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CHAPTER 3 PERFORMANCE OF MODULATION FORMATS ON DWDM...
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CHAPTER 3
PERFORMANCE OF MODULATION FORMATS ON
DWDM OPTICAL SYSTEMS
3.1 INTRODUCTION
The need for higher transmission rate in Dense Wavelength
Division optical systems necessitates the selection of a suitable modulation
format for the efficiency of the optical system and has been a key issue in
recent research (Vassilieva et al 2001). Generally modulation formats are
classified into On Off Keying (OOK) and Phase Shift Keying (PSK)
techniques (Idler et al 2003). Over the last few years, novel modulation
formats with improved performance than the NRZ scheme have been
suggested and investigated, (Matsuda et al 1998). From the literature it is
found that by adapting a Return-to-Zero (RZ) format we can improve the
receiver sensitivity and non-linear tolerance (Winzer and Kalmar 1999,
Caspar et al 1999), but at the extra cost of one additional modulator and drive
circuitry in the transmitter.
Phase modulation combined with a balanced receiver offers a very
attractive 3 dB improved receiver sensitivity compared to OOK, (Ferber et al
2003). Recently, many OOK formats with additional phase modulation have
been shown to perform very well under certain circumstances, for example
Chirped-RZ scheme (Bergano et al 1998) which however, adds further
complexity to the transmitter. Four-level phase modulation - Differential
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Quadrature Phase Shift Keying (DQPSK) has also been studied recently
(Griffin et al 2003, Wree et al 2002, Cho et al 2003).
In this chapter the impact of the fiber non-linear effect is studied for
a 32 channel DWDM system for RZ and NRZ modulation formats using the
OPT package and the spectrum of Duobinary and CSRZ modulation formats
are also observed. The spectrum is also observed for a single channel system
as by, (Binh and Csematony 2003) and a sixteen channel system for RZ and
NRZ modulation formats with phase shift keying by carrying out a
simulation using MATLAB simulink for our fiber and data rate specifications.
The multi channel CSRZ – DQPSK modulation format for 16
Unequal spacing channels is also simulated and the Q factor is observed. In
addition, the impact of filtering techniques and dispersion compensating
techniques are also analyzed in terms of Q factor and Eye Opening Penalty to
identify methods for improving the spectral efficiency.
3.2 IMPACT OF NON-LINEAR EFFECTS ON MODULATION
FORMATS
3.2.1 RZ, NRZ and CSRZ
All modulation formats can be divided into NRZ-based and RZ-
based modulation formats, (Mohs et al 2000). In this thesis, we have made an
effort to study these modulation formats on a 32 channel DWDM system at
40 Gb/s bit rate per channel accounting for the impact of non-linear effects
like XPM and FWM.
A simulation scenario is set up initially as shown in Figure 3.1,
(Hodzic 2004) and applied in this work for our specifications. The system and
fiber parameters used for the simulation are listed in Tables 3.1 and 3.2
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respectively. The channels allotted are chosen between 191.9 and 195 THz
with 100 GHz spacing in the C band, with EDFA based amplification and
dispersion compensating fibers. The transmission data are coded into NRZ
sequences and RZ sequences and the corresponding Q factors are measured
and plotted with and without the inclusion of non-linear effects. The Q factors
obtained with and without the non-linear effects are plotted with respect to the
channel numbers as shown in Figure 3.2.
The Quality Factor in this case is defined as the center frequency
divided by the signal bandwidth in Hertz. The non-linearity comes into
picture when the signal power per channel is higher, in this case 6 mWs.
When the power is reduced to 2 mWs the non-linear effects observed are
negligible.
Figure 3.1 Simulation setup for 32 channel DWDM system
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Table 3.1 Simulation parameters for the system at 40 Gb/s
Parameter Values
Rise time of electrical signal MZM extinction ratio Duo binary low pass filter Duo binary low pass filter cut off frequency Laser frequencies Laser Line width Simulation bits PRBS length NLSE step size Sample rate EDFA noise figure Splice loss Optical filter Optical filter 3 dB bandwidth
5 ps Infinite Fourth order Bessel type 11.7 GHz 193.1 THz - 195 THz 10 Hz 1024 210 – 1 1000 m (or) 0.06 rad 32 samples / bit 5 dB 0 dB Second Order Super Gaussian 100.0 GHz
Table 3.2 Fiber parameters used in the simulation
for 32 channels , Keiser(2000)
Parameters SMF DCF
Length 80 km Variable ( 14.5 kms)
Attenuation 0.2 dB/km 0.25 dB/km
GVD 16 ps/nm/km -72 ps/nm/km
Slope 0.08 ps/nm2 0.08 ps/nm2
Effective Area 80 µm2 30 µm2
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Figure 3.2 Q-factor performance of NRZ modulation format
It is observed from Figure 3.2 that the Q factor is degraded with the
inclusion of non-linearities. It is also noted that the maximum Q factor
degradation is seen for the center channel. When the impact of non-linearity is
not included the center channel has a Q factor of 18 which gets reduced to 13
in the presence of non-linear effects. Hence we come to the conclusion that
non-linear effects will affect the spectral efficiency and the performance
degradation is maximum for the channels placed in the center.
