Aston Data Explorer

Rodriguez-Rodriguez, Valia

Garcia Agustin, Daysi

Dataset - Gait Speed declines in older adults - Havana cohort

Edwards, Katie Leigh

Example video clips of stimuli used for research examining the effect of others' facial expressions on eating behaviour

Garcia-Dominguez, Antonio

Aggregated results from use of the AutoFeedback system in the 2020-21 and 2021-22 editions of CS1OOP (Object-Oriented Programming)

Li, Yiming

CRLB approaching phase and channel estimation code

Tan, Mingming

High-Speed Long-Haul Multi-Channel Nonlinear Optical Communication Systems Influenced by Equalization Enhanced Phase Noise

Ellis, Andrew

Ali, Abdallah

Salgado, Nelson

Sygletos, Stylianos

Mitigation of Nonlinear Effects in Optical Communications using Digital and Optical Techniques.

Bastamova, Mariia

Gordienko, Vladimir

Impact of Pump Phase Modulation on Fibre Optical Parametric 
Amplifier Performance for 16-QAM Signal Amplification  
Mariia Bastamova (1), Vladimir Gordienko(1), Andrew Ellis (1) 
 
(1)Aston Institute of Photonic Technologies, Aston University, Birmingham, B4 7ET 
210114416@aston.ac.uk 
 
