scottk/oopt-gnpy
1
0
Fork 0
mirror of https://github.com/Telecominfraproject/oopt-gnpy.git synced 2025-11-02 02:57:52 +00:00
Code Issues Packages Projects Releases 10 Wiki Activity

Add NLI Evaluation in Fiber class (#33)

Browse Source 
* Add Travis-CI configuration for continous integration
* Add GN-Model Documentation and auto-doc integration
* Add GN model to Fiber
* Unit of measure conversion adapted to SI
This commit is contained in:
Mattia Cantono
2018-02-28 17:53:58 +01:00
committed by James
parent 48f9c448e4
commit ba06a0e104
15 changed files with 2244 additions and 48 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
.travis.yml
View File

@@ -1 +1,9 @@
language: python
python:
- "3.6"
# command to install dependencies
install:
- pip install -r requirements.txt
# command to run tests
script:
- pytest

8
README.rst
View File

@@ -3,8 +3,7 @@ gnpy
====
|docs|
|docs| |build|
Gaussian Noise (GN) modeling library
@@ -52,3 +51,8 @@ The key to unbundling is the ability to accurately plan and predict the performa
:target: http://gnpy.readthedocs.io/en/develop/?badge=develop
:alt: Documentation Status
:scale: 100%
.. |build| image:: https://travis-ci.org/mcantono/gnpy.svg?branch=develop
:target: https://travis-ci.org/mcantono/gnpy
:alt: Build Status
:scale: 100%

1850
docs/biblio.bib Normal file
View File

File diff suppressed because it is too large Load Diff

19
docs/conf.py
View File

@@ -17,10 +17,9 @@
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
import os
import sys
sys.path.insert(0, os.path.abspath('../'))
# -- General configuration ------------------------------------------------
@@ -33,7 +32,7 @@
# ones.
extensions = ['sphinx.ext.autodoc',
'sphinx.ext.mathjax',
'sphinx.ext.githubpages']
'sphinx.ext.githubpages','sphinxcontrib.bibtex']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
@@ -41,8 +40,8 @@ templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
source_suffix = ['.rst', '.md']
# The master toctree document.
master_doc = 'index'
@@ -85,7 +84,11 @@ todo_include_todos = False
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'default'
on_rtd = os.environ.get('READTHEDOCS') == 'True'
if on_rtd:
html_theme = 'default'
else:
html_theme = 'alabaster'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the