The Q factor obtained with and without the non-linear effects in
this case of RZ shaping, is shown in Figure 3.3. In the case of RZ format, the
width of the optical signal is smaller than its bit period. It is observed from the
figure that there is degradation in Q factor due to non-linearity, however the Q
factor obtained is much higher than the case of NRZ formatting. The reason
for its superior performance is probably due to its ‘return-to-zero’
characteristic.
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Figure 3.3 Q-factor performance of RZ modulation format
Duo binary format whose spectral width is half of the standard
NRZ format is a possible way to increase spectral efficiency but it’s resistance
to nonlinear effects is not so large because the phase information is vulnerable
to interaction with Amplified spontaneous emission noise and SPM
(Yonennaga et al 1995, Keiser et al 2000). Thus this type is suitable only for
short distance DWDM systems. The spectrum of the Duo binary signaling
scheme obtained with our simulation for the design parameters from Table 3.1
is shown in Figure 3.4.
The narrow pulse nature of RZ format has a wider spectrum
leading to less spectrum efficiency in a DWDM system. To overcome this
difficulty and to improve the spectrum efficiency Carrier-suppressed RZ
(CSRZ) modulation has been recently proposed for high bit rate transmission
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Figure 3.4 Spectrum of the duo binary signal
systems, and has been intensively investigated in numerical and experimental
works (Miyamoto et al 2000). The transmitter and the fiber section for the
CSRZ format is shown in Figures 3.5 and 3.6, respectively as in Bosco et al
(2002). The spectrum obtained is shown in Figure 3.7. The carrier component
of the CSRZ signal spectrum is suppressed due to the external modulation at
“zero” point in the second MZM.
The CSRZ pulses possess a RZ signal shape with an optical phase
difference of π between adjacent bits. We have carried out investigation with
the CSRZ modulation format along with the phase modulations in this thesis.
The spectrum of the CSRZ modulation has been studied in this work for our
specification in the Table 3.1.
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Figure 3.5 Transmitter section of CSRZ format
Figure 3.6 Fiber section of CSRZ format
40 Gbs
CW laser
20 G clock
MZM2
20 G clock
MZM1
Loop control Number = 6
EDFA 1 EDFA 2 EDFA 3
SMF SMF SMF
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Figure 3.7 Spectrum of CSRZ format
3.2.2 Optimal Pre-Transmission Filter for DWDM Systems
A spectral efficiency of 0.1 b/s/Hz is realized with 100 channels of
100 GHz spacing and 10 Gb/s rate per channel. Spectral efficiency is
generally defined as the ratio of the per channel data rate to the channel
spacing in WDM systems. One way to improve the spectral efficiency is to
increase the data rate from 10 to 40 Gb/s and maintaining an optimum
channel spacing ( Ito et al 2000). The minimum channel spacing is limited by
the non-linear effects and the data rate efficiency depends on the modulation
format. Due to reduced spectral width, CSRZ modulation shows an increased
dispersion tolerance and is also more robust to non-linear impairments than
conventional RZ and NRZ systems, (Miyamoto et al 1999).
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The non-linear tolerance of CSRZ modulation can be enhanced by
the implementation of pre-chirp at the transmitter side (Sano and Miyamoto
2001) but the amount of pre-chirp has to be carefully optimized in order to
avoid the increase of linear cross talk and waveform distortions. The
robustness of CSRZ modulation to narrow band filtering can be improved and
hence can be beneficial for DWDM systems (Morita and Edagawa 2003). In
the reported literature, various transmission filters like Butterworth,
Chebychev, Dielectric, Fiber Bragg Grating, Arrayed Waveguide Grating and
Mech-Zender Interferometer Filter have been analyzed (Hodzic et al 2003).
