On the Impact of Optimal Modulation and FEC Overhead on Future Optical Networks

The potential of optimum selection of modulation and forward error correction (FEC) overhead (OH) in future transparent nonlinear optical mesh networks is studied from an information theory perspective. Different network topologies are studied as well as both ideal soft-decision (SD) and hard-decision (HD) FEC based on demap-and-decode (bit-wise) receivers. When compared to the de-facto QPSK with 7% OH, our results show large gains in network throughput. When compared to SD-FEC, HD-FEC is shown to cause network throughput losses of 12%, 15%, and 20% for a country, continental, and global network topology, respectively. Furthermore, it is shown that most of the theoretically possible gains can be achieved by using one modulation format and only two OHs. This is in contrast to the infinite number of OHs required in the ideal case. The obtained optimal OHs are between 5% and 80%, which highlights the potential advantage of using FEC with high OHs.Comment: Some minor typos were correcte

Approximating the Partially Coherent Additive White Gaussian Noise Channel in Polar Coordinates

We consider the partially coherent additive white Gaussian noise channel (PCAWGN) in optical communications and review the derivation of the exact channel conditional probability model in a closed-form solution in polar coordinates. In addition, we derive a reduced-complexity approximation by replacing the Rician and Tikhonov distributions describing amplitude and phase components, respectively, with their Gaussian approximation under certain assumptions of high SNR and low phase noise or jitter. Our proposal significantly reduces the hardware complexity by removing the modified Bessel functions involved in the exact solution. Furthermore, we compare the proposed approximation with a different metric previously found in the literature and observe that for maximumlikelihood hard symbol decision, both models are in perfect agreement with the optimal detector. However, our model not only reduces the required number of multiplications from 12 to 8 and additions from 9 to 3 (per computed symbol) but also reduces the information loss by at least one and up to several orders of magnitude with respect to the previously published metric when used to compute the channel achievable information rate (AIR). In all the simulation cases, we use QAM constellations of orders 4, 8, 16, and 32 as test input symbol sets

Design Considerations for Low-Margin Elastic Optical Networks in the Nonlinear Regime

We demonstrate from a system design perspective, that nonlinearity can be exploited, to minimize the impact of system margins on the system performance, both for point-to-point links and elastic optical networks. A nonlinear interaction causes a 2 dB reduction in launch power to be reduced to <0.25 dB signal-to-noise ratio (SNR) penalty and likewise, a 2 dB peak-peak (pk-pk) perturbation to the output power of an optical amplifier incurs <0.25 dB SNR penalty (for 5, 10 and 20 spans). Extending this to a gain ripple of 1 dB pk-pk with an internode spacing of 5x80 km, 10x80 km and 20x80 km the penalty is 0.4 dB, 1.5 dB and 5.1 dB, respectively, with pre-emphasis reducing this to 0.01 dB, 0.3 dB and 1.2 dB respectively. In elastic optical networks we consider the nonlinear relationship between SNR, margin and the fraction of capacity available. We consider scaling internode distances of a 9-node German scale network (DT9) such that the initial network diameter increases from 1,120 km to 6,720 km (six-fold scaling). We generate 1,000 different topologies based on the scaled DT9 node locations to quantify the impact of margin. For the unscaled DT9 network a 3 dB margin results in, on average, a 21% reduction in network throughput, however when the internode spacing is increased six-fold to a continental scale network, the network throughput is reduced by 40%, on average, for the same 3 dB margin.RJV acknowledges funding from EPSRC and BT through an iCASE studentship. SJS and DJI acknowledge funding through the EPSRC Programme Grant TRANSNET EP/R035342/1

Why compensating fibre nonlinearity will never meet capacity demands

Current research efforts are focussed on overcoming the apparent limits of communication in single mode optical fibre resulting from distortion due to fibre nonlinearity. It has been experimentally demonstrated that this Kerr nonlinearity limit is not a fundamental limit; thus it is pertinent to review where the fundamental limits of optical communications lie, and direct future research on this basis. This paper details recently presented results. The work herein briefly reviews the intrinsic limits of optical communication over standard single mode optical fibre (SMF), and shows that the empirical limits of silica fibre power handling and transceiver design both introduce a practical upper bound to the capacity of communication using SMF, on the order of 1 Pbit/s. Transmission rates exceeding 1 Pbit/s are shown to be possible, however, with currently available optical fibres, attempts to transmit beyond this rate by simply increasing optical power will lead to an asymptotically zero fractional increase in capacity.Comment: 4 pages, 2 figure

