Methods and systems for the performance analysis of fiber optic networks

In general, the present invention provides novel approaches to signal propagation modeling that utilize the following: 1) geographic segmentation is applied by separating a large fiber optic network into individual non-overlapping segments, defined by optical add/drop placements; 2) impairment segmentation is applied, such that optical noise, self-phase, cross-phase, four-wave mixing, and other impairments are all treated separately; 3) each impairment is calculated by the most efficient approach to achieve the minimum required accuracy, the approaches being fully numeric, semi-analytic, or empirical; 4) impairment concatenation rules are applied to compute an overall impairment experienced by a signal that traverses more than one segment; and 5) impairment scaling rules are applied to rapidly estimate changes in configuration that can lead to improved performance (i.e. higher capacity, longer distance, or lower cost).

FIELD OF THE INVENTION

The present invention relates generally to the telecommunications and optical networking fields. More specifically, the present invention relates to methods and systems for the performance analysis of fiber optic networks.

BACKGROUND OF THE INVENTION

Wavelength multiplexed optical networks are continually increasing in both functionality and complexity. This increased functionality is driven by, among other things, the provision of optical add/drop multiplexers and optical switching elements that permit a rich variety wavelength connectivity and exchange between fibers and nodes in optical networks. The increased complexity is driven by, among other things, increasing bit rates of individual channels, increasing channel counts, increasing channel densities, and increasing transmission distances.

The demands placed by such optical networks on signal quality mandate the use of specialized tools to calculate signal impairments as they propagate between origination and termination points. These signal impairments are associated with a wide range of linear and nonlinear effects which can act on a single wavelength channel or couple multiple wavelength channels.

Conventional approaches to optical signal propagation modeling can be grouped into two broad categories. First, there are fully numeric approaches. These approaches solve electromagnetic wave propagation equations, and generally account for nonlinear effects in the propagation medium (i.e. fiber). The resources required for such approaches, including computer memory requirements and computational time, grow rapidly with increased system complexity. Given the current state of desktop computers, it takes several hours to simulate the propagation of a relatively few 10 Gb/s channels over ˜1000 km of fiber. Desktop computer power is not sufficient for simulations involving more than ˜16 channels of 10 Gb/s each. Typical commercial systems can have up to 192 channels of 10 Gb/s each, and a typical North American fiber network extends over ˜25000 km. Further, network performance optimization can require propagation to be recomputed several times as an optimal solution is sought. For example, signal power can be changed, as well as the placement of signal conditioning elements, such as dispersion compensation modules, optical amplifiers, and the like, Signal channel spacing, bit rate, etc. can also be changed. Clearly, an alternative approach to optical signal propagation modeling is needed.

Second, there are semi-analytic or empirical approaches to optical signal propagation modeling. These approaches typically divide signal propagation into separate components, each component associated with a particular propagation effect. Examples of such propagation effects are Amplified Spontaneous Emission (ASE) noise accumulation associated with optical amplifiers, Self-Phase Modulation (SPM) associated with single-channel fiber nonlinearity, Cross-Phase Modulation (XPM) associated with a fiber nonlinearity coupling multiple adjacent channels, and the like. Each effect can be assumed to be independent of the others if each contributes only a small overall distortion to the signal. Calculations are typically carried out on a complete end-to-end link, starting at the point where an optical signal is generated and ending at the electrical receiver. In general, the semi-analytic or empirical approaches to optical signal propagation modeling provide computational efficiency, but sacrifice accuracy. One area of deficiency associated with these approaches involves their application to richly interconnected optical networks. A small change in one area of a network can impact optically coupled signals spanning a large geographic area, and thus require extensive recomputation.

In general, conventional approaches to optical signal propagation modeling have the following limitations which preclude their use in richly interconnected optical networks: 1) they assume that all wavelength signals have the same origination and termination points; 2) they account for nonlinear effects simultaneously (i.e. no differentiation); 3) they are computationally impractical for systems with fully populated channels; 4) they make optimization very difficult, if not impossible, as small configuration changes require full recomputation; and 5) they do not lend themselves to distributed calculations (i.e. parallelized calculations).

Thus, what is needed is a novel approach that overcomes the above limitations, while still providing sufficient accuracy.

BRIEF SUMMARY OF THE INVENTION

In general, the approach of the present invention overcomes the above limitations, while still providing sufficient accuracy, via the following: 1) geographic segmentation is applied by separating a large fiber optic network into individual non-overlapping segments, defined by optical add/drop placements; 2) impairment segmentation is applied, such that optical noise, self-phase, cross-phase, four-wave mixing, and other impairments are all treated separately; 3) each impairment is calculated by the most efficient approach to achieve the minimum required accuracy, the approaches being fully numeric, semi-analytic, or empirical; 4) impairment concatenation rules are applied to compute an overall impairment experienced by a signal that traverses more than one segment; and 5) impairment scaling rules are applied to rapidly estimate changes in configuration that can lead to improved performance (i.e. higher capacity, longer distance, or lower cost).

