Optical Line Systems Research Articles

Semi-automatic line-system provisioning with an integrated physical-parameter-aware methodology: field verification and operational feasibility

We propose methods and an architecture to conduct measurements and optimize newly installed optical fiber line systems semi-automatically using integrated physics-aware technologies in a data center interconnection (DCI) transmission scenario. We demonstrate, for the first time to our knowledge, digital longitudinal monitoring (DLM) and optical line system (OLS) physical parameter calibration working together in real-time to extract physical link parameters for fast optical fiber line systems provisioning. Our methodology has the following advantages over traditional design: a minimized footprint at user sites, accurate estimation of the necessary optical network characteristics via complementary telemetry technologies, and the capability to conduct all operation work remotely. The last feature is crucial, as it enables remote operation to implement network design settings for immediate response to quality of transmission (QoT) degradation and reversion in the case of unforeseen problems. We successfully performed semi-automatic line system provisioning over field fiber network facilities at Duke University, Durham, North Carolina. The tasks of parameter retrieval, equipment setting optimization, and system setup/provisioning were completed within 1 h. The field operation was supervised by on-duty personnel who could access the system remotely from different time zones. By comparing Q-factor estimates calculated from the extracted link parameters with measured results from 400G transceivers, we confirmed that our methodology has a reduction in the QoT prediction errors ( ±0.3dB ) over existing designs ( ±0.6dB ).

Enabling IP-optical integration in core and metro networks [Invited

IP-optical (Internet protocol and optical) integration, or “IP over DWDM (dense wavelength division multiplexing),” is a concept spanning over two decades since the advent of coherent DSP (digital signal processing). In recent years, coherent DSP technology has progressed to a point where it can be integrated on small pluggable form factors that can be equipped in optical line systems and disaggregated transponders or directly in IP routers to support a range of metro and core applications. The option of equipping pluggable digital coherent optics (DCOs) directly in IP routers has the biggest potential for operational cost savings but hinges on the availability of a unified management environment with a single pane of glass to coordinate and automate IP routing and optical transport functions. This work presents alternative software-defined networking (SDN) architectures and evaluates the challenges associated with the evolution to IP over DWDM network architectures. It demonstrates the first, to our knowledge, implementation of the Transport API supporting colored pluggable interfaces in routers in a real network testbed. This work contributes to the realization of end-to-end network management for IP-optical networks, offering operators comprehensive visibility into multi-layer and multi-domain services and empowering revenue generation.

Local and global optimization methods for optical line control based on quality of transmission

The ever-increasing demand for data traffic in recent decades has pushed network operators to give importance to the aspect of infrastructure control to facilitate its scalability and maximize its capacity. A generic lightpath (LP) is deployed starting from a traffic request between a given pair of nodes in a network. LPs are operated in the network based on an estimate of the quality of transmission (QoT), which is derived from the physical layer characteristics of a selected route. Regardless of the model used to estimate QoT, it is necessary to calibrate the model to maximize its accuracy and define minimum design margins. The model calibration process depends significantly on the type of data that can be collected in the field (i.e., type of metric, resolution) and therefore on the available monitoring devices. In this work, a systematic evaluation of the QoT estimation is carried out on a multi-span erbium-doped-fiber-amplified optical line system (OLS) using in the first case only total power monitors and in the second experimentally emulating optical channel monitors (OCMs). Given the type of monitoring devices available, three different physical models are calibrated, and six optimization methods are used to define the optimal configuration of the target gain and tilt parameters of the optical amplifiers, jointly optimizing the working point of all amplifiers (global approach) or proceeding span by span (local approach). Subsequently, the OLS was set in each configuration obtained, and the generalized signal-to-noise ratio (GSNR) profile was measured at the end.

Open Networking Engine (ONE): An Orchestration Tool for Open Optical Line System

Open Networking Engine (ONE): An Orchestration Tool for Open Optical Line System

Spatially disaggregated model for self-channel interference in mixed fiber optical network segments

Coherent transmission technology is widely diffused in backbone optical uncompensated transmission (UT) network segments. In metro and access network segments instead, intensity-modulated direct detected channels at 10 Gbps requiring inline dispersion compensation units (DCUs) are still largely used. While the transition to coherent technology is foreseen in the dispersion managed (DM) network segment, in the meantime, it would be handy to route coherent channels through DM network segments to fully exploit deployed hardware and available spectrum and improve network flexibility. In this scenario, self-channel interference (SCI) exhibits strongly coherent accumulation due to the presence of DCUs, which is not well modeled by traditional analytical models for non-linear interference estimation used for path feasibility assessment. In this paper, we propose a semi-analytical model able to fill this gap. We show that our proposed model is able to provide a quick, conservative estimation of the SCI noise on both UT and DM systems, including optical line system configurations with mixed fiber types.

