CN108173600A - Stokes spatial coherence optical modulation formats recognition methods based on adaptive non-iterative cluster - Google Patents
Stokes spatial coherence optical modulation formats recognition methods based on adaptive non-iterative cluster Download PDFInfo
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- H04B10/60—Receivers
- H04B10/61—Coherent receivers
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Abstract
The present invention provides a kind of relevant optical modulation formats recognition methods based on adaptive non-iterative cluster, distribution of this method according to signal on the least square plane of stokes spaces, two simple parameters of structure establish the selection that decision diagram carries out cluster centre, simultaneously according to the situation of different modulating form and signal-to-noise ratio, the decision threshold of cluster centre on decision diagram is adaptive selected, so as to fulfill higher accuracy of identification.This method be both utilized Stokes modulating methods to coherent optical heterodyne communicatio big phase noise and the advantages of insensitive polarization interference, break through the limitation that such algorithm is relatively low to higher order modulation formats accuracy of identification, algorithm complexity is higher simultaneously, it realizes in the higher recognition correct rate compared with higher order modulation formats under low signal-to-noise ratio, without iterative calculation, there is relatively low complexity.
Description
Technical field
The present invention relates to coherent optical communication system research fields, are gathered more particularly, to one kind based on adaptive non-iterative
The Stokes spatial coherence optical modulation formats recognition methods of class.
Background technology
In order to improve spectrum efficiency and ensure the quality of communication service, the cognition optic network with adaptive-bandwidth transceiver
(CON) it is considered as one of main selection of next-generation Networks of Fiber Communications.The transmitter of CON can be according to dynamic network rings
The modulation format of signal is adaptive selected in the demand of border and user.Therefore coherent optical heterodyne communicatio is needed to be identified with modulation format
Function, so as to make the subsequent digital signal processing algorithm related with modulation format (such as adaptive equalization, carrier phase recovery
Algorithm etc.) reach its optimal performance, and final realize correctly demodulates.It is different from the modulation format identification in wireless communication system,
Modulation format identification in coherent optical communication system is faced with polarization interference, polarization mode dispersion, nonlinear fiber, coherent light and connects
The new challenge that the fiber channels such as big phase noise of receipts machine lesion ribbon is come.For above challenge, there are many towards relevant in recent years
The modulation format recognition methods of optical communication system is suggested, in these methods, the modulation format identification based on Stokes spaces
Method obtains extensive concern due to crosstalk insensitive its big phase noise between coherent optical heterodyne communicatio, polarization.
But existing Stokes spatial modulations format identification method still has higher computation complexity (if desired for iteration
Calculate etc.).And due to high order modulation signal (such as 16QAM, 32QAM, 64QAM) in Stokes spaces mapping points compared with
More, distribution is more intensive, so such algorithm high-precision relatively difficult to achieve to higher order modulation formats identifies.Therefore, how to design
The Stokes spatial modulation format identification methods of relatively low computation complexity, recognizable higher order modulation formats are provided, at present still
It is a challenge.
Invention content
The shortcomings that it is an object of the invention to overcome the prior art and deficiency provide a kind of based on adaptive non-iterative cluster
Stokes spatial coherence optical modulation formats recognition methods, this method is in addition to having to polarization interference, the big phase of coherent optical heterodyne communicatio
The fiber channels damage such as position noise has outside preferable tolerance, also with relatively low computation complexity, and can realize to height
The identification of rank modulation format can realize the identification of higher accuracy under relatively low optical signal to noise ratio.
