1. Introduction
High-order optical quadrature-amplitude-modulation (QAM) formats, such as 16, 64, and 256QAM, have attracted significant attention because of their spectrally-efficient transmission characteristics in dense wavelength-division multiplexing (WDM) as well as polarization multiplexing (POLMUX) environments [
1
A. H. Gnauck, P. J. Winzer, S. Chandrasekhar, X. Liu, B. Zhu, and D. W. Peckham, “10×224-Gb/s WDM transmission of 28-Gbaud PDM 16-QAM on a 50-GHz grid transmission over 1,200 km of fiber,” in 2010 OSA Technical Digest of Optical Fiber Communication Conference
(Optical Society of America, 2010), PDPB8.
–
3
M. Nakazawa, S. Okamoto, T. Omiya, K. Kasai, and M. Yoshida, “256 QAM (64 Gbit/s) coherent optical transmission over 160 km with an optical bandwidth of 5.4 GHz,” in 2010 OSA Technical Digest of Optical Fiber Communication Conference
(Optical Society of America, 2010), OMJ5.
]. The recent development of digital coherent optical receivers may enable the use of such sophisticated modulation formats in the near future [
4
D.-S. Ly-Gagnon, S. Tsukamoto, K. Katoh, and K. Kikuchi, “Coherent detection of optical quadrature phase-shift keying signals with carrier phase estimation,” J. Lightwave Technol.
24, 12–21 (2006). [CrossRef]
,
5
H. Sun, K.-T. Wu, and K. Roberts, “Real-time measurements of a 40 Gb/s coherent system,” Opt. Express
16, 873–879 (2008). [CrossRef] [PubMed]
]. We have theoretically shown that 16QAM signals at 12.5 Gsymbol/s can be placed on the 25-GHz-spaced grid, and ultra long-haul transmission of 2,000 km with the spectral efficiency as high as 4 bit/s/Hz is possible even under the influence of self-phase modulation (SPM) and cross-phase modulation (XPM) between WDM and POLMUX channels [
6
K. Kikuchi, “Ultra long-haul optical transmission characteristics of wavelength-division multiplexed dual-polarisation 16-quadrature-amplitude-modulation signals,” Electron. Lett.
46, 433–434 (2010). [CrossRef]
]. Although the spectral efficiency can potentially be improved with higher-order QAM formats such as 64QAM and 256QAM, their transmission characteristics may suffer more seriously from nonlinear impairments due to SPM and XPM; however, neither experiments nor theoretical works have been reported so far.
This paper aims at analyzing the performance of WDM POLMUX high-order QAM transmission in a systematic manner. We assume that the dispersion-unmanaged link consists only of large-core single-mode fibers (SMFs), whereas the accumulated group-velocity dispersion (GVD) is fully compensated for at the digital coherent receiver. First, we conduct intensive computer simulations on bit-error rates (BERs) in WDM POLMUX QAM transmission systems and find maximum transmission distances under the influence of nonlinear impairments. Next, to elucidate the physics behind such nonlinear transmission characteristics, we calculate the distribution of constellation points for a 4QAM signal as functions of the the launched power, the transmission distance, and the symbol rate. These calculation results clearly show that waveform distortion stemming from the interplay between GVD and nonlinearity of fibers generates the Gaussian distribution for the constellation points. The variance of such Gaussian distribution is proportional to the transmission distance and the cube of the launched power, while it is independent of the symbol rate. The extension of these results to higher-order QAM formats leads to a closed-form formula for BER. From this BER formula, we derive simple laws that express the maximum transmission distance and the optimum power as functions of the QAM order and the symbol rate. The validity of these laws are confirmed by the simulation results.
