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In this paper, a novel optical signal to noise ratio (OSNR) monitoring method using 2-dimension (2-D) phase portrait is proposed and demonstrated, which is generated by using a single low-speed sampling channel with software synchronization technique. Moreover, variable phase difference is proposed to generate the X-Y pairs, which increases the tolerance of synchronization accuracy significantly. This method is a cost effective solution with simple system setup.
© 2015 Optical Society of America
1. Introduction
In high-speed optical transmission systems, optical signal to noise ratio (OSNR) is one of the most important parameters that determine system performance. OSNR monitoring plays a vital role in the system management of optical fiber transmission systems [1]. So far, there are several methods that have been proposed for OSNR monitoring, which are based on the optical characteristics of signal and noise [2–4]. Recently, the OSNR monitoring methods based on electrical sampling have attracted much attention [5, 6], which show their simple setup, easy operation, and convenient maintenance. They use a 1-dimension (1-D) signal amplitude histogram to monitor OSNR or Q-factor, but the monitoring accuracy is seriously affected by other optical effects [5–7]. In [8], the monitoring setup using two sampling channels was proposed to generate a 2-dimension (2-D) phase portrait, which exhibited particular pattern evolutions induced by different optical impairments. Since the 2-D plot delivers more information of waveform distortion than that of the 1-D histogram, multiple optical impairments could be isolated from the pattern evolution of the 2-D phase portrait. The OSNR monitoring parameter was derived by using simple statistical pattern recognition on the 2-D phase portrait [9–11]. However, it is an expensive approach to depict the 2-D phase portrait by using two high bandwidth samplers. Thus, we proposed and demonstrated that a single sampler is used to generate the 2-D phase portrait for OSNR monitoring [12]. Although this method reduces the monitoring setup cost in some degree, the usage of the related sampling frequency limits the application scope. For the un-related sampling, software synchronized technique was proposed to reconstruct eye diagram or constellation diagram for OSNR monitoring [13, 14]. The aliasing frequency generated by the un-related low-speed sampling is estimated by using fast Fourier transform (FFT) and phase reference detection method, which is the key part of the software synchronization technique. However, this method requires highly accurate estimation of the aliasing frequency for synchronization. A certain amount of the estimated aliasing frequency offset causes the re-timing shift of the eye diagram re-construction, which seriously affects the monitoring accuracy. In [15], we have proposed to use software synchronized single channel sampling (SCS) technique to depict the 2-D phase portrait with half-symbol phase difference for OSNR monitoring, which has certain tolerance to the aliasing frequency estimation offset, and increases the application scope.
In this paper, the working principle of the 2-D phase portrait generation using software synchronized SCS is introduced. Then, in order to increase the tolerance of the aliasing frequency estimation offset, variable phase difference is proposed for the phase portrait generation. In the following experimental demonstration, OSNR monitoring is successfully performed by using the proposed method in different modulation formats in the presence of certain amount of chromatic dispersion (CD).
2. Working principle and experimental setup
The experimental setup of the proposed OSNR monitoring method is shown in Fig. 1. In this paper, 10.7-Gb/s NRZ-OOK, NRZ-DPSK, and RZ-DPSK signals are tested respectively in the proposed OSNR monitoring system. The modulated signal is coupled with a 0.8-nm noise source after a certain period of standard single mode fiber (SSMF) transmission. The signal OSNR is tuned by varying the output power of the noise source, while different values of residual CD are introduced by using different lengths of SSMF. Then, an Erbium-doped fiber amplifier (EDFA) is used to compensate power loss after the fiber transmission. Before receiver, the redundant noise is removed by a 0.65-nm optical band pass filter (OBPF). In this paper, the receivers of the NRZ-OOK and RZ-DPSK signals are composed of a single photo detector and a linear electrical amplifier, while an optical delay interferometer (DI) is employed to demodulate the phase modulated signal before the photo detector in the NRZ-DPSK system. By using this approach, the significant waveform peak variation induced by optical noise can be obtained. In the experimental demonstration, a single 16-GHz-bandwidth sampling channel of real-time oscilloscope is triggered by un-related low sampling frequency (~9.77 MHz), as an analog to digital converter (ADC), which derives the discrete waveform sequence. The rest part of the monitoring setup is the data processing, which includes the sampling sequence synchronization and the OSNR monitoring parameter derivation.