To achieve the above object, the technical solution taken of the present invention is：
Based on the Stokes spatial coherence optical modulation formats recognition methods of adaptive non-iterative cluster, include the following steps：
(1) training stage：The sample signal of different modulating form is set, the sample signal of different modulating form is carried out
Stokes space reflections are simultaneously fitted its least square plane, rotate Stokes spacing waves, make its least square plane and S2And S3
The plane of axis composition overlaps；First is calculated to the signal on least square plane after the sample signal mapping of different modulating form to know
Other characteristic parameter as first class node of decision tree classifier, distinguishes signal and belongs to higher order modulation formats { PM-
32QAM, PM-64QAM } or low-order-modulated form { PM-QPSK, PM-8QAM, PM-16QAM }；For higher order modulation formats
The sample signal of { PM-32QAM, PM-64QAM }, according to some region of point on its least square plane in Stokes spaces
Cloth is different, calculates the second identification feature parameter, second class node as decision tree classifier；For low-order-modulated form
The sample signal of { PM-QPSK, PM-8QAM, PM-16QAM } calculates it in least square using adaptive non-iterative clustering algorithm
Cluster centre number in plane, and third identification feature parameter, the 4th identification feature parameter are calculated based on cluster centre number,
Respectively as third and fourth class node of decision tree classifier；Then for different optical signal to noise ratio situations, according to optimal shellfish
This principle of classification of leaf construction decision tree structure and decision threshold value improve the construction of decision tree classifier；
(2) cognitive phase：The orthogonal polarization signal of X, Y two-way that current coherent optical heterodyne communicatio is received carries out Stokes
Space reflection is simultaneously fitted its least square plane, then calculates signal on the least square plane of Stokes spaces after mapping the
First identification feature parameter is input in the decision tree classifier of step (1) foundation by one identification feature parameter, and principium identification is worked as
The modulation format of front signal is adjusted for low order { PM-QPSK, PM-8QAM, PM-16QAM } or high-order { PM-32QAM, PM-64QAM }
Form processed；If high-order { PM-32QAM, PM-64QAM } modulation format, to the signal meter on the least square plane of Stokes spaces
The second identification feature parameter is calculated, the second identification feature parameter is input in the decision tree classifier of step (1) foundation, determined
The specific modulation format of current demand signal；If low order { PM-QPSK, PM-8QAM, PM-16QAM } modulation format, using adaptive
Non-iterative clustering algorithm calculates its cluster centre number on least square plane, and calculates third based on cluster centre number
Third identification feature parameter, the 4th identification feature parameter are input to step by identification feature parameter, the 4th identification feature parameter
(1) in the decision tree classifier established, the specific modulation format of current demand signal is determined.
Preferably, the polarization signal that X, Y two-way are orthogonal in described (2) carries out the specific formula of Stokes space reflections such as
Under：
Wherein, ax、ayThe amplitude of X, Y two-way orthogonal polarization signals is represented respectively,For X, Y two-way orthogonal polarization signals it
Between phase difference, ex、eyX, Y two-way orthogonal polarization signals are represented respectively,E is represented respectivelyx、eyConjugation；S0、S1、S2、S3Table
Show each parameter after Stokes space reflections, wherein S0Represent the general power of optical signal；(S1, S2, S3) represent the three-dimensional seat formed
Mark represents the different polarization states of optical signal；Specific to each reference axis, S1Represent the linearly polarized light in horizontal direction, S2Represent 45
Spend the linearly polarized light on angular direction, S3Represent circularly polarized light.
Preferably, described (1) using singular value decomposition algorithm fit mapping after signal in Stokes spaces most
Small two multiply plane.
Preferably, the first identification feature expressed as parameters is as follows：
Wherein R represents the first identification feature parameter, and Ω is defined as on least square plane | S2| ＞ 1 and | S3| the area of ＞ 1
Domain, LSP represent least square plane.
Preferably, the data point that the second identification feature expressed as parameters is included for a certain region on least square plane
Quantity, wherein a certain region is expressed as by two circular arc S on the least square plane2 2+S3 2=[max (| S3|)-0.15]2、
S2 2+S3 2=[max (| S3|)-0.45]2And two straight line S3=tan (0.7) S2、S3=tan (0.87) S2The region surrounded,
max(|S3|) represent on least square plane in the coordinate of all data points | S3| maximum value.
