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1850 IEEE PHOTONICS TECHNOLOGY LETTERS, VOL. 30, NO. 21, NOVEMBER 1, 2018
Blind and Noise-Tolerant ModulationFormat Identification
Junfeng Zhang , Mingyi Gao , Senior Member, IEEE, Wei Chen , Yang Ye, Yuanyuan Ma, Wei Chen,
Yonghu Yan, Hongliang Ren , and Gangxiang Shen , Senior Member, IEEE
Abstract— We propose and experimentally demonstrate a sim-ple, blind digital modulation format identification scheme fora quadrature amplitude modulation (QAM) coherent opticalcommunication system. In the proposed scheme, we utilize adensity-peak clustering algorithm to determine the number ofamplitude levels of the signal as the feature for modulationformat identification. Simulations are carried out to investigatethe application of the proposed scheme to various modulationsignals. Experiments are performed to verify the robustness ofthe method to amplified spontaneous emission noise and fibernonlinearities. We experimentally achieved a 95% identificationaccuracy for a quadrature phase shift keying signal with a lowestoptical-signal-noise-ratio (OSNR) of 13.2 dB, a 16-QAM signalwith a lowest OSNR of 13.3 dB, and a 64-QAM signal with alowest OSNR of 19.7 dB.
Index Terms— Coherent optical communication, digital signalprocessing, modulation format identification.
I. INTRODUCTION
TO INCREASE the spectral usage of a conventionalwavelength division multiplexing (WDM) scheme with
fixed wavelength grids, an elastic optical network with aflexible gird is proposed to adaptively arrange the networkresources [1]. To support such an on-demand cognitive opticalnetwork, the control plane requires some indispensable infor-mation of the physical layer, such as modulation formats ofthe transmitted signals, available wavelength bandwidths andpotential fiber routes [2]. Furthermore, the modulation-format-identification (MFI) module is also significant in a coher-ent optical receiver, where some indispensable digital signalprocessing (DSP) algorithms, such as adaptive equalization,carrier phase recovery and symbol decision, are dependent onthe signals’ modulation format. Therefore, a blind MFI schemeis necessary for future cognitive optical networks.
Manuscript received August 2, 2018; revised August 27, 2018; acceptedAugust 30, 2018. Date of publication September 13, 2018; date of currentversion November 6, 2018. This work was supported in part by the OpenFund of BUPT under Grant IPOC 2017B001, in part by the Suzhou IndustryTechnological Innovation Projects under Grant SYG201809, in part by theNational Natural Science Foundation of China under Project 61307082, andin part by the Natural Science Foundation of Zhejiang Province under GrantLY16F050009. (Corresponding author: Mingyi Gao.)
J. Zhang, M. Gao, W. Chen, Y. Ye, Y. Ma, and G. Shen are with theSchool of Electronic and Information Engineering, Soochow University,Suzhou 215006, China (e-mail: [email protected]; [email protected]; [email protected]; [email protected];[email protected]; [email protected]).
W. Chen and Y. Yan are with the Key Laboratory of New Fiber Technologyof Suzhou City, Jiangsu Hengtong Fiber Science and Technology Corporation,Suzhou 215200, China (e-mail: [email protected]; [email protected]).
H. Ren is with the College of Information Engineering, Zhejiang Universityof Technology, Hangzhou 310023, China (e-mail: [email protected]).
Color versions of one or more of the figures in this letter are availableonline at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/LPT.2018.2869913
Intelligent MFI is roughly divided into two types,a decision-theory type and a feature-extraction type. The MFImethod based on decision theory is a multihypothesis verifi-cation problem called Bayesian theory, which always requiressubstantial prior knowledge [3]–[5]. In contrast, the MFImethod based on the feature extraction can be blind withoutthe use of training sequences. A variety of signal’ featurescan be extracted for the blind MFI, where the amplitude andconstellation features are frequently applied.