Machine Learning based Noise Estimation in Optical Fiber Communication Networks

In this paper, we discuss a machine learning based approach to jointly estimating both linear and nonlinear noise contributions in an optical fiber communication link. We will expound the rational for utilizing machine learning for this problem, before discussing current progress and then concluding with future research directions.The authors gratefully acknowledge the donation of equipment, funding and support from Ciena. This research was performed under the auspices of a Ciena university collaborative research grant. S. J. Savory also acknowledges support from UK EPSRC (through the project INSIGHT EP/L026155/2)

Data-Driven Erbium-doped Fiber Amplifier Gain Modeling Using Gaussian Process Regression

We propose a data-driven erbium-doped fiber amplifier (EDFA) gain model utilizing Gaussian process regression (GPR). An additive Laplacian and radial-basis function kernel is proposed for the GPR and was found to outperform deep neural network (DNN) methods while additionally providing prediction uncertainty. Performance is measured using mean absolute error (MAE) averaged across five different EDFAs with three manufacturers. The GPR achieves an MAE of 0.1 dB using 30 training samples in contrast to the DNN that achieves an MAE of 0.25 dB using 3000 training samples. Additionally, we demonstrate that active learning can be used to improverobustness and repeatability of convergence

Maximizing the information throughput of ultra-wideband fiber-optic communication systems

Maximized information rates of ultra-wideband (typically, beyond 100~nm modulated bandwidth) lumped-amplified fiber-optic communication systems have been thoroughly examined accounting for the wavelength dependencies of optical fiber parameters in conjunction with the impact of the inelastic inter-channel stimulated Raman scattering (SRS). Three strategies to maximize point-to-point link throughput were proposed: optimizations of non-uniformly and uniformly distributed launch power per channel and the optimization based on adjusting to the target 3 dB ratio between the power of linear amplified spontaneous emission and nonlinear interference noise. The results clearly emphasize the possibility to approach nearly optimal system performance by means of implementing pragmatic engineering sub-optimal optimization strategies

200 Gb/s/ λ Bidirectional Coherent PON Solutions Demonstrated over Field Installed Fiber

We demonstrate 200 Gb/s bidirectional coherent PON solutions using a simplified optical network unit (ONU) over 19 km of field-installed fiber. The ONU receiver is a single-polarization heterodyne detector with either a balanced or a single-ended photodetector and the transmitter is based on dual-polarization electro-absorption modulated laser (EML). The optical line terminal (OLT) uses standard dual-polarization coherent transceivers. The downstream solutions achieve 37 dB and 30.5 dB power budget for the receiver with balanced and single-ended photodiodes, respectively. For the upstream, two line rates have been investigated: 200 Gb/s/λ for symmetrical transmission and 100 Gb/s/λ for asymmetric transmission achieving a power budget of 30.1 dB and 40.9 dB respectively

Scalable Capacity Estimation for Nonlinear Elastic All--Optical Core Networks

Routing and wavelength assignment (RWA) algorithms must strike a balance between finding routes with high quality of transmission (QoT) and finding routes that will not interfere with allocating future traffic. Too much emphasis on the first will concentrate traffic along major routes causing congestion whilst too much emphasis on the second will cause individual transceivers to operate below their capabilities increasing both cost and power consumption. This paper presents a low--complexity algorithm that shows that focusing on wavelength packing allows for greater overall traffic whilst giving only slight penalties for latency and required transceivers. Our algorithm comfortably outperforms kSP--FF routing for the same complexity and typically betters congestion aware routing whilst reducing complexity. We show these results on 4 simplified networks based on deployed topologies before replicating them on 2,000 artificially generated topologies based on real node locations in Germany and the USA. Capacity for each topology was found with an integer linear program to which our algorithm compares favorably suggesting it provides a scalable alternative to global optimization.RJV thanks EPSRC and BT through an iCASE studentship EP/N509103/1 1775341; DJI and SJS thank the EPSRC Programme Grant TRANSNET EP/R035342/