The organization of the paper is as follows: In Sec. 2, we describe the simulation model of the transmission system and the digital coherent receiver. Section 3 presents simulation results on WDM POLMUX 4QAM, 16QAM, 64QAM and 256QAM transmission characteristics. Section 4 discusses how the distribution of constellation points of a 4QAM signal spreads out through GVD and nonlinearity of fibers for transmission. In Sec. 5, using the results obtained in Sec. 4, we derive closed-form formula for BER of QAM signals in WDM and POLMUX environments, which yields simple laws for high-order QAM transmission. Section 6 is the conclusion of the paper.
2. Simulation model
The model of the transmission system and the digital coherent receiver is shown in
Fig. 1. The dispersion-unmanaged link has 80-km-long SMF spans. We assume large-core SMFs, which have the GVD value
D of 19 ps/nm/km (
β
2 = −24 ps
2/km), the nonlinearity coefficient
γ of 1.1 /W/km, and the loss coefficient
α of 0.17 dB/km at the wavelength of 1550 nm [
8
T. Kato, M. Hirano, M. Onishi, and M. Nishimura, “Ultra-low nonlinearity low-loss pure silica core fibre for long-haul WDM transmission,” Electron. Lett.
35, 1615–1617 (1999). [CrossRef]
].
Fig. 1 Simulation model of QAM transmission and digital coherent detection.
We use the 50% return-to-zero (RZ) waveform for the envelope of the complex amplitude of the signal electric field.
Differentially-encoded optical QAM signals are filtered out by root Nyquist filters with the roll-off parameter of 0.3 before transmission. An erbium-doped fiber amplifier (EDFA) with the noise figure of 4 dB compensates for the loss of each span. Linewidths of transmitter lasers are assumed to be negligible in our calculations to investigate only the nonlinear effect. The WDM channel spacing is twice as large as the symbol rate, and the maximum number of WDM channels is five.
WDM POLMUX 4, 16, 64 and 256QAM transmission characteristics are numerically analyzed based on
the two-mode nonlinear Schrödinger equations given as
where
Ex
and
Ey
denote electric fields of the WDM signal with
x- and
y-polarization components,
respectively. In these equations, randomly-distributed linear birefringence is
assumed along the link [
7
S. G. Evangelides Jr., L. F. Mollenauer, J. P. Gordon, and N. S. Bergano, “Polarization multiplexing with solitons,” J. Lightwave Technol.
10, 28–35 (1992). [CrossRef]
].
At the receiver, the incoming signal of each polarization is detected with a phase-diversity homodyne receiver where the linewidth of a local laser is negligible. The received complex amplitude of each polarization tributary is filtered out by a root Nyquist filter with the roll-off parameter of 0.3 to select the center WDM channel. The accumulated GVD is compensated for by a fixed equalizer. After the data are resampled so as to keep one sample per symbol, the carrier phase is estimated by the 4-th power algorithm [
9
M. Seimetz, “Laser linewidth limitations for optical systems with high order modulation employing feed forward digital carrier phase estimation,” in 2008 OSA Technical Digest of Optical Fiber Communication Conference
(Optical Society of America, 2008), OTuM2.
]. Finally, a nine-tap FIR filter equalizes the signal in an adaptive manner based on the decision-directed least-mean-square (DD-LMS) algorithm. After the equalizing process is converged, we differentially decode the symbol and count the number of bit errors. The total number of symbols is 2
15 per channel. We have not introduced nonlinearity compensation based on the back-propagation method [
11
E. Ip and J. M. Kahn, “Compensation of dispersion and nonlinear impairments using digital backpropagation,” J. Lightwave Technol.
26, 3416–3425 (2008). [CrossRef]
], because the real-time implementation of this method is very difficult due to huge computational complexity especially in WDM systems.
3. Simulation results
We calculate QAM transmission characteristics of a single channel, co-polarized three
WDM channels, co-polarized five WDM channels, and POLMUX five WDM channels. We
select the center WDM channel for BER estimation, which is most seriously affected
by XPM between WDM channels. In the case of POLMUX transmission, we evaluate BERs of
one of the two polarization tributaries. The number of spans n for
each QAM format is determined such that the BER of the polarization tributary of the
center WDM channels becomes lower than 10−3 at the optimum
launched power, when POLMUX five WDM channels are transmitted.