Channel power equalization based on joint optimization of EDFA and ROADM configuration in open optical network.

Deterioration of the signal-to-noise ratio (SNR) is an important challenge in ultra-long multi optical line system (OLS) optical transmission systems. The non-uniform gain and cascading of the Erbium-doped fiber amplifier (EDFA) lead to SNR deterioration in transmission systems. In this paper, we propose two channel power equalization methods based on joint optimization of EDFA and Reconfigurable optical add-drop multiplexer (ROADM) configurations: 1) reinforcement learning (RL)-based channel power equalization (RL-PE) and 2) covariance matrix adaptive evolution strategy (CMA-ES) channel power equalization (CMA-PE). The simulation results indicate that the power equalization effect was improved by 1.9 dB through the CMA-PE method, while the RL-PE method led to a 1.5 dB improvement in an ultra-long 80-channel 7-OLS transmission system.

Advanced DSP-Based Monitoring for Spatially Resolved and Wavelength-Dependent Amplifier Gain Estimation and Fault Location in C+L-Band Systems

The development of efficient anomaly detection schemes is a key element for the implementation of autonomous optical networks as they can help telecom operators to automate the location of defective devices and track the overall performance of the network infrastructure. In that regard, the exploitation of receiver based digital signal processing (DSP) for optical performance monitoring has shown to be a promising enabler for detection of spatially resolved and wavelength-dependent properties and anomalies in optical fiber links. In this work, we study the benefits of applying DSP-based longitudinal power estimation on multiple wavelength division multiplexing (WDM) channels allocated in the optical grid to infer wavelength-wise characteristics of a C+L-band optical line system. In that context, we show that the applied scheme can successfully recreate a visualization of the spatial evolution of the gain tilt imposed by in-line optical amplifiers. Additionally, we propose the utilization of advanced DSP tools based on wavelet-denoising to enhance the performance of an anomaly detection approach. The proposed method not only can improve accuracy of the fault location, by reducing positioning uncertainty, but it also delivers more uniform readings of the anomaly signatures.

Iterative supervised learning approach using transceiver bit-error-rate measurements for optical line system optimization

Defining the working points of optical amplifiers is a key factor when managing optical networks, particularly for the quality of transmission (QoT) of deployed connections. However, given the lack of knowledge of physical layer parameters, in many cases operators use these infrastructures suboptimally. In this work, a methodology is presented that optimizes the QoT of an optical line system (OLS) by setting the working points of the erbium-doped fiber amplifiers (EDFAs), by analysis of simulations that use synthetic data derived from experimental characterization of commercial devices. The procedure is divided into three phases: a physical layer characterization, a design process, and an iterative supervised learning approach. Within the first phase, a novel amplifier physical layer characterization is used, exploiting a simple EDFA model that allows an efficient estimation of the OLS behavior, knowing only the setting operative ranges of the devices. The results show that the satisfactory outcome produced during the design phase is further improved by the iterative supervised learning approach. The latter approach is implemented for single OLSs between couples of adjacent reconfigurable optical add and drop multiplexers, each equipped with a certain set of transceivers, enabling the QoT estimation of the specific OLS.

Demonstration of ML-Assisted Soft-Failure Localization Based on Network Digital Twins

In optical transport networks, failure localization is usually triggered as a response to alarms and significant anomalous behaviors. However, the recent evolution of network control and management leveraging software-defined networking (SDN) and streaming-based telemetry opens up new possibilities for automated methods that can localize even subtle anomalies, the so-called soft failures. This paper reports the experimental demonstration of a machine-learning-based soft-failure localization framework in a small-scale laboratory setup. The SDN telemetry setup includes ONOS-controlled transponders using NETCONF and an optical line system (OLS) providing telemetry via an OLS domain controller. A shallow artificial neural network (ANN) accomplishes ML-based failure localization with principal component analysis to reduce non-essential information. The ANN is trained by combining field data and synthetic data generated in a digital network twin. The field data trains the ANN to tolerate statistical variations in the network telemetry without failures, while the synthetic data generates artificial single-failure scenarios. We show that the soft-failure localization scheme successfully pinpoints the faulty element in all single failures generated in transponders, fibers, and amplifiers. We also demonstrate the system’s ability to deal with double-failure scenarios.

