U.S. patent application number 15/603810 was filed with the patent office on 2018-11-29 for adjustment of control parameters of section of optical fiber network.
This patent application is currently assigned to Ciena Corporation. The applicant listed for this patent is David Boertjes, James Harley, Kim B. Roberts. Invention is credited to David Boertjes, James Harley, Kim B. Roberts.
Application Number | 20180343058 15/603810 |
Document ID | / |
Family ID | 64315608 |
Filed Date | 2018-11-29 |
United States Patent
Application |
20180343058 |
Kind Code |
A1 |
Harley; James ; et
al. |
November 29, 2018 |
Adjustment of Control Parameters of Section of Optical Fiber
Network
Abstract
Adjustment of one or more control parameters of a section of an
optical fiber network involves taking measurements of optical
signals in the section, deriving estimated data from the
measurements and from knowledge of the section, where the estimated
data is a function of optical nonlinearity and of amplified
spontaneous emission, and applying one or more control algorithms
using the estimated data to adjust the one or more control
parameters.
Inventors: |
Harley; James; (Nepean,
CA) ; Roberts; Kim B.; (Ottawa, CA) ;
Boertjes; David; (Nepean, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Harley; James
Roberts; Kim B.
Boertjes; David |
Nepean
Ottawa
Nepean |
|
CA
CA
CA |
|
|
Assignee: |
Ciena Corporation
Hanover
MD
|
Family ID: |
64315608 |
Appl. No.: |
15/603810 |
Filed: |
May 24, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04J 14/0221 20130101;
H04J 14/0275 20130101; H04B 10/0793 20130101; H04B 10/07955
20130101; H04B 10/27 20130101; H04B 10/25891 20200501 |
International
Class: |
H04B 10/25 20060101
H04B010/25; H04B 10/079 20060101 H04B010/079; H04J 14/02 20060101
H04J014/02; H04B 10/27 20060101 H04B010/27 |
Claims
1. A method for adjustment of one or more control parameters of a
section of an optical fiber network, the method comprising: taking
measurements of optical signals in the section; deriving estimated
data from the measurements and from knowledge of the section, where
the estimated data is a function of optical nonlinearity and of
amplified spontaneous emission; evaluating gradients of an
objective function using the measurements and the estimated data;
and applying one or more control algorithms using at least the
gradients to adjust the one or more control parameters.
2. The method as recited in claim 24, wherein the measurements
include a total output power.
3. The method as recited in claim 24, wherein the measurements
include per-channel optical power or power spectral density.
4. The method as recited in claim 3, wherein the measurements of
per-channel optical power or power spectral density are taken at
fewer geographic locations than a number of geographic sites in the
section having optical amplifier devices.
5. The method as recited in claim 4, wherein measurements of
per-channel optical power or power spectral density are taken at
two geographic locations and the section has more than three
geographic sites having optical amplifier devices.
6. The method as recited in claim 1, wherein the control parameters
include per-channel power control.
7. The method as recited in claim 6, wherein the control parameters
include loss values of a wavelength selective switch (WSS) node in
the section.
8. The method as recited in claim 1, wherein the control parameters
include total power control.
9. The method as recited in claim 8, wherein the control parameters
include target total output power values or target gain values of
one or more optical amplifier devices in the section.
10. The method as recited in claim 1, wherein the control
parameters include per-channel power control and total power
control, and the control algorithm to adjust per-channel power
control is substantially decoupled from the control algorithm to
adjust total power control.
11. The method as recited in claim 1, wherein the estimated data
includes estimated per-channel optical power or estimated power
spectral density at the output of one or more optical amplifier
devices in the section.
12. The method as recited in claim 1, wherein applying the one or
more control algorithms to adjust the one or more control
parameters comprises adjusting the one or more control parameters
in a direction of one or more dimensions of those gradients.
13. The method as recited in claim 12, wherein the optical signals
comprise two or more bands of channels that are independently
amplified through the section, and optimization of the objective
function jointly optimizes over all channels of the two or more
bands.
14. The method as recited in claim 12, wherein a goal of the
objective function is to minimize degradations through the
section.
15. The method as recited in claim 14, wherein optimization of the
objective function minimizes a weighted sum of ratios of the total
noise power from amplified spontaneous emission and optical
nonlinearities to the power of the optical signals.
16. The method as recited in claim 15, wherein optimization of the
objective function maximizes a capacity-distance product of the
optical fiber network.
17. The method as recited in claim 15, wherein optimization of the
objective function maximizes capacity of the optical fiber
network.
18. The method as recited in claim 12, wherein a goal of the
objective function is to maximize capacity or reliability or both
of the optical fiber network by reallocating margin between
channels.
19. The method as recited in claim 18, wherein the objective
function incorporates a concave value function of margin of one or
more channels propagated through the section.
20. The method as recited in claim 19, wherein for a channel
propagated through the section, the gradients incorporate a
function of information provided by a receiver modem that receives
that channel.
21. The method as recited in claim 24, wherein the optical fiber
network comprises multiple sections, and multiple instances of the
one or more control algorithms operate in parallel.
22. The method as recited in claim 21, wherein a topology of the
optical fiber network comprises a branch topology.
23. The method as recited in claim 21, wherein a topology of the
optical fiber network comprises a mesh topology.
24. A method for adjustment of one or more control parameters of a
section of an optical fiber network, the method comprising: taking
measurements of optical signals in the section; deriving estimated
data from the measurements and from knowledge of the section, where
the estimated data is a function of optical nonlinearity and of
amplified spontaneous emission; forming an objective function that
combines performance of multiple channels; and applying one or more
control algorithms using the estimated data and the objective
function to adjust the one or more control parameters.
25. (canceled)
Description
TECHNICAL FIELD
[0001] This document relates to the technical field of optical
communications and specifically to the control of components in an
optical fiber network.
BACKGROUND
[0002] Current best practices for determining optical parameters in
an optical fiber network look at equalizing the ratio of amplified
spontaneous emission (ASE) to signal power on channels over an
optical section while respecting channel power limits to manage the
fiber optical nonlinear effects. This equalization addresses the
strong power tilt that can accumulate across spans of optical fiber
mainly due to Stimulated Raman Scattering (SRS). These methods rely
heavily on offline simulations to determine good control
parameters, such as peak power. This is operationally burdensome
and error prone.
[0003] U.S. Pat. No. 9,438,369 describes increasing capacity by
optimization after nonlinear modeling. U.S. Pat. No. 8,364,036
describes controlling optical power within domains, and exchanging
state information between domains. U.S. Pat. No. 8,781,317
describes methods to measure phase nonlinearities. U.S. Pat. No.
