CN109587091A - The coherent optical communication system modulation format recognition methods of logic-based regression algorithm - Google Patents
The coherent optical communication system modulation format recognition methods of logic-based regression algorithm Download PDFInfo
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- 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
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- 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
- H04B10/616—Details of the electronic signal processing in coherent optical receivers
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- 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
- H04B10/616—Details of the electronic signal processing in coherent optical receivers
- H04B10/6161—Compensation of chromatic dispersion
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- 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
- H04B10/616—Details of the electronic signal processing in coherent optical receivers
- H04B10/6164—Estimation or correction of the frequency offset between the received optical signal and the optical local oscillator
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- 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
- H04B10/616—Details of the electronic signal processing in coherent optical receivers
- H04B10/6165—Estimation of the phase of the received optical signal, phase error estimation or phase error correction
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Abstract
The invention discloses a kind of coherent optical communication system modulation format recognition methods of logic-based regression algorithm, include the following steps: S1: transmitting original signal；S2: receive the pretreatment of signal；S3: the classification of PSK and QAM signal are carried out；S4: it is modulated format identification；The present invention solves the problems, such as that algorithm complexity of the existing technology is big compared with high and cost input.
Description
Technical field
The invention belongs to coherent fiber communication technical fields, and in particular to a kind of coherent light of logic-based regression algorithm is logical
Believe system modulation format identification method.
Background technique
Since bandwidth is deficient and cloud service, global ip flow continue to be exponentially increased.Optical-fiber network is being evolved into more flexible
With adaptivity framework.Next-generation optical fiber telecommunications system can be according to the demand assignment bandwidth and modulation format of each user.For
Realize the network of intelligence, an important component part of dynamic receiver is exactly modulation format identification module, the module root
Corresponding receiver is configured according to the pattern for receiving signal.Prior art has proposed more modulation format identification technologies, such as
According to amplitude distribution, non-linear power spectrum, the cluster in Stokes Space, the planisphere based on convolutional neural networks (CNN)
Identification etc..
Above-mentioned algorithm complexity is higher, lower in order to realize under the premise of keeping stability in optical communication system
Time delay needs a kind of higher scheme of and robustness lower with complexity；And when the prior art improves system, need
Equipment is added, cost input is big.
Summary of the invention
For above-mentioned deficiency in the prior art, the present invention proposes that a kind of complexity is low, robustness is preferable, it is next to be suitable for
For the coherent optical communication system modulation format recognition methods of more flexible and adaptive logic-based regression algorithm, reduce into
This investment, for solving the problems, such as that algorithm complexity of the existing technology is big compared with high and cost input.
In order to achieve the above object of the invention, the technical solution adopted by the present invention are as follows:
A kind of coherent optical communication system modulation format recognition methods of logic-based regression algorithm, includes the following steps:
S1: receive the pretreatment of signal: receiving the signal in modulated two polarization states, and located in advance
Reason, signal after being pre-processed；
S2: the classification of PSK and QAM signal are carried out: selection one pre-process after signal and calculate its amplitude variance, and according to
The distribution characteristics of amplitude variance is classified；
Class categories include psk signal and QAM signal；
S3: it is modulated format identification: successively carrying out 4 power operations to signal after classification and Fourier transform obtains transformation
Signal afterwards calculates its variance and mean value, and is divided according to corresponding to signal after the distribution characteristics and Current Transform of variance and mean value
Class classification is carried out specific modulation format identification, is obtained the modulation format type of signal using logic-based regression algorithm.
Further, in step S1, pretreatment includes the dispersion compensation processing and pre-equalization process successively carried out.
Further, pre-equalization process is carried out using CMA algorithm.
Further, in step S2, the calculation formula of amplitude variance are as follows:
D=D (| E'|)
In formula, d is amplitude variance；| | it is modulus operation；D () is that variance seeks operation；E' is the pretreatment of selection
Signal afterwards.
Further, in step S3,4 power operations are successively carried out to signal after classification and Fourier transform obtains after converting
Signal, its calculation formula is:
EFFT=| FFT [(E ')4]|
In formula, EFFTFor signal after transformation；(·)4For 4 power operations；FFT () is Fourier transformation computation；| | it is
Modulus operation.