Various MFI schemes of the feature-extraction type havebeen proposed [6]–[10]. According to the location of theMFI module in a coherent optical receiver, there are twocategories. When the MFI module is prior to the carrier-phase-recovery module, an amplitude feature is extractedas the identification reference. The amplitude-identificationfeatures include the peak-to-average power ratio (PAPR) ofthe signals’ amplitude, the PAPR for the Fourier transformof the received samples after different power operations,the cumulative distribution function of the received signal’snormalized amplitude, the amplitude histogram and the ampli-tude distribution in Stokes space [6]–[11]. When the MFImodule follows the carrier-phase-recovery module, the con-stellation diagram or eye diagram can be directly distinguishedby the convolutional neural network without need of anyother features [12], [13]. For the pretraining MFI techniques,such as artificial neural networks (ANN) [9], deep neuralnetworks (DNN) [10] and convolutional neural network (CNN)algorithms [12], [13], the MFI performance relies heavilyon training samples, as a large amount of training samplesare required to optimize the model parameters. Therefore,a simple and blind MFI technique with higher noise-toleranceis attractive.
In this letter, we propose a blind MFI scheme of QAM sig-nals based on the difference of the decision graph’s logarithmof the fast search and find of density peaks (FSFDP) clusteringalgorithm. When the data of the acquired signals from theanalog-to-digital converter is processed by the modulation-format transparent chromatic dispersion compensation andclock recovery algorithms, the number of amplitude circlesof the signals is used as the identification feature parameter.First, the nonlinear clustering of the amplitude circles isconverted into the linear clustering by mapping the ampli-tude circles into an amplitude diagonal. Then, the FSFDPclustering algorithm is used to find the cluster centers of theamplitude diagonal. Finally, the difference of the decisiongraph’s logarithm is calculated to yield the number of theamplitude circles for the MFI identification. We experimen-tally demonstrated the proposed MFI scheme and achieved a95% identification accuracy for a QPSK signal with a lowest
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ZHANG et al.: BLIND AND NOISE-TOLERANT MODULATION FORMAT IDENTIFICATION 1851
Fig. 1. Amplitude diagrams of 4/16/64-QAM signals with the OSNRs of17-dB, 21-dB and 26-dB, respectively.
optical-signal-noise-ratio (OSNR) of 13.2 dB, a 16-QAMsignal with a lowest OSNR of 13.3 dB and 64-QAM signalwith a lowest OSNR of 19.7 dB. The proposed MFI scheme isalso useful for a polarization-multiplexed system if the blindpolarization demultiplexing is implemented before the clockrecovery. We verified that the proposed scheme is robust tofiber nonlinearities in a single-mode-fiber (SMF) transmissionexperiment, and the identification accuracy is independent ofthe 16/64-QAM signals’ launched power into the SMFs.
II. PRINCIPLES OF THE PROPOSED MFI METHOD
Compared with the conventional K-means clustering algo-rithm to partition n observations into known k clusters,the FSFDP clustering algorithm can fast track the clustercenters by plotting the decision graph without prior knowledgeof the clusters’ number [11], [13], [14]. Inspired by its goodclustering performance, we apply the FSFDP clustering algo-rithm to process the amplitude data of the signals and identifythe number of amplitude levels. Fig. 1 shows the amplitudediagrams of 4/16/64-QAM signals with various OSNRs. It isclear that signals of various modulation types have differentnumbers of amplitude circles. With the increase of modulationtypes and noise, it is challenging to discriminate betweenambiguous amplitude diagrams and the improved method willbe discussed later.
The FSFDP clustering algorithm utilizes a decision graphto find the cluster centers. For our application, the key is tofirst find the centers of the signals’ amplitude circles and thenautomatically calculate the number of amplitude circles. The64-QAM signal with 26-dB OSNR is used as an example toexplain the principle and the key procedures of the proposedMFI scheme, as shown in Fig. 2.
First, to achieve a better recognition process, data pre-processing is critical. The data preprocessing normalizes thepower of the received clock-recovered data based on theaverage energy, written by
pi = xi
/√√√√ k∑i=1
|xi |2/
k (1)
where xi is the clock-recovered data and k is the number ofdata.
Next, the objective is to determine the number of amplitudecircles in Fig. 1 for the MFI. However, it is very challengingto find the cluster centers of the nonlinear amplitude circles.Therefore, a proper conversion is necessary. In Fig. 2 (a),we plot the 64-QAM signal in polar coordinates, where thehorizontal axis, θ , is the same for all signals with a rangefrom –π to π , and the difference between various signalslies in the vertical axis, ρ. However, every amplitude levelhas a variation range dominated by noise. The greater the
Fig. 2. The procedures of the proposed MFI method.
noise is, the wider the amplitude level. We plot the amplitudediagonal (i.e., the magnitude of complex signal as the abscissaand the ordinate) in Fig 2 (b). The object becomes to searchthe cluster centers of the amplitude diagonal in Fig. 2 (b).Thus, the nonlinear clustering is translated into a linear oneby mapping the amplitude circles onto the amplitude diagonal.
