1 January 2007 Impact of uncompensated dispersion on non-Gaussian statistics in duobinary transmission
Author Affiliations +
Optical Engineering, 46(1), 010501 (2007). doi:10.1117/1.2430507
We show through numerical simulation of 10.7-Gbits/s dense wavelength-division multiplexed (DWDM) duobinary transmission over 800 km of nonzero-dispersion-shifted fiber that uncompensated dispersion can introduce significant departures from Gaussian statistics in the receiver current.
Lobanov, Raghavan, Downie, Mauro, Sauer, and Hurley: Impact of uncompensated dispersion on non-Gaussian statistics in duobinary transmission



The conventional method of calculating the bit error ratio (BER) using Q -factor analysis assumes that the statistics of the received current is Gaussian. Whereas for nonreturn to zero (NRZ)-type systems employing direct detection, the assumption of Gaussian statistics yields satisfactory results due to a fortunate cancellation of errors,1 phase-shift-keyed modulation and balanced detection show non-Gaussian receiver current statistics.2, 3, 4, 5, 6 Recently7 we showed that duobinary transmission over 600km of nonzero-dispersion-shifted fiber (NZDSF) also exhibits significant departures from Gaussian statistics through a comparison between experiment and simulations in a 38-channel 10.7-Gbits/s dense wavelength-division multiplexed (DWDM) system, wherein our numerical predictions of non-Gaussian statistics were correctly confirmed by experiment.

In this paper, we demonstrate that while uncompensated dispersion plays the dominant role in inducing departures from Gaussian statistics, duobinary signaling inherently exhibits non-Gaussian statistics due to its intrinsically strong intersymbol interference. This is especially true when duobinary is generated using a low-pass filter at the transmitter.7 We model an 8-channel 10.7-Gbits/s DWDM system over 800km of NZDSF with no in-line dispersion compensation and show that standard Q -factor analysis relying on Gaussian statistics can severely underpredict the true system performance, which can only be captured by taking into account non-Gaussian statistics.2, 3, 4, 5, 6 The results are important because there has been considerable recent interest8 in increasing system reach with no in-line compensation using a combination of duobinary modulation and uncompensated low-dispersion NZDSF; in such a scenario, Gaussian statistics yields erroneous results.


System Description

Figure 1 shows the basic DWDM setup. The system consisted of eight optical channels spaced by 50GHz ranging from 1547 to 1550nm , each carrying duobinary signals. The transmission medium of the link consisted of eight spans of 100km of Corning® LEAF® optical fiber. The fiber dispersion is about 4ps(nmkm) at 1550nm . The pseudorandom bit sequence (PRBS) had a length of 291 . All amplifiers in the link were single-stage erbium doped fiber amplifiers (EDFAs). The average launch power into each channel was about 1dBm . At the transmitter, we used a low-pass fifth-order Bessel electrical filter with a half width at half maximum (HWHM) of 2.5GHz . At the receiver, we used third-order Bessel optical filter with a HWHM of 15GHz and a fifth-order Bessel electrical filter with a HWHM of 6GHz . To elucidate the impact of uncompensated dispersion, we introduced varying amounts of lumped precompensation that compensated for 0, 25, and 50% of the cumulative dispersion value at 1550nm . In our case, since the cumulative dispersion value over the transmission fiber was nominally around 3200psnm at 1550nm , we introduced lumped dispersion-compensating fiber (DCF) that compensated for 0, 800, and 1600psnm . We compared these cases against a reference system impaired only by amplified spontaneous emission (ASE) noise. The optical SNR (OSNR) in all the four cases was around 21dB . We estimated the Q -factor at the end of the transmission system using two methods. In the first method, we used an accurate BER estimator based on a Karhunen-Loève expansion technique2, 3, 4, 5, 6 that does not make any assumptions about the nature of the received current; this method has produced accurate results,4, 5, 6 especially for balanced differential-phase-shift-keyed (DPSK) detection. The calculated BER was then converted to Q -factor. In the second method, we realized that there was significant patterning or banding caused by the interplay between dispersion and different bit sequences. Consequently, we applied the analysis first laid out by Anderson and Lyle9 to take into account intersymbol interference. The method divides the received data into different bit patterns (rails) and then assumes that the received current for each of the different patterns obeys Gaussian statistics with different means and variances and computes the Q -factor due to each of these individual patterns. The method then computes the average Q -factor using the individual Q -factors.

Fig. 1

Schematic of DWDM setup. mzm=mach-Zender modulator.



Results and Discussion

We present the results of our analysis in Fig. 2 through a plot of Q (in 20-log decibels) of the worst channel as a function of accumulated dispersion (in picoseconds per nanometer). We see that the discrepancy between the predictions of Gaussian statistics and those from the accurate BER estimator depends strongly on the amount of accumulated dispersion. Whereas the difference between Gaussian and non-Gaussian statistics leads to a Q difference of about 1.2dB for a completely noise dominated system, the difference increases to about 2.7dB for a system with no dispersion compensation. The difference in the strongly uncompensated regime is particularly important because recent work has focused on precisely this regime in demonstrating8 the value of duobinary transmission over uncompensated NZDSF. Thus, use of Gaussian statistics would severely underpredict the reach advantage of this system. Note here that although we report the results of the worst channel in Fig. 2, the differences among the various channels are negligible because the relative difference in accumulated dispersion among the various channels is small. In a system having a larger number of channels (difference in dispersion amongst channels), the channel with the largest accumulated dispersion would suffer the worst penalty.