Preferably, the third identification feature expressed as parameters changes for the data point on least square plane by adaptively non-
The cluster centre point number obtained later for clustering algorithm cluster.
Preferably, the 4th identification feature expressed as parameters changes for the data point on least square plane by adaptively non-
In all cluster centre points obtained later for clustering algorithm cluster, local density p meets condition
Cluster centre point number, wherein max (ρ) represents the density value of the data point of local density maximum in all data point.
Preferably, the detailed process that the adaptive non-iterative clustering algorithm calculates cluster centre number is as follows：
Its local density p of each data point calculation and relatively minimal distance δ two on the least square plane of Stokes spaces
A parameter；Two-dimentional decision diagram is built by the two parameters, wherein cluster centre point is located at the upper right side of decision diagram, cluster centre point
Meet the following conditions：And δ ＞ a, wherein ρ represent the local density of data point, max (ρ) represents all
The density value of the data point of local density maximum in data point, a are self-adaptive decision threshold value.
Preferably, the self-adaptive decision threshold value a is determined in the following manner：Make self-adaptive decision threshold value a since 0 by
It is cumulative plus, when increasing to some numerical value b so that the data in region in decision diagram between two straight line δ=b and δ=b-0.02
The number of point is not less than the 2.5% of total number, and numerical value b is the value of a.
Preferably, the local density p of the data point and the calculating process of relatively minimal distance δ are as follows：
Wherein, dijFor the distance between i-th of data point and j-th data point, dcFor self-defined constant, it is relatively minimal away from
From δiRefer to that i-th of data point is more than ρ to local densityi, and with a distance from the data point nearest from i-th of data point.
Compared with prior art, the advantages of the present invention are：
1st, relevant optical modulation formats recognition methods provided by the invention, the carrier phase introduced with coherent optical heterodyne communicatio are made an uproar
Mixing crosstalk between sound, frequency shift (FS) and two polarization signals is unrelated, can be placed on adaptive equalization, frequency deviation compensation, carrier wave phase
Before the relevant algorithm of the modulation formats such as bit recovery, modulation format information is provided for these algorithms, so as to ensure that signal can be into
Restore to work(, demodulate and correctly adjudicate.
2nd, relevant optical modulation formats recognition methods provided by the invention using adaptive non-iterative clustering algorithm to signal into
Row pretreatment relative to common Iterative Clustering, has less calculating step, so as to complicated with relatively low calculating
Degree.
3rd, relevant optical modulation formats recognition methods provided by the invention can be to the higher order modulation formats such as 32QAM, 64QAM reality
Now based on stokes spaces modulation format identification, be both utilized Stokes modulating methods to coherent optical heterodyne communicatio big phase
The advantages of noise and insensitive polarization interference, while the more indiscernible limitation to higher order modulation formats of such algorithm is broken through, it realizes
In the higher recognition correct rate compared with higher order modulation formats under low signal-to-noise ratio.
Description of the drawings
Fig. 1 is the flow diagram of method provided by the invention.
Distribution schematic diagrams of the Fig. 2 for each signal on ideally Stokes spaces least square plane.
Fig. 3 is the two-dimentional decision diagram that signal-to-noise ratio is signal under 33dB.
Specific embodiment
To be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention, with reference to
The drawings and specific embodiments, how the present invention is further explained implements.