In this section, the performances of a Gaussian Filter, a Second
order Super Gaussian Filter and a Flat top Bragg Grating Filter are compared
by simulating the setup shown in Figure 3.8. Narrow band filters with sharp
filter edges and flat pass band represent the optimal solution for DWDM
systems Teraxion (2002). For the numerical investigation presented here, the
characteristics of flat top filters are emulated using Gaussian filters of higher
order degree n 2.In this work, we have measured the performance of the
filter by finding the Q factor .The frequency response of such a Super
Gaussian filter is defined by the transfer function, Hodzic (2004),
2n
3dB
( ln 2 2(f fc)T(f ) expf
(3.1)
where fc is the filter central frequency, n defines the order of the filter and
Δf3db represents the 3-dB optical bandwidth of the filter. The increase in filter
order results in increased steepness of the filter edges. In this section, we have
used a second order Super Gaussian filter. A binary sequence of 29 bits is
considered to assess the performance of a single channel case. As these filters
have a real transfer function, delay distortion does not occur. Using low
dispersion fibers reduces the problem of dispersion, but increases the effect of
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FWM and using high dispersion fibers results in decreasing the effects of
XPM, (Kai Song 2000). The simulation parameters used are listed Table 3.3.
Figure 3.9 shows the eye diagram with measured Q factor in our simulation
using OPT simulation package.
Figure 3.8 Simulation set up for filter performance study
Table 3.3 Simulation parameters used in the setup to
study filter performance
S.No. Parameter Values
1 Number of loops 09
2. Input power per channel 0.25 mW
3. SMF and DCF lengths 80 kms and 14.5 kms per span
4. EDFA parameters Noise Figure 4 dB, Gain 30 dB, two per span
5. WDM channels, data rate 192.5 -193.7 THz, 40 Gb/s
6. Transmitter filter bandwidth 80 GHz bandwidth
Figure 3.9 Q factor measured at the receiver output
Tx Filter Fiber EDFA Detector
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Figure 3.10 shows the Q factor measured for our specification and
simulation parameters at the receiver output for varying fiber lengths for
various filters like Bragg Grating, Gaussian, Super Gaussian and Fabry Perot
Filters. The reduction in Q factor for increasing distances indicates the
necessity for a dispersion management technique. Super Gaussian filter of
higher order, in this case four, is observed to give the highest Q factor. Bragg
grating Filter shows a performance close to that of the Super Gaussian filter.
Super Gaussian filter show better tolerance to dispersion and nonlinearity due
to it’s reduced spectral width as the order increases. Hence more number of
channels could be incorporated in the available spectral width of the optical
channel resulting in an improvement in the spectral efficiency. This filter has
no negative side lobes hence this can be incorporated for the dispersion
managing schemes to limit the interactions of XPM and FWM effects.
Figure 3.10 Receiver output Q factor variation with distance
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3.3 MODELING THE PHASE SHIFT KEYING METHODS
Phase Shift Keying (PSK) uses the phase of the optical carrier to
encode information. The Differential PSK has larger resistance to non linear
effects at higher data rates, (Rhode et al 2000).Wree et al (2002) investigated
the improvement in the spectral efficiency for RZ DQPSK using balanced
detection. The Differential Quadrature Phase Shift Keying (DQPSK)
modulation characterized by a symbol rate that is only half of the nominal bit
rate, results in better narrow-band filtering characteristics and an improved
dispersion and PMD tolerance (Hoshida et al 2003). The interaction between
the ASE noise and the non-linearities will modify the probability density
function used for BER calculation. It has been shown that (Ho 2004), non-
linear noise induced by ASE does not have the characteristics of a Gaussian
pdf.
The general architecture used for system modeling is shown in
Figure 3.11, (Binh and cheung 2005). The pseudo random data bit sequence is
simulated using the Bernoulli binary generator available in the
communication block set of the SIMULINK. CW LASER is modeled by the
sine wave generator available in the signal processing block set.
The main section for simulating NRZ/RZ-DPSK modulation
techniques is shown in Figure 3.12, consisting of DPSK block and the Phase
Modulation (PM) block, ( Liem et al 2005). The NRZ electrical signal is first
encoded by DPSK encoder and the encoded electrical signal is then used to
drive an electro-optic phase modulator to generate a DPSK optical signal. The
expanded simulation block for DPSK and PM are shown in Figures 3.13 and
3.14, respectively, ( Liem et al 2005). Inside the PM block, the input port 1 is
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fed with the optical carrier and input port 2 is fed with the DPSK electrical
data. To produce the NRZ-DPSK optical signal, the complex phase shift
block is then used to phase shift the optical phase by π when the data bit is ‘1’
and a phase shift of zero when the data bit is ‘0’. In the case of RZ DPSK an
additional Intensity modulator is added and in this additional MZIM, the
NRZ-DPSK optical signal from output of the PM block will be sampled by a
pulse generator to achieve the desired RZ-DPSK signal. The sampling pulse
train is synchronized with the input electrical sequence and is at the same rate
as the data rate. The results are observed and studied for the simulink models
taken from the Binh etal 2006 for our date rate and specifications towards the
spectral improvement.
In the receiver block, the NRZ-DPSK optical signal is demodulated
and passed through a low pass filter to remove the carrier. A one-bit-delay
Mach-Zehnder Interferometer (MZI) is usually used as a DPSK optical
receiver as shown in Figure 3.15, ( Binh et al 2006).