7,894,721 describes global optical control where receiver changes
are correlated to network perturbations. U.S. Pat. No. 7,457,538
describes performance monitoring using the analog-to-digital
converter of the receiver. U.S. Pat. No. 7,376,358 describes
location-specific monitoring of nonlinearities. U.S. Pat. No.
7,356,256 describes digital monitoring along the optical line. US
Patent Publication No. 2016/0315711 describes controlling the
optical spectral density in a section.
SUMMARY
[0004] Through the latest innovations, optical networks are capable
of dynamically changing optical paths, and flexible transceivers
are capable of changing modulation formats and other transmission
parameters. In this environment, optical line control that provides
good performance, scalability, and self-optimization is
desirable.
[0005] Adjustment of one or more control parameters of a section of
an optical fiber network involves taking measurements of optical
signals in the section, deriving estimated data from the
measurements and from knowledge of the section, where the estimated
data is a function of optical nonlinearity and of amplified
spontaneous emission, and applying one or more control algorithms
using the estimated data to adjust the one or more control
parameters.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 illustrates a method for adjustment of control
parameters in section of an optical fiber network;
[0007] FIG. 2 illustrates an example section of an optical fiber
network;
[0008] FIG. 3 illustrates an example concave value function of
excess margin; and
[0009] FIG. 4 illustrates a first derivative of the example concave
value function.
DETAILED DESCRIPTION
[0010] Optical network topologies can range from simple unamplified
point-to-point, to branching chains of reconfigurable optical add
drop multiplexer (ROADM) sections, up to a full multi-connected
mesh that spans a continent.
[0011] In wavelength division multiplexing (WDM) systems, an
optical fiber network connects wavelength selective switch (WSS)
nodes via spans of optical fibers and optical amplifier devices.
Pairs of flexible coherent transceivers are connected over paths
through the optical fiber network. Different channels are
propagated through different paths in the network. A flexible
coherent transceiver can be reconfigured allowing transmission
parameters, for example, modulation scheme, to be adapted to the
selected path.
[0012] Some elements of the optical fiber network have the ability
to do some level of per-channel power control. Such elements may
include, for example, the transmitter portions of the flexible
coherent transceivers, a variable optical attenuator (VOA) under
software control, and optical equalizers. In another example,
per-channel power is controllable by provisioning a wavelength
selective switch (WSS) node with loss values. A WSS node has
switching capabilities and per-channel power control.
[0013] Some elements of the optical fiber network have the ability
to do some level of total power control. Such elements may include,
for example, optical amplifier devices. For example, the gain of an
optical amplifier device is controllable by provisioning the
optical amplifier device with a target gain. Equivalently, the
total output power (TOP) of an optical amplifier device is
controllable by provisioning the optical amplifier device with a
target total output power.
[0014] Some optical amplifier devices also have the ability to do
some level of per-channel power control, by provisioning the
optical amplifier device with a target gain tilt profile. For
simplicity, this document focuses on the following control
parameters of a section of an optical fiber network: the loss
values of a WSS node, which affect the launch powers of the signals
launched into the optical fibers, and the target gain values (or
target TOP values) of optical amplifier devices.
[0015] FIG. 1 is a flowchart illustration of a method for
adjustment of control parameters in a section of an optical fiber
network. A section may comprise most or all of the optical fiber
network. If the optical fiber network is small, the section may
indeed comprise all of the network. However, it is generally
advantageous for the method to control a single point-to-point
section of optical amplifier devices and spans of optical fiber
between two nodes that contain ROADM, WSS, or other switching
hardware that may be present.
[0016] At 2, measurements of optical signals are taken at various
locations in the section. The measurements may include per-channel
optical power (also referred to as power spectral density,
especially in a flexible grid system) and total output power. For
example, an optical power monitor (OPM) device is able to measure
per-channel optical power by switching the optical connection to
its input. Due to the cost of an OPM device, there is generally not
an OPM device at each optical amplifier device. Taps and
photodiodes may be placed, for example, at the input and at the
output of the optical amplifier devices. Each photodiode is
operative to measure the total optical power at the location of the
tap. At locations where there is an OPM device and a tap and
photodiode, the measurement of total optical power may be used to
calibrate the per-channel optical power measured by the OPM
device.
[0017] At 4, estimated data is derived from the measurements, from
the target values, and from knowledge of the section and its
components. The estimated data may include, for example, the
estimated per-channel optical power at the output of the optical
amplifier devices, the estimated incremental amplified spontaneous
emission (ASE) power introduced by the optical amplifier devices,
and the estimated self-phase modulation (SPM) and cross-phase
modulation (XPM) variance accumulated in the section. The estimated
data may be derived using a modeling engine that models the
propagation of signals through the components of the section.
Alternatively, the estimation of nonlinearities and noise may be
derived from specific measurements of parameters as described in
U.S. Pat. No. 8,594,499, U.S. Pat. No. 7,356,256, U.S. Pat. No.
6,128,111, U.S. Pat. No. 6,687,464, U.S. Pat. No. 6,839,523, U.S.
Pat. No. 7,376,358, U.S. Pat. No. 6,072,614, U.S. Pat. No.
6,064,501, and U.S. Pat. No. 5,513,029.
[0018] The estimated data is then used in a control algorithm to
adjust the control parameters. Various control algorithms are
contemplated. For example, the control algorithm may make use of
gradients and slew-rate limited steepest descent. At 6, gradients
of an objective function are evaluated, using the measurements and
the estimated data. The values of the gradients are inaccurate, for
at least the reason that the measurements are noisy, the knowledge
of the section and its components may be inaccurate or incomplete,
the modeling engine is inaccurate, and the estimated data is
inaccurate. Some of the channels propagated through the section
carry live traffic. That is, some of the channels are in-service
channels carrying traffic for customers. It is therefore important
not to adjust the components of the section in a manner that would
jeopardize or degrade or destabilize the in-service channels.
[0019] At 8, the values of the gradients are used in steepest
descent algorithms to adjust control parameters of the section by a
small step in a direction of optimization of the objective
function. That is, small adjustments are applied to target values
such as loss values of a WSS node and the target gain (or target
total output power) of an optical amplifier device. Steepest
descent algorithms are known to be noise tolerant and to be very
safe if small steps are taken. The values of some control
parameters that are adjusted may be set points for algorithms that
control other control parameters. For example, a value of a
per-channel optical power out of a WSS node may be a set point for
an algorithm that adjusts the loss of the relevant pixels of that
WSS node. A total power may be a set point for an algorithm that
adjusts total gain, which may be a set point for a digital control
loop which adjusts a VOA loss and pump currents. A pump current may
be a set point for an analog loop which adjusts a Field Effect
Transistor (FET) bias.