Figures 2(a)–2(d) show BERs of 4QAM,
16QAM, 64QAM, and 256QAM signals, respectively, calculated as a function of the
launched average power
Pave
. Powers of all channels are
changed simultaneously. Green, red, black, and blue curves represent BERs when we
transmit a single channel, co-polarized three WDM channels, co-polarized five WDM
channels, and POLMUX five WDM channels, respectively. The symbol rate is 12.5
Gsymbol/s and the WDM channel spacing 25 GHz. Numbers of spans
n
corresponding to (a)–(d) are 160, 37, 10, and 3, respectively, resulting in
total transmission distances of 12,800 km, 2,960 km, 800 km, and 240 km. Note that
the optimum power is about −9 dBm ~ −8 dBm, which is almost common
in all of the QAM orders. Above this value, the BER performance is degraded by
nonlinear impairments stemming from SPM and XPM between WDM channels and POLMUX
channels.
Fig. 2 BER characteristics of QAM transmission systems. (a): 4QAM, (b): 16QAM, (c):
64QAM, and (d): 256QAM. Green, red, black, and blue curves represent BERs
when we transmit a single channel, co-polarized three WDM channels,
co-polarized five WDM channels, and POLMUX five WDM channels, respectively.
The symbol rate is 12.5 Gsymbol/s, and the WDM channel spacing is 25 GHz.
Numbers of spans n corresponding to (a)–(d) are
160, 37, 10, and 3, respectively.
Dots in
Fig. 3 show the maximum number of
spans
n as a function of the order of QAM
m, when
POLMUX five WDM channels are transmitted. We find that
n is
inversely proportional to
m as seen from the solid line and that
transmission distances of 64QAM and 256QAM systems are severely limited below 1,000
km.
Fig. 3 Maximum number of spans n as a function of the order of QAM
m. The solid line shows the relation of
n ∝
m
−1.
Next, changing the symbol rate, we perform similar simulations.
Figure 4 shows maximum numbers of spans
n
for 4, 16, 64, and 256QAM transmission as a function of the symbol rate
B. Solid lines represent the slope of
n
∝
B
−2/3, which is in good agreement with
simulation results in the range above 25 Gsymbol/s. On the other hand, the optimum
launched power
Popt
to obtain the minimum BER for 4QAM
is plotted as a function of the symbol rate
B in
Fig. 5. In higher-order QAM transmission systems, we have
similar
B-versus-
Popt
characteristics
within the error range. The solid line represents the slope of
Popt
∝
B
1/3.
Simulation results differ from this line significantly in the range below 25
Gsymbol/s. The theoretical background for these dependencies on
B
will be discussed in Sec. 5 in detail.
Fig. 4 Maximum number of spans n for 4, 16, 64, and 256QAM as
functions of the symbol rate. POLMUX five WDM channels are transmitted, and
the WDM channel spacing is twice as large as the symbol rate. Solid lines
represent the slope of n ∝
B
−2/3.
Fig. 5 Optimum power levels Popt
for 4QAM transmission
as a function of the symbol rate. POLMUX five WDM channels are transmitted
through 160 spans, and the WDM channel spacing is twice as large as the
symbol rate. The solid line represents the slope of
Popt
∝
B
1/3.
4. Distribution of constellation points spread by nonlinear effects
In this section, to understand the physics behind the simulation results in Sec. 3,
we discuss how constellation points of a 4QAM signal spread out by nonlinear
effects. We consider transmission of five-channel WDM and POLMUX signals. The WDM
channel spacing is twice as large as the symbol rate. The received complex
amplitude is normalized such that the mean square of its absolute value is unity. In
such a case, when the carrier-to-noise ratio (CNR) is high enough, the distance
between constellation points is 2 as shown in
Fig.
6, where
stands for the variance of the distribution.