Further, in step S3, the calculation formula of variance and mean value:
In formula, σ2For variance；D () is that variance seeks operation；E is mean value；E () is that mean value seeks operation；EFFTTo become
Change rear signal.
Further, variance seeks the specific formula of operation are as follows:
In formula, D () is that variance seeks operation；hmFor the value of current sampling point；For the mean value of current demand signal；M is sampling
Point variable；M is current signal sample point sum.
Further, mean value seeks the specific formula of operation are as follows:
In formula, E () is that mean value seeks operation；hiFor the value of current sampling point；M is sampling point variable；M is current demand signal
Total number of sample points.
Further, in step S4, using logistic regression algorithm, it is fitted a cubic polynomial curve, is specifically adjusted
Format identification processed.
Beneficial effects of the present invention:
The present invention utilizes the logistic regression Logistic of low complex degree under the premise of not changing coherent receiver configuration
Regression algorithm realizes modulation format identification function, greatly reduces complexity, and logistic regression algorithm have compared with
Good robustness has important practical significance and application prospect in next-generation optical fiber telecommunications system, and sets without addition
It is standby, reduce cost input.
Detailed description of the invention
Fig. 1 is the coherent optical communication system modulation format recognition methods flow chart of logic-based regression algorithm；
Fig. 2 is the variance comparison diagram of the amplitude of each modulation format；
Fig. 3 is amplitude variance distribution map；
Fig. 4 is variance-distribution of mean value figure of psk signal and QAM signal；
Fig. 5 is the recognition correct rate comparison diagram of each modulation format；
Fig. 6 is coherent optical communication system structural block diagram.
Specific embodiment
A specific embodiment of the invention is described below, in order to facilitate understanding by those skilled in the art this hair
It is bright, it should be apparent that the present invention is not limited to the ranges of specific embodiment, for those skilled in the art,
As long as various change is in the spirit and scope of the present invention that the attached claims limit and determine, these variations are aobvious and easy
See, all are using the innovation and creation of present inventive concept in the column of protection.
A kind of coherent optical communication system modulation format recognition methods of logic-based regression algorithm, as shown in Figure 1, including such as
Lower step:
S1: receive the pretreatment of signal: receiving the signal E in modulated two polarization statesxAnd Ey, and use number
Word signal processing module is pre-processed, signal E' after being pre-processedxAnd E'y；
Pretreatment includes the dispersion compensation processing and pre-equalization process successively carried out；
Dispersion compensation processing, the signal after obtaining dispersion compensation are carried out firstWith
By dispersion compensation treated signalWithUsing based on CMA preequalization algorithm carry out preliminary demultiplexing and
Channel equalization obtains signal E' after pre-equalization processxAnd E'y, and as signal after pretreatment；
S2: the classification of PSK and QAM signal are carried out: signal E' after one pretreatment of selectionxOr E'yAnd calculate its amplitude variance
D, and classified according to amplitude variance d and signal modulation mode；
Class categories include psk signal and QAM signal；
Theoretical foundation: as shown in Fig. 2, the amplitude of psk signal is a Gaussian Profile, and the amplitude of QAM signal is multiple
The superposition of Gaussian Profile, as shown in figure 3, the difference of its variance can increase with the increase of signal-to-noise ratio, in QAM and PSK amplitude
A threshold value is arranged in the intermediate of variance, carries out the classification of psk signal and QAM signal；
S3: it is modulated format identification: successively carrying out 4 power operations to signal after classification and Fourier transform obtains transformation
Signal E afterwardsFFT, calculate its variances sigma2With mean value e, and according to variances sigma2With signal institute after the distribution characteristics and Current Transform of mean value e
Corresponding class categories are fitted a cubic polynomial curve using logic-based regression algorithm, as shown in figure 4, carrying out specific
Modulation format identification, obtains the modulation format type of signal, recognition result accuracy is as shown in Figure 5；
Theoretical foundation: 4 power operations of signal, which can eliminate phase phase difference, isPoint；
Distinguish 16QAM and QPSK: according to the E of 16QAM and QPSKFFTDistribution map has a peak value, and 16QAM has 3 width altogether
Degree, wherein the phase difference there are two amplitude isThere is a peak value after eliminating the transformation of phase phase difference by 4 powers, together
It manages, is differed between each point of QPSKThere is a peak value after eliminating the transformation of phase phase difference by 4 powers；
Distinguish the E of QPSK and 8PSK:QPSKFFTDistribution map has a peak value, the E of 8PSKFFTDistribution map does not have peak value, according to
Its variances sigma2With mean value e, show that the distribution of its Mean-Variance is different, the cubic polynomial being fitted according to logic-based regression algorithm
Curve distinguishes；
Similarly, 16QAM and 32QAM are distinguished.
