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In this paper, we propose a novel method to depict 2-dimension (2-D) phase portrait by using single-channel-sampling (SCS) technique, which can be used for optical performance monitoring. Single low speed sampling scheme reduces cost and complexity of monitoring system setup significantly. In the experimental demonstration, optical signal to noise ratio (OSNR) is monitored from 10 dB to 30 dB by statistical analysis on pattern evolution of the generated 2-D phase portrait in both NRZ and RZ systems. The chromatic dispersion (CD) influence on OSNR monitoring performance is also investigated.
©2014 Optical Society of America
1. Introduction
OSNR is one of the most important parameters on physical layer of optical fiber transmission system [1]. During fiber transmission, amplified spontaneous emission (ASE) noise is generated. It is a non-reversible effect that cannot be compensated in optical domain directly. Thus, OSNR monitoring is important for intelligent system management in future smart high-speed optical network. Compared with OSNR monitoring method based on filtering effect [2], nonlinear effect [3], and polarization characteristic [4], electrical-sampling technique based OSNR monitoring method shows its advantages on simple setup and easy maintenance. Among many electrical-sampling based OSNR monitoring methods, Q-factor monitoring relying on eye diagram requires derivation of signal amplitude at particular time slot [5]. Asynchronous sampling method is proposed to monitor optical impairments by histogram of received signal waveform [6, 7]. However, it is difficult to distinguish multiple optical effects via 1-dimension (1-D) histogram. In [8], 2-dimension (2-D) phase portrait is proposed for multiple optical impairments monitoring. The portrait is depicted by superposition of the X-Y pairs in 2-D coordinate system, which are sampled by two sampling channels with internal time delay. The X-Y pairs display the relative intensity between two addresses on electrical signal, which are with fixed electrical phase difference. Since the pattern evolutions in phase portrait induced by different optical impairments are diverse, it is possible to separate and quantize multiple impairments by using 2-D phase portrait. In [9–11], statistical analysis on 2-D phase portrait provides a simple and efficient approach to monitor OSNR and CD in different modulation formats. However, by using electrical-sampling technique, high bandwidth sampler is necessary to display waveform distortion induced by optical impairments, so two samplers are the major cost in monitoring setup. Thus, we propose to derive X-Y pairs by using single-channel-sampling (SCS) technique, which could reduce the monitoring setup cost significantly. Moreover, as discussed in [7], for OSNR monitoring, the monitoring components induced noise power should be much smaller than the noise power of monitored signal, which means that larger optical input power is preferred. Since SCS method samples the received signal directly without 3-dB split, it can also save 3-dB power budget for the monitored signal, compared with two-channel-sampling (TCS) method.
In this paper, we experimentally demonstrate the generation of phase portrait by SCS method in both NRZ and RZ systems. Additionally, OSNR is estimated from the statistical analysis on the generated phase portrait.
2. Operation principle
2.1 Working principle of single channel sampling scheme
The schematic diagrams of TCS method and SCS method are shown in Fig. 1(a). SCS saves one sampler cost, while the simplified setup is easier to be maintained and operated. In Fig. 1(b), the derivation procedures of X-Y pairs by these two methods are shown and compared. For TCS method, two samples (x and y) are obtained by two sampling channels separately with short and fixed time delay (Δt) as X-Y pair. Typically, time delay (Δt) is less than one symbol duration. In SCS method, since single sampler continuously samples waveform with a relative slower frequency (fs), the time interval between continuous two samples, S1 and S2, can be expressed as 1/fs, which can also be described as n × T + τ (n is an integer number; T is one symbol duration; τ is the remaining time that is less than one symbol duration). Once data rate and sampling rate are known, τ can be obtained.
Fig. 1 (a) Schematic diagram of the two-channel-sampling method and single-channel-sampling method. (b) Working principle of these two methods.