In the third step, we will calculate the key parameters of theFSFDP clustering algorithm, i.e., density ρi and the distance,δi, away from the points of higher density. Here, the Gaussiankernel function is used as the density, ρi, of data i, defined by,
ρi =∑j∈Is
e−(di j /dc)2
(2)
where Is = {1, 2, . . . , N} is the label set of the data points, anddi j is the distance of data i from data j . The cutoff distanceis dc = ω∗d f (N(N−1)/50), where f (·) is the rounding function,N is the number of the data points, and ω is the weightparameter. The minimum distance between the data and otherdata with higher density is δi . However, in the case of datawith the largest local density, δi is the largest distance of alldata. The cluster centers are these data points with relativelylarge local densities ρi and large δi. Thus, the decision graph,i.e., the ρi versus δi curve, can be used to fast search for thecluster centers, as shown in Fig. 2 (c), where red dots aredesignated as the cluster centers and black dots denote theother cluster points or the cluster halos. Normally, the numberof cluster centers can be observed by plotting the γ curve,as shown in Fig. 2 (d), where γ is defined as,
γi = ρiδi , i ∈ Is (3)
1852 IEEE PHOTONICS TECHNOLOGY LETTERS, VOL. 30, NO. 21, NOVEMBER 1, 2018
In Fig. 2 (d), we plot 20 larger γ values and arrange themin descending order. It can be seen that there is a variationbetween the ninth point and the tenth point in Fig. 2 (d).However, the difference is challenging to distinguish and isnot obvious.
To achieve a distinct difference between the cluster centerswith other points, we take the logarithm of the γ values,as shown in Fig. 2 (e). The difference between the ninth pointand the tenth point is distinct, and we can clearly observe thecluster centers, as shown by the higher columns in Fig. 2 (e).To automatically achieve the number of the cluster centers,we calculate the difference of the adjacent logarithm elements,
Y = di f f (x)
= [x(2) − x(1), x(3) − x(2), . . . , x(m) − x(m − 1)] (4)
The difference of the logarithm γ curve is shown in Fig. 2 (f)and the corresponding n value with the maximum absolutedifference is the number of the cluster centers. We candetermine the modulation formats of QAM signals accordingto the obtained n values.
III. MFI SYSTEM AND RESULTS
A. Simulation SystemFirst, a numerical simulation is performed to verify the
feasibility and further evaluate the performance of the pro-posed MFI method. In the simulation system, the 12.5-Gbaud4/8/16/32/64/256-QAM signals are transmitted through anadditive Gaussian white noise channel. The OSNR valuesdetermine the amplitude distributions of these signals and havesignificant influence on the MFI performance.
Fig. 3 plots the number of the amplitude circles and theidentification accuracy as functions of the OSNR values. Theamplitude distribution and the calculated n of the 256-QAMsignal with a 30-dB OSNR are inserted in Fig. 3 (a). We utilize100 testing data points for each modulation of each OSNR.However, for the more challenging signals with ambiguousamplitude diagrams, the number of the identified amplitudecircles may not be the same in each test. Therefore, we use theaverage number of the identified amplitude circles of all testsin this work. The proposed MFI scheme can work to distin-guish these QAM signals in different OSNR ranges. Moreover,the challenging QPSK and 8-QAM signals with OSNRs lessthan 10 dB have a divergent amplitude distribution, and theywill be falsely identified and given larger amplitude numbers,as shown in Fig. 3 (a). For the lower OSNRs of approximately12 dB, it is challenging to identify more modulations andpractical higher-order QAM signals, such as 32/64/256-QAMsignals, cannot work in such low OSNRs because of theamount of error. As shown in Fig. 3 (b), the lowest OSNRsto achieve a 95% identification accuracy are 7 dB for QPSK,10 dB for 8-QAM, 16 dB for 16-QAM, 21 dB for 32-QAM,22-dB for 64-QAM and 33 dB for 256-QAM signals.