In one exemplary embodiment of the present invention, a method for the performance analysis of fiber optic networks includes, given a fiber optic network of interest, applying geographic segmentation to the fiber optic network by separating the fiber optic network into a predetermined number of individual non-overlapping segments defined by one or more optical add/drop placements; given a plurality of impairments of interest, applying impairment segmentation to the plurality of impairments such that each of the plurality of impairments is treated separately; calculating each of the plurality of impairments using a predetermined method selected to achieye a predetermined minimum required accuracy; applying one or more concatenation rules to compute an overall impairment experienced by a signal that traverses more than one segment; and applying one or more impairment scaling rules to estimate changes in configuration that lead to improved fiber optic network performance.

In another exemplary embodiment of the present invention, a system for the performance analysis of fiber optic networks includes, given a fiber optic network of interest, a first algorithm operable for applying geographic segmentation to the fiber optic network by separating the fiber optic network into a predetermined number of individual non-overlapping segments defined by one or more optical add/drop placements; given a plurality of impairments of interest, a second algorithm operable for applying impairment segmentation to the plurality of impairments such that each of the plurality of impairments is treated separately; a third algorithm operable for calculating each of the plurality of impairments using a predetermined method selected to achieve a predetermined minimum required accuracy; a fourth algorithm operable for applying one or more concatenation rules to compute an overall impairment experienced by a signal that traverses more than one segment; and a fifth algorithm operable for applying one or more impairment scaling rules to estimate changes in configuration that lead to improved fiber optic network performance.

In a further exemplary embodiment of the present invention, a method for the performance analysis of fiber optic networks includes, given a fiber optic network of interest, separating the fiber optic network into a plurality of segments (geographic segmentation); performing an optical line amplifier (OLA) chain analysis of each of the plurality of segments; and performing an impairment analysis of each of the plurality of segments, wherein the impairment analysis includes a plurality of impairment sub-analyses, each of the plurality of impairment sub-analyses limited to an analysis of one predetermined impairment (impairment segmentation).

DETAILED DESCRIPTION OF THE INVENTION

In general, the present invention provides novel approaches to signal propagation modeling that utilize the following: 1) geographic segmentation is applied by separating a large fiber optic network into individual non-overlapping segments, defined by optical add/drop placements; 2) impairment segmentation is applied, such that optical noise, self-phase, cross-phase, four-wave mixing, and other impairments are all treated separately; 3) each impairment is calculated by the most efficient approach to achieve the minimum required accuracy, the approaches being fully numeric, semi-analytic, or empirical; 4) impairment concatenation rules are applied to compute an overall impairment experienced by a signal that traverses more than one segment; and 5) impairment scaling rules are applied to rapidly estimate changes in configuration that can lead to improved performance (i.e. higher capacity, longer distance, or lower cost).

The methods and systems of the present invention are best illustrated by first considering some specific conventional impairment examples. Optical Amplified Spontaneous Emission (ASE) noise accumulation associated with optical amplifiers can be expressed via Optical Signal-to-Noise Ratio (OSNR), which can be computed using a relatively simple equation, assuming uniform span distribution, as:
OSNRsegment=Pch+58−Lspan−10 log(Nspan)−NF,  (1)
where segment is defined as a potion of the network between two Optical Add/Drop Multiplexers (OADMs), Pchis the channel power launched into the fiber span, Lspanis the loss of a fiber span between two optical amplifiers, Nspanis the number of fiber spans comprising a segment, and NF is the noise figure of the optical amplifier. It should be noted that more complicated approaches to modeling OSNRsegmentrely on numeric or empirical solutions.

If a signal traverses more than one segment, overall OSNR can be computed via a concatenation equation, such as:
I/OSNRtot=Σallsegments(1/OSNRsegment i).  (2)

Self-Phase Modulation (SPM) associated with single-channel fiber nonlinearity requires an accurate calculation of the optical waveform distortion as it propagates through the fiber. Thus, a typical approach requires a Split Step Fourier numeric method to be applied at the segment level, as illustrated inFIG. 1.

Subsequent to a calculation for each segment, a simplified segment model is extracted from the results. A simple form is represented by three calculations, as illustrated inFIG. 2.

Signal propagation that spans multiple segments can be computed by a simple concatenation of individual segment models. Thus, a change in one segment only impacts the local model, and complete recalculation can be achieved rapidly and efficiently.

Other impairments, such as Cross-Phase Modulation (XPM) associated with a fiber nonlinearity coupling multiple adjacent channels, Four-Wave Mixing (FWM), Stimulated Raman Scattering (SRS), Multi-Path Interference MPI), Polarization Mode Dispersion (PMD), and the like are handled in a substantially similar manner.