[0020] The method illustrated in FIG. 1 may be repeated over the
lifetime of use of the optical fiber network. For example, the
method may be repeated every few seconds for 25 years. It is not
necessary that all control parameters be adjusted in each iteration
of the method. Various changes occur over time, yielding updated
measurement data, updated estimated data, updated values for the
gradients, an updated direction of optimization of the objective
function, and updated adjustments to the control parameters.
[0021] The optical fiber network may be partitioned into sections
arbitrarily. For simplicity, this document focuses on an example
section that enables transmission of a set of optical signals along
a particular transmission direction from a first WSS node to a
second WSS node. (Signals are also directed along the opposite
transmission direction, where the roles of ingress and egress are
reversed. However, so as not to obscure the description of the
technology, transmission along that opposite direction is not
illustrated and is not discussed in this document.)
[0022] FIG. 2 illustrates an example section 10 of an optical fiber
network. An ingress WSS node 12 is connected to an egress WSS node
14 via spans 16 of optical fiber. The length of a span 16 of
optical fiber is typically in the range of approximately 80 km to
approximately 100 km. The spans 16 of optical fiber are coupled via
optical amplifier devices 18. An optical pre-amplifier device 20 in
the ingress WSS node 12 is optically coupled to the first span 16
of optical fiber. An optical pre-amplifier device 20 in the egress
WSS node 14 is optically coupled to the final span 16 of optical
fiber. One can index the spans 16 and the optical (pre-)amplifier
devices 18,20 by an index j, with N representing the total number
of spans of optical fiber coupling the ingress WSS node 12 to the
egress WSS node 14. For example, one can refer to the optical
pre-amplifier device 20 in the ingress WSS node 12 as the first
optical amplifier device, whose output is launched into the first
span of optical fiber. Similarly, the output of the optical
amplifier device j is launched into the span j of optical
fiber.
[0023] As discussed above, the measurements taken at various
locations in the section may include per-channel optical power
(also referred to as power spectral density, especially in a
flexible grid system) measured by OPM devices and total output
power measured by photodiodes. In the example section 10, OPM
devices 22 at the ingress WSS node 12 and at the egress WSS node 14
are able to measure per-channel optical power across the spectrum
at the output of the respective optical pre-amplifier device 20. In
the example section 10, taps and photodiodes are present at the
input and at the output of each optical (pre-)amplifier device
18,20 and are illustrated in FIG. 2 by small black squares. Each
optical amplifier device 18 is comprised, together with its
respective taps and photodiodes and together with a shelf processor
24, in a network element 26. For simplicity, only one such network
element 26 is illustrated in FIG. 2. There is a shelf processor 28
comprised in the ingress WSS node 12 and a shelf processor 30
comprised in the egress WSS node 14.
[0024] Each photodiode is operative to measure the total optical
power at the location of its respective tap. At the output of the
optical amplifier device j, the photodiode measures the total
output power, which includes both optical signal power and ASE
power. The per-channel power measured by the OPM device 22 is
reliable only in terms of relative power across the spectrum,
because the loss along a cable 32 coupling the output of the
optical pre-amplifier device 20 to the OPM device 22 is generally
not known. The total output power measured by the photodiode at the
output of the optical pre-amplifier device 20 in the ingress WSS
node 12 can be used to calibrate the per-channel optical power
measured by the OPM device 22, thus yielding a calibrated set of
per-channel optical power measurements {P.sub.1[i]}, where
P.sub.1[i] is the power of the channel i launched into the first
span of optical fiber. The total output power measured by the
photodiode at the output of the optical pre-amplifier device 20 in
the egress WSS node 14 can be used to calibrate the per-channel
optical power measured by the OPM device 22, thus yielding a
calibrated set of per-channel optical power measurements
{P.sub.N+1[i]}, where P.sub.N+1[i] is the power of the channel i
output from the optical pre-amplifier device 20 in the egress WSS
node 14. The integration of per-channel optical power measurements
to yield an aggregate power, comparison of the aggregate power to
the measured total optical power, and calibration may be performed
by firmware (not shown) in the WSS node 12,14. Alternatively, the
integration, comparison and calibration may be performed by any
other suitable firmware executed by a processor within the example
section 10. Conventional optical power units are dBm. In this
document, the per-channel optical power measurements {P.sub.j[i]}
are conveniently measured in units of Nepers relative to a Watt,
because it is more convenient for the calculus of equations
appearing hereinbelow.
[0025] A control system embedded in the section is operative to
provision certain components of the section with specific target
values. For example, the control system is operative to provision
the ingress WSS node 12 with loss values, and to provision the
optical amplifier devices 18 and the optical pre-amplifier devices
20 with respective target gain values or target TOP values. The
control system comprises, for example, hardware (not shown) located
in the ingress WSS node 12, hardware (not shown) located in the
optical amplifier devices 18 and in the optical pre-amplifier
devices 20, and control firmware 34 executed by any one of the
shelf processors within the section 10, for example, the shelf
processor 30 comprised in the egress WSS node 14. The control
firmware 34 is stored in non-transitory computer-readable media
coupled to the shelf processor.
[0026] In an alternative implementation, the control firmware 34 is
executed by an external processor (not shown) that is in
communication with the controllable elements of the section. The
external processor may be located in a physical server or may be
virtualized as part of a cloud infrastructure. The apparatus in
which the external processor is located may also store the control
firmware 34 in non-transitory computer-readable media that is
accessible by the external processor.
[0027] As discussed above, estimated data is derived from the
measurements, from the target values, and from knowledge of the
section and its components. The estimated data may be derived using
a modeling engine that models the propagation of signals through
the components of the section.
[0028] The knowledge of the section and its components may include
"known characteristics". Manufacturers and/or distributors of the
components may provide some of the known characteristics. Other
known characteristics may be determined by testing and/or
calibrating the components. Still other known characteristics may
be provided by inspection of the section. The known characteristics
may include, for example, the topology of the section, one or more
optical amplifier characteristics such as amplifier type (e.g.
Erbium-doped fiber amplifier (EDFA), distributed Raman amplifier,
lumped Raman amplifier), noise figure, ripple, spectral hole
burning, and Total Output Power (TOP) limit, and one or more
optical fiber characteristics such as fiber type, span length,
nonlinear coefficients, effective area, loss coefficients, total
loss, chromatic dispersion, and Stimulated Raman Scattering
(SRS).
[0029] Measured data (raw and/or calibrated), control data, and
(optionally) known characteristics, are communicated within the
section 10 over an optical service channel (OSC), also known as an
optical supervisory channel. The WSS nodes 12,14 and the network
elements 26 each comprise circuitry 36 to support the OSC.
