Accurate quality of transmission (QoT) estimations are essential enablers for future low-margin dynamic optical network operations. However, physical parameter measurement uncertainties and other intractable signal propagation effects degrade the accuracy of QoT estimation, especially in live production networks. The recent trend of network disaggregation further exacerbates the issue, and a vendor-agnostic accurate QoT estimator is much needed. In this paper, we study Gaussian-noise-model-based QoT estimation in a large-scale disaggregated and partially loaded live production network with monitored physical layer data spanning across 8 months. We first propose refining the signal power measurements by combining the inline amplifier and optical channel monitor (OCM) power measurements, followed by estimating the gain and noise power profiles of each inline amplifier, which in turn improves QoT estimation accuracy. We further introduce an optical multiplex section and frequency bias to the analytical model to incorporate intractable location-specific and spectral effects in the network and proposed data-driven parameter optimizations to learn the biases as well as erbium-doped fiber amplifier noise figures. The (mean, standard deviation) of the QoT estimation errors were reduced from (−0.1043, 0.6037) dB using average amplifier power and (−0.7875, 0.6337) dB using OCM power to (−0.0964, 0.4649) dB after input parameter refinements and were further reduced to (0.0046, 0.2377) dB with data-driven parameter optimization. The proposed methodologies are simple procedures that network operators can adopt to optimize analytical-model-based QoT estimators and/or serve as feature engineering procedures preceding machine-learning-based QoT in realistic disaggregated live production networks.

There has been a lot of effort in our industry to develop analytical and numerical models for accurate estimation of quality of transmission (QoT), which is essential to efficiently design and operate optical transport networks. However, most researchers do not have access to real optical network data to validate and improve their models. We provide data from our production optical transport network on GitHub, hoping it can be of some help for our community to advance in this area. This letter describes how to use the open-source data.

We experimentally demonstrated an optical phase shifted quantizer using a cascade step-size MMI (CS-MMI), which was fabricated on a commercially available 220-nm SOI platform via multi-project wafer (MPW) process. An experimental setup was built to test the ability of the CS-MMI acting as a quantizer. The experimental results show that the proposed CS-MMI-based quantizer has an effective number of bit (ENOB) of 3.31bit, which is a little slighter than the ideal ENOB of 3.32bit. The operation range is 12 nm for ENOB≥3 bit. Moreover, the insertion loss of the CS-MMI is −1.26 dB at 1560 nm, the performance of the fabricated device agrees well with simulation results.

An optical analog-to-digital converter (OADC) scheme with enhanced bit resolution by using a multimode interference (MMI) coupler as optical quantization is proposed. The mathematical simulation model was established to verify the feasibility and to investigate the robustness of the scheme. Simulation results show that 20 quantization levels (corresponding to 4.32 of effective number of bits (ENOB)) are realized by using only 6 channels, which indicates that the scheme requires much fewer quantization channels or modulators to realize the same amount of ENOB. The scheme is robust and potential for integration.

Optical-fiber networks, compensated for path-length fluctuations, enable long-distance dissemination of a radio-frequency signal for precise spectroscopic calibration at remote sites in environmental, industrial and energy-monitoring applications.

We proposed an artificial neural network (ANN)-based QoT estimator for large-scale mixed-rate disaggregated metro DCI networks with an estimation error standard deviation of 0.3 dB, outperforming analytical-based methods with vendor-specific transponder SNR characterization.