124
docs/gn_model.rst Normal file
View File

@@ -0,0 +1,124 @@
The QoT estimation in the PSE framework of TIP-OOPT
=======================================================
QoT-E including ASE noise and NLI accumulation
----------------------------------------------
The operations of PSE simulative framework are based on the capability to estimate the QoT of one
or more channels operating lightpaths over a given network route. For
backbone transport networks, we can suppose that transceivers are
operating polarization-division-multiplexed multilevel modulation
formats with DSP-based coherent receivers, including equalization. For
the optical links, we focus on state-of-the-art amplified and
uncompensated fiber links, connecting network nodes including ROADMs,
where add and drop operations on data traffic are performed. In such a
transmission scenario, it is well accepted
:cite:`vacondio_nonlinear_2012,bononi_modeling_2012,carena_modeling_2012,mecozzi_nonlinear_2012,secondini_analytical_2012,johannisson_perturbation_2013,dar_properties_2013,serena_alternative_2013,secondini_achievable_2013,poggiolini_gn-model_2014,dar_accumulation_2014,poggiolini_analytical_2011,savory_approximations_2013,bononi_single-_2013,johannisson_modeling_2014`
to assume that transmission performances are limited by the amplified
spontaneous emission (ASE) noise generated by optical amplifiers and and
by nonlinear propagation effects: accumulation of a Gaussian disturbance
defined as nonlinear interference (NLI) and generation of phase noise.
State-of-the-art DSP in commercial transceivers are typically able to
compensate for most of the phase noise through carrier-phase estimator
(CPE) algorithms, for modulation formats with cardinality up to 16, per
polarization state
:cite:`poggiolini_recent_2017,schmidt_experimental_2015,fehenberger_experimental_2016`.
So, for backbone networks covering medium-to-wide geographical areas, we
can suppose that propagation is limited by the accumulation of two
Gaussian disturbances: the ASE noise and the NLI. Additional impairments
such as filtering effects introduced by ROADMs can be considered as
additional equivalent power penalties depending on the ratio between the
channel bandwidth and the ROADMs filters and the number of traversed
ROADMs (hops) of the route under analysis. Modeling the two major
sources of impairments as Gaussian disturbances, and being the receivers
*coherent*, the unique QoT parameter determining the bit error rate
(BER) for the considered transmission scenario is the generalized
signal-to-noise ratio (SNR) defined as
.. math::
{\text{SNR}}= L_F \frac{P_{\text{ch}}}{P_{\text{ASE}}+P_{\text{NLI}}} = L_F \left(\frac{1}{{\text{SNR}}_{\text{LIN}}}+\frac{1}{{\text{SNR}}_{\text{NL}}}\right)^{-1}
where :math:`P_{\text{ch}}` is the channel power,
:math:`P_{\text{ASE}}` and :math:`P_{\text{NLI}}` are the power levels of the disturbances
in the channel bandwidth for ASE noise and NLI, respectively.
:math:`L_F` is a parameter assuming values smaller or equal than one
that summarizes the equivalent power penalty loss such as
filtering effects. Note that for state-of-the art equipment, filtering
effects can be typically neglected over routes with few hops
:cite:`rahman_mitigation_2014,foggi_overcoming_2015`.
To properly estimate :math:`P_{\text{ch}}` and :math:`P_{\text{ASE}}`
the transmitted power at the beginning of the considered route must be
known, and losses and amplifiers gain and noise figure, including their
variation with frequency, must be characterized. So, the evaluation of
:math:`{\text{SNR}}_{\text{LIN}}` *just* requires an accurate
knowledge of equipment, which is not a trivial aspect, but it is not
related to physical-model issues. For the evaluation of the NLI, several
models have been proposed and validated in the technical literature
:cite:`vacondio_nonlinear_2012,bononi_modeling_2012,carena_modeling_2012,mecozzi_nonlinear_2012,secondini_analytical_2012,johannisson_perturbation_2013,dar_properties_2013,serena_alternative_2013,secondini_achievable_2013,poggiolini_gn-model_2014,dar_accumulation_2014,poggiolini_analytical_2011,savory_approximations_2013,bononi_single-_2013,johannisson_modeling_2014`.
The decision about which model to test within the PSE activities was
driven by requirements of the entire PSE framework:
i. the model must be *local*, i.e., related individually to each network element (i.e. fiber span) generating NLI, independently of preceding and subsequent elements; and
ii. the related computational time must be compatible with interactive operations.
So, the choice fell on the Gaussian Noise
(GN) model with incoherent accumulation of NLI over fiber spans
:cite:`poggiolini_gn-model_2014`. We implemented both the
exact GN-model evaluation of NLI based on a double integral (Eq. (11) of
:cite:`poggiolini_gn-model_2014`) and its analytical
approximation (Eq. (120-121) of