Fig. 6 Distributions of constellation points of a 4QAM signal. The complex amplitude
is normalized such that the mean square of its absolute value is unity. stands for the variance of the
distribution.
Let the symbol rate be 12.5 Gsymbol/s, the WDM channel spacing 25 GHz, and the
number of spans 160. We consider the real part of the complex amplitude ranging from
−2 to +2 in
Fig. 6. This
range is divided into 1,000 sections, and the number of data falling in each section
is shown as a histogram for 2
15 sampled data. Black curves in
Figs. 7(a), 7(b), and 7(c) show distributions
of the real part of the received complex amplitude for launched average powers
Pave
of −14 dBm, −9 dBm, and
−4 dBm, respectively. On the other hand, red curves are Gaussian
distributions fitted to black ones.
Fig. 7 Distributions of the real part of the received complex amplitude. (a):
Pave
=−14 dBm, (b):
Pave
= −9 dBm, and (c):
Pave
=−4 dBm. Black
curves show simulation results, and red ones are Gaussian fits. Five-channel
WDM and POLMUX signals are transmitted. The symbol rate is 12.5 Gsymbol/s,
the WDM channel spacing 25 GHz, and the number of spans 160.
When
Pave
=−14 dBm, the BER performance
is determined by amplified spontaneous emission (ASE) from EDFAs as shown in
Fig. 2(a). On the other hand, when
Pave
=−4 dBm, the BER
performance is degraded by nonlinear effects. At an intermediate power such as
Pave
=−9 dBm, both of ASE and
nonlinear effects spread out the distribution of constellation points. Note that
distributions are Gaussian in all of the three cases.
Next, we calculate the variance
of the Gaussian distribution as a function of the
launched average power
Pave
. In
Fig. 8, the blue curve is obtained when
γ = 0 and ASE is included,
i.e., the linear case, whereas the red curve are obtained when
ASE is neglected and fiber nonlinearity is included
i.e., the
nonlinear case. The black curve is calculated when both of ASE and fiber
nonlinearity are taken into account. Since the black curve is simply equal to the
sum of the red and blue curves, we find that the nonlinear impairment stems from
waveform distortion due to GVD and fiber nonlinearity rather than ASE noise enhanced
by fiber nonlinearity,
i.e., Gordon-Mollenauer phase noise
[
10
J. P. Gordon and L. F. Mollenauer, “Phase noise in photonic communications systems using linear amplifiers,” Opt. Lett.
15, 1351–1353 (1990). [CrossRef] [PubMed]
]. In the linear case,
ASE determines the distribution of constellation points, and it follows that
is inversely proportional to
Pave
. On the other hand, in the nonlinear case,
we find that
is proportional to
. This fact is understood as follows: First,
inter-symbol interference (ISI) due to GVD generates the power fluctuation
δ
P proportional to
Pave
in all of the channels. Then, SPM and XPM
induces the phase fluctuation
γδ
P
per unit length in the channel under consideration, which in turn spreads out the
distribution of constellation points through GVD; thus,
is proportional to
(
γ
Pave
)
2.
Fig. 8 Variance of the Gaussian distribution of the
constellation points as a function of the launched power. The blue curve is
obtained when γ = 0 and ASE is included,
whereas the red curve is obtained when ASE is neglected and fiber
nonlinearity is included. The black curve is calculated when both of ASE and
fiber nonlinearity are taken into account.
We move on to the dependence of
on the transmission distance. In this calculation,
ASE is neglected and only fiber nonlinearity is taken into account. We demodulate
the signal at the end of each span and obtain the distribution of constellation
points, fixing the launched power
Pave
at −7
dBm.
Figure 9 shows
as a function of the transmission distance. The red
curve and the black curve are those obtained when symbol rates are 100 Gsymbol/s and
12.5 Gsymbol/s, respectively. We find that
is proportional to the number of spans
n, as shown by blue fitted lines. This result shows that
evolution of the complex amplitude along the link obeys the two-dimensional
random-walk model: from one span to the next, the signal sequence takes a random
step from its last position, which results in the linear increase in the variance as
a function of the number of spans.
