Optical Transport Networks Research Articles

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.

Machine learning models for alarm classification and failure localization in optical transport networks

The increase in computing capacity and the huge amount of available data have significantly accelerated the use of artificial intelligence and machine learning algorithms to solve complex real-life problems. In this context, optical networks can benefit from the power of machine learning algorithms to find innovative solutions to complex problems, in particular, by improving failure management. Failure management is an essential functionality of a network management system (NMS) to avoid degradation of signal quality or even the interruption of service. Existing failure management solutions are based on simple static rules, not suitable for modern optical networks characterized by a complex design and a large number of management parameters. In this paper, we use machine learning algorithms with data extracted from an NMS connected to an experimental setup to perform a rapid diagnosis and locate failures. We compare several machine learning models to identify, in the first step, correlated alarms that are related to the same type of failure—unexpected power attenuation—and to localize, in the second step, the position of this failure in the network. Using the four datasets generated by the experimental setup, results show that for both alarm classification and failure localization steps, the best performance is obtained by the light gradient boosting machine classifier with an F1-score greater than 90% and average precision and area under the curve scores close to 1.

Transparent vs Translucent Multi-Band Optical Networking: Capacity and Energy Analyses

Multi-band optical fiber transmission is generally proposed for capacity upgrades in optical transport networks. To comprehensively assess the potential of multi-band transmission, key metrics such as the potential capacity increase, energy consumption, and the number of required interfaces must be evaluated for different transmission scenarios. We consider progressive spectral exploitation, starting from the C-band only and up to C+L+S+U-band transmission, for both transparent and translucent solutions that exploit optical signal regeneration. By considering accurate state-of-the-art physical layer models, we derive a networking performance metric that enables the comparison of different solutions in terms of capacity allocation and energy consumption. For a translucent network design, different regenerator placement algorithms are compared, with the aim of minimizing energy consumption. The proposed network-wide numerical analysis shows that, for spectral occupations exceeding the C+L-band, translucent solutions can significantly increase network capacity, while leading to a similar energy consumption per transmitted bit as in the transparent design case, but they require the deployment of additional line interfaces. Significantly, these results provide evidence that the transparent exploitation of an additional transmission band produces a capacity increment that is at least comparable to that of a translucent solution based on already-in-use bands. Since this is attained at the expense of fewer line interfaces, it is a key finding suggesting that extending the number of bands supported is a cost-effective approach to scaling the capacity of existing fiber infrastructures.

QoS-aware hybrid cloudlet placement over joint fiber and wireless backhaul access network

Over the last decades, the emerging paradigms, e.g., centralized mobile cloud computing (MCC), multi-access edge computing (MEC), and collaborative computing offloading (CCO), have attracted extensive attention in adaptive 5G low latency communication for Tactile Internet. Besides, a novel integrated wireless optical transport network emerges in access networks and supports joint fronthaul and backhaul services. Therefore, considering that the task offloading response time, the needs of proximity, and the ultra-dense 5G small cell deployment, a novelty hybrid cloudlet deployment scheme over fiber and wireless backhaul-aware infrastructure is created with efficient cost optimization in mind. In this work, the envisioned Fiber Wireless networks (FiWi) consists of optical backhaul and wireless fronthaul (i.e., integrated access network and backhaul link), whereby base stations (BSs) with fiber and wireless backhauls are referred as wired-BS (WBS) and unwired-BS (UBS), respectively. Therefore, to meet the quality of service (QoS) delay constraints, cloudlets can be deployed in either UBSs, WBS, or remote node (RN). However, we apply mixed-integer line programming (MILP) to resolve the convex optimization problem in terms of minimization deployment cost. Besides, we describe the QoS-aware hybrid cloudlet placement cost algorithm for WBS and UBS coverage areas against different network conditions. It was shown through experimental measurements that the proposed architecture can achieve the scalability in different deployment scenarios. Also, the dependency of minimization cloudlet deployment cost on variable network parameters in terms of user density, network framework, and network QoS can be validated.

