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Advances in MLSE Techniques for Dispersion-Tolerant Optical Systems

Advances in MLSE Techniques for Dispersion-Tolerant Optical Systems. Pierluigi Poggiolini Acknowledgements Robert Killey, Seb Savory, Yannis Benlachtar, University College London Gabriella Bosco, Politecnico di Torino, Italy Josep Prat, Mireia Omeilla, Universitat Politecnica de Catalunya.

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Advances in MLSE Techniques for Dispersion-Tolerant Optical Systems

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  1. Advances in MLSE Techniques for Dispersion-Tolerant Optical Systems Pierluigi Poggiolini Acknowledgements Robert Killey, Seb Savory, Yannis Benlachtar, University College London Gabriella Bosco, Politecnico di Torino, Italy Josep Prat, Mireia Omeilla, Universitat Politecnica de Catalunya this work was sponsored by the e-Photon/ONe+ Project

  2. References • The results presented in this talk have been published also at ECOC 2006 • “Branch Metrics for Effective Long-Haul MLSE IMDD Receivers”, P. Poggiolini, G. Bosco, J. Prat, R. I. Killey, S. Savory, paper We2.5.4, ECOC 2006 • “1,040 km Uncompensated IMDD Transmission over G.652 Fiber at 10 Gbit/s using a Reduced-State SQRT-Metric MLSE Receiver”, P.Poggiolini (1), G.Bosco (1), S.Savory (2), Y.Benlachtar (2), R.I.Killey (2), J. Prat (3), post deadline paper 4.4.6, ECOC 2006

  3. Outline • Motivations • Advances in MLSE branch-metric design for long-haul uncompensated transmission • MLSE experiments over 1,040 km of uncompensated standard single-mode fiber • Conclusions

  4. Motivations • Recent papers have suggested that MLSE should have the potential for reaching long-haul distances without any dispersion compensation • 700 km G.652 fiber at 10.7 Gbit/s have been reached using simulationBosco, G.; Poggiolini, P. “Long-Distance Effectiveness of MLSE IMDD Receivers”, IEEE Photonics Technol. Letters, Volume 18,  Issue 9,  May 1, 2006 Page(s):1037 - 1039 • 600 km of G.652 fiber have been demonstrated experimentally (off-line) at 10.7 Gbit/s N. Alic, G. C. Papen, R. E. Saperstein, R. Jiang, C. Marki, Y. Fainman and S. Radic “Experimental Demonstration of 10 Gb/s NRZ Extended Dispersion-Limited Reach over 600km-SMF Link without Optical Dispersion Compensation,” OFC 2006, paper OWB7, March 5-9, 2006.

  5. Problems for long-haul MLSE • MLSE processor complexity grows exponentially with accumulated dispersion • To practically enable uncompensated long-haul with MLSE, complexity must be minimized (as much as possible) • The processor complexity depends on: • number of MLSE trellis states • amount of processing per state

  6. Reducing complexity • Reducing complexity can be done by: • reducing the number of trellis states for a given accumulated dispersion various reduced-states techniques possible • decreasing the amount of processing per state devising a low-complexity branch-metric let’s start from this aspect

  7. An example of a four-state trellis 1 1 1 1 11 11 11 11 11 0 0 0 0 10 10 10 10 10 1 1 1 1 0 0 0 0 1 1 1 1 01 01 01 01 01 0 0 0 0 1 1 1 1 00 00 00 00 00 0 0 0 0 4 states

  8. noisy RXsignal Per-state processing 0 1 01101

  9. actual noisy RX waveform sample state and branch-dependent parameters The metric function m • The RX operates on the samples of the waveforms • Many studies have found 2 samples per waveform sufficient, 1 may be enough with some penalty • Two make things simple, we assume one sample • The distance d could then be computed in principle as a function of one variable and a few parameters:

  10. Which function m ? • Theory states that the optimum metric function m depends on the statistical distribution of noise on the RX signal • For optical systems, it cannot be “written down” analytically • It is possible to estimate it at run-time using “histograms” and store it in lookup tables • The existing MLSE commercial implementation resorts to lookup tables • This approach does not scale well with increasing number of states (long-haul); for instance: • 512 states, 2 samples per bit  2048 lookup tables