In the present embodiment, in step S3, the calculation formula of amplitude variance are as follows:
D=D (| E'|)
In formula, d is amplitude variance；| | it is modulus operation；D () is that variance seeks operation；E' is the pretreatment of selection
Signal afterwards.
In the present embodiment, in step S4,4 power operations are successively carried out to signal after classification and Fourier transform obtains transformation
Signal afterwards, its calculation formula is:
EFFT=| FFT [(E ')4]|
In formula, EFFTFor signal after transformation；(·)4For 4 power operations；FFT () is Fourier transformation computation；| | it is
Modulus operation.
In the present embodiment, in step S4, the calculation formula of variance and mean value:
In formula, σ2For variance；D () is that variance seeks operation；E is mean value；E () is that mean value seeks operation；EFFTTo become
Change rear signal；
Variance seeks the specific formula of operation are as follows:
In formula, D () is that variance seeks operation；hmFor the value of current sampling point；For the mean value of current demand signal；M is sampling
Point variable；M is current signal sample point sum.
Mean value seeks the specific formula of operation are as follows:
In formula, E () is that mean value seeks operation；hiFor the value of current sampling point；M is sampling point variable；M is current demand signal
Total number of sample points.
In the present embodiment, used coherent optical communication system, as shown in fig. 6, including sequentially connected transmitting terminal, transmission
Link and receiving end；
Transmitting terminal includes sequentially connected laser and coherent modulator, and the line width of laser is 100kHz, and its center
Wavelength is 1550nm；Coherent modulator includes two I/Q modulators being arranged in parallel, the both ends of two I/Q modulators respectively with swash
Light device and the connection of the first fiber amplifier；
Transmission link includes sequentially connected first fiber amplifier, optical fiber and the second fiber amplifier；
Receiving end includes sequentially connected preamplifier, coherent demodulation receiver and digital signal processing module；
Coherent modulator is connect with the first fiber amplifier, and the second fiber amplifier is connect with coherent demodulation receiver；The
One fiber amplifier and the second fiber amplifier are erbium-doped optical fiber amplifier EDFA；
Digital signal processing module includes that the dispersion successively carried out is mended for carrying out Digital Signal Processing, Digital Signal Processing
It repays, clock recovery, CMA equilibrium, modulation format identification, adaptive equalization, offset estimation, phase recovery and bit decision and solution
Code, wherein dispersion compensation, clock recovery and CMA preequalization are without obtaining modulation format information.
The present invention propose a kind of complexity is low, robustness preferably, be suitable for it is next-generation more flexible and adaptive based on
The coherent optical communication system modulation format recognition methods of logistic regression algorithm, reduces cost input, solves the prior art and deposit
Algorithm complexity is higher and the big problem of cost input.
Claims (9)
1. a kind of coherent optical communication system modulation format recognition methods of logic-based regression algorithm, which is characterized in that including such as
Lower step:
S1: receive the pretreatment of signal: receiving the signal in modulated two polarization states, and pre-processed,
Signal after being pre-processed；
S2: the classification of PSK and QAM signal are carried out: selection one pre-process after signal and calculate its amplitude variance, and according to amplitude
The distribution characteristics of variance is classified；
The class categories include psk signal and QAM signal；
S3: it is modulated format identification: successively carrying out 4 power operations to signal after classification and Fourier transform obtains after converting and believes
Number, calculate its variance and mean value, and the classification class according to corresponding to signal after the distribution characteristics and Current Transform of variance and mean value
Not, using logic-based regression algorithm, specific modulation format identification is carried out, the modulation format type of signal is obtained.
2. the coherent optical communication system modulation format recognition methods of logic-based regression algorithm according to claim 1,
It is characterized in that, in the step S1, pretreatment includes the dispersion compensation processing and pre-equalization process successively carried out.
3. the coherent optical communication system modulation format recognition methods of logic-based regression algorithm according to claim 2,
It is characterized in that, carries out pre-equalization process using CMA algorithm.