Abstract We examine impact of fibre optical parametric amplifier pump phase modulation on signals 
complex amplitude via simulations. We find that in most practical scenarios the required SNR penalty 
for 16-QAM signals can be less than 0.1dB at BER of 0.03. ©2022 The Author(s) 
Introduction 
Fibre optical parametric amplifiers (FOPA) 
present a major research interest for fibre optic 
communications for their virtually wavelength 
unrestricted [1] abilities for broadband [2], phase-
sensitive [3] and transient-free [4] amplification. 
Although a significant progress in terms of 
realising polarisation-insensitive FOPA gain and 
mitigation of nonlinear crosstalk in FOPA has 
been achieved recently [5], the mitigation of 
stimulated Brillouin scattering (SBS) remains a 
challenge for FOPAs. The SBS limits the pump 
power employable by a parametric device and 
thus restricts its performance [6]. One common 
way to mitigate SBS relies on pump linewidth 
increase, most often via pump phase modulation 
known as dithering [7]. This technique consists in 
phase modulation of pump to evenly distribute 
pump power across bandwidth sufficient to 
mitigate SBS without inducing the pump 
amplitude modulation.  
Several theoretical and experimental papers 
have showed that dithering caused temporal 
variations of the FOPA gain. The gain variation 
occurs due to instantaneous pump frequency 
modulation induced by pump phase modulation 
[8-12]. This effect has been studied in detail but 
only for directly detected on-off keying signals. 
These studies therefore have not considered 
signal phase noise introduced by dithering and do 
not address penalties for quadrature modulated 
signals. Moreover, coherent detection relies on 
signal amplitude rather than signal power, which 
has not been addressed in previous study as 
well. Overall, results of the existing impact of 
dithering studies in FOPA are not applicable for 
coherently detected QAM signals widely used in 
modern optical communications.  
In this paper we numerically simulate the 
impact of pump phase modulation on FOPA 
performance when amplifying 16-QAM signals. 
First, we derive the dependency of the amplitude 
gain and the amplified signal phase shift on the 
pump frequency. Then, we use these data to 
calculate BER as a function of signal SNR for a 
range of pump linewidths across the FOPA gain 
bandwidth. We find that although dithering-
induced impairments increase quickly with the 
pump linewidth, the pump linewidth of 5 GHz 
sufficient for SBS mitigation in most FOPAs 
allows to keep the required SNR penalty for 
coherently detected 16QAM signals below 0.1 dB 
at BER of 0.03 at all signal wavelengths within the 
3 dB gain bandwidth of the simulated FOPA. 
Simulation 
We examine the impact of pump phase 
modulation on signal performance arising from 
induced instantaneous pump frequency 
modulation. The latter causes temporal variation 
of FOPA gain and phase shift and thus introduces 
amplitude and phase noise for an amplified 
signal. Therefore, we first calculate FOPA gain in 
the complex domain as a function of pump 
frequency, then use it to calculate complex 
amplitudes of an amplified 16-QAM signal for a 
range of instantaneous pump frequencies, and 
finally derive the signal BER considering 
temporal fluctuations of the output signal 
complex amplitude.  
We calculate complex amplitude gain ℎ3 in 
FOPA defined as the ratio between output and 
input signal amplitudes (Eq. (1)) to consider both 
power amplification and phase shifts introduced 
by FOPA [13].  Eq. (1) assumes no loss, no pump 
depletion case, where 𝑔 is the gain coefficient 
given by Eq. (2), 𝑘 is the total propagation 
constant, 𝐿 = 214 m is the gain fibre length, 𝑃 = 
1 W is the pump power, and 𝛾 = 14 W-1km-1 is the 
gain fibre nonlinearity coefficient. The total 
propagation constant 𝑘 is the key parameter 
because it depends on the group velocity 
dispersion at the pump wavelength 𝛽2 and the 
frequency offset of signal from the pump Δ𝜔 (Eq. 
(3)), which are the two parameters modified by 
the instantaneous pump frequency modulation. 
ℎ3 =
𝐴𝑜𝑢𝑡
𝐴𝑖𝑛
= [cosh(𝑔𝐿) + 𝑖
𝑘
2𝑔
sinh⁡(𝑔𝐿)] ×
× exp [𝑖 (2𝛾𝑃 −
𝑘
2
)𝐿] ;
 (1) 
𝑔 = √(𝛾𝑃)2 − 𝑘2 4⁄ ; (2) 
𝑘 = 2𝛾𝑃 + 𝛽2 ∙ Δ𝜔
2. (3) 
Fig. 1 shows spectra of the amplitude gain 
modulus |ℎ3⁡| and phase calculated for pump 
wavelength of 1563.7 nm, zero-dispersion 
wavelength of 1562.9 nm, and dispersion slope 
of 43 s·m-3. The amplitude gain modulus is the 
square root of the power gain, so its peak of 
10 dB corresponds to the power gain of 20 dB. 
The amplitude gain phase represents the phase 
shift introduced to the signal during amplification.  
Therefore, both values have to be taken into 
account when calculating impact of pump phase 
modulation on phase and amplitude modulated 
signals, e.g. M-QAM. 
We simulate the impact of pump dithering on 
the amplified signal for different pump 
bandwidths by calculating ℎ3 at the signal 
wavelength for 20 pump frequencies distributed 
across an examined pump bandwidth. Then, the 
calculated values of ℎ3 were used to derive a set 
of output signal complex amplitudes 
corresponding to different pump frequencies for 
each point of the 16-QAM constellation diagram. 
We consequently calculated error probability for 
every combination of a constellation point and ℎ3 
value. We assumed Gaussian noise distribution 
for each case, because although temporal 
fluctuations of ℎ3 affect overall noise distribution 
of the output signal, at every instant the output 
signal has the same noise distribution as the 
input signal which was assumed Gaussian. 
Hence, error probability for each point was found 
as a sum of error functions shown by Eq. (4), 
where 𝜇 is the centre of Gaussian distribution 
defined by the output signal complex amplitude, 
𝑥0 is the decision threshold scaled for all points 
by the same ℎ3 corresponding to the central of 
the simulated pump frequencies, and 𝜎 is the 
standard deviation defined by the input signal 
SNR [14]. The symbol error rate is found as an 
average of all error probabilities assuming all 
constellation points and instantaneous pump 
frequencies to be equally probable. The BER is 
found as a ¼ of the symbol error rate due to Gray 
coding used for 16-QAM signals [15]. 
𝐵𝐸𝑅 =
1
2
𝑒𝑓𝑟𝑐 (
𝑥0 − 𝜇
𝜎√2
) (4) 
 
Results and Discussion 
Fig. 2 shows simulated BER of a 16QAM signal 
at the FOPA gain peak wavelength versus the 
signal SNR for a set of typical pump linewidths [7, 
8]. Fig. 2 additionally shows a theoretical curve 
(black) for Gaussian-noise limited 16-QAM 
signals obtained with Eq. (5) [15]. A perfect 
match between the theoretical curve and our 
simulated curves for pump linewidths ≤1 GHz 
implies no performance penalties for such pump 
linewidths and confirms our model. To 
characterise the impact of pump phase 
modulation of higher pump linewidths for a range 
of signal wavelengths across the FOPA gain 
spectrum we define the required SNR penalty as 
the SNR difference between the simulated BER 
curve and the theoretical (back-to-back) curve at 
the BER level of 0.03 (close to the forward error 
correction limit of modern transponders). Thus, 
the required SNR penalty for the 20 GHz pump 
linewidth is 1.04 dB (Fig. 2).  
 