B. Experimental SystemNext, the experiments are performed to evaluate the per-
formance of the proposed MFI methods, i.e., the robustnessof the identification accuracy to the amplified spontaneousemission (ASE) noise and fiber nonlinearities. The exper-imental setup consists of the transmitter, the transmissionSMF and the coherent optical receiver, as shown in Fig. 4.
Fig. 3. (a) Calculated average number of the identification amplitude circlesand (b) identification accuracy vs. OSNR (dB).
Fig. 4. Experimental setup of the blind MFI scheme.
The transmitter can emit the 12.5-Gbaud QPSK, 16-QAMand 64-QAM signals, depending on the levels of electricalsignals from the arbitrary waveform generator (AWG). Forthe ASE robustness measurement, the transmitted signal isattenuated by a variable optical attenuator (VOA) to changethe signal power into an erbium-doped fiber amplifier (EDFA),which can vary the signals’ OSNR values. For the robustnessmeasurement to the fiber nonlinearities, the amplified signalby the EDFA is attenuated by another VOA to adjust thelaunched power into the standard SMF. Before the standardcoherent optical receiver, the signal power is adjusted to a
ZHANG et al.: BLIND AND NOISE-TOLERANT MODULATION FORMAT IDENTIFICATION 1853
Fig. 5. (a) Measured average number of the identification amplitude circlesand (b) identification accuracy vs. OSNR (dB) for 4/16/64-QAM signals andinserts are the amplitude diagram and the difference γ curve of the 64-QAMsignal with 20.8-dB OSNR.
Fig. 6. Measured average number of the identification amplitude circles andidentification accuracy vs. the launched signal power into SMF (dBm) of (a)16-QAM signal and (b) 64-QAM signal.
suitable level by the final-stage EDFA and VOA. The acquireddata from the real-time oscilloscope is first processed by themodulation-format transparent finite impulse response (FIR)filter-based chromatic dispersion compensation and Gardner-based clock recovery algorithms. The proposed MFI algorithmis inserted and then the modulation-format dependent DSPalgorithms are assigned according to the obtained modulation-format information.
By adjusting the first VOA, we can measure the iden-tification accuracy with the different OSNR values, wherethe transmission SMF is skipped. Fig. 5 plots the measurednumber of the amplitude circles and the identification accuracyagainst the various OSNR values. The lowest OSNRs toachieve 95% identification accuracy are 13.2 dB for QPSK,13.3 dB for 16-QAM and 19.7-dB for 64-QAM signals, wherewe use 50 testing data points for each modulation-formatsignal. The measured results have a similarity to the simulatedresults, as shown in Fig. 3 and Fig. 5. The OSNR deviationcomes from the imperfect fiber channel, the optical-electricalcomponents in practice and the applied modulation signals.
To evaluate the robustness of the proposed MFI to fibernonlinearities, we inserted the SMF transmission part (includ-ing EDFA, VOA and SMFs) prior to the aforementionedpreamplifier. Because the 16-QAM and 64-QAM signals arevery susceptible to the fiber nonlinearities, we measure theiridentification accuracy with a different launched signal powerinto the SMF. The 240-km SMF for the 16-QAM signal andthe 80-km SMF for the 64-QAM signal are used; the detailedbit-error-ratio performance was described in [15].
Fig. 6 plots the identification accuracy of the 16-QAMsignal and the 64-QAM signal with the different launchedsignal powers into the SMFs. The insets are the ampli-
tude diagrams and the constellation diagrams with theminimum signal power and the maximum signal power. Fromthe inserted constellation diagrams, we can clearly observethe effects of the ASE noise and the fiber nonlinearities,i.e., the blurry constellation diagrams and the phase-rotatedconstellation diagrams. Because fiber nonlinearities cause theconstellation diagrams of the signals to deteriorate instead ofthe amplitude diagrams, the proposed MFI scheme is robust tofiber nonlinearities, and the measured identification accuracyis kept constant, as shown by triangle-marked curves in Fig. 6.
IV. CONCLUSION
In this work, we propose a blind MFI scheme based on adensity-peak clustering algorithm and extract the normalizedamplitude diagonal as the feature reference. The proposedMFI scheme utilizes the difference of the decision graph’slogarithm to determine the number of amplitude circles ofthe QAM signals without the use of training sequences.We experimentally demonstrated the proposed MFI schemefor the 4/16/64-QAM signals and verified its strong robustnessto fiber nonlinearities.
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