For example, XPM-induced phase and amplitude impairments can be computed for separate segments, and concatenation of these impairments performed for multi-segment signal propagation, as described in “Cross-Phase Modulation in Multispan WDM Systems With Arbitrary Modulation Formats,” G. Goeger, M. Wrage, and W. Fischler, IEEE Photon. Techn. Lett., Vol. 16, No. 8, August 2004, pp. 1858-1860; and in “Cross-Phase Modulation in Multispan WDM Optical Fiber Systems,” R. Hui, K. Demarest, and C. T. Allen, J. Lightwave Techn., Vol. 17, No. 6, June 1999, pp. 1018-1026. Further, under some simplifying assumptions, the XPM-induced noise variance, σ2XPM, can be shown to scale with channel power, Pch, and with channel spacing, Δλ, as shown in the following equation:
σ2XPM/Ps2∝Pch2/Δλ2(3)
Rapid power and channel density optimization can be realized.

The impairment due to FWM can be computed and concatenated as described in “Effect of Four-Wave Mixing on WDM Optical Systems: A Statistical Analysis,” S. Betty, M. Giaconi, and M. Nardini, IEEE Photon. Techn. Lett., Vol. 15, No. 8, August 2003, pp. 1079-1081. Further, under some simplifying assumptions, the FWM-induced noise variance, σ2XPM, can be shown to scale with channel power, Pch, and with channel spacing, Δλ, as shown in the following equation:
σ2XPM/Ps2∝Pch2/Δλ4(4)
Rapid power and channel density optimization can be realized.

MPI is related primarily to the physical imperfections and reflections encountered in deployed fiber optic links, and is measured as residual delayed signal power. It is generally independent of signal power and channel spacing. Concatenation is a simple addition of powers from each separate segment.

PMD is also generally independent of signal power and channel spacing, although some second-order interactions with nonlinear effects do exist. Concatenation of individual segment PMD can be done under the assumption of random accumulation, and is expressed by the following equation, with differential group delay between polarizations computed as:

Computation, concatenation, and scaling rules for other impairments can be similarly obtained.

Again, the present invention provides novel approaches to signal propagation modeling that utilize the following as illustrated inFIG. 4: 1) geographic segmentation is applied by separating a large fiber optic network into individual non-overlapping segments, defined by optical add/drop placements (step90); 2) impairment segmentation is applied, such that optical noise, self-phase, cross-phase, four-wave mixing, and other impairments are all treated separately (step92); 3) each impairment is calculated by the most efficient approach to achieve the minimum required accuracy, the approaches being fully numeric, semi-analytic, or empirical (step94); 4) impairment concatenation rules are applied to compute an overall impairment experienced by a signal that traverses more than one segment (step96); and 5) impairment scaling rules are applied to rapidly estimate changes in configuration that can lead to improved performance (i.e. higher capacity, longer distance, or lower cost) (step98).FIG. 5illustrates an exemplary embodiment of a fiber optic network100with geographic segments102,104. The fiber optic network100includes OADMs106. As described herein, geographic segmentation is applied by separating the fiber optic network100to include individual non-overlapping segments, i.e. the geographic segments102,104. These individual non-overlapping segments are defined by the OADM placements.

Referring toFIG. 3a, in one exemplary embodiment, the method10for the performance analysis of fiber optic networks of the present invention comprises an optimization based on quality (Q) (Block12). First, a rule of thumb for setting channel power is applied, the rule of thumb based on fiber type, span count, channel type and count, and/or the like (Block14). Second, a new channel plan is defined (Block16). Third, a new channel plan cross-section between each OADM is defined (Block18). Next, an optical line amplifier (OLA) chain analysis is performed for sections 1, 2, . . . , N (including ASE, double Rayleigh backscattering (DRBS), channel power (P/ch), and/or the like) (Blocks20,22,24). Optionally, parallelized processing is performed. Computational load balancing can also be utilized.

Referring toFIG. 3b, the method10then includes the application of both geographic and impairment segmentation (i.e. sections and impairments are decoupled). This results in the following blocks, SPM Section128, SPM Section230, . . . , SPM Section N32, XPM Section134, FWM Section136, MPI-DRBS Section138, XPM Section240, FWM Section242, MPI-DRBS Section244, . . . XPM Section N46, FWM Section N48, and MPI-DRBS Section N50. Optionally, parallelized processing is performed. Run-times can also be balanced by combining several faster modules together to align completion times.

Referring toFIG. 3c, the method10then includes concatenating each effect, following a channel routing matrix (Block52). Optionally, parallelized processing is performed. Once the effects are concatenated, the channel Q's are computed (Block54), and impairments, noise sources, and/or the like are analyzed (Block58).

Referring toFIG. 3d, the method10then includes determining whether all channel Q's pass (Block60). If yes, options are suggested for improving the overall system (i.e. higher capacity, longer reach, cheaper components, and/or the like) (Block64), and the final result is an optimization based on Q (Block66). If no, options are suggested for making the overall system pass based on scaling rules for individual impairments (i.e. modified channel plan, different amps, and/or the like) (Block68), and one or more of these options are selected for modifying the overall system (Block70). If there is a viable option (Block72), then the system configuration is modified accordingly and the method is repeated. If there is not a viable option (Block72), then the final result is a sub-optimal outcome based on Q (Block66).

It should be noted that the any/all of the methods described above can be implemented as one or more algorithms resident in software on a desktop computer or the like, as appropriate.