The sampler is triggered by the un-related low-speed sampling frequency (fs), which generates the aliasing frequency (fa), as Fig. 2(a) shows. The sample interval between the neighbor samplers can be expressed by n × T + τ (n is an integer number; T is one-symbol duration; τ is the within one-symbol time shift between neighbor samples). There is a particular relationship between the aliasing frequency and the sampling frequency as expressed in Eq. (1) [14].
After the aliasing frequency is estimated, the electrical phase difference (τ/T) between adjacent samples can be obtained by Eq. (1). The accumulated phase change of the whole sample sequence can be obtained as Fig. 2(b) shows. From the synchronized sample sequence, the nearest sample pairs, whose phase differences are equal to half-symbol period (∆t/T = 0.5), are selected as X-Y pairs to be depicted in a 2-D coordinate system, which is an equivalent portrait of the half-symbol delay-tap sampling (DTS) plot [12].
The aliasing frequency estimation is in charged by FFT based reference phase detection [13, 14]. However, when the aliasing frequency estimation has certain estimation frequency offset (fo), it generates the drift phase (∆τ/T) in the obtained phase difference between the samples, as expressed in Eq. (2). More seriously, the drift phase is accumulated in the synchronized sample sequence, which causes the re-timing error of the eye diagram reconstruction. As shown in Fig. 3(a) and 3(b), when the aliasing frequency offset (FO) reaches 500 Hz, the recovered eye of NRZ-DPSK signal suffers serious re-timing error, which is formed by 10,000 samples. Since the interval between the generated X-Y pair is several samples apart, the accumulated drift phase between the sample pair is too small to influence the phase portrait pattern, as shown in Fig. 3(c) and 3(d). However, as the estimated aliasing FO increases to 10 kHz, the plot pattern is changed by the phase difference with large drift phase, where the phase difference between the X-Y pair deviates from half-symbol period in large degree, as shown in Fig. 3(e).
Fig. 3 For 30-dB OSNR NRZ-DPSK signal, the reconstructed eye diagram, when the aliasing FO is (a) 0 Hz, (b) 500 Hz; half-symbol phase difference phase portrait, when the aliasing FO is (c) 0 Hz, (d) 500 Hz, (e) 10 kHz.
For the conventional 2-D phase portrait, the time delay or phase difference between the sample pair is a fixed value, such as tenth-symbol or half-symbol duration [9–15]. The pattern of the tenth-symbol delay phase portrait is totally different from that of the half-symbol delay phase portrait. However, a slight deviation of the phase difference does not change the pattern obviously. As the plots in Fig. 4 show, the phase portraits are generated by using different phase differences (∆t/T). The patterns generated by using the X-Y pairs with slightly different phase differences (∆t/T = 0.5 and 0.47) are similar in all the three tested modulation formats respectively. Thus, the OSNR monitoring relying on these two different phase portraits would have similar monitoring performance.
Fig. 4 Phase portrait generated by using different phase differences in different modulation formats.
Therefore, the sample pairs, whose phase differences are within a particular range (2•to/T), are proposed to be selected from the sample sequence to depict the 2-D phase portrait, as shown in Fig. 5(a), where l is an integer number. For example, the phase difference between the sample S1 and its partner Sp can be in the range from (Δt-to)/T to (Δt + to)/T, so that the sample pairs with the variable phase difference would be easier to be found along the sample sequence. Consequently, the interval between the sample pairs is reduced, which reduces the accumulated drift phase, such that the tolerance of the aliasing FO is increased. As the variable half-symbol phase difference phase portrait (to/T = 0.03) shows in Fig. 5(b), 10-kHz aliasing FO does not change the phase portrait pattern of NRZ-DPSK signal obviously.