Fig. 1 is the schematic diagram of modulation format recognition methods provided by the invention, as shown in Figure 1, method provided by the invention
There are two steps, respectively training stage and cognitive phase for tool, specific as follows shown：
First, the training stage
1) stokes space reflections and least square plane fitting
Acquire the sample letter of a plurality of { PM-QPSK, PM-8QAM, PM-16QAM, PM-32QAM, PM-64QAM } modulation format
Number, the sample signal of different modulating form is subjected to Stokes space reflections.Wherein, sample signal is being represented by X, Y two-way just
The polarization signal of friendship, the specific formula for carrying out Stokes space reflections are as follows：
Wherein, ax、ayThe amplitude of X, Y two-way orthogonal polarization signals is represented respectively,For X, Y two-way orthogonal polarization signals it
Between phase difference, ex、eyX, Y two-way orthogonal polarization signals are represented respectively,E is represented respectivelyx、eyConjugation.For Stokes skies
Between map after each parameter meaning, wherein S0Represent the general power of optical signal.(S1, S2, S3) form three-dimensional coordinate, represent light
The different polarization states of signal.Specific to each reference axis, S1Represent the linearly polarized light in horizontal direction, S2Represent 45 degree of angular direction
On linearly polarized light, S3Represent circularly polarized light.Here optical signal refers to the optical signal of palarization multiplexing.
Least square plane of the signal in Stokes spaces after mapping is fitted using singular value decomposition algorithm, and then
Find mapping distribution of the signal on least square plane, while rotate Stokes spacing waves, make its least square plane with
S2And S3The plane of axis composition overlaps.Distribution signals of the Fig. 2 for each signal on ideally Stokes spaces least square plane
Figure.
2) identification feature parameter is calculated
S1. the sample of { PM-QPSK, PM-8QAM, PM-16QAM, PM-32QAM, PM-64QAM } modulation format is believed
Number, the signal after mapping it on least square plane calculates the first identification feature parameter respectively.First identification feature parameter
Calculating process represents as follows：
Wherein R represents the first identification feature parameter, and Ω is defined as on least square plane | S2| ＞ 1 and | S3| the area of ＞ 1
Domain, LSP represent least square plane.
S2. for the sample signal of higher order modulation formats { PM-32QAM, PM-64QAM }, least square is put down after mapping it
Signal on face calculates the second identification feature parameter respectively, and the calculating process of the second identification feature parameter represents as follows：
The quantity of data point that second identification feature expressed as parameters is included for a certain region on least square plane, wherein institute
A certain region on least square plane is stated to be expressed as by two circular arc S2 2+S3 2=[max (| S3|)-0.15]2、S2 2+S3 2=[max
(|S3|)-0.45]2And two straight line S3=tan (0.7) S2、S3=tan (0.87) S2The region surrounded, max (| S3|) table
Show on least square plane in the coordinate of all data points | S3| maximum value.
S3. for the sample signal of low-order-modulated form { PM-QPSK, PM-8QAM, PM-16QAM }, using adaptive non-
Iterative Clustering calculates its cluster centre number on least square plane, and calculates third based on cluster centre number and know
Other characteristic parameter, the 4th identification feature parameter.Third identification feature expressed as parameters passes through for the data point on least square plane
The cluster centre point number that adaptive non-iterative clustering algorithm cluster obtains later.4th identification feature expressed as parameters is minimum two
The data point multiplied in plane is clustered by adaptive non-iterative clustering algorithm in all cluster centre points obtained later, local close
Degree ρ meets conditionCluster centre point number, wherein max (ρ) represents all data point
The density value of the data point of middle local density maximum.
In the present embodiment, the detailed process that the adaptive non-iterative clustering algorithm calculates cluster centre number is as follows：
Its local density p of each data point calculation and relatively minimal distance δ two on the least square plane of Stokes spaces
A parameter；Two-dimentional decision diagram is built by the two parameters, as shown in figure 3, wherein cluster centre point is located at the upper right side of decision diagram,
Cluster centre point meets the following conditions：And δ ＞ a, wherein ρ represent the local density of data point, max (ρ)
Represent the density value of the data point of local density maximum in all data point, a is self-adaptive decision threshold value.
Self-adaptive decision threshold value a is determined in the following manner：Self-adaptive decision threshold value a is made to be gradually increased since 0, works as increasing
Add to some numerical value b so that the number of the data point in region in decision diagram between two straight line δ=b and δ=b-0.02 is not small
In the 2.5% of total number, numerical value b is the value of a.