Fig. 2: BER versus SNR for different pump bandwidth for a 
16-QAM signal amplified at the FOPA gain peak wavelength. 
 
 
Fig. 1: Gain Amplitude spectra and Gain Phase shift spectra.  
𝐵𝐸𝑅 =
3
2
𝑒𝑓𝑟𝑐 (√
𝑆𝑁𝑅
10
) (5) 
 
Figure 3 shows comparison of required SNR 
penalties for several pump linewidths and a range 
of signal wavelengths across the FOPA gain 
bandwidth. Gain spectrum calculated as |ℎ3⁡|
2 is 
shown for a reference. Expectedly, Fig. 3 shows 
that the SNR penalties increase with the pump 
linewidth. The SNR penalty also increases with 
the signal wavelength offset from the pump 
wavelength of 1563.7 nm until peaking at the gain 
spectrum edge. However, essentially only 
wavelength range within the 3 dB FOPA gain 
bandwidth presents interest for signal 
amplification. Then, the biggest SNR penalty 
occurs at the edge of this band furthermost from 
the pump (1527 nm) and remains low at the level 
of 0.1 dB for the pump linewidth of 5 GHz or 
reaches 2.2 dB for the pump linewidth of 20 GHz. 
The pump linewidth of 5 GHz allows to increase 
the SBS threshold by >20 dB if bandwidth-
efficient dithering is employed [8] which is 
sufficient for most FOPA applications [2], 
although pump linewidth of 20 GHz and more 
might be required for some ultra-high gain 
applications [7]. 
Our results present two spectacular features. 
First, we observe much less signal performance 
penalty with 16QAM signal than in previous 
studies of on-off keying signals (up to 10 dB 
penalty on Q2) [9]. Second, we observe a 
completely different signal penalty distribution 
across the FOPA gain bandwidth as compared to 
penalties being minimal at the gain peak and 
have two maxima at the FOPA gain slopes in [9]. 
We understand these differences arise due to 
quadrature-modulation and coherent detection of 
the signal. Indeed, in case of directly detected on-
off keying signals the only source of signal 
degradation is power gain modulation due to 
instantaneous pump frequency modulation. 
Then, the largest degradation occurs where 
power gain has the largest slopes. However, 
coherently-detected signals rely on electric field 
amplitude being square root of power, so the 
impact of power gain fluctuations on the signal 
quality is mitigated. On the other hand, 
quadrature-modulated signals are susceptible to 
induced signal phase modulation. The correlation 
between the SNR penalty dependency on the 
signal wavelength and the complex gain 
amplitude phase spectrum (Fig. 1) implies that 
the induced phase noise is the major source of 
16QAM signal degradation due to pump phase 
modulation in FOPA. This explains not only 
quantitative but also qualitative difference 
between the SNR penalties of directly-detected 
on-off keying signals and coherently-detected 
QAM signals.  
Conclusions 
For the first time we have numerically studied the 
required SNR penalty for coherently-detected 16-
QAM signals due to pump phase modulation in 
single pump FOPA. Our results and comparison 
with previous works indicate that the induced 
phase noise is the major source of degradation 
for coherently detected QAM signals, whilst the 
impact of induced gain fluctuations is mitigated. 
Consequently, we find that the pump linewidth of 
5 GHz sufficient for SBS mitigation in most 
FOPAs induces the maximum required SNR 
penalty for 16QAM signal of only 0.1 dB at BER 
of 0.03 within the 3 dB FOPA gain bandwidth. 
Acknowledgements 
This paper was funded in part by the EPSRC, 
grant number EP/S003436/1 (PHOS). Underlying 
data is available at [16]. 
 