[0030] The modeling engine models the propagation of signals
through components of the section. Specifically, the modeling
engine employs fiber models for the spans 16 of optical fiber in
the section 10 and employs amplifier models for the optical
(pre-)amplifier devices 18,20. Modeling firmware 38 that uses the
modeling engine is executed by any one of the shelf processors
within the section 10, for example, the shelf processor 24
comprised in the network element 26. The estimated data derived by
the modeling engine may include, for example, the estimated
per-channel optical power {P.sub.j[i]} at the output of the optical
amplifier j, where P.sub.j[i] is the power, measured in units of
Nepers, of the channel i launched into the span j of optical fiber,
and the estimated incremental ASE power {ASE.sub.j[i]} at the
output of the optical amplifier j. The modeling engine may employ
known techniques to derive the power evolution of the optical
signals through the section and to derive the incremental ASE
power.
[0031] The accuracy of the estimated per-channel optical power at
each of the fiber interfaces is important. Stimulated Raman
Scattering (SRS) may impart in the range of approximately 1 dB to
approximately 2 dB power tilt across the C band (1525 nm to 1565
nm) and in the range of approximately 3 dB to approximately 4 dB
power tilt across the L band (1565 nm to 1610 nm). These power
tilts may accumulate between spans where there is no WSS node to
equalize the tilts. Channels at different optical powers experience
very different optical degradation in terms of ASE (at low channel
power) and optical nonlinearities (at high channel power). Good
modeling of the SRS tilt per span of optical fiber is part of what
contributes to accurate estimated per-channel optical powers and
accurate estimated incremental ASE powers.
[0032] Once the estimated per-channel optical powers are of
sufficient accuracy (which could be determined, for example, by
comparing the estimated per-channel optical powers for the output
of the optical pre-amplifier device 20 in the egress WSS node 14
with the calibrated set of per-channel optical power measurements
{P.sub.N+1 [i]}), the modeling engine may derive the estimated
self-phase modulation (SPM) and cross-phase modulation (XPM)
variance accumulated in the section 10. The estimated data is thus
a function of optical nonlinearity and of ASE.
[0033] The modeling engine may model nonlinear interactions within
the spans 16 of optical fiber as Gaussian noise, as described in P.
Poggiolini, "The GN Model of Non-Linear Propagation in
Uncompensated Coherent Optical Systems", Journal of Lightwave
Technology, Vol. 30, No. 24, Dec. 15, 2012; P. Poggiolini et al.
"The GN Model of Fiber Non-Linear Propagation and its
Applications", Journal of Lightwave Technology, Vol. 32, No. 4,
Feb. 14, 2014. Alternatively, the modeling engine may employ a
different model of the nonlinear interactions, for example, full
non-linear Schrodinger Equation solutions using Fast Fourier
transform (FFT) or finite difference methods.
[0034] As described above, gradients of an objective function are
evaluated, using the measurements and the estimated data.
[0035] In one aspect, the goal of the objective function is to
minimize the total degradation through the section. Optimization of
this objective function minimizes a weighted sum of ratios of the
total noise power from ASE and optical nonlinearities to the power
of the optical signals. This objective function is suitable for
systems where there is no software connection to convey information
from the receiver modem to the section.
[0036] An example objective function V.sub.1 for a section, with
the goal of minimizing the total degradation through the section,
is given in Equations (1) and (2):
V 1 = i = 1 N CH C [ i ] LNSR [ i ] = i = 1 N CH j = 1 N C [ i ]
LNSR j [ i ] ( 1 ) LNSR j [ i ] = A S E j [ i ] e P j [ i ] + k = 1
N CH NL j [ i , k ] e 2 P j [ k ] ( 2 ) ##EQU00001##
[0037] In Equation (1), LNSR[i] denotes the incremental line
noise-to-signal ratio (NSR) for the channel i in the section, which
can be expressed as a weighted sum over spans j of optical fiber of
the incremental line NSR for the channel i in the span j, denoted
LNSR.sub.j[i]. C[i] is a customer-defined weighting value for the
channel i to optionally bias the objective function for particular
higher-value signals. N.sub.CH denotes the number of channels in
the signals in the section, and N denotes the number of spans j of
optical fiber in the section. C[i] may be a customer-defined
weighting value. Alternatively, C[i] may be defined in a different
manner. For example, when C[i]=Baudrate[i] the objective function
V.sub.1 will converge to control parameters that maximize the
capacity-bandwidth product in the optical fiber network. In another
example, when C[i]=Baudrate[i].times.SNR[i] where SNR[i] is an
estimate of the signal-to-noise ratio (SNR) in linear units at the
receiver modem whose channel i traverses the section, the objective
function V.sub.1 will converge to control parameters that maximize
the capacity of the optical fiber network.
[0038] In Equation (2), P.sub.j[i] is the power of the channel i at
the output of the optical amplifier j, which is launched into the
optical fiber of the span j, ASE.sub.j[i] is the incremental ASE
power on the channel i at the output of the optical amplifier j,
and NL.sub.j[i,k] is the SPM/XPM nonlinear coefficient for Kerr
interactions between the channel i and the channel k at the span j.
The power P.sub.j[i] is measured in units of Nepers relative to a
Watt. The second term in Equation (2) is a summation over all
channel powers that impact the LNSR of the channel i at the span j.
Where nonlinear interactions in one span are independent of
nonlinear interactions in another span, the local optimum of this
objective function V.sub.1 is the global optimum. The paper I.
Roberts, J. M. Kahn, D. Boertjes, "Convex Channel Power
Optimization in Nonlinear WDM Systems using Gaussian Noise Model",
Journal of Lightwave Technology, Vol. 34, No. 13, Jul. 1, 2016
proves that the second term in Equation (2) is a convex function in
the power P.sub.1 [i] when assuming a Gaussian noise nonlinearity
model.
[0039] The following discussion derives the gradients of the
example objective function V.sub.1, which are evaluated to provide
a direction for adjustment of control parameters. A gradient vector
.gradient.V.sub.1j for control of the ingress WSS node 12 is
derived. A gain gradient for TOP control of the optical amplifier
devices 18 is derived.
[0040] The example objective function V.sub.1 given in Equation (1)
can be expressed as the sum over all spans j in the section of an
example span objective function V.sub.1j, which is given in
Equation (3):
V.sub.1j=.SIGMA..sub.i=1.sup.N.sup.CHC[i]LNSR.sub.j[i] (3)
[0041] The partial derivative of the example span objective
function V.sub.1j with respect to channel power for channel i in
the span j of optical fiber is given by Equation (4):
.differential. V 1 j .differential. P j [ i ] = k = 1 N CH C [ k ]
.differential. LNSR j [ k ] .differential. P j [ i ] = - C [ i ] A
S E j [ i ] e P j [ i ] + k = 1 N CH 2 C [ k ] NL j [ i , k ] e 2 P
j [ i ] ( 4 ) ##EQU00002##
[0042] where the channel power P.sub.j[i] is fixed and the sum is
over XPM/SPM terms over all channels dependent on channel power
P.sub.j[i].
