Long-Term Observation of PMD and SOP on Installed Fiber Routes

Woodward, S.L.; Nelson, L.E.; Schneider, Charles R.; Knox, Laurie A.; O’Sullivan, Maurice; Laperle, Charles; Moyer, M.; Foo, S.

doi:10.1109/lpt.2013.2290473

Cited by 21 publications

(8 citation statements)

References 13 publications

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“…It was not necessary to stabilize the fiber in the experiment in order to ensure a static channel. In a deployed fiber, the channel impulse response would need to be updated regularly to track the state of polarization (most likely hourly for buried single-mode fiber [18]) and modal cross-talk (the relevant timescales for this are not well-known).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Focusing Over Optical Fiber Using Time Reversal

Piels

Silva

Estaran

et al. 2015

IEEE Photon. Technol. Lett.

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A time-reversal array in multimode fiber is proposed for lossless remotely controlled switching using passive optical splitters. The signal to be transmitted is digitally predistorted so that it is routed through the physical layer in order to arrive at only one receiver in an array. The system performance in the presence of an additive white Gaussian noise, modal group delay, and timing error is investigated numerically for single-mode fiber and 10-mode fiber. Focusing using a two-transmitter array and 44 km of single-mode fiber is demonstrated experimentally for 3-GBd QPSK signals with a bit error rate below the forward error correction limit.Index Terms-Optical fiber communication, access networks, digital signal processing.

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Section: Experiments and Resultsmentioning

confidence: 99%

Focusing Over Optical Fiber Using Time Reversal

Piels

Silva

Estaran

et al. 2015

IEEE Photon. Technol. Lett.

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“…These variations could be caused by the physical properties of the network and could correspond to the deterioration or aging of the fibers caused either by external temperature acting on them, or by the central office's (CO) activities such as maintenance. Analyses that are mainly focused on data distribution and correlation studies, presented in [2,18,19], have indeed shown that there may be a relationship between BER and temperature as well as between BER and daily changes characterizing human activities in the CO. Thus, external factors are the hourly outside temperature (T) at the receiving site location and human activity.…”

Section: A Databases Descriptionmentioning

confidence: 99%

Multivariate Machine Learning Models for Short-Term Forecast of Lightpath Performance

Allogba

Aladin

Tremblay

2021

J. Lightwave Technol.

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Machine Learning (ML) is emerging as a promising solution for managing the physical layer complexity of heterogeneous dynamic optical networks transporting multiple applications in a software defined network (SDN) context, namely for performance prediction. We propose two multivariate neural network models based on Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) methods, trained with field performance data and features, for predicting lightpath signal-to-noise ratio (SNR) over forecast horizons of up to 4 days. The best performance is achieved by using a 5-feature LSTM multivariate model over forecast horizons of up to 96 hours, with an absolute maximum error (AME) of 0.90 dB, compared to 0.91 dB and 0.97 dB for the GRU and LSTM univariate models, respectively, and 1.21 dB for a persistence model. The 2-feature multivariate models obtained through feature engineering perform better than their univariate counterparts for forecast horizons of up to 40 hours. Lastly, we explore the concept of transfer learning (TL) by testing the trained multivariate LSTM and univariate GRU models on field data from two lightpaths carried on the same route. The TL models underperform the naive model for the lightpath carried in a different optical fiber. However, for the lightpath carried in the same optical fiber on a portion of the same route, the LSTM-based TL model outperforms the naive model with a difference of up to 0.11 dB at a 96-hour forecast horizon, compared to 0.30 dB for the lightpath in the source domain, while using 3 times less training data.

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“…The most detailed long-term PMD measurement to date was an 18-month field observation of SOP and corresponding PMD on three buried fiber links employing 40 Gbps transmissions [65]. Fig.…”

Section: Pmd Monitoring Campaignsmentioning

confidence: 99%

“…Long-term SOP and DGD observation for a) short route and b) long route. [65] It was revealed that SOP and PMD changes were in the order of days and the PMD was given by Maxwellian distribution. However, the longer fiber paths revealed the least activity and no correlation between variation in the SOP and the mean DGD was observed.…”

Section: Pmd Monitoring Campaignsmentioning

confidence: 99%

Characterization of dual-polarization LTE radio over a free-space optical turbulence channel

et al. 2015

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A dual polarization (DP) radio over a free-space optical (FSO) communication link using a long-term evolution (LTE) radio signal is proposed and analyzed under different turbulence channel conditions. Radio signal transmission over the DP FSO channel is experimentally verified by means of error vector magnitude (EVM) statistics. We demonstrate that such a system, employing a 64 quadrature amplitude modulation at the frequency bands of 800 MHz and 2.6 GHz, evinces reliability with <8% of EVM in a turbulent channel. Based on the results, we show that transmitting the LTE signal over the FSO channel is a potential solution for last-mile access or backbone networks, when using multiple-input multiple-output based DP signals.

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Long-Term Observation of PMD and SOP on Installed Fiber Routes

Cited by 21 publications

References 13 publications

Focusing Over Optical Fiber Using Time Reversal

Focusing Over Optical Fiber Using Time Reversal

Multivariate Machine Learning Models for Short-Term Forecast of Lightpath Performance

Characterization of dual-polarization LTE radio over a free-space optical turbulence channel

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