Fig 5 - uploaded by Ye Tian
Content may be subject to copyright.
OSNR measurement errors for 32-Gbaud PDM-QPSK signal after 37.5-GHz filter with (a) different degrees of polynomial and (b) different measurement times of γ n s .
Source publication
A novel in-band optical signal to noise ratio (OSNR) measurement technique based on polynomial fitting function with high performance is proposed and experimentally demonstrated. By introducing a polynomial function to fit the normalized autocorrelation function of the signal, the OSNR can be measured without the requirement for any prior knowledge...
Contexts in source publication
Context 1
... we investigate the impact of degree of polynomial (N) and measurement times (M) of γ ns on the performance. As shown in Fig. 5(a), when N is set as 1, 2, and 3, the corresponding fitting functions are γ s (τ ) ≈ 1 − c 1 τ 2 , γ s (τ ) ≈ 1 − c 1 τ 2 − c 2 τ 4 , and γ s (τ ) ≈ 1 − c 1 τ 2 − c 2 τ 4 − c 3 τ 6 , respectively. The results indicate that N must be greater than or equal to 2; otherwise, big errors will be caused (N = 1). And the performance of ...
Context 2
... that N must be greater than or equal to 2; otherwise, big errors will be caused (N = 1). And the performance of measurement is almost the same when N is greater than or equal to 2. Therefore, in our experiments, N is set as 2, i.e., γ s (τ ) ≈ 1 − c 1 τ 2 − c 2 τ 4 . The experimental results of different measurement times of γ ns are shown in Fig. 5(b). When M is set as 3, the power spectra of noisy signals are measured three times, and the corresponding NACFs of noisy signals are γ 1 ns , γ 2 ns , and γ 3 ns . The maximum and minimum of the 3 NACFs are removed and γ ns = q =3 q=1 γ q ns − max(γ q ns ) − min(γ q ns ). When M is set as 4, the power spectra of noisy signals are ...
Context 3
... set as 4, the power spectra of noisy signals are measured four times, and the corresponding NACFs of noisy signals are γ 1 ns , . . . , γ 4 ns . We remove the maximum and minimum of the 4 NACFs and compute the average of remaining two NACFs, i.e., γ ns = 1 2 { q =4 q=1 γ q ns − max(γ q ns ) − min(γ q ns )}, and likewise for M = 5. The results in Fig. 5(b) show that when M ≥ 3, the performance of OSNR measurement is similar; therefore, we use 3 NACFs of noisy signals to determine the final γ ns (τ ) considering the real-time operation and the stability of ...
Citations
... (a) The injected current of the HVDC system is determined by the commutation bus voltage, and the relationship between the injected current and the commutation bus voltage can reflect the overall performance of the HVDC control system. (b) Polynomial function fitting is widely used in nonlinear system modeling and has many advantages, such as simple expression, good performance for finite data points, and low errors [23][24][25]. ...
Fault steady-state analysis method can help to analyze the variation law of electrical quantities, clarify the fault characteristics, and provide the basis of power system design, protection scheme formulation, short-circuit current limitation and electrical equipment selection. Existing methods of fault steady-state analysis for alternating current (AC) system with line-commutated converter (LCC) high voltage direct current (HVDC) infeed are not suitable for high-severity AC system faults and existing equivalent models of LCC-HVDC system are inaccurate under asymmetrical AC system fault. Hence, a more accurate method is proposed in this paper. First, according to the relationship between the injected current of LCC-HVDC system and the fault phase voltage of commutation bus, equivalent models of the LCC-HVDC system under symmetrical fault and asymmetrical fault of AC system are established respectively. Based on the equivalent models of LCC-HVDC system and the fault sequence network of AC system, a fault steady-state analysis method for AC system with LCC-HVDC infeed is proposed. The proposed method is validated on CIGRE Benchmark HVDC model using PSCAD/EMTDC software. The results show that the proposed method is accurate and suitable for faults of different severities in AC system with LCC-HVDC infeed.
... A healthy quality of optical transmission links is essential for the next generation optical transparent network [1,2]. With the help of optical performance monitoring (OPM), we can provide precise information about the channel condition and the real-time quality of optical signals, which can be utilized for the optimization of the transmission performance and management of the network [3]. ...
We propose a cascaded neural network (NN) to simultaneously identify the modulation formats and monitor the optical-signal-to-noise ratio (OSNR). In the second-level network, it is a single deep NN (DNN) rather than multiple sub-networks, which makes the architecture more compact and can save the resource for real implementation. However, since the data set is constituted from all modulation formats, the universality can be guaranteed but not for the accuracy and the complexity. To accelerate the estimation process and improve the accuracy, we introduce the transfer learning (TL) and reconstruct the data set with a part from all of the modulation formats for universality and another part from a specific modulation format for TL to pursue higher accuracy. In the experiment, we compare the proposed cascaded single neural network (CSNN) with or without TL, cascaded multiple neural networks (CMNN) and adaptive multi-task learning (MTL) for MFI and OSNR monitoring. In the first-level NN, all of the three schemes can achieve the accuracy of MFI as 100 %. In the second-level NN, the CSNN with TL (TL-CSNN) can significantly improve the training speed and decline the RMSE of 0.19 dB compared with CSNN without TL. The TL-CSNN also has faster convergence speed and is more stable compared with CMNN and adaptive MTL.