Fig. 3: Gain spectrum and SNR penalty for different pump bandwidths and signal wavelengths. 
References 
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[5] V. Gordienko, F. M. Ferreira, C. B. Gaur, and 
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2–6, 2021,  
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[7] T. Torounidis, P. A. Andrekson, and B. E. 
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(2021) 
DOI:10.17036/researchdata.aston.ac.uk.000
00547 
 
 
 
 
 


Impact of Pump Dithering on Fibre Optical Parametric Amplifier Performance for 16-QAM Signal Amplification

Phillips, Ian

Harper, Paul

Forysiak, Wladek

Raman amplification optimization in short-reach high data rate coherent transmission systems

Spickett, Corinne M.

Pitt, Andrew R,

Sousa, Bebiana

Medeiros, Romina

 
1 
 
Online Data for “Identification and relative quantification of 3-nitrotyrosine residues in fibrinogen 
nitrated in vitro and fibrinogen from ischemic stroke patient plasma using LC-MSMS” 
Free Radic Biol Med. 2021 Feb 4;165:334-347. doi: 10.1016/j.freeradbiomed.2021.01.049  
Online ahead of print. 
https://www.sciencedirect.com/science/article/pii/S0891584921000666 
Authors: Authors: Romina Medeiros, Bebiana Sousa, Silvina Rossi, Catarina Afonso, Luis Bonino, 
Andrew Pitt, Elizabeth Lopez, Corinne Spickett, Graciela Borthagaray 
Address for Correspondence regarding data: c.m.spickett@aston.ac.uk 
 
 
Cohort Data 
Numerical data on age and CRP levels supporting Table 1 are supplied in the form of an Excel 
spreadsheet: 
FRBM-D-20-00396 Age & CRP data 
 
 
LC-MSMS Data 
The data are divided into separate folders according to the whether the data is in-vitro or clinical 
samples and if it is information dependent acquisition (IDA) or MRM data.  
For the IDA in vitro data there are four folders, a control and 3 treatment levels, 10, 100 and 200 mol 
equivalent of peroxynitrite. Each folder contains data from 2 preparations. There are single data runs 
for files beginning 2018_06_16 and triplicates for files beginning 2018_04_27. The data were acquired 
on an ABSCIEX 5600 TripleTOF instrument and wiff and wiff.scan files are supplied. 
For the IDA clinical data there are two files, one containing control and the other the stroke data. 
Control samples names begin with CTL and stroke sample with ACV. There are 3 replicates of each 
sample identified by the final letter A, B or C. The data were acquired on an ABSCIEX 5600 TripleTOF 
instrument and wiff and wiff.scan files are supplied. 
For the MRM Clinical data there are two files, one containing control and the other the stroke data. 
Control samples names begin with CTL and stroke sample with ACV. The data were acquired on an 
ABSCIEX 5500 QTrap instrument and wiff and wiff.scan files are supplied. This analysis was run on a 
subset of samples 
For the SRM Clinical data there are two files, one containing control and the other the stroke data. 
Control samples names begin with CTL and stroke sample with ACV. There are 3 replicates of each 
sample identified by the final letter A, B or C. The data were acquired on an ABSCIEX 5500 QTrap 
instrument and wiff and wiff.scan files are supplied. 
 
 
 
2 
 
Analysis of nitrotyrosine in peroxynitrite-treated fibrinogen 
IDA in vitro data 
Folder: IDA In vitro control 
o 2018-04-27 Fg n_1 
o 2018-04-27 Fg n_2 
o 2018-04-27 Fg n_3 
Folder: IDA In vitro 200 mol equivalent peroxynitrite 
o 2018-04-27 Fg 1_1 
o 2018-04-27 Fg 1_2 
o 2018-04-27 Fg 1_3 
 
Folder: IDA In vitro 100 mol equivalent peroxynitrite 
o 2018-04-27 Fg 2_1 
o 2018-04-27 Fg 2_2 
o 2018-04-27 Fg 2_3 
 
Folder: IDA In vitro 10 mol equivalent peroxynitrite 
o 2018-04-27 Fg 3_1 
o 2018-04-27 Fg 3_2 
o 2018-04-27 Fg 3_3 
 
IDA Clinical Data 
Folder: IDA Clinical Control 
o CTL26.A 
o CTL26.B 
o CTL26.C 
o CTL27.A 
o CTL27.B 
o CTL27.C 
o CTL28.A 
o CTL28.B 
o CTL28.C 
o CTL29.A 
o CTL29.B 
o CTL29.C 
o CTL30.A 
o CTL30.B 
o CTL30.C 
o CTL31.A 
 