In a DPSK balanced receiver, a photodiode is used at each MZI
output and then the two photocurrents are combined (logical subtract) to
double the signal level (Binh et al 2005). The spectrum of the NRZ-DPSK
and RZ-DPSK modulated signals obtained from simulation are shown in
Figures 3.16 and 3.17, respectively for our specification. Based on the
investigation made by the models proposed by Binh et al monash university
we have simulated the RZ and NRZ DPSK spectrum for our data rate and
specifications as discussed.
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Figure 3.11 General architecture for system modeling (Binh et al 2006)
Encoder signal shaping circuit
Input
Modulator
Optical source
Optical fiber
Optical
fiber EDFA
Optical detector
Decoder Demodulator
Amplifier
Output
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Figure 3.12 Main section for simulation of NRZ/RZ-DPSK modulation techniques
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Figure 3.13 Expansion of DPSK block
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Figure 3.14 Phase modulation block
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Figure 3.15 Receiver block
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Figure 3.16 NRZ -DPSK spectrum at fiber input
Figure 3.17 Spectrum of the RZ-DPSK
Frequency (GHz)
Frequency (GHz)
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An optical transmission with very high spectral efficiency is one of
the objectives of this research work. Various modulation formats for very
high spectral efficiency have been investigated in the literature, such as
Duobinary (Yonenaga and Kuwano 1997), NRZ VSB (Idler et al 2002) and
Narrow Band RZ (Gnauck et al 2003) to name a few.
A common feature of all these techniques is that of narrow signal
bandwidth with a reduction in the symbol rate. DQPSK is another spectrally
efficient modulation format by which two bits per symbol is transmitted.
DQPSK is a four-symbol format equivalent to phases of {0, π/2, π, 3π/2}.
Depending on the desired di-bit combination to be encoded, the difference in
phase, Δφ, between the two adjacent symbols (optical carrier pulses) is varied
systematically, (Griffin and carter 2002). The signal spectra before and after a
fiber length of 450 Kms for RZ-DQPSK are as depicted in Figures 3.18 and
3.19, respectively. The general simulation block, received signal eye-diagram
and signal spectra for CSRZ-DQPSK are shown in Figures 3.20 ,3.21 and
3.22, respectively. In order to built the simulation block in simulink for CSRZ
DQPSK we have studied the models and lay out for NRZ / RZ DPSK and
RZ DQPSK from ( Binh and Laville 2005) series of Monash university and
the results which are discussed in this chapter for the above formats are
obtained for our model parameters .Hence in our work the model of CSRZ –
DQPSK has been developed based on the other modulation formats
simulation , the results are obtained for the CSRZ DQPSK spectrum and eye
pattern at the detector output (Ramprasad and Meenakshi 2005) .
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Figure 3.18 RZ-DQPSK spectrum before the fiber
Figure 3.19 RZ-DQPSK after propagating 450 kms in the fiber
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Figure 3.20 CSRZ-DQPSK simulation set up
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Figure 3.21 Receiver eye pattern CSRZ-DQPSK
Figure 3.22 Spectrum of CSRZ-DQPSK
Time (bit period)
Am
plitu
de
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3.4 EYE OPENING PENALTY
A broadened eye opening is seen at the receiver for ideal optical
transmission. The comparison of the different modulation formats is done
with reference to the Eye Opening Penalty (EOP) which is defined as the ratio
of the difference in the eye openings of the mark and space state to the
difference in their corresponding variance.
In order to compare the transmission characteristics of PSK-based
modulation formats, a 40 Gb/s DWDM transmission system with Unequally
Spaced Channels and 7 spans of SSMF fibers of span length 80 kms is
considered. The EOP values are measured from the simulation model shown
in Figure 3.23 using MATLAB SIMULINK by varying the transmission
distance for 16 channels with USC scheme. The transmitter, receiver and fiber
parameters used are as listed in Tables 3.4 and 3.5.
Figure 3.24 provides a comparison of the EOP at different fiber
spans for the NRZ-DPSK, RZ-DPSK and the CSRZ-DQPSK modulation
formats. The figure shows that CSRZ-DQPSK offers the best transmission
results up to 560 kms. This is certainly due to its low symbol rate, which
suppresses the effects of fiber dispersion and subsequent nonlinear effects on
the signal as well as good narrow-band filtering characteristics. However
when compared to RZ-DPSK, the signal quality steadily decreases at greater
transmission spans. This can be attributed to the effects of ASE induced phase
noise, which accumulate as the transmission distance increases, resulting in
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Figure 3.23 Simulation model for measuring EOP and Q factor
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Table 3.4 Simulation parameters used to determine Q factor for USC and ESC scheme.