:cite:`poggiolini_analytical_2011`). We performed several
validation analyses comparing results of the two implementations with
split-step simulations over wide bandwidths
:cite:`pilori_ffss_2017`, and results clearly showed that
for fiber types with chromatic dispersion roughly larger than 4
ps/nm/km, the analytical approximation ensures an excellent accuracy
with a computational time compatible with real-time operations.
The Gaussian Noise Model to evaluate the NLI
--------------------------------------------
As previously stated, fiber propagation of multilevel modulation formats relying on the polarization-division-multiplexing
generates impairments that can be summarized as a disturbance called nonlinear interference (NLI),
when exploiting a DSP-based coherent receiver, as in all state-of-the-art equipment.
From a practical point of view, the NLI can be modeled as an additive
Gaussian random process added by each fiber span, and whose strength depends on the cube of the input power spectral density and
on the fiber-span parameters.
Since the introduction in the market in 2007 of the first transponder based on such a transmission technique, the scientific
community has intensively worked to define the propagation behavior of such a trasnmission technique.
First, the role of in-line chromatic dispersion compensation has been investigated, deducing that besides being
not essential, it is indeed detrimental for performances :cite:`curri_dispersion_2008`.
Then, it has been observed that the fiber propagation impairments are practically summarized by the sole NLI, being all the other
phenomena compensated for by the blind equalizer implemented in the receiver DSP :cite:`carena_statistical_2010`.
Once these assessments have been accepted by the community, several prestigious research groups have started to work
on deriving analytical models able to estimating the NLI accumulation, and consequentially the generalized SNR that sets the BER,
according to the transponder BER vs. SNR performance.
Many models delivering different levels of accuracy have been developed and validated. As previously clarified, for the purposes
of the PSE framework, the GN-model with incoherent accumulation of NLI over fiber spans has been selected as adequate.
The reason for such a choice is first such a model being a "local" model, so related to each fiber spans, independently of
the preceding and succeeding network elements. The other model characteristic driving the choice is
the availability of a closed form for the model, so permitting a real-time evaluation, as required by the PSE framework.
For a detailed derivation of the model, please refer to :cite:`poggiolini_analytical_2011`, while a qualitative description
can be summarized as in the following.
The GN-model assumes that the channel comb propagating in the fiber is well approximated by unpolarized spectrally shaped
Gaussian noise. In such a scenario, supposing to rely - as in state-of-the-art equipment - on a receiver entirely compensating for linear propagation effects, propagation in the fiber only excites the four-wave mixing (FWM) process among the continuity of
the tones occupying the bandwidth. Such a FWM generates an unpolarized complex Gaussian disturbance in each spectral slot
that can be easily evaluated extending the FWM theory from a set of discrete tones - the standard FWM theory introduced back in the 90s by Inoue :cite:`Innoue-FWM`- to a continuity of tones, possibly spectrally shaped.
Signals propagating in the fiber are not equivalent to Gaussian noise, but thanks to the absence of in-line compensation for choromatic dispersion,
the become so, over short distances.
So, the Gaussian noise model with incoherent accumulation of NLI has estensively proved to be a quick yet accurate and conservative tool
to estimate propagation impairments of fiber propagation.
Note that the GN-model has not been derived with the aim of an *exact* performance estimation, but to pursue a conservative performance prediction. So, considering these characteristics, and the fact that the NLI is always a secondary effect with respect to the ASE noise accumulation, and - most importantly - that typically linear propagation parameters (losses, gains and noise figures) are known within
a variation range, a QoT estimator based on the GN model is adequate to deliver performance predictions in terms of a reasonable SNR range, rather than an exact value.
As final remark, it must be clarified that the GN-model is adequate to be used when relying on a relatively narrow bandwidth up to few THz. When exceeding such a bandwidth occupation, the GN-model must be generalized introducing the interaction with the Stimulated
Raman Scattering in order to give a proper estimation for all channels :cite:`cantono2018modeling`.
This will be the main upgrade required within the PSE framework.
.. bibliography:: biblio.bib

