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

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)

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

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

Realising high sensitivity at 40 Gbit/s and 100 Gbit/s

We experimentally investigate modulation formats for realizing high data rate and high power sensitivity using coherent reception with low noise-figure optical preamplification. 40 Gbit/s PS-QPSK exhibits a sensitivity of 4.3 photons/bit while 100 Gbit/s PDM-QPSK exhibits a sensitivity of 5.3 photons/bit at 3.8×10-3 BER

Techniques for applying reinforcement learning to routing and wavelength assignment problems in optical fiber communication networks

We propose a novel application of reinforcement learning (RL) with invalid action masking and a novel training methodology for routing and wavelength assignment (RWA) in fixed-grid optical networks and demonstrate the generalizability of the learned policy to a realistic traffic matrix unseen during training. Through the introduction of invalid action masking and a new training method, the applicability of RL to RWA in fixed-grid networks is extended from considering connection requests between nodes to servicing demands of a given bit rate, such that lightpaths can be used to service multiple demands subject to capacity constraints. We outline the additional challenges involved for this RWA problem, for which we found that standard RL had low performance compared to that of baseline heuristics, in comparison with the connection requests RWA problem considered in the literature. Thus, we propose invalid action masking and a novel training method to improve the efficacy of the RL agent. With invalid action masking, domain knowledge is embedded in the RL model to constrain the action space of the RL agent to lightpaths that can support the current request, reducing the size of the action space and thus increasing the efficacy of the agent. In the proposed training method, the RL model is trained on a simplified version of the problem and evaluated on the target RWA problem, increasing the efficacy of the agent compared with training directly on the target problem. RL with invalid action masking and this training method outperforms standard RL and three state-of-the-art heuristics, namely, k shortest path first fit, first-fit k shortest path, and k shortest path most utilized, consistently across uniform and nonuniform traffic in terms of the number of accepted transmission requests for two real-world core topologies, NSFNET and COST - 239. The RWA runtime of the proposed RL model is comparable to that of these heuristic approaches, demonstrating the potential for real-world applicability. Moreover, we show that the RL agent trained on uniform traffic is able to generalize well to a realistic nonuniform traffic distribution not seen during training, thus outperforming the heuristics for this traffic. Visualization of the learned RWA policy reveals an RWA strategy that differs significantly from those of the heuristic baselines in terms of the distribution of services across channels and the distribution across links