S.No. Parameter Values used in the model
1. PRBS 2.5 Gb/s,223-1,10Gb/s
2. Pulse generator NRZ /RZ Encoding
3. Laser Diode 3 mw power per channel line width 10 MHz
4. Modulator MUX
Mech zender modulator Gaussian MUX filter at the transmitter
5. EDFA Gain 12 db, Noise factor 4 dB.
6. Fiber Nonlinear dispersive fiber length 60 km , attenuation 0.22db/km
7. Optical filter Trapezoidal filter with Zero dB bandwidth 45 GHz, Cut off Bandwidth 50 GHz, Cut off Magnitude 30 dB.
8. Photo detector Responsivity 1 A/W ,Dark current 10 nA
9. Low pass Filter Bessel filter Bandwidth 1.875G Hz
10. Power per channel Variable power in milli watts per channel
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Table 3.5 Non-linear dispersive fiber parameters used in the simulation
Lucent (2001)
Parameters Values
α attenuation 0.25 dB/km
Input coupling efficiency -1 dB
Output coupling efficiency 0.022dB
GVD constant 4.5 ps/nm/km
Dispersion slope constant 0.11ps/nm2/km
Effective Area 72 m2
N2 constant 2.6 e-20 m2/w
Peak Raman gain coefficient 9.9 e-014 m/w
Pump wavelength 1000 nm
Raman self shift time 5 fsec
Figure 3.24 EOP measurements for the modulation formats
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larger phase fluctuations and possible errors in the detection process.
RZ-DPSK can cope better with these effects and is less severely affected over
the entire transmission span, because of better error tolerance at the receiver
compared to DQPSK. NRZ-DPSK has higher EOP values and larger
variations in the EOP due to its reduced non-linear tolerance, (Ramprasad and
Meenakshi 2005 ).
3.5 IMPACT OF FWM ON MODULATION FORMATS
3.5.1 Q Factor Measurement
Based on the simulation model, the Q factor is measured for
various modulation formats namely RZ, NRZ, CSRZ and VSB-RZ. The block
diagram with optimum filter design to generate VSB-RZ and CSRZ are
obtained from the previous section. The fiber is modeled as non-linear and
dispersive thereby giving rise to FWM components in the DWDM system
considered for simulation. Table 3.6 shows the measured Q factor for NRZ
modulation with four channels at 10 Gb/s and other parameters same as
shown in Tables 3.4 and 3.5. The channel allocations using ESC technique
and orthogonal coding based USC techniques, for four channels are {193.1,
193.2, 193.3, 193.4} THz and {193.1, 193.3, 193.4, 193.8} THz respectively.
The corresponding results obtained for RZ categories are tabulated in
Tables 3.7, 3.8 and 3.9.
It is found from the Tables 3.6, 3.7, 3.8 and 3.9 that the Q factor for
both ESC and USC schemes decreases in spite of an increase in the channel
power of the system for all the modulation formats. This is due to the
presence of Four wave mixing effect. In general RZ format shows better Q
factor values and hence is more non-linear tolerant. The non-linear tolerant
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Table 3.6 Q factor measured for various input powers for ESC and
USC schemes under NRZ modulation - 4 channel DWDM
Power in mw 0.25 0.5 0.75 1.0 1.25 1.5
Q factor ( ESC ) 14.04 6.977 4.03 2.853 1.839 1.222
Q factor ( USC ) 16.97 9.553 6.904 4.982 3.079 2.998
Table 3.7 Q factor measured for various input powers for ESC and
USC schemes under RZ modulation - 4 channel DWDM
Power in mw 0.25 0.5 0.75 1.0 1.25 1.5
Q factor ( ESC ) 28.02 12.89 8.99 5.12 3.89 2.44
Q factor ( USC ) 32.24 18.78 13.98 9.88 6.98 6.99
Table 3.8 Q factor measured for various input powers for ESC and
USC schemes under CSRZ modulation - 4 channel DWDM
Power in mw 0.25 0.5 0.75 1.0 1.25 1.5
Q factor ( ESC ) 32.06 16.01 12.98 9.66 7.83 6.89
Q factor ( USC ) 38.27 24.53 16.24 15.45 12.37 12.23
Table 3.9 Q factor measured for various input powers for ESC and
USC schemes under VSB-RZ modulation - 4 channel
DWDM
Power in mw 0.25 0.5 0.75 1.0 1.25 1.5
Q factor ( ESC ) 33.85 17.55 14.56 11.66 9.39 8.222
Q factor ( USC ) 41.97 26.65 18.904 17.982 14.079 14.998
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characteristics is combined with the filtering effect at the transmitter side to
get vestigial side band RZ format which shows good overall performance in
the presence of FWM. Comparing Q factor for ESC and USC schemes under
constant input powers; it is observed that the Q factor values are higher for
USC than ESC irrespective of the modulation format implying that USC
schemes are more FWM tolerant, (Ram prasad and Meenakshi 2006).