Modelling non-linear interference in non-periodic and disaggregated optical network segments

We investigate the generation of nonlinear interference (NLI) within two disaggregated transmission scenarios, each considering a chain of three distinct optical line systems that contain fibers with different dispersion values, with 400G-ZR+ 64 GBd transmission simulated using the split-step Fourier method. Firstly, by separating the NLI into its main constituents: the self- and cross-phase modulations, we investigate the impact of accumulated dispersion upon NLI generation and compensate for the coherent accumulation of the former to produce a model that is fully spectrally and spatially separable, including for alien wavelengths. Considering ideal and optimized in-line amplification, we calculate the amplified spontaneous emission noise and combine this value with the recovered NLI to obtain the generalized signal-to-noise ratio. We show that this disaggregated model provides accurate and conservative results for both transmission scenarios, showing that abstracting these signals with a Gaussian noise approximation always results in a conservative prediction, even for non-uniform fiber dispersion scenarios.

Power control strategies and network performance assessment for C+L+S multiband optical transport

Spatial-division multiplexing (SDM) and band-division multiplexing (BDM) have emerged as solutions to expand the capacity of existing C-band wavelength-division multiplexing (WDM) optical systems and to deal with increasing traffic demands. An important difference between these two approaches is that BDM solutions enable data transmission over unused spectral bands of already-deployed optical fibers, whereas SDM solutions require the availability of additional fibers to replicate C-band WDM transmission. On the other hand, to properly design a multiband optical line system (OLS), the following fiber propagation effects have been taken into account in the analysis: (i) stimulated Raman scattering (SRS), which induces considerable power transfer among bands; (ii) frequency dependence of fiber parameters such as attenuation, dispersion, and nonlinear coefficients; and (iii) utilization of optical amplifiers with different doping materials, thus leading to different characteristics, e.g., in terms of noise figures. This work follows a two-step approach: First, we aim at maximizing and flattening the quality of transmission (QoT) when adding L- and -bands to a traditional WDM OLS where only the C-band is deployed. This is achieved by applying a multiband optimized optical power control for BDM upgrades, which consists of setting a pre-tilt and power offset in the line amplifiers, thus achieving a considerable increase in QoT, both in average value and flatness. Second, the SDM approach is used as a benchmark for the BDM approach by assessing network performance on three network topologies with different geographical footprints. We show that, with optical power properly optimized, BDM may enable an increase in network traffic, slightly less than an SDM upgrade but still comparable, without requiring additional fiber cables.

Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks

Increasing traffic demands are causing network operators to adopt disaggregated and open networking solutions to better exploit optical transmission capacity, and consequently enable a software-defined networking (SDN) approach to control and management that encompasses the WDM data transport layer. In these frameworks, a quality of transmission estimator (QoT-E) that gives the generalized signal-to-noise ratio (GSNR) is commonly used to compute the feasibility of transparent lightpaths (LP)s, taking into account the amplified spontaneous emission (ASE) noise and the nonlinear interference (NLI). In general, the ASE noise is the main contributor to the GSNR and is also the most challenging noise component to evaluate in a scenario with varying spectral loads, due to fluctuations in the optical amplifier responses. In this work, we propose a machine learning (ML) algorithm that is trained using different ASE-shaped spectral loads in order to predict the OSNR component of the GSNR; this methodology is subsequently used in combination with a QoT-E in the lightpath computation engine (L-PCE). We present an experiment on a point-to-point optical line system (OLS), including 9 commercial erbium-doped fiber amplifiers (EDFA)s used as black-boxes, each with variable gain and tilt values, and 8 fibers that are characterized by distinct physical parameters. Within this experiment, we receive the signal at the end of the OLS, measuring the bit-error-rate (BER) and the power spectrum, over 2520 different spectral loads. From this dataset, we extract the expected GSNRs and their linear and nonlinear components. Through joint application of a ML algorithm and the open-source GNPy library, we obtain a complete QoT-E, demonstrating that a reliable and accurate LP feasibility predictor may be implemented.

Simulative assessment of non-linear interference generation within disaggregated optical line systems

Lightpaths within optical line systems (OLS)s that deploy coherent optical technologies are mainly impaired by two additive Gaussian disturbances: the amplified spontaneous emission (ASE) noise from the optical amplifiers and the non-linear interference (NLI) from fiber propagation, together with some amount of phase noise, typically compensated for by the carrier phase estimator module within the digital signal processing (DSP) unit. The main obstacle in accurately modelling the physical layer of a disaggregated optical network arises from the spatially-coherent and spectrally-aggregated general behavior of the NLI generation. Within this paper, we perform an accurate split-step Fourier method (SSFM) physical layer simulation campaign over a wide range of fiber chromatic dispersion values that range from 2 to 16.7 ps / (nm·km) and channel symbol rates from 32 GBd to 85 GBd. For all the explored scenarios, we first show that the NLI generation in an OLS can be spectrally disaggregated in a practical manner by considering a superposition of self-channel (SC) and cross-channel (XC) NLI components only. Secondly, by considering the span-by-span generalized signal-to-noise ratio (GSNR) deterioration, we show that the XC-NLI accumulation components can also be considered as spatially disaggregated, leaving the SC-NLI as the only spatial coherency contribution. Consequently, by appropriately managing these coherent NLI contributions, we find that it is possible to produce a conservative physical layer model that is both spectrally and spatially disaggregated.

Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers

An ultra-fast machine learning based method for accurate predictions of gain and amplified spontaneous emission (ASE) noise profiles of Raman amplifiers is introduced. It is an alternative to high-complexity and time-consuming standard approaches, which are based on the numerical solution of sets of nonlinear differential equations. Main relevance resides on its possible application in real-time network controllers for future multi-band optical line systems where Raman amplification will be required to cope with capacities beyond the standard C–band. Here we consider as an example the C+L–band scenario with different input load conditions: full load and partial loads. For the case of full load it has been recently shown a neural network (NN) capable of highly accurate predictions. Real optical networks are not usually operated only in full load conditions: the load can dynamically vary over time and the behavior of the Raman amplifier depends on it. In this article we introduce a new NN model and we show its higher accuracy when the line system is not fully loaded: we define it as the load aware neural network. Applying this new approach we can predict both gain and ASE noise profiles in Raman amplifiers with high accuracy under any load conditions: we demonstrate almost 100% of maximum prediction errors to be lower than 0.5 dB.

Privacy-Preserving Multilayer In-Band Network Telemetry and Data Analytics: For Safety, Please do Not Report Plaintext Data

With the evolution of Internet infrastructure and network services, multilayer in-band network telemetry (ML-INT) and data analytics (DA) have been considered as key enabling techniques to realize real-time and fine-grained network monitoring, especially for backbone IP-over-Optical networks. However, the existing ML-INT&DA systems have privacy and security issues, because plaintext ML-INT data is reported from the data plane and gets analyzed in the control plane. In this work, we address these issues by designing a privacy-preserving ML-INT&DA system for IP-over-Optical networks. We first leverage vector homomorphic encryption (VHE) to design a lightweight encryption scheme, which overcomes the security breaches due to eavesdropping and preserves the delicate correlations buried in multi-dimensional ML-INT data. Then, we develop an effective data compression scheme to further encode the encrypted ML-INT data and make the results suitable for hash-based signature. The signature is for data certification and enables the DA in the control plane to verify the integrity of received ML-INT data. Hence, the threats from data tampering are removed. Next, we architect a deep learning (DL) model that can directly operate on encrypted ML-INT data for anomaly detection. Finally, we implement the proposed ML-INT&DA system, and experimentally demonstrate its effectiveness in a real IP over elastic optical network (IP-over-EON) testbed, whose key elements, i.e., optical line system (OLS), bandwidth-variable wavelength-selective switches (BV-WSS’) and programmable data plane (PDP) switches, are all commercial products.

Open and disaggregated optical transport networks for data center interconnects [Invited

In this paper, we present Alibaba’s practices and views on open and disaggregated optical transport networks, with a focus on metro data center interconnects (DCIs). We first discuss technology developments that have enabled open optical transport networks, followed by a presentation of our open optical network architecture for DCI networks. We then describe an overall data center network architecture and the application scenario of open and disaggregated DCI technology. Details of the point-to-point DCI network architecture and the need for reconfigurable optical add–drop multiplexers (ROADMs) in DCI networks are then discussed. We present the data models of DCI equipment, including optical terminal transponders, point-to-point optical line systems, and ROADMs, developed based on OpenConfig YANG models. The detailed design and implementation of a home-grown control and management software platform for DCI networks are also presented. We conclude with a discussion of the real deployment of the technology in Alibaba’s networks.

Using machine learning in an open optical line system controller

The reduction of system margin in open optical line systems (OLSs) requires the capability to predict the quality of transmission (QoT) within them. This quantity is given by the generalized signal-to-noise ratio (GSNR), including both the effects of amplified spontaneous emission (ASE) noise and nonlinear interference accumulation. Among these, estimating the ASE noise is the most challenging task due to the spectrally resolved working point of the erbium-doped fiber amplifiers (EDFAs), which depend on the spectral load, given the overall gain profile. An accurate GSNR estimation enables control of the power optimization and the possibility to automatically deploy lightpaths with a minimum margin in a reliable manner. We suppose an agnostic operation of the OLS, meaning that the EDFAs are operated as black boxes and rely only on telemetry data from the optical channel monitor at the end of the OLS. We acquire an experimental data set from an OLS made of 11 EDFAs and show that, without any knowledge of the system characteristics, an average extra margin of 2.28 dB is necessary to maintain a conservative threshold of QoT. Following this, we applied deep neural network machine-learning techniques, demonstrating a reduction in the needed margin average down to 0.15 dB.