2.2 X-Y pairs generation by self-delay scheme
By using SCS method, we can obtain discrete sample sequence (S1…Sn) with known sampling interval, which is shown in Fig. 2.There exists a sample (Sk + 1) that the time interval between Sk + 1 and S1 is k∙n × T + k × τ, which can also be expressed as m × T + ∆t (m is an integer number, ∆t is same as TCS method). The original sequence makes self-delay by k samples, so that Sk + 1 and S1 are composed of new X-Y pair with large time delay (m × T + ∆t). Then, the following samples between these two sequences are selected to form X-Y pairs by this way, which are depicted in 2-D coordinate system successively. The generated phase portrait delivers the relative intensity between two addresses on waveform, where is with fixed phase difference (2π∙∆t/T). It is equivalent to the phase portrait by using TCS method, which employs small time delay to generate same phase difference. In the experimental demonstration for SCS method, sampling oscilloscope is used to derive discrete waveform serial. A sampling clock, whose frequency is fd/256 (fd = data rate), is employed to obtain accurate and known sampling time interval for SCS method demonstration.
2.3 Experimental demonstration setup
The experimental setup of our proposed monitoring method is shown in Fig. 3. 10-Gb/s NRZ-OOK and NRZ-DPSK are modulated by intensity modulator with different bias voltages, while 20-Gb/s NRZ-DQPSK and 50-Gb/s RZ-DQPSK are modulated by I/Q modulator separately. After optical fiber transmission, the optical signal is coupled with a 1.6-nm bandwidth noise source. An EDFA is used to compensate power loss in the fiber transmission. Before photo detection, another 0.65-nm optical band pass filter is used to remove redundant noise. For NRZ-OOK and RZ-DQPSK system, we employ single photo detector (42 GHz) to detect optical signal directly, and then, use sampling oscilloscope (20 GHz) to sample the detected electrical signal. For NRZ-DPSK and NRZ-DQPSK signals, a delay interferometer (DI) is used to demodulate phase modulated signal before the photo detector. The input power of photo detector is kept at −3 dBm. In this paper, we demonstrate OSNR monitoring from 10 dB to 30 dB. At the same time, residual CD is introduced by the optical fibers with different distances, in order to investigate OSNR monitoring performance under residual CD impairment.
2.4 Obtained 2-D phase portrait and OSNR monitoring parameter calculation algorithm
By using our proposed method, equivalent tenth symbol (∆t/T = 1/2) delay and equivalent half symbol (∆t/T = 1/2) delay phase portraits in 10-Gb/s NRZ-OOK system are shown in Figs. 4(a) and 4(b). In order to monitor OSNR, it is straight-forward to derive “space” and “mark” levels information from diagonal direction of the phase portrait generated by equivalent half symbol delay. Moreover, the portrait generated by equivalent tenth symbol delay derives more types of relative intensities than the one depicted by conventional TCS method, which uses less than one symbol time delay [9]. The central area of equivalent tenth symbol delay phase portrait is filled by the extra types of relative intensities, so that it is difficult to derive OSNR monitoring parameter from the diagonal direction of portrait. When m is larger than two, the phase portraits generated by using same ∆t and different m have same patterns.
Fig. 4 In the case of 30-dB OSNR (a) Equivalent 1/10 symbol delay phase portrait of 10-Gb/s NRZ-OOK; (b) Equivalent 1/2 symbol delay phase portrait of 10-Gb/s NRZ-OOK; (c) Equivalent 1/2 symbol delay phase portrait of 10-Gb/s NRZ-DPSK; (d) Equivalent 1/2 symbol delay phase portrait of 50-Gb/s RZ-DQPSK.
In this work, OSNR monitoring is based on statistical analysis on equivalent half symbol delay phase portrait. In NRZ-DPSK and NRZ-DQPSK systems, phase portrait is depicted by phase demodulated signal as shown in Fig. 4(c). For RZ-DQPSK signal, its pulse carver’s waveform is used to generate phase portrait by using direct detection on the monitored optical signal, whose equivalent half symbol delay phase portrait has line pattern for periodic waveform, as shown in Fig. 4(d), which is same as the one that uses half symbol delay in [11].