Figures 3.25 and 3.26 show the spectrum of the 4 channel ESC and
USC schemes. The strength or the power of the inter modulation FWM cross
talk products falling on the desired channels for both ESC and USC scheme
are shown. It is seen that many cross talk inter modulation products are
present in the desired band of the four channels for ESC and lesser inter
modulation products are present in the desired channel band as cross talk
components for the USC scheme.
Figure 3.25 Output spectrum of four channels in ESC
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Figure 3.26 Output spectrum of four channels in USC
3.5.2 Simulation Results for 16 Channels
The simulation is carried out for 16 DWDM channels with the same
system parameters shown in Tables 3.4 and 3.5 but the data rate is chosen as
40 Gb/s per channel. The results obtained for NRZ, RZ, CS-RZ and VSB-RZ
are tabulated as shown in Tables 3.10, 3.11, 3.12 and 3.13 respectively.
Table 3.10 Q factor measured for various input powers for ESC and
USC Schemes under NRZ modulation - 16 channel DWDM
Power in mw 0.25 0.5 0.75 1.0 1.25 1.5
Q factor ( ESC ) 11.09 4.977 2.03 1.84 1.2 1.0
Q factor ( USC ) 14.34 7.78 4.98 4.45 4.34 3.45
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Table 3.11 Q factor measured for various input powers for ESC and
USC Schemes under RZ modulation - 16 channel DWDM
Power in mw 0.25 0.5 0.75 1.0 1.25 1.5
Q factor ( ESC ) 26.23 11.45 06.56 3.67 3.78 2.87
Q factor ( USC ) 36.26 16.78 11.57 07.76 04.76 04.56
Table 3.12 Q factor measured for various input powers for ESC and
USC Schemes under CSRZ modulation - 16 channel DWDM
Power in mw 0.25 0.5 0.75 1.0 1.25 1.5
Q factor ( ESC ) 31.67 15.78 11.64 7.546 5.76 4.676
Q factor ( USC ) 36.454 22.656 14.75 13.565 10.75 10.87
Table 3.13 Q factor measured for various input powers for ESC and
USC Schemes under VSB-RZ modulation - 16 channel
DWDM
Power in mw 0.25 0.5 0.75 1.0 1.25 1.5
Q factor ( ESC ) 31.66 15.45 12.77 9.46 7.77 6.74
Q factor ( USC ) 37.45 24.36 16.26 15.47 12.57 12.11
On observing the tabulated values for 16 channel system, it is
observed that the Q factor values for USC are reasonable even at higher
channel powers and is almost double that of ESC schemes. Hence we
conclude that, better suppression of Four wave mixing effect at higher data
rate is achieved by the Unequal Spacing Channel allocation using orthogonal
optical codes. Further, the comparison of Q factor values suggests that a
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combination of orthogonal code based USC and VSB-RZ formatting can
effectively combat FWM even at higher channel powers. Figures 3.27 and
3.28 show the received Eye diagram for NRZ and RZ coded 16 channel
DWDM system under ESC and USC schemes, respectively. The eye is seen
to be more distinct for RZ under USC compared to that of NRZ under ESC.
Figure 3.27 Measured Q factor and Eye diagram for NRZ under ESC
Figure 3.28 Measured Q factor and Eye diagram for RZ under USC
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The Q factor performance of PSK formats namely, DPSK, DQPSK
and CSRZ- DQPSK modulations are also investigated at high data rates of 40
Gb/s, in a 16 channel DWDM system for a propagation distance of
600 kms in a NZDSF. Tables 3.14, 3.15 and 3.16 show the output Q factor
measured for DPSK , DQPSK and CSRZ-DPSK modulation formats with
balanced detection using the simulation parameters given in Tables 3.4 and
3.5.
Table 3.14 Q factor measured for various input powers for ESC and
USC Schemes under DPSK - 16 channel DWDM
Power mw 0.25 0.5 0.75 1. 0 1.25
Q (ESC) 27 18 12 10 08
Q( USC) 32 20 14 13 11
Table 3.15 Q factor measured for various input powers for ESC and
USC Schemes under DQPSK - 16 channel DWDM
Power mw 0.25 0.5 0.75 1. 0 1.25
Q (ESC) 29 21 18 15 11
Q( USC) 36 22 17 16 13
Table 3.16 Q factor measured for various input powers for ESC and
USC Schemes under CSRZ DQPSK - 16 channel DWDM
Power mw 0.25 0.5 0.75 1.0 1.25
Q ( ESC) 29 22 18 15 11
Q ( USC) 37 24 18 17 13
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It is found from Table 3.16 that the Q factor for the CSRZ-DQPSK
modulation format shows better values at all power levels. Hence a
combination of OOC based USC with the CSRZ-DQPSK shows better
resistance towards the four wave mixing effect and gives better Q factor
values. It is also inferred that at high powers the performance of DQPSK and
CSRZ-DQPSK are similar and yield the same value of Q factors. Thus these
two modulation formats perform equally well in the presence of non-linear
effects.