  11. Suboptimum analytical metrics • Instead of resorting to look-up tables, we would like to use a a simple analytical expressions for m , though suboptimum • A suboptimum analytical metric can be derived from the arbitrary assumption of a certain (incorrect) noise distribution • In this work we have analyzed: • chi-square • gaussian • gaussian, stationary • We also propose a new metric, that assumes a gaussian stationary noise distribution over the square-root of the signal • None of these assumptions is exact, but some metrics are: • simpler • better performing • easier to implement

  12. (∙)2 TX MLSE Simulated test system ASE noise • Transmitter: NRZ rectangular pulses, 10.7 Gbit/s 5-pole 8 GHz Bessel filter, ideal MZ modulator • Fiber: G.652 SMF (D=16 ps/nm/km) • Optical RX filter: 2nd order Supergaussian filter with bandwidth 35 GHz • PD filter: 5-pole Bessel filter with bandwidth 7 GHz • MLSE: Viterbi algorithm with variable number of states and 2 samples per bit fiber PD filter Optical filter +

  13. Simulation details • Monte-Carlo direct error count • PRBS 215-1 • Total test sequence length: 217 (131072 bits) • target BER=10-3 (130 errors expected, for a 95% confidence interval of ±0.15 dB over the OSNR) • A/D converter ideal (infinite resolution) • OSNR (over 0.1 nm) for target BERis assessed at various distances and with different metrics

  14. sample of noisy RX waveform sample of expected waveform (no noise) OSNR-related parameter independent of trellis state Chi-square metric

  15. Chi-square metric significance • Two recent papers have shown chi-square to be near-optimum • N. Alic, G.C. Papen, R.E. Saperstein, L.B. Milstein, and Y. Fainman, “Signal statistics and maximum likelihood sequence estimation in intensity modulated fiber optic links containing a single optical preamplifier,” Optics Express, vol. 13, no. 5, June 2005, pp.4568-4579 • T. Foggi, E. Forestieri, G. Colavolpe, G. Prati, "Maximum Likelihood Sequence Detection With Closed-Form Metrics in OOK Optical Systems Impaired by GVD and PMD" IEEE Journal of Lightwave Technology , vol 24, n°8, august 2006, pp 3073-3087 • As a result, chi-square is a good “benchmark” for all metrics

  16. 64 128 512 2048 states 32 4096 8 4 Chi-Square Results BER=10-3 16 15 14 OSNR [dB] 13 12 no MLSE Chi-square 11 10 0 100 200 300 400 500 600 700 L [km]

  17. statistical variance of expected waveform sample with noise statistical average of expected waveform sample with noise sample of noisy RX waveform Gaussian metric

  18. Gaussian metric results BER=10-3 16 15 14 OSNR [dB] 13 12 no MLSE Chi-square 11 Gaussian 10 0 100 200 300 400 500 600 700 L [km]

  19. statistical average of expected waveform sample with noise sample of noisy RX waveform Gaussian stationary metric • We assume that all variances are identical, for all branches • As a result, variances can be eliminated:

  20. Gaussian stationary results BER=10-3 16 15 14 OSNR [dB] 13 12 no MLSE Chi-square 11 Gaussian S Gaussian 10 0 100 200 300 400 500 600 700 L [km]

  21. statistical average of square root of expected waveform sample with noise square rootof sample of noisy RX waveform The SQRT metric • We propose a new metric, theSQRT metric:

  22. SQRT Metric Results BER=10-3 16 15 14 OSNR [dB] 13 no MLSE 12 Chi-square Gaussian S Gaussian 11 SQRT 10 0 100 200 300 400 500 600 700 L [km]

  23. Summing up so far • The SQRT metric seems to combine: • near optimum performance • low run-time complexity • [low channel identification complexity]

  24. The next goal • Use the SQRT metric to carry out a long-haul MLSE experiment reaching much farther than previous attempts

  25. Best experimental result so far • 600 km of G.652 fiber at 10.7Gbit/s, experimental [2] N. Alic, G. C. Papen, R. E. Saperstein, R. Jiang, C. Marki, Y. Fainman and S. Radic “Experimental Demonstration of 10 Gb/s NRZ Extended Dispersion-Limited Reach over 600km-SMF Link without Optical Dispersion Compensation,” OFC 2006, paper OWB7, March 5-9, 2006. • it was done with the help of a very narrow (12GHz) TX optical filter • the TX curtailed the optical spectrum and therefore mitigated the impact of CD • the eye diagram is already closed at the TX: the system is not really NRZ-IMMD