According to the different portrait patterns in NRZ and RZ signals, OSNR monitoring parameter extraction algorithm is divided into two types. For NRZ signals (including NRZ-OOK, NRZ-D(Q)PSK), points along diagonal and horizontal directions of the pattern are acquired to calculate bimodal distribution parameters, including mean values (m) and standard deviations (σ), as shown in Figs. 5(a)-5(c). In [9, 10], authors propose that OSNR is estimated by their defined factor F, based on bimodal distribution along diagonal direction. Since bimodal distance at diagonal direction in NRZ-DPSK and DQPSK systems varies with CD, we propose to use bimodal distance on horizontal direction to represent signal power, which is more robust to CD effect. Moreover, bimodal variation along diagonal direction is used to convey ASE noise induced two-level variation, so that the OSNR monitoring factor is expressed in Eq. (1) for NRZ systems.
Fig. 5 NRZ-DPSK equivalent half symbol delay phase portrait, (a) Points on diagonal and horizontal directions are derived; (b) Bimodal distribution along diagonal direction; (c) Bimodal distribution along horizontal direction.
For RZ-DQPSK system, points below the highlighted line are extracted to calculate uni-modal distribution on horizontal direction, including mean value (mu) and standard deviation (σu), as shown in Fig. 6. This cluster of points represents relative intensity of waveform peak to valley, which is related to OSNR variation. Thus, OSNR monitoring factor of RZ-DQPSK system is expressed as Eq. (2), which is proposed in [11].
Fig. 6 RZ-DQPSK equivalent half symbol delay phase portrait, (a) Points below red line are derived; (b) Uni-modal distribution of the selected points on horizontal direction
3. Experiment result and discussion
In Fig. 7, the experimental result shows that the phase portrait derived by our proposed SCS method can successfully be used to monitor OSNR in both NRZ and RZ systems. The calculated OSNR monitoring factor has been converted to corresponding OSNR value by using polynomial fitting, and each value in Fig. 7 is an average value of ten times estimation, where 20,000 X-Y pairs are used to calculate monitoring parameter each time. In 10-Gb/s NRZ-OOK system, OSNR monitoring error can be kept below 1-dB, when CD is below 340 ps/nm. In 10-Gb/s NRZ-DPSK system, OSNR monitoring error can be kept within 1-dB monitoring from 15-dB to 25-dB OSNR monitoring at demonstrated CD range. It is significant to obtain accurate OSNR monitoring performance from 15 dB to 25 dB. As CD increases, the pattern width on diagonal direction becomes narrow, so that the points from the two parts on diagonal direction are overlapped in lower OSNR case. Thus, it is difficult to derive accurate bimodal statistical parameters, which reduces OSNR monitoring dynamic range and increases monitoring error. When OSNR is high, large CD induces strong waveform distortion that overrides OSNR induced variation, which also increases OSNR monitoring error, as the inserter in Fig. 7(b) shows. Moreover, for 20-Gb/s NRZ-DQPSK system, OSNR monitoring is more sensitive to CD effect. The OSNR monitoring can be kept within 1-dB error from 15 dB to 25 dB, when CD is below 255 ps/nm. After data rate increases to 50 Gb/s, CD effect will significantly influence OSNR monitoring performance, due to narrower pulse width. As the experimental result shows in Fig. 7(d), OSNR monitoring in 50-Gb/s RZ-DQPSK system is sensitive to CD. When CD is at 55 ps/nm, the OSNR monitoring errors reach 2 dB from 10 dB to 20 dB. Moreover, the monitoring dynamic range is also decreased.
Fig. 7 Experimental results for OSNR monitoring of the proposed method at present of CD in (a) 10-Gb/s NRZ-OOK; (b) 10-Gb/s NRZ-DPSK; (c) 20-Gb/s NRZ-DQPSK; (d) 50-Gb/s RZ-DQPSK.
