OFC/NFOEC: Measurement of Equivalent Zero-Dispersion Wavelength Distribution for…
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Check out Mark Filer and Sorin Tibuleac's slides from their presentation at OFC this week!
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OFC/NFOEC: Measurement of Equivalent Zero-Dispersion Wavelength Distribution for…
- 1. Measurement of Equivalent Zero- Dispersion Wavelength Distribution for Capacity Increase in FWM-limited Networks Mark Filer and Sorin Tibuleac ADVA Optical Networking, Atlanta GA mfiler@advaoptical.com OFC/NFOEC 2013, Anaheim CA (NW4E.4)
- 2. What is Four-Wave Mixing? • Generally describes any third order process in which the interaction of three fields lead to the generation of a fourth f1 f2 f3 f123,213 f123,213 f132,312 f132,312 f231,321 f231,321 f113113 f f112 f223 f223 f112 f221 f221 f332332 f f331 f321 f312 f221 f1 f2 f2 f3 • Several approaches to minimizing FWM-induced crosstalk • Reduced channel count • Limited transmission distance • Raman amps + low launch power • L-band • S-USCA OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 3. Zero-Dispersion Wavelength • Generation of FWM components: phase-matching condition • Knowledge of fiber CD properties critical for optimal channel allocation • Fiber zero-dispersion wavelength (ZDW) characteristics • ZDW is non-uniform along fiber length • Larger ZDW variation in older DSFs which are still used today • Challenge: standard method for in-field dispersion measurement is Modulation Phase Shift (MPS) method • MPS yields average ZDW over entire link; however… • Near-end ZDW features more important than far-end • Solutions: previous methods complex, equipment-intensive We propose a new method of determining equivalent ZDW 1) Simplified in-field measurement of degenerate FWM efficiency 2) Derivation of equivalent ZDW distribution using measured data as input to search-and-optimization numerical algorithm OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 4. In-Field Measurement Technique • Probe with (swept) pump induces degenerate FWM • Use OSA or OPM to obtain FWM efficiency vs frequency • Repeat for multiple fprobe values • Take average of results f fprobe fpump fFWM λ0 λ0 OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 5. In-Field Measurement Technique (2) • Concept demonstrated on 112km DSF comprised of 6 spools • Known ZDW’s (from MPS method) • Four probe wavelengths (1541, 1545, 1561, 1565 nm) λ0,2 λ0,1 λ0,3 λ0,4 OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 6. Equivalent ZDW distribution • Next step: utilize inverse method for determining equivalent ZDW distribution from measured data • Multiple ZDW distributions possible for same solution • Physical distribution of ZDW not as critical as accurate modeling of FWM efficiency of span • Simple example: OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 7. Search-and-Optimization Algorithm • Search-and-optimization routine: Genetic Algorithm • Merit function: experimental FWM efficiency spectrum • Optimization parameters: n fiber segments, ZDWs {λ0,1 , λ0,2 ,…, λ0,n} L1 L2 Ln λ0,1 λ0,2 λ0,n generations max time size m FWM efficiency exceeded exceeded or stall new population initial merit function rank stop? ks∙m kx∙m km∙m population evaluation select xover mutate Terminology solution • candidate solution: ZDW set {λ0,1 , λ0,2 ,…, λ0,n} • population: a set of candidate solutions (length m) • generation: one population lifetime • selection: candidates retained (top ks) • crossover: combinations of best candidates (kx) • mutation: random changes to a candidate (km) OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 8. Example for 112km span DSF • Measurement performed on 112km span DSF comprised of 6 shorter spools each with different ZDW • Analytical FWM model* used to calculate FWM efficiency spectrum • Calculated and measured FWM efficiencies compared in a least-squares sense merit function • Genetic Algorithm parameters: • Fiber segments, n = 20 • Population size, m = 500 • Initial population, {λ0,1 , λ0,2 ,…, λ0,n} = 1550.43nm • Max generations = 225 • Selection, ks = 10% • Crossover, kx = 55% • Mutation, km = 35% * J. Stay et al, proc. OFC/NFOEC, 2008, paper JWA14 OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 9. Result for 112km span DSF • GA merit function designed to emphasize matching of spectral “peaks” with less weighting given to regions of low FWM efficiency • Final solution set which yielded result above input to GA-based S- USCA optimization tool* * J. Stay et al, proc. OFC/NFOEC, 2008, paper JWA14 OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 10. Application to S-USCA scheme • S-USCA tool produces optimal channel plan given number of channels and channel range/spacing • Increase in channel capacity realized when inputting equivalent ZDW distribution vs inputting MPS-measured (i.e., average) ZDW 72ch 58ch OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 11. S-USCA channel distributions compared • Optimal allocation of channels for e.g., 70 of 96 channels differ greatly given knowledge of ZDW distribution average ZDW Optimized for distributed ZDW Optimized for channel number OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 12. Conclusions • Demonstrated technique for characterizing equivalent ZDW distribution for maximized transmission capacity 1. FWM efficiency of span measured 2. Equivalent ZDW distribution extracted from inverse search-and- optimization numerical algorithm • Result used to generate optimized S-USCA for given network • May be combined with additional mitigation procedures • Power pre-emphasis • Raman amplification • Additional benefits realized when combined with: • Higher-order phase-modulated formats (mPSK vs OOK) • High-coding gain SD-FEC (BERs > 10-2) • electronic equalization • coherent technologies OFC/NFOEC 2013, Anaheim CA (NW4E.4) © 2013 ADVA Optical Networking. All rights reserved. Confidential.