Characterization of the optical spectrum as a service

Encouraged by the emerging disaggregation trend in optical transport networks, operators are willing to offer flexible and future-proof optical spectrum as a service (OSaaS) instead of classical capacity services. To define this modern service in commercial offerings, a commonly accepted, reliable, and easy-to-use service characterization method has to be developed. This is explicitly challenging in terrestrial brown-field, black-box scenarios, where open line system (OLS) data are unavailable or outdated to employ calculation or simulation-based methods. The objective of this work is to identify a characterization method that reliably captures wavelength-dependent performance variations, possible filtering penalties, and the operation regime of the optical spectrum services. First, all OSaaS configuration options in the OLS are systemized into four primary configurations, and their operational aspects are discussed. Then, two of the most used OSaaS configurations are tested in the lab and in a live network environment, providing links up to 5738 km in length. Generalized signal-to-noise ratio profiles are captured with a channel probing method, using a single-probe sweep and multiple simultaneous probes. Then, the accuracy and suitability of the amplified spontaneous emission loaded optical signal-to-noise ratio profile is tested in a lab environment. Finally, we discuss the required time and hardware resources to characterize the service and bring out the pros and cons for each investigated profile. In conclusion, we propose a single-probe sweep as a reliable and easy-to-use method to characterize the OSaaS in terrestrial brown-field networks.

Optical access evolutions towards SDN and disaggregated hardware: an operator perspective [Invited

In this paper, we review recent advances of the optical access network in the path toward Cloud-computing-related technologies, software defined networking, and network function virtualization. We present the new network management architectures proposed by standardization bodies such as the Broadband Forum and non-profit organizations such as the Open Networking Foundation and propose to use today’s Orange-France Operation Support System as a baseline to discuss different evolution paths. We also provide a review of recent advances in the optical line terminal (OLT) hardware evolution, from white-box OLTs to general purpose hardware, and identify the remaining related challenges, such as energy consumption and interoperability. We also show how Cloud-computing technologies in optical access networks could remove the frontier between network segments (home local area network, optical transport network, and mobile network) by deeply improving the synergy among them.

Accurate QoT estimation for the optimized design of optical transport network based on advanced deep learning model

Increasing traffic demand in the backbone optical network urges the initiation of new solutions that allow an increment in the transmission/network capacity without a considerable rise in service costs. To attain this objective, simpler and quicker network reconfiguration processes is required. A significant issue intrinsically correlated with network reconfiguration is the quality of transmission (QoT) (BER, SNR, Q-factor, chromatic dispersion and polarization mode dispersion) estimation. Predicting the QoT of already established light-paths enable network operators to respond proactively to performance deterioration, which helps in enhancing or sustaining quality of service (QoS). Recently, several deep learning-based approaches have been presented to estimate the QoT of an optical network. This paper proposes an advanced deep learning model comprising a long short-term memory (LSTM) network along with three fully connected layers that leverage the transmission quality metrics collected from the network’s control plane to predict QoT of light-path. The three additional fully connected layers help the model learn the more complex patterns in the data that a basic LSTM cannot learn on its own and improve the model’s performance by three orders of magnitude. Moreover, the proposed model doesn’t require a large data-set to provide high accuracy results, which decreases the control plane’s load. Our model was tested on a data-set that contains data extracted from Microsoft’s optical backbone network in North America. We compare the performance of the proposed model with reference approaches viz; SVM, MLP, NN, and LSTM to show if such models could be considered for predicting the estimated received Q-factor and chromatic dispersion of established light-paths. Results exhibit that the proposed model achieves the best performance across multiple channels of this data-set and even in a case when the size of the training data-set is small. The proposed model provides high prediction accuracy, which is perfect for the next-generation agile optical network.