[0043] A gradient vector .gradient.V.sub.1j for a span j comprises
the partial derivative
.differential. V 1 j .differential. P j [ i ] ##EQU00003##
for each channel i from 1 to N.sub.CH. The partial derivative of
the example objective function V.sub.1 with respect to WSS loss for
the channel k in the section is given by Equation (5):
.differential. V 1 .differential. P [ k ] = j = 1 N .differential.
V 1 j .differential. P j [ k ] = j = 1 N [ - C [ k ] ASE j [ k ] e
P j [ k ] + i = 1 N CH 2 C [ k ] NL j [ i , k ] e 2 P j [ i ] ] ( 5
) ##EQU00004##
where P[k] is the power of the channel k out of the ingress WSS
node 12 which affects all spans in the section.
[0044] A gradient vector .gradient.V.sub.1 for the section
comprises the partial derivative
.differential.V.sub.1/.differential.P[k] for each channel k from 1
to N.sub.CH. The gradient vector .gradient.V.sub.1 can be evaluated
from the customer values C[i], the known characteristics
NL.sub.j[i,k], and the measured or estimated data {P.sub.j[i]} and
{ASE.sub.j[i]}.
[0045] The incremental ASE power on the channel i induced by the
optical amplifier device j is given by the well known Equation
(6):
ASE.sub.j[i]=h*v*B.sub.e(NF.sub.j[i]*G.sub.j[i]-1) (6)
where h is Planck's constant, v is the optical frequency, B.sub.e
is the electrical bandwidth of the noise filtering in the receiver,
NF.sub.j[i] is the noise figure for the channel i of the optical
amplifier device j, and G.sub.j[i] is the gain for the channel i of
the optical amplifier device j.
[0046] The gradient of the ratio of the ASE power to the signal
power term with respect to gain G.sub.j[i] is given by Equation
(7):
G j [ i ] ( ASE j [ i ] e P j [ i ] ) .apprxeq. - h v NF j [ i ] B
e e P j + 1 IN [ i ] ( 7 ) ##EQU00005##
where P.sub.j+1.sup.IN[i] is the channel power at the input to the
next optical amplifier device. This gradient is approximately the
negative of the ratio of the incremental ASE power to signal power
of the next optical amplifier device.
[0047] The gain gradient for the optical amplifier device j,
averaged over all wavelengths, can be formed as given by Equation
(8):
.differential. V 1 j .differential. G j = k = 1 N CH [ - C [ k ]
ASE j + 1 [ k ] e P j + 1 [ k ] + i = 1 N CH 2 C [ i ] NL j [ i , k
] e 2 P j [ i ] ] ( 8 ) ##EQU00006##
[0048] The gain gradient for the optical amplifier device j,
averaged over all wavelengths, can be evaluated from the customer
values C[i], the known characteristics NL.sub.j[i,k], and the
measured or estimated data {P.sub.j[i]} and {ASE.sub.j[i]}.
[0049] As described above, the values of the gradients are used in
steepest descent algorithms to adjust control parameters of the
section by a small step in a direction of optimization of the
objective function.
[0050] Small adjustments are applied to loss values of a WSS node
and to target TOP values of optical amplifier devices. The steepest
descent algorithm is applied to the WSS node while assuming that
the gains of the optical amplifier devices are fixed. The steepest
descent algorithm is applied to all of the optical amplifier
devices in parallel while assuming that the WSS pixel drive values
are fixed.
[0051] For example, two loops may be run in parallel with a
decoupling factor, as expressed in the vector Equation (9),
Equation (10) and Equation (11):
WSS_PowerTarget NEW = WSS_PowerTarget - ( MAXSTEP max ( V 1 ) * [ V
1 - mean ( V 1 ) ] ) ( 9 ) TOP_Target NEW = TOP_Target - sign [
.differential. V 1 j .differential. G j * 0.1 * MAXSTEP ] ( 10 ) if
TOP_Target NEW .gtoreq. TOP LIMIT , set TOP_Target NEW = TOP LIMIT
( 11 ) ##EQU00007##
where WSS_PowerTarget.sub.NEW and WSS_PowerTarget have values for
each channel k from 1 to N.sub.CH, the decoupling factor in this
example is 0.1, and the target TOP for the optical amplifier device
j is subject to an upper limit. An example MAXSTEP is 0.2 dB.
[0052] TOP control is used to decouple incremental SNR optimization
in this section from changes occurring in other sections of the
optical fiber network. The subtraction of the change in average
power (which is denoted mean (.gradient.V.sub.1) in Equation (9)
but is not quite equal to the average of the changes) keeps the WSS
output power constant, and the power launched into the first span
is controlled by the TOP of the first amplifier. This scaling is
important when one or more of the amplifiers reaches the limit of
their TOP and can provide no more power. With this scaling, the
allocation of that limited power between the wavelengths is cleanly
optimized.
[0053] Note also that there is no reliance on a second derivative
for step size, and that this simple algorithm is robust to
noise.
[0054] As mentioned above, where nonlinear interactions in one span
are independent of nonlinear interactions in another span, the
local optimum of this objective function V.sub.1 is the global
optimum. Operationally, this permits the adjustment of the control
parameters for one section to be performed in parallel to the
adjustment of the control parameters for other sections of the
optical fiber network. For example, the two loops expressed in the
vector Equation (9), Equation (10) and Equation (11) may be run in
parallel independently for several sections of the optical fiber
network.
[0055] In another aspect, the goal of the objective function is to
maximize the capacity or the reliability or both of a network by
allocating margin to channels that are at higher risk of failure at
their designated capacities by taking away margin from channels
with plenty of margin. This objective function is suitable for
systems where, for at least some channels, there is a software
connection to convey information to the section (or to the external
processor) from the receiver modem that receives that channel. This
objective function can also be used protect channels in service
while trialing a new channel to see if it can sustain a particular
high capacity. Another value of this objective function is to
assist channels that are experiencing a slow low-probability
degradation event such as polarization dependent loss (PDL) by
improving this weakened channel's line SNR at the expense of other
channels that have higher margin.