3.6 IMPACT OF DISPERSION COMPENSATION ON
MODULATION FORMATS
Dispersion compensation is basically classified as: (a) pre-chirp
techniques at the transmitter side, (b) dispersion compensation in the
transmission line (in-line compensation) and (c) dispersion compensation at
the receiver side, ( Keiser 2000 ).
In pre-chirp, a chirp with the opposite sign of that of the fiber is
introduced at the transmitter for reducing the GVD effects in the fiber.. The
pre-chirp can be realized by several methods, namely by exploiting the
internal chirp of the laser source (Wedding et al 1994) or of an external
modulator (Gnauck et al 1991, Henmi et al 1994), by the implementation of
complex transmitter structures using additional components such as phase
modulators (Khosravani and Willner 2001). The impact of Dispersion slope
for NRZ and DPSK modulation formats at higher bit rate has to be taken into
consideration ( Castanon and Hoshida (2002) . Hayee and Willner (1997)
showed the concept of pre and post compensation of the fiber in the presence
of non linearities and dispersion at the data rate of 10 Gb/s.
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The main application area of these technique are the cost effective,
optical short-reach systems (e.g. MANs) with smaller channel bit rates, but in
combination with dispersion compensation techniques they can enable a
performance improvement even in high-bit rate transmission systems over
long distances (Sano etal 2000). In-line dispersion compensation is realized in
the optical domain. This is achieved by chirped fiber gratings, using DCF
fibers or phase conjugators. The post-chirp techniques at the receiver side are
characterized by the compensation of the chromatic dispersion in electrical
domain by Maximum Likelihood Detection (Otte and Rosenkranz 2000).
In this section we have estimated the optimal length of dispersion
compensating fibers to be used to realize a high Q factor for various input
power levels. The simulation is done for 16 channels with the data rate of
40 Gb/s per channel propagating over a non-linear dispersive fiber, and the Q
factor is determined for various modulation formats. The performance of the
non-linear fiber is also studied at higher data rate for various modulation
formats towards the improvement of Q factor.
The impact of chromatic dispersion becomes larger with a system
upgrade to higher channel bit rates greater than 10 Gb/s. The performance of
NRZ, RZ and DQPSK modulation formats are analyzed with pre, post and
symmetrical dispersion compensation techniques for a constant SMF length
of 80 kms and a varying length of DCF. One loop in our simulation includes
SMF of fixed length 80 km and EDFA 1 of gain 20 dB, noise figure 4 dB and
the variable length DCF followed by EDFA 2 of 12.6 dB gain. The other fiber
parameters used are listed in Table 3.17. The receiver side of our simulation
has a photo detector with low pass Bessel filter of cut off frequency 0.75
times the bit rate. Table 3.18 lists the Q factors measured for various lengths
of DCF based post compensation for the NRZ, RZ and DQPSK modulation
formats.
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It is observed that the Q factor is maximized for a certain length of
DCF and degrades if the length is increased or decreased from the optimum
value. It is found from Table 3.18 that, the optimum DCF length for best Q-
factor is different for different modulation formats. For example, in the NRZ
format, a maximum Q factor of 13.66 is achieved for a DCF length of 15 kms.
For RZ format the optimum DCF length is 13 kms giving a maximum Q
factor of 15.79 and for DQPSK the optimum DCF length is 12 kms giving a
maximum Q factor of 17.34.
Table 3.17 Fiber parameters used in the simulation for studying the
dispersion compensation schemes
Parameters SMF DCF
Length 80 km Variable
Attenuation 0.2 dB/km 0.25 db/km
GVD 16 ps/nm/km -72 ps/nm/km
Slope 0.08 ps/nm2 0.08 ps/nm2
Effective Area 80 µm2 30 µm2
Table 3.18 Q factor measured at Input power of 2 dBm - Post
compensation using DCF
SMF in km DCF length in km
Q factor (NRZ)
Q factor (RZ)
Q factor (DQPSK)
80 10 12.29 14.59 15.11 80 11 12.98 14.88 15.23 80 12 13.08 15.67 17.34 80 13 12.45 15.79 15.45 80 14 13.27 15.26 16.12 80 15 13.66 14.79 16.33 80 18 12.33 14.23 15.67 80 20 12.00 13.37 15.76 80 22 11.10 13.19 14.35
105
Tables 3.19 and 3.20 list out the Q factors measured under Pre-
compensation and Symmetric compensation by DCF. In the case of
symmetrical compensation, the loop consists of a DCF of variable length,
EDFA 1 of 12.8 db gain with 4 db noise figure , SMF of length 80 kms ,
EDFA 2 of 20 dB gain , SMF of 80 kms, EDFA 3 of 20 dB gain, a DCF of
length 10 kms, and finally EDFA 4 of 12.8 dB.