- 13. Thank you mfiler@advaoptical.com OFC/NFOEC 2013, Anaheim CA (NW4E.4)
Editor's Notes
- The FWM efficiency may be measured in using a method similar to that presented in [7]. Two tunable-wavelength laser sources, one “pump” and one “probe,” are launched into fiber in order to produce a third (degenerate) FWM signal at frequency fFWM = 2·fpump - fprobe (Fig. 1a).The probe is held at a fixed frequency while the pump is swept over the wavelength region of interest, resulting in a plot of FWM efficiency versus frequency. This process is repeated for multiple probe frequency values, with the resulting FWM efficiency plots averaged together for a composite picture of the FWM efficiency. As pointed out in [7], the generated FWM products can have local maxima when the pump wavelength is not equal to the fiber’s ZDW. By measuring the efficiencies at multiple probe wavelengths, the fact that a peak value will always occur at the ZDW is utilized, and the dependency on the particular probe wavelength chosen is averaged out.
- Figure 1b shows that the sharpest peak in the FWM efficiency plot (red, left) corresponds precisely to the ZDW (blue, right) of the first 6 km spool in the long span. Accordingly, the second, third, and fourth highest peaks correspond well with the second, third, and fourth fiber spools in the span. Other peaks are indiscernible because signal powers are no longer high enough to generate FWM products (past the ~60 km mark), highlighting the need for a measurement technique which doesn’t average the dispersion over the entire length of the fiber.This is a compelling demonstration of the correlation existing between the ZDW of the different fiber segments, and the wavelengths and peak power of the degenerate FWM products. In a real network scenario, the distribution of ZDW shown above (blue, right) for the lab test example becomes the unknown, and therefore must be derived from the degenerate FWM spectra. This derivation is achieved with the inverse method described next.
- The inverse-method calculation takes the experimentally-measured FWM data as an input and generates an equivalent distribution of ZDW values along the fiber length. The term “equivalent” is applied throughout because there can exist multiple ZDW distributions over fiber length which can yield the same FWM efficiency result. The physical distribution of the ZDWs is not as important as accurate modeling of the FWM efficiency of the span.
- The experimental FWM efficiency spectrum represents the target, or “merit,” function which the search-and-optimization algorithm seeks to match. A merit function is defined as the absolute difference (or RMS value) between the target function and the calculated function. One example of a search-and-optimization routine well suited for such an application is Genetic Algorithm (GA), which can be easily implemented in e.g., MATLAB, although other search and optimization algorithms may be used.
- This GA-based inverse method was run for the example given in Sec. 2 with the 112 km fiber. In the calculation of FWM products, the FWM model used in [3] was utilized, with the resultant degenerate FWM efficiency profile compared with the plot of FWM mixing efficiency from Fig. 1 as the basic merit function. The algorithm divided the fiber into n = 20 segments, and was seeded with an initial population of 1550.43 nm (i.e., the average measured ZDW for the full 112 km span) for all segments. The GA parameters were population size = 500, generations = 225, selection = 10%, crossover = 55%, and mutation = 35%. The set of ZDW values resulting from the optimization yields the FWM efficiency spectrum shown in Fig. 2a. The merit function was designed to emphasize matching the main “peaks” with lesser importance given to matching the regions of spectrum with low FWM efficiency.
- This equivalent ZDW distribution may then be used as the fiber profile input to an S-USCA optimization tool. The tool employed here also utilizes the genetic algorithm to produce a channel plan with maximum capacity for a given channel count [3]. By accounting for the equivalent distribution of ZDWs through the fiber, an increase in channel capacity can be realized when compared to the assumption of a single, averaged ZDW value that would be obtained from a typical in-field fiber characterization. Figure 2b depicts FWM-induced crosstalk for a per-channel launch power of -3 dBm versus number of channels for 2 cases: (1) S-USCA plan optimized for the correct equivalent ZDW distribution (○’s), and (2) one optimized using only an average ZDW value for the entire span (□’s). Supposing a system crosstalk tolerance level of -20 dB, under the assumption of a distributed ZDW (case 1), a maximum of 76 channels may be deployed. The simple assumption of an average ZDW (case 2) would in practice limit the system to 68 channels for the same level of crosstalk, and increases levels of crosstalk by as much as 4 dB above what was expected (in this case) for high channel counts. In general, penalties generated by using the wrong ZDW may be even higher depending on the specific differences in the ZDW assumed and the actual distribution of ZDWs.
- the actual optimal placement of channels in the transmission band differs greatly once a distributed model of ZDWs is considered, additionally highlighting the need for a more sophisticated method of describing link dispersion than simple MPS dispersion measurement.
- The work contained herein demonstrates a technique for characterizing an equivalent zero-dispersion wavelength (ZDW) distribution on fibers with ZDWs in the transmission band in order to maximize transmission capacity. The FWM efficiency of a span is measured, and an equivalent ZDW distribution is extracted from an inverse search-and-optimization numerical algorithm, which can be used to generate an S-USCA scheme for a given network. This technique may be combined with additional mitigation procedures (e.g., power pre-emphasis, Raman amplification) in order to further increase total capacity. More recent developments in high-coding gain forward error correction, electronic equalization, and coherent detection allow transmission at higher bit error ratios, up to 10-2 (SD-FEC). This, coupled with higher-order phase-modulated formats which generate less FWM than amplitude-keyed predecessors, enables significant increases in capacity when utilizing an S-USCA approach.