based on adaptive non-iterative cluster |
-
2018
- 2018-09-08 CN CN201811046509.4A patent/CN109309640B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9729362B1 (en) * | 2013-03-20 | 2017-08-08 | Georgia Tech Research Corporation | Systems and methods for autonomous signal modulation format identification |
| CN107342962A (en) * | 2017-07-03 | 2017-11-10 | 北京邮电大学 | Deep learning intelligence Analysis On Constellation Map method based on convolutional neural networks |
| CN108173600A (en) * | 2017-12-26 | 2018-06-15 | 中山大学 | Stokes spatial coherence optical modulation formats recognition methods based on adaptive non-iterative cluster |
Non-Patent Citations (2)
| Title |
|---|
| FAISAL NADEEM KHAN,KANGPING ZHONG,WALED HUSSEIN AL-ARASHI,CHANGY: ""Modulation Format Identification in Coherent Receivers Using Deep Machine Learning"", 《IEEE PHOTONICS TECHNOLOGY LETTERS》 * |
| 蔡钟乐: ""弹性光网络中基于机器学习的调制格式识别技术及应用"", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110071885A (en) * | 2019-04-17 | 2019-07-30 | 成都华日通讯技术有限公司 | A kind of deep learning method of discrimination of PSK digital signal subclass Modulation Identification |
| CN113205000A (en) * | 2021-04-07 | 2021-08-03 | 江苏师范大学 | Modulation mode identification method based on Cauchy Score polar coordinate diagram |
| CN113518049A (en) * | 2021-04-13 | 2021-10-19 | 江苏师范大学 | Modulation identification method based on fractional low-order polar coordinate and deep learning |
| CN113518049B (en) * | 2021-04-13 | 2024-04-26 | 江苏师范大学 | Modulation identification method based on fractional low-order polar coordinates and deep learning |
| CN113537061A (en) * | 2021-07-16 | 2021-10-22 | 中天通信技术有限公司 | Format identification method, device and storage medium for two-dimensional quadrature amplitude modulation signal |
| CN113537061B (en) * | 2021-07-16 | 2024-03-26 | 中天通信技术有限公司 | Method, device and storage medium for identifying format of two-dimensional quadrature amplitude modulation signal |
| CN114500200A (en) * | 2022-02-22 | 2022-05-13 | 苏州大学 | Probability shaping QAM dynamic equalization and digital signal processing method |
| CN114500200B (en) * | 2022-02-22 | 2023-01-17 | 苏州大学 | Digital signal processing method, dynamic equalization method, apparatus, medium, and device |
| CN114531328A (en) * | 2022-04-24 | 2022-05-24 | 四川轻化工大学 | Modulation format identification method based on signal envelope flatness |
| CN114531328B (en) * | 2022-04-24 | 2022-07-01 | 四川轻化工大学 | Modulation format identification method based on signal envelope flatness |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109309640B (en) | 2021-11-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109309640A (en) | Fanaticism format identification method based on machine learning | |
| CN103944638B (en) | Optical signal modulation format recognition method and system based on nonlinear digital processing | |
| CN113141325B (en) | Training method, identification method and device for optical OFDM signal subcarrier modulation format identification model | |
| CN110233661B (en) | Long-short term memory neural network training method, channel parameter adjusting system and method | |
| CN105790849A (en) | Modulation format recognition method for coherent optical communication system | |
| CN105515650A (en) | Optical signal modulation format identification method based on spectral characteristics | |
| Yu et al. | A modified PSO assisted blind modulation format identification scheme for elastic optical networks | |
| Zhao et al. | A modulation format identification method based on information entropy analysis of received optical communication signal | |
| Zhang et al. | Blind and noise-tolerant modulation format identification | |
| CN113542172B (en) | Elastic optical network modulation format identification method and system based on improved PSO clustering | |
| Ma et al. | Modulation format identification based on constellation diagrams in adaptive optical OFDM systems | |
| Yang et al. | Modulation format identification using graph-based 2D stokes plane analysis for elastic optical network | |
| CN111935056A (en) | Signal demodulation method and related device | |
| Li et al. | Modulation format identification based on an improved RBF neural network trained with asynchronous amplitude histogram | |
| Feng et al. | Intelligent optical performance monitoring based on intensity and differential-phase features for digital coherent receivers | |
| CN114867018A (en) | Device identification authentication method, system and storage medium using carrier frequency offset | |
| Zhang et al. | A simple artificial neural network based joint modulation format identification and OSNR monitoring algorithm for elastic optical networks | |
| Cheng et al. | Improved modulation format identification based on Stokes parameters using combination of fuzzy c-means and hierarchical clustering in coherent optical communication system | |
| CN113364519B (en) | Optical performance monitoring method and device, electronic equipment and storage medium | |
| Yang et al. | Joint modulation format identification and OSNR estimation method based on trajectory information analysis | |
| CN113285762B (en) | Modulation format identification method based on relative entropy calculation | |
| Wang et al. | A novel modulation format identification based on amplitude histogram space | |
| CN113537061B (en) | Method, device and storage medium for identifying format of two-dimensional quadrature amplitude modulation signal | |
| CN111313974A (en) | Modulation format identification method for signal amplitude sequencing and ratio calculation | |
| Guo et al. | Optical Signal-to-Noise Ratio Monitoring Based On Back-Propagation Network With an Autoencoder |
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 |