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

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

Machine learning for optical fiber communication systems: An introduction and overview

Optical networks generate a vast amount of diagnostic, control and performance monitoring data. When information is extracted from this data, reconfigurable network elements and reconfigurable transceivers allow the network to adapt both to changes in the physical infrastructure but also changing traffic conditions. Machine learning is emerging as a disruptive technology for extracting useful information from this raw data to enable enhanced planning, monitoring and dynamic control. We provide a survey of the recent literature and highlight numerous promising avenues for machine learning applied to optical networks, including explainable machine learning, digital twins and approaches in which we embed our knowledge into the machine learning such as physics-informed machine learning for the physical layer and graph-based machine learning for the networking layer

Maximizing 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

GP Driven DBP

1) Waveforms.zip containing waveforms generated from experiments : This dataset contains 30 sample waveforms generated from the experimental system of a wavelength division multiplexing (WDM) optical network link. This link is with 4 channels, of 1000 km length, of 64GBd symbol rate and 64 QAM modulation format. They are in the format of ".mat" and can be viewed with Matlab. $$\$$2) sim_results.zip containing simulation results from the proposed Gaussian process (GP) driven digital backpropagation (DBP) approach : This dataset contains the resultant data generated from the simulations which are then used to generate the plots in the paper. Data files with respect to signal to noise ratio (SNR) data are represented in “.mat” format and can be accessed with Matlab while the other data files are in the format of “.pkl” and can be opened using Python language “pickle” module. The trained GP models are also stored in “.pkl” format and can be accessed as described above.$$\$$3) DBP_GP_data.zip containing all source files and remaining data: These files contain the source codes related to this work including GP implementation, DBP simulation and history matching. Sources are written in Python programming language and can be executed with any Python environment. Additionally, this contains datasets generated from the optimisation process in “.mat” format. See the main manuscript for more details.EPSRC Transnet grant number EP/R035342/