[0056] An arbitrary concave value function f is introduced that
takes as its argument the excess margin SNR.sub.M[i] on the channel
i as determined at the receiver modem. A positive value for
SNR.sub.M[i] indicates that total SNR (including ASE, nonlinear
effects, and internal receiver modem noise) currently experienced
by the channel i exceeds the SNR required for error-free
communications on that channel. A negative value for SNR.sub.M[i]
indicates that the total SNR currently experienced by the channel i
is less than the SNR required for error-free communications on that
channel. The concave value function f(SNR.sub.M[i]) expresses the
utility of extra margin on a channel and whether the channel is
better off sharing its excess margin. FIG. 3 illustrates an example
concave value function f having desirable properties, and FIG. 4
illustrates a first derivative f.sup.1 of the example concave value
function. It is scaled so that f(0)=0 and f.sup.1(0)=1. The example
concave value function f is neutral (has a value of zero) for zero
excess margin, decreases rapidly for negative excess margin, and
increases then quickly plateaus for positive excess margin.
[0057] An example objective function V.sub.2 for a section that
incorporates information from the receiver modem is the sum over
all controllable channels of a concave value function f of the
excess margin, as given in Equations (12) and (13):
V 2 = i = 1 N CH C [ i ] D [ i ] f ( SNR M [ i ] ) ( 12 ) SNR M [ i
] = - 10 log ( LNSR M [ i ] BLNSR M [ i ] ) ( 13 ) ##EQU00008##
[0058] In Equation (12), C[i] is a customer-defined weighting value
for the channel i to optionally bias the objective function for
particular higher-value signals. D [i] is a metric that is a
function of the geographic distance travelled by the channel i from
the transmitter to the receiver, or other such network value.
Adjusting the function for D allows an adaptation of the trade-off
between the use of an optical fiber (if optical fiber on this route
is a scarce resource and installing or acquiring rights to more
would be very expensive, then, for example, set D[i]=1), and
minimizing the cost of the transceivers (if optical fiber is
plentiful, then, for example, set D[i]=distance[i]).
[0059] In Equation (13), LNSR.sub.M [i] is the line NSR for the
channel i as measured at the receiver modem, and BLNSR.sub.M[i] is
a budgeted line NSR which factors in margin, implementation noise,
and target Required Noise to Signal Ratio (RNSR), required for the
modem to be error free under nominal conditions, from the capacity
commitment and forward error channel (FEC) performance for the
channel i.
[0060] There are many different ways in which the budgeted line NSR
for the channel i, BLNSR.sub.M[i], can be defined. For example, the
budgeted line NSR may be defined as given in Equation (14):
BLNSR M [ i ] = 1 m P ( FEC_NSR [ i ] - INSR [ i ] - m A ) ( 14 )
##EQU00009##
[0061] In Equation (14), FEC_NSR[i] is the NSR for the modulation
format for the channel i at the FEC threshold, INSR[i] is the modem
implementation noise for the channel i, m.sub.P is a multiplicative
margin applied to the line NSR, and m.sub.A is an additive noise
margin.
[0062] The example objective function V.sub.2 given in Equation
(12) can be expressed as the sum over all channels i of an example
channel objective function V.sub.2[i], which is given in Equation
(15):
V.sub.2[i]=C[i]D[i]f(SNR.sub.M[i]) (15)
[0063] The partial derivative of the example channel objective
function V.sub.2[i] with respect to channel power for channel i in
the span j of optical fiber is given by Equations (16), (17) and
(18):
.differential. V 2 [ i ] .differential. P j [ i ] = A [ i ]
.differential. .differential. P j [ i ] ( j = 1 N LNSR j [ i ] ) =
A [ i ] .differential. LNSR j [ i ] .differential. P j [ i ] ( 16 )
A [ i ] = - 10 C [ i ] D [ i ] f 1 ( SNR M [ i ] ) 2.30 LNSR M [ i
] ( 17 ) LNSR M [ i ] = j = 1 N LNSR j [ i ] ( 18 )
##EQU00010##
[0064] Equations (16) and (17) demonstrate the use of the chain
rule in the partial derivative, and introduce the concept of a
modem coefficient A[i] that encapsulates receiver modem
information. The modem coefficient A[i] multiplies the partial
derivative of the noise (LNSR) to power ratio of a specific
section.
[0065] Through proper scaling of the metric D [i], the modem
coefficient A[i] can be made equal to the first derivative of the
example concave value function f. A[i]=f.sup.1(SNR.sub.M[i]). The
receiver modem whose channel i traverses the section is capable of
determining the value of the modem coefficient A[i]. For channels
where receiver modem information is unavailable, the modem
coefficient A[i] can be set to equal the number 1.
[0066] The value of this proper scaling of the metric D [i] is to
ground the example concave value function f of measured margin onto
the example objective function V.sub.1 given in Equation (1) which
can be shown to either maximize capacity or the capacity-product
depending on the choice of the weighting value C[i]. When the modem
coefficient A[i] is set to equal the number 1 for all channels, the
derivative in Equation (16) for the example objective function
V.sub.2 is identical to the derivative in Equation (4) for the
example objective function V.sub.1. Thus the example concave value
function f which tends to help channels with less margin at the
expense of channels with more margin will operate around the
control parameters that are close to either maximizing capacity or
the capacity-distance product of the optical network.
[0067] The following discussion derives the gradients of the
example objective function V.sub.2, which are evaluated to provide
a direction for adjustment of control parameters. A gradient vector
.gradient.V.sub.2j for control of the ingress WSS node 12 is
derived. A gain gradient for TOP control of the optical amplifier
devices 18 is derived.
[0068] By comparing Equation (16) and Equation (3), it is apparent
that the gradients derived for the example objective function
V.sub.1 are applicable to the example objective function V.sub.2,
with the insertion of the modem coefficient A[i]. In cases where
the modem coefficient AN equals 1 for all channels, the gradients
derived for the example objective function V.sub.1 are identical to
the gradients derived for the example objective function
V.sub.2.
[0069] The partial derivative of the example function V.sub.2 with
respect to WSS loss for the channel kin the section is therefore
given by Equation (19):
.differential. V 2 .differential. P [ k ] = j = 1 N [ - A [ i ] C [
k ] ASE j [ k ] e P j [ k ] + k = 1 N CH 2 A [ i ] C [ k ] NL j [ i
, k ] e 2 P j [ i ] ] ( 19 ) ##EQU00011##
where A[i]=f.sup.1 (SNR.sub.M[i]).
[0070] A gradient vector .gradient.V.sub.2 for the section
comprises the partial derivative
.differential.V.sub.2/.differential.P[k] for each channel k from 1
to N.sub.CH. The gradient vector .gradient.V.sub.2 can be evaluated
from the modem coefficients A[i], the customer values C[i], the
known characteristics NL.sub.j[i,k], and the measured or estimated
data {P.sub.j[i]} and {ASE.sub.j[i]}.