41
docs/index.rst
View File

@@ -22,14 +22,16 @@ PSE WG Charter
- The PSE will influence and benefit from the innovation of the DTC, API, and OLS working groups.
- The PSE represents a step along the journey towards multi-layer optimization.
Features
--------
* GNPY simulation of an amplified optical link
Documentation
=============
The following pages are meant to describe specific implementation details and modeling assumptions behind GNpy.
.. toctree::
:maxdepth: 2
gn_model
Indices and tables
==================
@@ -38,10 +40,31 @@ Indices and tables
* :ref:`modindex`
* :ref:`search`
Contributors
============
Mattia Cantono, Alessio Ferrari, Vittorio Curri - Politecnico di Torino - Italy
Gilad Goldfarb, Brian Taylor - Facebook Inc.
James Powell
Gert Grammel - Juniper Inc.
Contributors in alphabetical order
==================================
+----------+------------+-----------------------+----------------------------+
| Name | Surname | Affiliation | Contact |
+==========+============+=======================+============================+
| Alessio | Ferrari | Politecnico di Torino | alessio.ferrari@polito.it |
+----------+------------+-----------------------+----------------------------+
| Brian | Taylor | Facebook | briantaylor@fb.com |
+----------+------------+-----------------------+----------------------------+
| David | Boertjes | Ciena | dboertje@ciena.com |
+----------+------------+-----------------------+----------------------------+
| Esther | Le Rouzic | Orange | esther.lerouzic@orange.com |
+----------+------------+-----------------------+----------------------------+
| Gabriele | Galimberti | Cisco | ggalimbe@cisco.com |
+----------+------------+-----------------------+----------------------------+
| Gert | Grammel | Juniper Networks | ggrammel@juniper.net |
+----------+------------+-----------------------+----------------------------+
| Gilad | Goldfarb | Facebook | giladg@fb.com |
+----------+------------+-----------------------+----------------------------+
| James | Powell | Consultant | james@dontusethiscode.com |
+----------+------------+-----------------------+----------------------------+
| Jeanluc | Auge | Orange | jeanluc.auge@orange.com |
+----------+------------+-----------------------+----------------------------+
| Mattia | Cantono | Politecnico di Torino | mattia.cantono@polito.it |
+----------+------------+-----------------------+----------------------------+
| Vittorio | Curri | Politecnico di Torino | vittorio.curri@polito.it |
+----------+------------+-----------------------+----------------------------+

70
docs/source/gnpy.core.rst Normal file
View File

@@ -0,0 +1,70 @@
gnpy\.core package
==================
Submodules
----------
gnpy\.core\.elements module
---------------------------
.. automodule:: gnpy.core.elements
:members:
:undoc-members:
:show-inheritance:
gnpy\.core\.execute module
--------------------------
.. automodule:: gnpy.core.execute
:members:
:undoc-members:
:show-inheritance:
gnpy\.core\.info module
-----------------------
.. automodule:: gnpy.core.info
:members:
:undoc-members:
:show-inheritance:
gnpy\.core\.network module
--------------------------
.. automodule:: gnpy.core.network
:members:
:undoc-members:
:show-inheritance:
gnpy\.core\.node module
-----------------------
.. automodule:: gnpy.core.node
:members:
:undoc-members:
:show-inheritance:
gnpy\.core\.units module
------------------------
.. automodule:: gnpy.core.units
:members:
:undoc-members:
:show-inheritance:
gnpy\.core\.utils module
------------------------
.. automodule:: gnpy.core.utils
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: gnpy.core
:members:
:undoc-members:
:show-inheritance:

17
docs/source/gnpy.rst Normal file
View File

@@ -0,0 +1,17 @@
gnpy package
============
Subpackages
-----------
.. toctree::
gnpy.core
Module contents
---------------
.. automodule:: gnpy
:members:
:undoc-members:
:show-inheritance:

7
docs/source/modules.rst Normal file
View File

@@ -0,0 +1,7 @@
gnpy
====
.. toctree::
:maxdepth: 4
gnpy

4
examples/edfa/edfa_example_network.json
View File

@@ -14,7 +14,9 @@
"params": {
"length": 80,
"loss_coef": 0.2,
"length_units": "km"
"length_units": "km",
"dispersion": 16.7E-6,
"gamma": 1.27E-3
},
"metadata": {
"latitude": 1,