Optimized Design and Challenges for C&L Band Optical Line Systems

Optical communications are the key enabler for the ever-increasing traffic growth as they allow multiplexing a number of carriers onto a single optical fiber. However, network deployments are becoming more challenging due to the large bandwidth increments. Indeed, the fiber planning process is a slow, costly, and strategic activity for network operators, who must decide where to rent or deploy additional capacity. Depending on the long-term view, the network operator will define a fiber plan which may require high capital expenditure (CAPEX) investments to deploy additional multiple fiber pairs between central offices. During the last few years, C&L-band technologies have evolved to a point where they are now strong candidates to extend the fiber lifetime of these high capacity links. This article aims to present the evolution of C&L-band systems from a network design perspective, covering transmission and optical amplifier capabilities, demand optimization phase, showing a record demonstration of 56.4 Tb/s C&L-band 800 km transmission using commercial C&L-band system technology.

OpenConfig and OpenROADM Automation of Operational Modes in Disaggregated Optical Networks

OpenConfig and OpenROADM are emerging as the most relevant initiatives to support partial disaggregation, in which the optical line system is provided by a single vendor while transponders can be provided, in pairs, by different vendors. This way, vendor lock-in is eliminated at the transponder level, without significantly impacting the transmission performance. Although the above initiatives have defined YANG models reaching a good level of maturity, there are still open issues that prevent the full deployment of vendor-neutral partially disaggregated solutions. Among these, the candidate transmission modes in a transponder, including proprietary solutions, are exposed in the YANG models as opaque attributes called Operational (OP) Modes, thus limiting the awareness and the effectiveness of the SDN controller in the selection of the most appropriate transmission mode. In this work, we focus on the open issues related to the configuration and adaptation of transmission parameters. In particular, we focus on the OpenConfig concept of OP Mode enabling the abstraction of transmission complexity but currently preventing an SDN controller to manage transponders in a fully vendor-neutral way. This work first estimates, through a simulative study, the network performance benefit that is achievable by optimizing the OP mode selection. Then, a telemetry-based automated solution is proposed, designed and implemented to enhance the OP mode concept in case of both provisioning and adaptation scenarios (e.g., upon failure), also considering the impact in the tributary/client network. In particular, the following components have been designed and implemented: (i) software agent for OpenConfig transponders; (ii) software agent for OpenROADM line systems, (iii) an automatic telemetry-assisted monitoring handler; and (iv) SDN control procedures implemented in the ONOS Controller. The proposed components and comprehensive solution have been evaluated in a network testbed encompassing multi-vendor network elements, successfully demonstrating a full vendor-neutral partially disaggregated provisioning and recovery operations.

Disaggregated edge-enabled C+L-band filterless metro networks

The recent interest in the upgrade and enhancements of metro transport networks and the availability of transponder cards with coherent receivers is opening the way to filterless solutions employing only passive splitters/couplers and optical amplifiers, potentially achieving significant capital expeditures and operating expenditures savings. However, the filterless option suffers from inefficiencies, mainly due to the broadcasting constraint and the reduced optical reach. To overcome such limitations, this paper proposes three complementary strategies to upgrade optical filterless metro networks (FMN). First, the number of supported channels is incremented by exploiting the full C + L -band. To this end, two design architectures (i.e., Single and Dual Region) are proposed and evaluated, targeting double capacity with respect to the standard C-band and an upgrade to cost reduction. Second, we investigate a dual-architecture solution, extending metro deployments with a low-cost filterless and unamplified L-band system. Its design trade-offs are evaluated to determine its suitability in providing direct low-latency connectivity between metro-access nodes with the aim of supporting edge-computing platforms. Finally, the flexibility of the FMN is extended by introducing disaggregated transponders with different bitrates (i.e., 100 Gbps and 400 Gbps) and configurable transmission parameters, such as the modulation format and the forward error correction). Such flexibility is exploited through the extension of the OpenConfig YANG model of the optical line system, thus enabling automatic spectrum and transmission parameter assignment by means of a centralized software-defined-network controller and achieving better resource utilization. Simulation and experimental results are provided, showing the effectiveness and the potential impact of filterless metro solutions in future deployments and low-cost network upgrades supporting edge/fog clusters and 5G.