4. the coherent optical communication system modulation format recognition methods of logic-based regression algorithm according to claim 3,
It is characterized in that, in the step S2, the calculation formula of amplitude variance are as follows:
D=D (| E'|)
In formula, d is amplitude variance；| | it is modulus operation；D () is that variance seeks operation；E' be selection pretreatment after believe
Number.
5. the coherent optical communication system modulation format recognition methods of logic-based regression algorithm according to claim 1,
It is characterized in that, in the step S3,4 power operations is successively carried out to signal after classification and Fourier transform obtains after converting and believes
Number, its calculation formula is:
EFFT=| FFT [(E')4]|
In formula, EFFTFor signal after transformation；(·)4For 4 power operations；FFT () is Fourier transformation computation；| | it is transported for modulus
It calculates.
6. the coherent optical communication system modulation format recognition methods of logic-based regression algorithm according to claim 1,
It is characterized in that, in the step S3, the calculation formula of variance and mean value:
In formula, σ2For variance；D () is that variance seeks operation；E is mean value；E () is that mean value seeks operation；EFFTAfter transformation
Signal.
7. the coherent optical communication system modulation format recognition methods of logic-based regression algorithm according to claim 6,
It is characterized in that, variance seeks the specific formula of operation are as follows:
In formula, D () is that variance seeks operation；hmFor the value of current sampling point；For the mean value of current demand signal；M is sampled point change
Amount；M is current signal sample point sum.
8. the coherent optical communication system modulation format recognition methods of logic-based regression algorithm according to claim 7,
It is characterized in that, mean value seeks the specific formula of operation are as follows:
In formula, E () is that mean value seeks operation；hiFor the value of current sampling point；M is sampling point variable；M is current signal sample
Point sum.
9. the coherent optical communication system modulation format recognition methods of logic-based regression algorithm according to claim 1,
It is characterized in that, in the step S3, using logistic regression algorithm, is fitted a cubic polynomial curve, carry out specific modulation lattice
Formula identification.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110048781A (en) * | 2019-04-17 | 2019-07-23 | 武汉邮电科学研究院有限公司 | A kind of recognition methods of optical signal modulation format and device |
| CN111313974A (en) * | 2020-02-14 | 2020-06-19 | 北京理工大学 | Modulation format identification method for signal amplitude sequencing and ratio calculation |
| CN115643141A (en) * | 2022-10-17 | 2023-01-24 | 西南交通大学 | Modulation format identification method of probability shaping coherent optical communication system based on signal nonlinear power change |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013051675A (en) * | 2011-07-26 | 2013-03-14 | Silver Spring Networks Inc | Systems and methods for multimode signal acquisition |
| CN103199945A (en) * | 2013-03-29 | 2013-07-10 | 南京信息职业技术学院 | Recognition method for cognizing radio signal modulation modes under low-signal-to-noise-ratio condition |
| CN103684600A (en) * | 2012-09-14 | 2014-03-26 | 富士通株式会社 | Updating device and method for equalizer coefficients, receiver and optical communication system |
| CN104009951A (en) * | 2014-06-17 | 2014-08-27 | 北京邮电大学 | Signal modulation mode recognition method and system based on multi-scale wavelet transformation |
| CN104301056A (en) * | 2014-11-03 | 2015-01-21 | 成都中星世通电子科技有限公司 | Frequency spectrum monitoring method based on signal feature analysis |
| CN104796366A (en) * | 2015-04-10 | 2015-07-22 | 长春理工大学 | Communication signal system identification system and method |
| CN106059969A (en) * | 2016-06-16 | 2016-10-26 | 中国科学院信息工程研究所 | Modulation identification method and device based on envelope square spectrum analysis |
| CN106330805A (en) * | 2016-08-29 | 2017-01-11 | 重庆会凌电子新技术有限公司 | Automatic signal modulation mode identification method and system |