Fig. 9 Variance of the Gaussian distribution of the
constellation points as a function of the number of spans. The launched
power Pave
is −7 dBm. The red curve and
the black curve are obtained when symbol rates are 100 Gsymbol/s and 12.5
Gsymbol/s, respectively. Blue lines are linear fits to these curves.
Finally, we discuss the symbol-rate-dependence of
in the nonlinear case.
Figure 10 shows
as a function of the symbol rate, when the launched
power
Pave
is −7 dBm, and the number of spans
100. The variance is almost constant when the symbol rate is larger than 25
Gsymbol/s; however, it increases significantly when the symbol rate is lower than 25
Gsymbol/s. This tendency is explained in terms of modulation instability
[
12
G. P. Agrawal, Nonlinear Fiber Optics (Academic Press, 1989), Chap. 5.
]. The bandwidth of
modulation-instability gain is about 1 GHz in our case. Therefore, when
B < 25 Gsymbol/s, the modulation-instability effect inside
the signal bandwidth cannot be ignored, and the variance of the Gaussian
distribution is enhanced.
Fig. 10 Variance of the Gaussian distribution of the
constellation points as a function of the symbol rate. The launched power
Pave
is −7 dBm, and the number
of spans 100.
In conclusion of Sec. 4, we have the following equation for
:
where
L is the span length and
Cp
is a constant. In the five-channel WDM and
POLMUX environments,
Cp
= 13.4
[km] above 50 Gsymbol/s; however, we need some corrections for
Cp
at lower symbol rates as shown in
Fig. 10.
In [
13
P. Poggiolini, A. Carena, V. Curri, G. Bosco, and F. Forghieri, “Analytical modeling of nonlinear propagation in uncompensated optical transmission links,” IEEE Photon. Technol. Lett.
23, 742–744 (2011). [CrossRef]
] and [
14
X. Chen and W. Shieh, “Closed-form expressions for nonlinear transmission performance of densely spaced coherent OFDM systems,” Opt. Express
18, 19039–19054 (2010). [CrossRef] [PubMed]
], closed-form expressions of
nonlinear ISI are derived. The dependence on
n and
Pave
expressed by
Eq. (3) is the same as that given in
[
13
P. Poggiolini, A. Carena, V. Curri, G. Bosco, and F. Forghieri, “Analytical modeling of nonlinear propagation in uncompensated optical transmission links,” IEEE Photon. Technol. Lett.
23, 742–744 (2011). [CrossRef]
] and [
14
X. Chen and W. Shieh, “Closed-form expressions for nonlinear transmission performance of densely spaced coherent OFDM systems,” Opt. Express
18, 19039–19054 (2010). [CrossRef] [PubMed]
]; however, the dependence on
B given by
Eq.
(3) is different from those derived in [
13
P. Poggiolini, A. Carena, V. Curri, G. Bosco, and F. Forghieri, “Analytical modeling of nonlinear propagation in uncompensated optical transmission links,” IEEE Photon. Technol. Lett.
23, 742–744 (2011). [CrossRef]
] and [
14
X. Chen and W. Shieh, “Closed-form expressions for nonlinear transmission performance of densely spaced coherent OFDM systems,” Opt. Express
18, 19039–19054 (2010). [CrossRef] [PubMed]
]. This may be because modulation instability is not taken
into consideration in these references.
5. BER formula for high-order QAM transmission systems
Based on
Eq. (3), we derive a
closed-form expression of BER. Let the minimum distance between QAM constellation
points be 2
δ and the variance of the distribution
σ
2. In such a case, the bit-error rate for
QAM signals is approximately given as
where erfc(*) is the complementary error
function, and
De
=1, 3/4, 7/12, and 15/32 for
differentially encoded 4QAM, 16QAM, 64QAM, and 256QAM, respectively. The average
power of the
m-th order QAM signal
Pave
(
m) is given as
In the linear region, the variance
stems from ASE accumulation and is given as
where
f denote the frequency of the
carrier,
G the amplifier gain, and
nsp
the spontaneous emission factor of amplifiers. On the other hand, the variance in
the nonlinear region is given from
Eq.