By using statistical analysis on monitoring parameter derivation, sample size used for statistical calculation is an important factor that determines accuracy and efficiency of monitoring system. As the result in Fig. 8 shows, when sample point size used for phase portrait depiction and monitoring parameter calculation reaches 10,000, standard deviation of the proposed OSNR monitoring method can be kept below 0.5 dB (the exhibited signal is 15-dB 50-Gb/s RZ-DQPSK). As sample size increases, the monitoring performance becomes more stable and accurate. Since the sampling rate can be at dozens of MHz, the derivation time of single phase portrait can be less than one micro-second.
Fig. 8 OSNR monitoring accuracy VS sample size used for portrait depiction and monitoring parameter calculation.
4. Conclusion
In this paper, we propose and experimentally demonstrate the generation of 2-D phase portrait depicted by our proposed SCS technique. By employing corresponding statistical analysis on the 2-D phase portraits, OSNR can be successfully monitored in both RZ and NRZ systems. Additionally, the phase portrait generated by our proposed method possesses potential for multiple optical impairment monitoring. More importantly, phase portrait derived by our proposed SCS scheme can save monitoring setup cost and 3-dB monitored signal power budget. Based on self-delay method, it is flexible to generate the phase portraits with different time delays.
Acknowledgment
This work is supported by AcRF Tier 1 Grant R-263-000-631-112 from MOE Singapore and R-2012-N-009 from National University of Singapore (Suzhou) Research Institute.
References and links
1. Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010). [CrossRef]
2. M. R. Chitgarha, S. Khaleghi, W. Daab, M. Ziyadi, A. Mohajerin-Ariaei, D. Rogawski, M. Tur, J. D. Touch, V. Vusirikala, W. Zhao, and A. E. Willner, “Demonstration of WDM OSNR performance monitoring and operating guidelines for pol-muxed 200-Gbit/s 16-QAM and 100-Gbit/s QPSK data channels,” in Optical Fiber Communication Conference/National Fiber Optic Engineers Conference 2013, OSA Technical Digest (online) (Optical Society of America, 2013), paper OTh3B.6. (2013).
3. Z. Chen, L. Yan, A. Yi, W. Pan, and B. Luo, “Simultaneous OSNR monitoring for two polarization tributaries of a PDM signal using a polarization-diversity nonlinear loop mirror based on FWM,” J. Lightwave Technol. 30(14), 2376–2381 (2012). [CrossRef]
4. J. H. Lee, D. K. Jung, C. H. Kim, and Y. C. Chung, “OSNR monitoring technique using polarization-nulling method,” IEEE Photon. Technol. Lett. 13(1), 88–90 (2001). [CrossRef]
5. I. Shake, H. Takara, and S. Kawanishi, “Simple Q factor monitoring for BER estimation using opened eye diagrams captured by high-speed asynchronous electrooptical sampling,” IEEE Photon. Technol. Lett. 15(4), 620–622 (2003). [CrossRef]
6. N. Hanik, A. Gladisch, C. Casper, and B. Strebel, “Application of amplitude histograms to monitor performance of optical channels,” Electron. Lett. 35(5), 403–404 (1999). [CrossRef]
7. R. S. Luís, A. Teixeira, and P. Monteiro, “Optical Signal-to-Noise Ratio estimation using reference asynchronous histograms,” J. Lightwave Technol. 27(6), 731–743 (2009). [CrossRef]
8. S. D. Dods and T. B. Anderson, “Optical performance monitoring technique using delay tap asynchronous waveform sampling,” in Optical Fiber Communication Conference and Exposition and The National Fiber Optic Engineers Conference, Technical Digest (CD) (Optical Society of America, 2006), paper OThP5 (2006).
9. B. Kozick, A. Maruta, and K. Kitayama, “Experimental investigation of delay-tap sampling technique for online monitoring of RZ-DQPSK signals,” IEEE Photon. Technol. Lett. 21(3), 179–181 (2009). [CrossRef]
10. F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical analysis of optical signal to noise ratio monitoring Using Delay-Tap Sampling,” IEEE Photon. Technol. Lett. 22(3), 149–151 (2010). [CrossRef]
11. F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photon. Technol. Lett. 22(11), 823–825 (2010). [CrossRef]