[0071] The gain gradient for the span j of optical fiber, averaged
over all wavelengths, can be formed as given by Equation (20):
.differential. V 2 j .differential. G j = k = 1 N CH [ - A [ i ] C
[ k ] ASE j + 1 [ k ] e P j + 1 [ k ] + i = 1 N CH 2 A [ i ] C [ i
] NL j [ i , k ] e 2 P j [ i ] ] ( 20 ) ##EQU00012##
where A[i]=f.sup.1(SNR.sub.M[i]).
[0072] The gain gradient for the optical amplifier device j,
averaged over all wavelengths, can be evaluated from the modem
coefficients A[i], the customer values C[i], the known
characteristics NL.sub.j[i,k], and the measured or estimated data
{P.sub.j[i]} and {ASE.sub.j[i]}.
[0073] The values of the gradients are used in steepest descent
algorithms to adjust control parameters of the section by a small
step in a direction of optimization of the objective function.
[0074] Small adjustments are applied to loss values of a WSS node
and to target TOP values of optical amplifier devices. The steepest
descent algorithm is applied to the WSS node while assuming that
the gains of the optical amplifier devices are fixed. The steepest
descent algorithm is applied to all of the optical amplifier
devices in parallel while assuming that the WSS pixel drive values
are fixed.
[0075] For example, two loops may be run in parallel with a
decoupling factor, as expressed in the vector Equation (21),
Equation (22) and Equation (23):
WSS_PowerTarget NEW = WSS_PowerTarget - ( MAXSTEP max ( V 2 ) * [ V
2 - mean ( V 2 ) ] ) ( 21 ) TOP_Target NEW = TOP_Target - sign [
.differential. V 2 j .differential. G j * 0.1 * MAXSTEP ] ( 22 ) if
TOP_Target NEW .gtoreq. TOP LIMIT , set TOP_Target NEW = TOP LIMIT
( 23 ) ##EQU00013##
where WSS_PowerTarget.sub.NEW and WSS_PowerTarget have values for
each channel k from 1 to N.sub.CH, the decoupling factor in this
example is 0.1, and the target TOP for the optical amplifier device
j is subject to an upper limit. An example MAXSTEP is 0.2 dB.
[0076] TOP control is used to decouple incremental SNR optimization
in this section from changes occurring other sections of the
optical fiber network. The subtraction of the change in average
power (which is denoted mean (.gradient.V.sub.2) in Equation (21)
but is not quite equal to the average of the changes) keeps the WSS
output power constant, and the power launched into the first span
is controlled by the TOP of the first amplifier. This scaling is
important when one or more of the amplifiers reaches the limit of
their TOP and can provide no more power. With this scaling, the
allocation of that limited power between the wavelengths is cleanly
optimized.
[0077] Note also that there is no reliance on a second derivative
for step size, and that this simple algorithm is robust to
noise.
[0078] When the ability for rapid introduction of new channels is
desired, idlers may be used to pre-allocate the effects of those
channels.
[0079] An ASE idler is treated by the first aspect (example
objective function V.sub.1) as any other channel, with the
appropriate XPM generator coefficient. The channel weight could be
set to a static value of one. In the second aspect (example
objective function V.sub.2), once a modem signal is switched to
replace this ASE, then the margin from that modem would be used to
calculate the new weight in the usual way. The diminished default
value is used again after the ASE is switched back in.
[0080] To not cause the XPM from ASE idlers, a limited number of
virtual idlers can be used. Virtual idlers are treated just like
ASE idlers, except that their XPM generator coefficient is set to
equal that of the modulation expected to be used. Virtual idlers do
not consume photons, so the TOP limits need to be reduced by the
virtual wattage.
[0081] A Boolean acceptance criteria should be used to decide on
the choice of a virtual idler versus an ASE idler in order to limit
the SRS impact of their sudden conversion to real signals, assuming
that all virtual idlers are allowed to switch at once. Define A to
be the integral of virtual power spectral density out of the WSS,
across a 1 THz interval centered at the wavelength of the candidate
virtual idler, including the virtual power of that candidate idler.
Define B to be the integral of real power spectral density across
the same 1 THz interval centered at the wavelength of the candidate
virtual idler. Choose epsilon to be a small positive number to
avoid division by zero, e.g. 100 microWatts. The virtual idler is
acceptable if A/(B+epsilon)<0.25.
[0082] In yet another aspect, the objective function is a
combination of the above two objective functions. For example, the
example objective function is given by Equation (24):
V.sub.3=V.sub.2-V.sub.1=.SIGMA..sub.i=1.sup.N.sup.CHC[i]D[i]f(SNR.sub.M[-
i])-.SIGMA..sub.i=1.sup.N.sup.CHC[i]LNSR[i] (24)
[0083] With this example objective function V.sub.3, the goal of
the objective function is to balance the goals of minimizing the
total degradation through the section with maximizing capacity or
reliability or both of the optical fiber network by re-allocating
margin among the channels that are propagated through the section.
The discussion above of deriving gradients and applying the
gradients in steepest descent algorithms is applicable also to the
example objective function V.sub.3.
[0084] Returning now to FIG. 2, consider how this example section
10 could be modified to independently amplify different bands of
transmission. For example, the section 10 could simultaneously
handle the C band (1525 nm to 1565 nm) and the L band (1565 nm to
1610 nm). The ingress WSS node 12 could have two independent WSS
filters to control individual channel powers for the C band and the
L band, respectively. Each of the optical (pre-)amplifier devices
18,20 could be replaced by a set of two optical (pre-)amplifier
devices, one for the C band and one for the L band. The C-band
channels and the L-band channels propagate through the same spans
16 of optical fiber, where there is fiber nonlinear interaction
between all the channels. That is, the nonlinear interaction in the
spans of optical fiber is across all channels being propagated,
including C-band channels and L-band channels. There is also strong
SRS which makes for significant power differences between channels
compared to the case of a single band, given that the SRS is
approximately proportional to the square of the optical
bandwidth.
[0085] In a variation of the first aspect, the example objective
function V.sub.1 applies to the full set of channels in the C band
and the L band.
[0086] The partial derivative of the example objective function
V.sub.1 with respect to C-band WSS loss for the channel k in the
section is given by Equation (25), where the channel k is in the C
band:
.differential. V 1 .differential. P [ k ] = j = 1 N .differential.
V 1 j .differential. P j [ k ] = j = 1 N [ - C [ k ] ASE j [ k ] e
P j [ k ] + i = 1 N CH 2 C [ k ] NL j [ i , k ] e 2 P j [ i ] ] (
25 ) ##EQU00014##
where P[k] is the power of the channel k out of the ingress WSS
node 12 which affects all spans in the section.
[0087] The partial derivative of the example objective function
V.sub.1 with respect to L-band WSS loss for the channel k in the
section is given by Equation (26), where the channel k is in the L
band:
.differential. V 1 .differential. P [ k ] = j = 1 N .differential.