(3) as
where we assume that each constellation point of the
high-order QAM has the same Gaussian distribution determined from the average power.
Since the total variance of the Gaussian distribution is given as the sum of
and
,
Eqs.
(4),
(5),
(6), and
(7) yield
where
From
Eq. (8), we find that when
the BER is minimized as
To keep BER
min
at a
certain value, for example 10
−3, the following relation must be
satisfied for any values of
m,
n, and
B:
where
C is a constant.
The analyses mentioned above lead to the following simple laws for QAM transmission
in fixed WDM and POLMUX environments:
The maximum number of spans
n to achieve a certain BER
has the following dependence on
m and
B:
The optimum power
Popt
to obtain the best BER
is dependent on neither
n nor
m;
however, its symbol-rate-dependence is given as
These laws can well explain simulation results given in
Figs. 3,
4, and
5.
Figure
3 shows that
n ∝
m
−1 when
B is fixed. On the
other hand, for a fixed
m,
Fig.
4 and
Fig. 5 lead to relations of
n ∝
B
−2/3 and
Popt
∝
B
1/3,
respectively, when
B is larger than 50 Gsymbol/s. Deviations of
simulation results from these relations at lower symbol rates are due to the fact
that
Cp
in
Eq.
(3) is enhanced by modulation instability. It should be noted here that
the larger GVD of fibers generally provides us with the better transmission
performance because of the smaller bandwidth of modulation-instability gain.
Equation (8) is a closed-form BER
formula applicable to WDM and POLMUX QAM transmission. Once
Cp
in
Eq.
(10) is determined from computer simulations in fixed WDM and POLMUX
environments, we can calculate BER of any QAM formats using signal parameters
(
n,
m,
B, and
Pave
) and link parameters (
L,
G,
nsp
, and
γ). For five-channel WDM and POLMUX transmission at
12.5 Gsymbol/s, which is discussed in Sec. 3, we need to use
Cp
=56.1 [km] read from
Fig. 10. Then, BER curves calculated for 4QAM,
16QAM, 64QAM, and 256QAM are respectively shown by the black, red, blue, and green
curves in
Fig. 11. Numbers of spans are the
same as those used in Sec. 3. Even using such a simple formula, we can obtain BER
curves for any QAM formats which are in reasonable agreement with full simulation
results given by
Fig. 2.
Fig. 11 BER curves calculated from the simple BER formula
Eq. (8). The symbol rate is 12.5
Gsymbol/s. The black, red, blue, and green curves are BER curves for 4QAM,
16QAM, 64QAM, and 256QAM, respectively. The symbol rate is 12.5 Gsymbol/s
and the WDM channel spacing is 25 GHz. Five-channel WDM and POLMUX signals
are transmitted, and numbers of spans are the same as those used in Sec.
3.
6. Conclusions
We have analyzed the performance of WDM POLMUX high-order QAM transmission in a systematic manner. First, we conduct intensive computer simulations on BERs and find maximum transmission distances under the influence of nonlinear impairments. The maximum number of spans is inversely proportional to the order of QAM and transmission distances of 64QAM and 256QAM systems are severely limited below 1,000 km at 12.5 Gsymbol/s.
Next, to elucidate the physics behind such nonlinear transmission characteristics, we calculate the distribution of constellation points for QAM signals and find that waveform distortion stemming from the interplay between GVD and nonlinearity of fibers generates the Gaussian distribution for the constellation points. Using the dependence of the variance of such Gaussian distribution on the transmission distance, the launched power, and the symbol rate, we derive a closed-form expression of BER. This BER formula leads to simple laws that determine the maximum transmission distance and the optimum power for each QAM format as a function of the symbol rate.