Wherein, the local density p of data point and the calculating process of relatively minimal distance δ are as follows：
Wherein, dijFor the distance between i-th of data point and j-th data point, dcFor self-defined constant, it is relatively minimal away from
From δiRefer to that i-th of data point is more than ρ to local densityi, and with a distance from the data point nearest from i-th of data point.
3) the identification feature parameter structure decision tree classifier based on calculating
Make the first identification feature parameter, the second identification feature parameter, third identification feature parameter, the 4th identification feature parameter
Respectively as first class node of decision tree classifier, second class node, third class node, the 4th classification
Then for different optical signal to noise ratio situations, decision tree structure and decision gate are constructed according to optimal Bayes's classification principle for node
Limit value improves the construction of decision tree classifier.
2nd, cognitive phase
In practical application, the polarization signal progress that X, Y two-way that current coherent optical heterodyne communicatio is received are orthogonal
Stokes space reflections are simultaneously fitted its least square plane, then to the signal after mapping on the least square plane of Stokes spaces
The first identification feature parameter is calculated, the first identification feature parameter is input in the decision tree classifier of step 3) foundation, tentatively
The modulation format for differentiating current demand signal is low order { PM-QPSK, PM-8QAM, PM-16QAM } or high-order { PM-32QAM, PM-
64QAM } modulation format；If high-order { PM-32QAM, PM-64QAM } modulation format, on the least square plane of Stokes spaces
Signal calculate the second identification feature parameter, by the second identification feature parameter be input to step 3) foundation decision tree classifier
In, determine the specific modulation format of current demand signal；It, should if low order { PM-QPSK, PM-8QAM, PM-16QAM } modulation format
Its cluster centre number on least square plane is calculated, and based on cluster centre number with adaptive non-iterative clustering algorithm
Third identification feature parameter, the 4th identification feature parameter are calculated, third identification feature parameter, the 4th identification feature parameter are inputted
In the decision tree classifier established to step 3), the specific modulation format of current demand signal is determined.
Finally illustrate, the foregoing is merely the embodiment of the present invention, are not intended to limit the scope of the invention, every
The equivalent structure or equivalent flow shift made using description of the invention and accompanying drawing content, is directly or indirectly used in other
Relevant technical field, is included within the scope of the present invention.
Claims (10)
1. the Stokes spatial coherence optical modulation formats recognition methods based on adaptive non-iterative cluster, it is characterised in that：Including
Following steps：
(1) training stage：The sample signal of different modulating form is set, the sample signal of different modulating form is subjected to Stokes
Space reflection is simultaneously fitted its least square plane, rotates Stokes spacing waves, makes its least square plane and S2And S3Axis forms
Plane overlap；First identification feature is calculated to the signal on least square plane after the sample signal mapping of different modulating form
Parameter as first class node of decision tree classifier, distinguishes signal and belongs to higher order modulation formats { PM-32QAM, PM-
64QAM } or low-order-modulated form { PM-QPSK, PM-8QAM, PM-16QAM }；For higher order modulation formats PM-32QAM,
PM-64QAM } sample signal, it is different according to distribution some region of on its least square plane in Stokes spaces, calculate
Second identification feature parameter, second class node as decision tree classifier；For low-order-modulated form PM-QPSK,
PM-8QAM, PM-16QAM } sample signal, calculate it on least square plane using adaptive non-iterative clustering algorithm
Cluster centre number, and third identification feature parameter, the 4th identification feature parameter are calculated based on cluster centre number, respectively as
Third and fourth class node of decision tree classifier；Then for different optical signal to noise ratio situations, according to optimal Bayes's classification
Principle constructs decision tree structure and decision threshold value, improves the construction of decision tree classifier；
(2) cognitive phase：The orthogonal polarization signal of X, Y two-way that current coherent optical heterodyne communicatio is received carries out Stokes spaces
It maps and is fitted its least square plane, then calculating first to the signal after mapping on the least square plane of Stokes spaces knows
First identification feature parameter is input in the decision tree classifier of step (1) foundation by other characteristic parameter, and principium identification is currently believed
Number modulation format modulate lattice for low order { PM-QPSK, PM-8QAM, PM-16QAM } or high-order { PM-32QAM, PM-64QAM }
Formula；If high-order { PM-32QAM, PM-64QAM } modulation format, the is calculated the signal on the least square plane of Stokes spaces
Second identification feature parameter is input in the decision tree classifier of step (1) foundation by two identification feature parameters, is determined current
The specific modulation format of signal；If low order { PM-QPSK, PM-8QAM, PM-16QAM } modulation format, change using adaptively non-
Its cluster centre number on least square plane is calculated, and third identification is calculated based on cluster centre number for clustering algorithm
Third identification feature parameter, the 4th identification feature parameter are input to step (1) and built by characteristic parameter, the 4th identification feature parameter
In vertical decision tree classifier, the specific modulation format of current demand signal is determined.