V 1 j .differential. P j [ k ] = j = 1 N [ - C [ k ] ASE j [ k ] e
P j [ k ] + i = 1 N CH 2 C [ k ] NL j [ i , k ] e 2 P j [ i ] ] (
26 ) ##EQU00015##
where P[k] is the power of the channel k out of the ingress WSS
node 12 which affects all spans in the section.
[0088] In Equation (25), the summation of the nonlinear interaction
is over all N.sub.CH channels i in the C band and in the L band. In
Equation (26), the summation of the nonlinear interaction is over
all N.sub.CH channels i in the C band and in the L band.
[0089] A gradient vector .gradient.V.sub.1(C) for the section for
the C band comprises the partial derivative
.differential.V.sub.1/.differential.P[k] for each channel k in the
C band from 1 to N.sub.CH.sup.C. A gradient vector
.gradient.V.sub.1(L) for the section for the L band comprises the
partial derivative .differential.V.sub.1/.differential.P[k] for
each channel k in the L band from 1 to N.sub.CH.sup.L. The gradient
vectors .gradient.V.sub.1(C) and .gradient.V.sub.1(L) can be
evaluated from the customer values C[i], the known characteristics
NL.sub.j[i,k], and the measured or estimated data {P.sub.j[i]} and
{ASE.sub.j[i]}.
[0090] The gain gradient for the optical amplifier device j,
averaged over all wavelengths in the C band, can be formed as given
by Equation (27):
.differential. V 1 j ( C ) .differential. G j = k = 1 N CH C [ - C
[ k ] ASE j + 1 [ k ] e P j + 1 [ k ] + i = 1 N CH 2 C [ i ] NL j [
i , k ] e 2 P j [ i ] ] ( 27 ) ##EQU00016##
where the outer summation is over the channels k in the C band, and
the inner summation is over all N.sub.CH channels i in the C band
and the L band.
[0091] The gain gradient for the optical amplifier device j,
averaged over all wavelengths in the L band, can be formed as given
by Equation (28):
.differential. V 1 j ( L ) .differential. G j = k = 1 N CH L [ - C
[ k ] ASE j + 1 [ k ] e P j + 1 [ k ] + i = 1 N CH 2 C [ i ] NL j [
i , k ] e 2 P j [ i ] ] ( 28 ) ##EQU00017##
where the outer summation is over the channels k in the L band, and
the inner summation is over all N.sub.CH channels i in the C band
and the L band. The total number of channels in the C band and the
L band, denoted N.sub.CH, is the sum of the number of channels in
the C band, denoted N.sub.CH.sup.L, and the number of channels in
the L band, denoted N.sub.CH.sup.L. That is,
N.sub.CH=N.sub.CH.sup.C-N.sub.CH.sup.L.
[0092] The gain gradients can be evaluated from the customer values
C[i], the known characteristics NL.sub.j[i,k], and the measured or
estimated data {P.sub.j[i]} and {ASE.sub.j[i]}.
[0093] Small adjustments are applied to loss values of a WSS node
and to target TOP values of optical amplifier devices. The steepest
descent algorithm is applied to the WSS node while assuming that
the gains of the optical amplifier devices are fixed. The steepest
descent algorithm is applied to all of the optical amplifier
devices in parallel while assuming that the WSS pixel drive values
are fixed.
[0094] For example, four loops may be run in parallel with a
decoupling factor, as expressed in the vector Equations (29) and
(30), Equations (31) and (32) and Equations (33) and (34):
WSS_PowerTarget NEW ( C ) = WSS_PowerTarget ( C ) - ( MAXSTEP max (
V 1 ( C ) ) * [ V 1 ( C ) - mean ( V 1 ( C ) ) ] ) ( 29 )
WSS_PowerTarget NEW ( L ) = WSS_PowerTarget ( L ) - ( MAXSTEP max (
V 1 ( L ) ) * [ V 1 ( L ) - mean ( V 1 ( L ) ) ] ) ( 30 )
TOP_Target NEW ( C ) = TOP_Target ( C ) - sign [ .differential. V 1
j ( C ) .differential. G j * 0.1 * MAXSTEP ( 31 ) if TOP_Target NEW
( C ) .gtoreq. TOP LIMIT ( C ) , set TOP_Target NEW ( C ) = TOP
LIMIT ( C ) ( 32 ) TOP_Target NEW ( L ) = TOP_Target ( L ) - sign [
.differential. V 1 j ( L ) .differential. G j * 0.1 * MAXSTEP ] (
33 ) if TOP_Target NEW ( L ) .gtoreq. TOP LIMIT ( L ) , set
TOP_Target NEW ( L ) = TOP LIMIT ( L ) ( 34 ) ##EQU00018##
where WSS_PowerTarget.sub.NEW(C) and WSS_PowerTarget (C) have
values for each channel k in the C band from 1 to N.sub.CH.sup.C,
WSS_PowerTarget.sub.NEW(L) and WSS_PowerTarget (L) have values for
each channel kin the L band from 1 to N.sub.CH.sup.L, the
decoupling factor in this example is 0.1, and the target TOP for
the optical amplifier device is subject to an upper limit
(dependent on the band). An example MAXSTEP is 0.2 dB.
[0095] In a variation of the second aspect, the example objective
function V.sub.2 applies to the full set of channels in the C band
and the L band. Similar equations and loops can be derived for that
case, for example, by replacing the customer values C[i] in
Equations (25) through (34) with the product of the customer values
C[i] and the modem coefficients A[i].
[0096] For clarity, the examples apply a Gaussian nonlinearity
noise model. The methods described in the document can be used
where other models of optical nonlinear interactions provide a
better representation, such as where the nonlinearities are not
substantially independent between spans.
[0097] For clarity, the examples use ASE from Erbium doped fiber
amplifiers (EDFAs). Other power or gain dependent degradations such
as double-Raleigh scattering from Raman amplifiers may be included
in the estimations.
[0098] Modern high capacity optical transmission systems use
coherent modems (also known as coherent transceivers). The
techniques described in this document may also be used with other
kinds of optical transmitters and receivers.
[0099] The description shows specific examples of objective
functions and the derivation of control algorithms from those
objective functions. Other objective functions may be used. Using a
convex objective function and deriving a control algorithm from an
objective function is convenient. However, other control algorithms
and methods may be used.
[0100] For simplicity, the control algorithms described in this
document have sole control of the control parameters of the
section. Other control algorithms or provisioning or constraints
may also be present or active.
[0101] The scope of the claims should not be limited by the details
set forth in the examples, but should be given the broadest
interpretation consistent with the description as a whole.
* * * * *