Table 3.19 Q factor measured at Input power of 2 dBm - Pre-
compensation using DCF
SMF in km
DC F length in km
Q factor (NRZ)
Q factor (RZ)
Qfactor (DQPSK)
80 10 19.80 21.08 23.08
80 11 21.30 22.29 24.29
80 12 21.09 22.98 27.66
80 13 18.42 24.45 26.66
80 14 17.13 19.27 21.23
80 15 16.04 18.66 21.88
80 18 14.42 15.43 17.34
80 20 14.00 14. 60 16.35
80 22 12.10 14.10 16.11
106
Table 3.20 Q factor measured at Input power of 2 dBm - Symmetrical
compensation using DCF
SMF in km
DCF length in km
Q factor (NRZ)
Q factor (RZ)
Q factor (DQPSK)
80 10 15.75 18.08 20.08
80 11 18.98 19.29 22.29
80 12 16.34 21.98 24.98
80 13 14.72 16.45 17.45
80 14 13.98 16.27 18.27
80 15 15.69 13.66 15.66
80 18 13.89 12.33 14.33
80 20 12.99 11.00 12.00
80 22 11.08 10.10 13.10
It can be concluded from these investigations that, an optimum
DCF length can give us the best Q factor performance and this optimum
length is also dependent on the modulation format as well as the location of
compensation. Comparing Pre, Post and Symmetrical dispersion
compensation techniques in terms of Q factor realized, it is observed that Pre-
compensation is the best option. It is also noted that the DQPSK modulation
format shows the best Q factor performance. DQPSK with pre-compensation
using DCF of length 12 km gives the highest Q-factor of 27.66.
Figures 3.29, 3.30 and 3.31 show the eye diagram and Q factor for
the NRZ, RZ and DQPSK modulation formats, under different dispersion
compensation techniques.
107
Figure 3.29 Q factor of NRZ under post compensation using DCF
Figure 3.30 Q factor of RZ under pre-compensation using DCF
108
Figure 3.31 Q factor of NRZ under symmetrical compensation using
DCF
3.7 SUMMARY
In this chapter a modulation format has been identified by
investigating its performance under Equally Spaced Channel and Unequally
Spaced Channel schemes for DWDM systems. Unequally Spaced Channel
assignment is studied using optical orthogonal coding technique. In addition,
the impact of filtering techniques, modulation formats and dispersion
compensating techniques in combating the linear and non-linear fiber
impairments are also analyzed in terms of Q factor and Eye Opening Penalty
to identify methods for improving the spectral efficiency.
The right choice of the optical filter used for transmitter and
receiver side is crucial especially in a system requiring more spectral
109
efficiency. The Q factor measured at the receiver output for various pre-
transmission filters like Bragg Grating, Gaussian, Super Gaussian and Fabry
Perot Filters shows a reduction in Q factor for increasing distances indicating
the necessity for a dispersion management technique. Super Gaussian filter of
higher order, is observed to give the highest Q factor with Bragg grating Filter
showing a closer performance.
In terms of Q factor and EOP measurements, it is concluded that
RZ based formats are more non-linear tolerant. In addition it is also concluded
that under constant input powers the Q factor values are higher for USC
scheme than the ESC scheme, irrespective of the modulation format. This is a
significant inference implying that USC schemes are more FWM tolerant.
Further, the comparison of Q factor values suggests that a combination of
orthogonal code based USC and CSRZ-DQPSK formatting can effectively
combat FWM even at higher channel powers. The Q-factor comparison of
PSK based formats namely DPSK, DQPSK and CSRZ-DQPSK suggests that
DQPSK and CSRZ-DQPSK have similar performance and are better FWM
tolerant.
The performances of the dispersion compensation schemes like
post, pre and symmetrical compensation techniques are observed to be
dependent on the modulation format as well as the DCF length. It can be
concluded from our investigations that, an optimum DCF length can give us
the best Q factor performance and this optimum length is also dependent on
the modulation format as well as the location of compensation. Comparing
Pre, Post and Symmetrical dispersion compensation techniques in terms of Q
factor realized, for different modulation formats it is concluded that DQPSK
modulation format with pre-compensation using optimum DCF length is the
best option.