2. the Stokes spatial coherence optical modulation formats identification side according to claim 1 based on adaptive non-iterative cluster
Method, it is characterised in that：The specific formula that the polarization signal that X, Y two-way are orthogonal in (2) carries out Stokes space reflections is as follows：
Wherein, ax、ayThe amplitude of X, Y two-way orthogonal polarization signals is represented respectively,Between X, Y two-way orthogonal polarization signals
Phase difference, ex、eyX, Y two-way orthogonal polarization signals are represented respectively,E is represented respectivelyx、eyConjugation；S0、S1、S2、S3It represents
Each parameter after Stokes space reflections, wherein S0Represent the general power of optical signal；(S1, S2, S3) represent the three-dimensional seat formed
Mark represents the different polarization states of optical signal；Specific to each reference axis, S1Represent the linearly polarized light in horizontal direction, S2Represent 45
Spend the linearly polarized light on angular direction, S3Represent circularly polarized light.
3. the Stokes spatial coherence optical modulation formats identification side according to claim 1 based on adaptive non-iterative cluster
Method, it is characterised in that：(1) fits minimum of the signal in Stokes spaces after mapping using singular value decomposition algorithm
Two multiply plane.
4. the Stokes spatial coherence optical modulation formats identification side according to claim 2 based on adaptive non-iterative cluster
Method, it is characterised in that：The first identification feature expressed as parameters is as follows：
Wherein R represents the first identification feature parameter, and Ω is defined as on least square plane | S2| ＞ 1 and | S3| the region of ＞ 1, LSP
Represent least square plane.
5. the Stokes spatial coherence optical modulation formats identification side according to claim 2 based on adaptive non-iterative cluster
Method, it is characterised in that：The data point that the second identification feature expressed as parameters is included for a certain region on least square plane
Quantity, wherein a certain region is expressed as by two circular arc S on the least square plane2 2+S3 2=[max (S3)-0.15]2、S2 2+
S3 2=[max (S3)-0.45]2And two straight line S3=tan (0.7) S2、S3=tan (0.87) S2The region surrounded, max
(S3) represent on least square plane in the coordinate of all data points | S3| maximum value.
6. the Stokes spatial coherence optical modulation formats identification side according to claim 2 based on adaptive non-iterative cluster
Method, it is characterised in that：The third identification feature expressed as parameters changes for the data point on least square plane by adaptively non-
The cluster centre point number obtained later for clustering algorithm cluster.
7. the Stokes spatial coherence optical modulation formats identification side according to claim 2 based on adaptive non-iterative cluster
Method, it is characterised in that：The 4th identification feature expressed as parameters changes for the data point on least square plane by adaptively non-
In all cluster centre points obtained later for clustering algorithm cluster, local density p meets condition
Cluster centre point number, wherein max (ρ) represents the density value of the data point of local density maximum in all data point.