94
gnpy/core/elements.py
View File

@@ -4,6 +4,9 @@
"""
Network elements class with SpectralInformation propagation using
__call__ and propagate methods
@author: Alessio Ferrari
@author: Mattia Cantono
@author: Vittorio Curri
@author: giladgoldfarb
@author: briantaylor
@author: jeanluc-auge
@@ -30,7 +33,7 @@ class Transceiver(Node):
osnr_nli = [lin2db(c.power.signal/c.power.nli) for c in spectral_info.carriers]
snr = [lin2db(c.power.signal/(c.power.nli+c.power.ase)) for c in spectral_info.carriers]
print('OSNR in signal bandwidth={}dB and in 0.1nm={}dB'.format(osnr_ase[0], osnr_ase_01nm[0]))
return snr
return snr
def __call__(self, spectral_info):
return spectral_info
@@ -39,9 +42,11 @@ class Fiber(Node):
def __init__(self, config):
super().__init__(config)
self.length = self.params.length * \
UNITS[self.params.length_units] #length in km
self.loss_coef = self.params.loss_coef #lineic loss dB/km
self.lin_loss_coef = self.params.loss_coef / 4.3429448190325184
UNITS[self.params.length_units] #length in m
self.loss_coef = self.params.loss_coef*1e-3 #lineic loss dB/m
self.lin_loss_coef = self.params.loss_coef / (20*np.log10(np.exp(1)))
self.dispersion = self.params.dispersion #s/m/m
self.gamma = self.params.gamma #1/W/m
#TODO discuss factor 2 in the linear lineic attenuation
def __repr__(self):
@@ -51,48 +56,87 @@ class Fiber(Node):
attenuation = self.length * self.loss_coef
return db2lin(attenuation)
def effective_length(self, loss_coef):
alpha_dict = self.dbkm_2_lin(loss_coef)
@property
def effective_length(self):
alpha_dict = self.dbkm_2_lin()
alpha = alpha_dict['alpha_acoef']
leff = 1 - np.exp(-2 * alpha * self.span_length)
leff = (1 - np.exp(-2 * alpha * self.length)) / (2*alpha)
return leff
def asymptotic_length(self, loss_coef):
alpha_dict = self.dbkm_2_lin(loss_coef)
@property
def asymptotic_length(self):
alpha_dict = self.dbkm_2_lin()
alpha = alpha_dict['alpha_acoef']
aleff = 1 / (2 * alpha)
return aleff
def dbkm_2_lin(self, loss_coef):
def beta2(self, ref_wavelength=None):
""" Returns beta2 from dispersion parameter.
Dispersion is entered in ps/nm/km.
Disperion can be a numpy array or a single value. If a
value ref_wavelength is not entered 1550e-9m will be assumed.
ref_wavelength can be a numpy array.
"""
#TODO: discuss beta2 as method or attribute
wl = 1550e-9 if ref_wavelength is None else ref_wavelength
D = np.abs(self.dispersion)
b2 = (wl**2) * D / (2 * np.pi * c) # 10^21 scales [ps^2/km]
return b2 # s/Hz/m
def dbkm_2_lin(self):
""" calculates the linear loss coefficient
"""
alpha_pcoef = loss_coef
alpha_acoef = alpha_pcoef / (2 * 4.3429448190325184)
alpha_pcoef = self.loss_coef
alpha_acoef = alpha_pcoef / (2 * 10*np.log10(np.exp(1)))
s = 'alpha_pcoef is linear loss coefficient in [dB/km^-1] units'
s = ''.join([s, "alpha_acoef is linear loss field amplitude \
coefficient in [km^-1] units"])
coefficient in [m^-1] units"])
d = {'alpha_pcoef': alpha_pcoef,
'alpha_acoef': alpha_acoef,
'description:': s}
return d
def beta2(self, dispersion, ref_wavelength=None):
""" Returns beta2 from dispersion parameter. Dispersion is entered in
ps/nm/km. Disperion can be a numpy array or a single value. If a
value ref_wavelength is not entered 1550e-9m will be assumed.
ref_wavelength can be a numpy array.
def _psi(self, carrier, interfering_carrier):
""" Calculates eq. 123 from arXiv:1209.0394.
"""
wl = 1550e-9 if ref_wavelength is None else ref_wavelength
D = np.abs(dispersion)
b2 = (10**21) * (wl**2) * D / (2 * np.pi * c) # 10^21 scales [ps^2/km]
return b2
if carrier.num_chan == interfering_carrier.num_chan: # SCI
psi = np.arcsinh(0.5 * np.pi**2 * self.asymptotic_length
* abs(self.beta2()) * carrier.baud_rate**2)
else: # XCI
delta_f = carrier.freq - interfering_carrier.freq
psi = np.arcsinh(np.pi**2 * self.asymptotic_length * abs(self.beta2()) *
carrier.baud_rate * (delta_f + 0.5 * interfering_carrier.baud_rate))
psi -= np.arcsinh(np.pi**2 * self.asymptotic_length * abs(self.beta2()) *
carrier.baud_rate * (delta_f - 0.5 * interfering_carrier.baud_rate))
return psi
def _gn_analytic(self, carrier, *carriers):
""" Computes the nonlinear interference power on a single carrier.
The method uses eq. 120 from arXiv:1209.0394.
:param carrier: the signal under analysis
:param carriers: the full WDM comb
:return: carrier_nli: the amount of nonlinear interference in W on the under analysis
"""
g_nli = 0
for interfering_carrier in carriers:
psi = self._psi(carrier, interfering_carrier)
g_nli += (interfering_carrier.power.signal/interfering_carrier.baud_rate)**2 *\
(carrier.power.signal/carrier.baud_rate) * psi
g_nli *= (16 / 27) * (self.gamma * self.effective_length)**2 /\
(2 * np.pi * abs(self.beta2()) * self.asymptotic_length)
carrier_nli = carrier.baud_rate*g_nli
return carrier_nli
def propagate(self, *carriers):
#TODO integrate and call the gn.ole module to calculate carrier nli noise in fiber
carrier_nli = db2lin(-28)*1e-3 #temporary Cte nli noise is added for debug
i=0
for carrier in carriers:
pwr = carrier.power
carrier_nli = self._gn_analytic(carrier, *carriers)
pwr = pwr._replace(signal=pwr.signal/self.lin_attenuation(),
nonlinear_interference=(pwr.nli+carrier_nli)/self.lin_attenuation(),
amplified_spontaneous_emission=pwr.ase/self.lin_attenuation())