Fig. 2

System performance in 20logQ (in decibels) versus accumulated dispersion (in picoseconds per nanometer).


Note that even in the case of a completely ASE-dominated system, departures from Gaussian statistics are significant, leading to more than 1dB discrepancy in system performance. This shows that duobinary transmission inherently exhibits non-Gaussian statistics—uncompensated dispersion further emphasizes departures from Gaussian statistics. Therefore, whereas strong non-Gaussian statistics is induced due to accumulated dispersion and would be observed in other modulation formats, e.g., NRZ/ return to zero (RZ) as well, the duobinary modulation format presents an interesting case for two reasons. First, due to the strong patterning inherent in duobinary, this modulation format exhibits departures from Gaussian statistics even in an ASE-limited system. Second, duobinary is a significant modulation format in dispersion-dominated systems because of its tolerance to dispersion. Consequently, duobinary arguably presents a more interesting test case of non-Gaussian statistics than other amplitude-shift-keyed (ASK) formats.

To examine the cause of the discrepancy between Gaussian and non-Gaussian statistics in this uncompensated dispersion regime, in Fig. 3, we display an eye diagram of the worst channel after 800km of uncompensated transmission. Here we have purposely ignored the effects of fiber nonlinearity and ASE noise to emphasize dispersion effects. We see that there are distinct banded levels reflecting the strong interplay between the spectra associated with different duobinary bit sequences and the fiber dispersion. Thus, the received current shows non-Gaussian statistics.

Fig. 3

Eye diagram of voltage corresponding to received current of worst channel after 800km of propagation with no dispersion compensation.


We can see the departures from Gaussian statistics more directly through a comparison of the probability density functions (pdfs) of the receiver current obtained from a Gaussian assumption and those obtained from the accurate BER estimator in Fig. 4. In the main figure, we show the pdfs of the 1’s and 0’s received currents from Gaussian approximation (dashed) and accurate (non-Gaussian) calculation (solid); in the inset, we magnify the tails of the pdfs to focus on the region where the tails intersect and where the BER will be actually calculated. The figure shows that while the regions near the centroid of the pdfs calculated by both the methods are similar, the tails are dramatically different. Since the BER depends on the tails of the PDFs and not the main body of the PDFs, Gaussian statistics and Q -factor analysis produce erroneous results.

Fig. 4

Graph of pdfs of voltage corresponding to received current from Gaussian assumption (dashed) and non-Gaussian calculation (solid).




Using simulations of eight-channel 10.7Gbitss duobinary transmission over 800km of NZDSF with no dispersion compensation we demonstrated that uncompensated dispersion can induce strongly non-Gaussian statistics of the receiver current. We showed that the use of Gaussian approximation and Q -factor analysis severely underpredicts the system performance.


1.  D. Marcuse, “Calculation of bit-error probability for a lightwave system with optical amplifiers and post-detection Gaussian noise,” J. Lightwave Technol.0733-8724 10.1109/50.76665 9, 505–513 (1991). Google Scholar

2.  J. S. Lee and C. S. Shim, “Bit error rate analysis of optically preamplified receivers using an eigenfunction expansion method in optical frequency domain,” J. Lightwave Technol.0733-8724 12, 1224 (1994). Google Scholar

3.  P. J. Winzer, S. Chandrasekhar, and H. Kim, “Impact of filtering on RZ-DPSK reception,” IEEE Photonics Technol. Lett.1041-1135 10.1109/LPT.2003.811140 15, 840 (2003). Google Scholar

4.  A. H. Gnauck and P. J. Winzer, “Optical phase-shift-keyed transmission,” J. Lightwave Technol.0733-8724 10.1109/JLT.2004.840357 23, 115 (2005). Google Scholar

5.  J. Wang and J. Kahn, “Impact of chromatic and polarization mode dispersions on DPSK systems using interferometric demodulation and direct detection, ”J. Lightwave Technol.0733-8724 10.1109/JLT.2003.822101 22, 362 (2004). Google Scholar

6.  E. Forestieri, “Evaluating the error probability in lightwave systems with chromatic dispersion, arbitrary pulse shape and pre- and postdetection filtering,” J. Lightwave Technol.0733-8724 10.1109/50.896209 18, 1493 (2000). Google Scholar

7.  S. Lobanov, S. Raghavan, J. Downie, M. Sauer, and J. Hurley, “Influence of non-Gaussian statistics in duobinary transmission,” in Proc. CLEO 2006, pp. (2006). Google Scholar

8.  J. Downie, M. Sauer, and J. Hurley, “Flexible 10.7Gbs DWDM transmission over up to 1200km without optical in-line or post-compensation of dispersion using MLSE-EDC,” in Proc. OFC 2006, pp. (2006). Google Scholar

9.  C. J. Anderson and J. A. Lyle, “Technique for evaluating system performance using Q in numerical simulations exhibiting intersymbol interference,” Electron. Lett.0013-5194 10.1049/el:19940045 30, 71 (1994). Google Scholar

Sergey Lobanov, Srikanth Raghavan, John D. Downie, Yihong Mauro, Michael Sauer, Jason Hurley, "Impact of uncompensated dispersion on non-Gaussian statistics in duobinary transmission," Optical Engineering 46(1), 010501 (1 January 2007). http://dx.doi.org/10.1117/1.2430507

Statistical analysis


Dense wavelength division multiplexing


Error analysis

Linear filtering


Back to Top