8. the Stokes spatial coherence light modulations clustered according to claim 1~7 any one of them based on adaptive non-iterative
Format identification method, it is characterised in that：The adaptive non-iterative clustering algorithm calculates the detailed process of cluster centre number such as
Under：
Two ginsengs of its local density p of each data point calculation and relatively minimal distance δ on the least square plane of Stokes spaces
Number；Two-dimentional decision diagram is built by the two parameters, wherein cluster centre point is located at the upper right side of decision diagram, and cluster centre point meets
The following conditions：And δ ＞ a, wherein ρ represent the local density of data point, max (ρ) represents all data
The density value of the data point of local density maximum in point, a are self-adaptive decision threshold value.
9. the Stokes spatial coherence optical modulation formats identification side according to claim 8 based on adaptive non-iterative cluster
Method, it is characterised in that：The self-adaptive decision threshold value a is determined in the following manner：Make self-adaptive decision threshold value a since 0 by
It is cumulative plus, when increasing to some numerical value b so that the data in region in decision diagram between two straight line δ=b and δ=b-0.02
The number of point is not less than the 2.5% of total number, and numerical value b is the value of a.
10. the Stokes spatial coherences optical modulation formats identification according to claim 8 based on adaptive non-iterative cluster
Method, it is characterised in that：The local density p of the data point and the calculating process of relatively minimal distance δ are as follows：
Wherein, dijFor the distance between i-th of data point and j-th data point, dcFor self-defined constant, relatively minimal distance δi
Refer to that i-th of data point is more than ρ to local densityi, and with a distance from the data point nearest from i-th of data point.
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Cited By (7)
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| CN109309640B (en) * | 2018-09-08 | 2021-11-05 | 苏州大学 | Blind signal format identification method based on machine learning |
| CN109309640A (en) * | 2018-09-08 | 2019-02-05 | 苏州大学 | Fanaticism format identification method based on machine learning |
| CN109347776A (en) * | 2018-11-26 | 2019-02-15 | 北京理工大学 | A kind of optic communication format modulation signal recognition methods of differential phase Amplitude Ratio |
| CN109347776B (en) * | 2018-11-26 | 2020-06-16 | 北京理工大学 | Method for identifying modulation format of optical communication signal with differential phase-to-amplitude ratio |
| CN109743276A (en) * | 2018-12-11 | 2019-05-10 | 中国联合网络通信集团有限公司 | A kind of recognition methods of optical signal modulation format and device |
| CN109743276B (en) * | 2018-12-11 | 2021-02-23 | 中国联合网络通信集团有限公司 | Method and device for identifying optical signal modulation format |
| CN109474336A (en) * | 2019-01-08 | 2019-03-15 | 北京科技大学 | A kind of optical signal-to-noise ratio monitoring device and monitoring method |
| CN109474336B (en) * | 2019-01-08 | 2020-11-06 | 北京科技大学 | Optical signal-to-noise ratio monitoring device and monitoring method |
| CN111343116A (en) * | 2020-02-25 | 2020-06-26 | 杭州电子科技大学 | Modulation identification method based on constellation locus diagram under dense signal environment |
| CN111343116B (en) * | 2020-02-25 | 2022-06-10 | 杭州电子科技大学 | Modulation identification method based on constellation locus diagram under dense signal environment |
| CN111371503A (en) * | 2020-03-17 | 2020-07-03 | 华南师范大学 | Method for blind polarization demultiplexing in probability shaping constellation modulation coherent optical communication system |
| CN113542172A (en) * | 2021-07-12 | 2021-10-22 | 聊城大学 | Elastic optical network modulation format identification method and system based on improved PSO clustering |
| CN113542172B (en) * | 2021-07-12 | 2023-11-14 | 聊城大学 | Elastic optical network modulation format identification method and system based on improved PSO clustering |
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