4
gnpy/core/units.py
View File

@@ -1,2 +1,2 @@
UNITS = {'m': 1e-3,
'km': 1}
UNITS = {'m': 1,
'km': 1E3}

27
requirements.txt
View File

@@ -1,11 +1,36 @@
alabaster==0.7.10
attrs==17.4.0
Babel==2.5.3
certifi==2017.11.5
chardet==3.0.4
cycler==0.10.0
decorator==4.1.2
docutils==0.14
idna==2.6
imagesize==0.7.1
Jinja2==2.10
latexcodec==1.0.5
MarkupSafe==1.0
matplotlib==2.1.0
networkx==2.0
numpy==1.13.3
scipy==1.0.0
oset==0.1.3
pluggy==0.6.0
py==1.5.2
pybtex==0.21
pybtex-docutils==0.2.1
Pygments==2.2.0
pyparsing==2.2.0
pytest==3.3.2
python-dateutil==2.6.1
pytz==2017.3
PyYAML==3.12
requests==2.18.4
scipy==1.0.0
six==1.11.0
snowballstemmer==1.2.1
Sphinx==1.6.6
sphinxcontrib-bibtex==0.3.6
sphinxcontrib-websupport==1.0.1
urllib3==1.22
xlrd==1.1.0

3
tests/__init__.py Normal file
View File

@@ -0,0 +1,3 @@
#!/usr/bin/env python
from .test_utilities import *

16
tests/test_utilities.py Normal file
View File

@@ -0,0 +1,16 @@
#!/usr/bin/env python
import pytest
from gnpy.core.utils import db2lin
def test_db2lin():
assert pytest.approx(10.0)==db2lin(10.0)
if __name__ == '__main__':
from logging import getLogger, basicConfig, INFO
logger = getLogger(__name__)
basicConfig(level=INFO)
logger.info(f'Running {test}')
test_db2lin()