| CN106789788A (en) * | 2016-12-26 | 2017-05-31 | 北京邮电大学 | A kind of wireless digital signal Modulation Mode Recognition method and device |
| CN106899531A (en) * | 2017-03-01 | 2017-06-27 | 西安电子科技大学 | A kind of method of identification satellite modulation mode of communication signal |
| US9729362B1 (en) * | 2013-03-20 | 2017-08-08 | Georgia Tech Research Corporation | Systems and methods for autonomous signal modulation format identification |
| CN108768907A (en) * | 2018-01-05 | 2018-11-06 | 南京邮电大学 | A kind of Modulation Identification method based on temporal characteristics statistic and BP neural network |
| CN108880692A (en) * | 2018-06-05 | 2018-11-23 | 西南交通大学 | A kind of identification of coherent optical communication system modulation format and optical signal to noise ratio monitoring method |
| CN108900454A (en) * | 2018-06-28 | 2018-11-27 | 北京邮电大学 | A kind of modulation system parameter blind detection method and device based on machine learning |
| CN109067678A (en) * | 2018-06-22 | 2018-12-21 | 达新宇 | Based on Higher Order Cumulants WFRFT signal cascade Modulation Identification method, wireless communication system |
-
2019
- 2019-01-23 CN CN201910062075.5A patent/CN109587091B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013051675A (en) * | 2011-07-26 | 2013-03-14 | Silver Spring Networks Inc | Systems and methods for multimode signal acquisition |
| CN103684600A (en) * | 2012-09-14 | 2014-03-26 | 富士通株式会社 | Updating device and method for equalizer coefficients, receiver and optical communication system |
| US9729362B1 (en) * | 2013-03-20 | 2017-08-08 | Georgia Tech Research Corporation | Systems and methods for autonomous signal modulation format identification |
| CN103199945A (en) * | 2013-03-29 | 2013-07-10 | 南京信息职业技术学院 | Recognition method for cognizing radio signal modulation modes under low-signal-to-noise-ratio condition |
| CN104009951A (en) * | 2014-06-17 | 2014-08-27 | 北京邮电大学 | Signal modulation mode recognition method and system based on multi-scale wavelet transformation |
| CN104301056A (en) * | 2014-11-03 | 2015-01-21 | 成都中星世通电子科技有限公司 | Frequency spectrum monitoring method based on signal feature analysis |
| CN104796366A (en) * | 2015-04-10 | 2015-07-22 | 长春理工大学 | Communication signal system identification system and method |
| CN106059969A (en) * | 2016-06-16 | 2016-10-26 | 中国科学院信息工程研究所 | Modulation identification method and device based on envelope square spectrum analysis |
| CN106330805A (en) * | 2016-08-29 | 2017-01-11 | 重庆会凌电子新技术有限公司 | Automatic signal modulation mode identification method and system |
| CN106789788A (en) * | 2016-12-26 | 2017-05-31 | 北京邮电大学 | A kind of wireless digital signal Modulation Mode Recognition method and device |
| CN106899531A (en) * | 2017-03-01 | 2017-06-27 | 西安电子科技大学 | A kind of method of identification satellite modulation mode of communication signal |
| CN108768907A (en) * | 2018-01-05 | 2018-11-06 | 南京邮电大学 | A kind of Modulation Identification method based on temporal characteristics statistic and BP neural network |
| CN108880692A (en) * | 2018-06-05 | 2018-11-23 | 西南交通大学 | A kind of identification of coherent optical communication system modulation format and optical signal to noise ratio monitoring method |
| CN109067678A (en) * | 2018-06-22 | 2018-12-21 | 达新宇 | Based on Higher Order Cumulants WFRFT signal cascade Modulation Identification method, wireless communication system |
| CN108900454A (en) * | 2018-06-28 | 2018-11-27 | 北京邮电大学 | A kind of modulation system parameter blind detection method and device based on machine learning |
Non-Patent Citations (2)
| Title |
|---|
| 常婧云: "无线射频信号的发射源及信息处理方式的识别", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 贺辉刚: "通信信号在低信噪比条件下的调制识别研究与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110048781A (en) * | 2019-04-17 | 2019-07-23 | 武汉邮电科学研究院有限公司 | A kind of recognition methods of optical signal modulation format and device |
| CN111313974A (en) * | 2020-02-14 | 2020-06-19 | 北京理工大学 | Modulation format identification method for signal amplitude sequencing and ratio calculation |
| CN115643141A (en) * | 2022-10-17 | 2023-01-24 | 西南交通大学 | Modulation format identification method of probability shaping coherent optical communication system based on signal nonlinear power change |
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