Fig. 5 (a) Schematic diagram of phase portrait generated by variable phase difference, (b) variable half-symbol phase difference phase portrait under 10-kHz aliasing FO.
For the OSNR monitoring parameter derivation, statistical pattern recognition is employed to derive the statistical parameter from the phase portrait [15]. For NRZ-OOK and NRZ-DPSK signals, the bimodal distribution along the diagonal direction of the pattern is derived for the monitoring parameter calculation, while the uni-modal distribution of the line pattern end of RZ-DPSK signal is adopted.
3. Experimental results and discussions
Firstly, the OSNR monitoring error induced by the phase difference deviation is investigated in the scheme, which uses the fixed phase difference phase portrait. As the experimental results show in Fig. 6(a), when the phase difference deviates from half symbol, the OSNR monitoring errors increase in the NRZ-DPSK and RZ-DPSK systems. In high OSNR cases, the OSNR monitoring is more sensitive to the phase difference deviation. When the deviation of the phase difference is less than 0.03 times one symbol period, the OSNR monitoring errors can be kept in an acceptable range. However, for the NRZ-OOK signal, the OSNR monitoring errors are almost within 1 dB, since its continuous mark level feature leads to similar pattern in the demonstrated phase difference range, which can refer to the phase portraits in Fig. 4. Thus, the variable phase difference range (to/T = 0.03) is adopted to generate the phase portrait for OSNR monitoring. In Fig. 6(b)-6(d), the OSNR monitoring error versus the aliasing FO is investigated in the three tested modulation formats respectively after using the variable phase difference. In the NRZ-DPSK and RZ-DPSK systems, the OSNR monitoring errors can almost be kept below 1 dB as the aliasing frequency estimation offset increases up to 10 kHz in the demonstrated OSNR values. For the NRZ-OOK format, due to its insensitivity to the phase difference deviation, its half-symbol phase portrait is more robust to the tested aliasing frequency estimation offset.
Fig. 6 (a) OSNR monitoring error versus the phase difference deviation; OSNR monitoring error versus aliasing frequency offset in (b) NRZ-DPSK, (c) RZ-DPSK, (d) NRZ-OOK.
In Fig. 7, the experimental results exhibit the OSNR monitoring performance in the presence of both aliasing FO and CD effect in the three tested formats. The aliasing frequency estimation offset varies from 0 Hz to 10 kHz, while the CD is changed from 0 ps/nm to 425 ps/nm simultaneously. When the two factors co-exist in the monitoring system, the proposed OSNR monitoring method can keep good monitoring accuracy from 10 dB to 25 dB. Beyond 25 dB, the OSNR monitoring performances of both the NRZ-DPSK and RZ-DPSK systems are quite poor, since they are more sensitive to the CD induced pattern distortion.
Fig. 7 OSNR monitoring performance in the presence of CD variation and aliasing frequency offset in (a) NRZ-DPSK, (b) RZ-DPSK, (c) NRZ-OOK.
4. Conclusion
In this paper, the OSNR monitoring method using single channel sampling with software synchronization technique is proposed and experimentally demonstrated. The utilization of the un-related sampling scheme extends the application scope of single channel sampling technique. More importantly, by using the proposed variable phase difference phase portrait, the tolerance of the aliasing frequency estimation offset is significantly improved, which reduces the accuracy requirement of software synchronization technique. Moreover, this method significantly reduces monitoring system setup complexity and cost. In addition, the proposed phase portrait is available for other optical impairments monitoring.
Acknowledgment
The authors would like to thank the support from MOE Singapore of AcRF Tier 2 Grant MOE2013-T2-2-145 and NNSFC Grant F010902.
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