3 
 
o CTL31.B 
o CTL31.C 
o CTL32.A 
o CTL32.B 
o CTL32.C 
o CTL33.A 
o CTL33.B 
o CTL33.C 
o CTL34.A 
o CTL34.B 
o CTL34.C 
 
 
  
Folder: IDA Clinical Stroke 
o ACV1.A 
o ACV1.B 
o ACV1.C 
o ACV2.A 
o ACV2.B 
o ACV2.C 
o ACV3.A 
o ACV3.B 
o ACV3.C 
o ACV4.A 
o ACV4.B 
o ACV4.C 
o ACV5.A 
o ACV5.B 
o ACV5.C 
o ACV6.A 
o ACV6.B 
o ACV6.C 
o ACV7.A 
o ACV7.B 
o ACV7.C 
o ACV8.A 
o ACV8.B 
o ACV8.C 
o ACV9.A 
o ACV9.B 
o ACV9.C 
o ACV10.A 
o ACV10.B 
o ACV10.C o  
 
4 
 
MRM clinical data 
Folder: MRM clinical Control 
o CTL26 
o CTL27 
o CTL28 
o CTL29 
o CTL30 
o CTL31 
o CTL32 
o CTL33 
o CTL34 
 
 
Folder: MRM clinical stroke 
 
o ACV1 
o ACV2 
o ACV3 
o ACV4 
o ACV5 
o ACV6 
o ACV7 
o ACV8 
o ACV9 
o ACV10 
 
 
Folder: SRM clinical Control 
o CTL35.A 
o CTL35.B 
o CTL35.C  
o CTL36.A 
o CTL36.B 
o CTL36.C  
o CTL37.A 
o CTL37.B 
o CTL37.C  
o CTL38.A 
o CTL38.B 
o CTL38.C  
o CTL39.A 
o CTL39.B 
o CTL39.C  
 
5 
 
o CTL40.A 
o CTL40.B 
o CTL40.C  
o CTL41.A 
o CTL41.B 
o CTL41.C  
o CTL42.A 
o CTL42.B 
o CTL42.C  
o CTL43.A 
o CTL43.B 
o CTL43.C 
 
Folder: SRM clinical Stroke 
o ACV1.A 
o ACV1.B 
o ACV1.C 
o ACV2.A 
o ACV2.B 
o ACV2.C 
o ACV3.A 
o ACV3.B 
o ACV3.C 
o ACV4.A 
o ACV4.B 
o ACV4.C 
o ACV5.A 
o ACV5.B 
o ACV5.C 
o ACV6.A 
o ACV6.B 
o ACV6.C 
o ACV7.A 
o ACV7.B 
o ACV7.C 
o ACV8.A 
o ACV8.B 
o ACV8.C 
o ACV9.A 
o ACV9.B 
o ACV9.C 
o ACV10.A 
o ACV10.B 
o ACV10.C o  
o ACV11.A 
o ACV11.B 
 
6 
 
o ACV11.C 
o ACV12.A 
o ACV12.B 
o ACV12.C 
o ACV13.A 
o ACV13.B 
o ACV13.C 
o ACV14.A 
o ACV14.B 
o ACV14.C 
o ACV15.A 
o ACV15.B 
o ACV15.C 
o ACV16.A 
o ACV16.B 
o ACV16.C 
o ACV17.A 
o ACV17.B 
o ACV17.C 
o ACV18.A 
o ACV18.B 
o ACV18.C 
o ACV19.A 
o ACV19.B 
o ACV19.C 
o ACV20.A 
o ACV20.B 
o ACV21.A 
o ACV21.B 
o ACV21.C 
o ACV22.A 
o ACV22.B 
o ACV22.C 
o ACV23.A 
o ACV23.B 
o ACV23.C 
o ACV24.A 
o ACV24.B 
o ACV25.A 
o ACV25.B 
o ACV25.C o  
  
 


Identification and relative quantification of 3-nitrotyrosine residues in fibrinogen nitrated in vitro and fibrinogen from ischemic stroke patient plasma using LC-MS/MS.

Ferreira, Filipe

Gaur, Chandra

Doran, Nick

Looped Polarization-Insensitive Fiber Optical Parametric Amplifiers for Broadband High Gain Applications

Aston University

Not a member yet