  26. Eye diagram at TX of [2] (Narrowly Filtered) NF-OOK

  27. Objectives • we decided to try with at least 1,000 km of G.652 fiber • we wanted to avoid any kind of optical mitigation • no dispersion compensation • no narrow optical filters either at TX or RX • we wanted to try and do this while keeping MLSE processor complexity as low as possible by • minimizing the number of trellis states • using a simple branch metric (SQRT)

  28. -3 dBm 1551.65 nm 13 recirculations 1,040 km 17,680 ps/nm gaussian, 50 GHz Experimental set-up

  29. Eye diagram at output of TX

  30. Experimental Eye Diagram at 1,040 km Amplitude (a.u.) 0 1 2 Bit Intervals

  31. Signal sampling • a TekTronix TDS6154C real-time oscilloscopewas used to sample the electrical RX signal • the A/D resolution was about 5 bits • 2 samples per bit were used by the MLSE processor • BER was evaluated over 1,000,000 bits, 1 full cycle of the 2^20-1 PRBS

  32. MLSE complexity reduction • the number of trellis states was minimized using a reduced-state technique similar to: D.E. Crivelli, H.S. Carrer, M.R. Hueda, “On the performance of reduced-state Viterbi receivers in IM/DD optical transmission systems,” Proc. of ECOC 2004, paper We.4.P.083, Stockholm (Sweden), 5-9 Nov. 2004

  33. Baseline reference performance • As a suitable baseline reference we measured the performance of a benchmark system: • operating in back-to-back (no dispersion) • without MLSE processor • with the same TX and RX hardware as the MLSE system… • …except for the electrical filters that were replaced by two SONET filters to remove any eye closure • Baseline at BER=10-3 was: OSNR=11.5 dB (over 0.1 nm)

  34. 11.5 dB Results

  35. 10.2 dB Results

  36. 5.2 dB Results

  37. 3.8 dB Results

  38. 3.2 dB Results

  39. Comments • a residual 3.2 dB penalty is present even with 8192 states • computer simulations have been run to investigate this penalty • some of it is due to the finite-resolution of the A/D converter(between 1 and 1.5 dB) • about 2 dB are unaccounted for but show up in simulations as well • whether this further penalty can be removed is currently not known (theoretical results from Prati et al. show it to be there)

  40. Final Conclusions • 1,000 km can be achieved in the lab, off-line, with good results • 1,000 km are probably still too challenging for a practical implementation (require 2048 states or more) • However 500 km should be possible with only 256 states, which is within reach of today’s technology • MLSE is here to stay – we will likely see a lot more of it in the new generations of systems

  41. Reducing the sampling rate? • Great progress would be made by reducing the sampling rate to one sample per bit, rather than two • We set out to re-process the experimental data with the goal to obtain the best possible results with one sample per bit • Note that “equal processing complexity” is roughly obtained using either: • N states with 2 samples per bit • 2N states with 1 sample per bit • If same performance, the latter case is probably preferable, since the critical A/D circuit runs at halved speed  less expensive, higher resolution,…

  42. Data re-processing • We numerically applied post-detection re-filtering over the experimental data, using different bandwidths: • 2 to 7 GHz in steps of 1 GHz (Bessel, 5 pole) • We passed only one sample per bit to the MLSE processor • The best post-detection filter bandwidth turned out to be 4 GHz

  43. Results

  44. Results

  45. Results

  46. Results

  47. Results

  48. Results

  49. Results

  50. Final Conclusions • The SQRT metric together with proper state-reduction algorithm provide an effective combination • Off-line experiments prove that the scheme works, also with one sample per bit only • 1,000 km can be achieved in the lab, off-line, with good results • 1,000 km are probably still too challenging for a practical implementation (require 2048 states or more) • However 500 km should be possible with only 256 states, which is within reach of today’s technology • MLSE is here to stay – we will likely see a lot more of it in the new generations of systems

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