... With the rapid growth of flexibility, scalability and reliability on optical fiber communication, the next generation optical network becomes more complex and transparent. A healthy quality of optical signals and transmission links becomes more essential to be obtained [1,2]. In order to reduce the operating costs, ensure optimum resource utilization and guarantee adequate operation and management of dynamic optical networks, the optical performance monitoring (OPM) has been a key enabling technology [3]. ...
We propose a transfer learning assisted deep neural network (DNN) method for optical-signal-to-noise ratio (OSNR) monitoring and realize fast remodel to response to various system parameters changing, e.g. optical launch power, residual chromatic dispersion (CD) and bit rate. By transferring the hyper-parameters of DNN at the initial stage, we can fast response to the channel variation with fewer training set size and calculations to save consumptions. For feature extraction processing, we use amplitude histograms of received 56-Gb/s QPSK signals as the input for DNN at the initial stage, which shows the root mean squared error (RMSE) of OSNR estimation is less than 0.1 dB with the OSNRs ranging from 5 to 35 dB. Then, we change several system parameters and find superior capabilities of fast remodeling and data resource saving with the proposed method. The required training epochs have about four times reduction, and the required training set size is only one-fifth compared to retraining the network without any accuracy penalty. The DNN assisted by transfer learning can save resources and will be beneficial for real-time application on OSNR estimation.
In the letter, a new frequency offset generation method based on polynomial fitting is proposed. The frequency offset can be obtained by sampling the fitted curve. By adjusting the coefficients of the polynomial, different forms of frequency offset can be generated. Different from existing window-based frequency diverse arrays (FDAs) which adopt traditional window functions to generate frequency offset and suffer from high sidelobe levels (SLLs) and low range resolution, the performance of Poly-FDA can be more flexibly regulated by fitting different forms of polynomial curves. Moreover, the particle swarm optimization (PSO) algorithm is exploited into the coefficient optimization process to obtain the optimal polynomial curve and enhanced beampattern property. Numerical examples demonstrate the superiority and effectiveness of the proposed Poly-FDA with lower SLL and narrower beamwidth other than the existing window-based FDAs and FDA of other frequency offset forms.
To extract important information about the nonlinear signals, this letter makes the utmost of the fitting advantages of Gaussian and polynomial functions, and proposes a nonlinear signal model with broader applications. Then we focus on the parameter estimation issues of the proposed models in the presence of noises. The stability factor recursive algorithm is devised based on the increasing noisy data, which makes full use of the information from the nonlinear signals. Applying the hierarchical identification principle, a two-stage recursive algorithm with higher computational efficiency is developed for the nonlinear signals. The simulation results test the effectiveness of the proposed algorithms from the aspects of estimation accuracy and prediction effect.
Deformation has a significant effect on the machining accuracy of the monolithic components. Fluctuant initial residual stress is one of the factors causing deformation. In this paper, initial residual stresses of six specimens of 7050-T7451 aluminum alloy plates with 65 mm thickness and 70 mm thickness are measured respectively. Fluctuant initial residual stresses are fitted by segmented polynomials of best uniform approximation. An analytical model in terms of the deformation and initial residual stress is established for single-sided stringer parts. The results show that the weight of web-induced deformation in the whole deformation of the part is about 74%–98.5% and the weight sidewall-induced deformation is about 1.5%–26%. Therefore, minimizing the web-induced deformation is the key to minimize the whole part deformation. The condition number calculated by coefficients matrix of initial residual stress inside the web is defined in the model. The value of condition number is determined by initial residual stress inside the web and its fluctuant degree, which is positively correlated with web-induced deformation when the structural stiffness is invariant. By selecting the blank with a small fluctuant amplitude of stress or changing the workpiece position, condition number will be decreased, and the deformation is minimized.
We propose a novel in-band optical signal to noise ratio (OSNR) monitoring technique in digital coherent receiver based on fractional Fourier transform (FrFT), which employs the power aggregation property of linear frequency modulation (LFM) signal in fractional domain. In our method, the LFM signal with extremely low power is added on the data signal. Thus, the received signal exists a peak in fractional domain, which can be used to indirectly differentiate the data signal power from noise thereby calculating the OSNR. Meanwhile, the proposed technique does not occupy additional spectrum resource and avoids any additional hardware on top of the standard digital coherent receiver. Experimental results of a 10 Gbaud quadrature phase-shift keying (QPSK) system show that the proposed technique is highly accurate, with the monitoring error less than 0.55 dB in the OSNR range of 9–29 dB for back-to-back transmission and 0.9 dB in the OSNR range of 11–25 dB for 960 km standard single mode fiber (SSMF) transmission.