On the benefits of point-to-multipoint coherent optics for multilayer capacity planning in ring networks with varying traffic profiles

Point-to-multipoint (P2MP) coherent optics using digital subcarrier multiplexing have recently been proposed as a promising new technology to reduce the cost and complexity of optical transport networks, particularly those in metro aggregation scenarios with hub-and-spoke (H&S) traffic patterns. In this paper, we perform a case study on fault tolerant ring networks with more general traffic profiles to assess the benefits of P2MP optics for alternative network scenarios such as those appearing in metro rings. We propose mixed integer linear programming formulations for multilayer capacity planning to minimize total transceiver costs, considering both point-to-point and P2MP transceivers, as well as a heuristic approach for the more complex P2MP case. Results indicate that P2MP optics can bring several advantages for all tested traffic profiles, ranging from pure uniform flows to mostly H&S. Transceiver cost savings are achieved in various cases, greater at higher network loads and for traffic patterns that are mostly H&S. Results also show that P2MP optics prompt a multilayer redesign of the network with significantly reduced spectrum usage and a reduced number of IP hops, resulting in lower optical–electronic–optical and IP processing latency and lower IP layer equipment costs for all traffic profiles.

Advances in SDN control and telemetry for beyond 100G disaggregated optical networks [Invited

This tutorial considers the evolution of software-defined networking (SDN) control for optical transport networks (OTNs) in disaggregated scenarios, focusing on its requirements and challenges when applied to “beyond 100G” networks, a term that jointly refers to the use of coherent technology, data rates beyond 100G, and the evolution of OTN standards to support rates such as 200G, 400G, or 800G. The tutorial covers use-case-driven SDN development, new challenges, and requirements, such as the need to account for physical impairments, multiband/space-division multiplexing control, improved fault/alarm management, and optical telemetry and streaming. Finally, the tutorial provides an overview of new trends such as network sharing.

Operator view on optical transport network automation in a multi-vendor context [Invited

Network automation is the current challenge that operators face to accelerate end-to-end service delivery and improve network operations in the context of rapidly growing capacity needs. Optical networks have long been built using vendors’ turn-key solutions, resulting in complexity for the operator to automate its multi-vendor networks in a consistent way. Questioning the vendors’ lock-in in disaggregating the optical transmission systems (i.e., splitting the system into parts, where each part can be provided by a different vendor) provides advantages (e.g., cost) but also additional complexity. Openness and standardization appear as key for these multi-vendor scenarios. The path is not easy, but it is not a matter of choice: opening and interoperability are obligations that we face because of the need for automation. The journey that we detail in this paper is the one that we think will be sustainable from our side. The opposite, i.e., continue building vendors’ silos and staking per-vendor specialized applications, is not sustainable. We propose a step-by-step approach, starting with the non-disaggregated situation, followed by partial and full disaggregation architectures to the last evolution towards data centric networking. We present and discuss implementations that Orange has been contributing to and identify some gaps the industry should address. We show that current works in the communities of open source, open initiatives, and standardization bodies are addressing all these steps, and in this respect can accelerate the deployment of automation solutions in current and future optical transport networks.

Routing in Elastic Optical Networks Based on Deep Reinforcement Learning

Modern optical network schemes consist of eighty channels involving fixed grids and offering speeds of up to 400 Gbps per channel rendering the allocated bandwidths insufficient during certain timeframes and underutilized during others. These speeds will become inefficient when compared to the accelerated growth of traditional traffic, which threatens to be tripled by 2025. In order to exploit the capacity of elastic optical networks, efficient algorithms are essential. This article assesses Deep reinforcement learning RMCSA algorithms, focused on providing high availability to the allocation of routing, spectrum and modulation resources over a given network topology, in order to determine how to allocate physical resources in optical transport networks and increase capacity while accounting for traffic variability and demands. Reinforcement learning is currently used in many scientific fields for the solution of different problems. In optical networks, this work will use this method to determine the best path and resources for each type of traffic-related demand. Simulations are performed by increasing volumes of variable traffic over a fourteen-node topology. The results show a significant 40% improvement in blocking probability for 22 links with 100 frequency slots compared to current transmission systems. They also reflect low latency, higher speeds, and high availability, which can deliver better quality of service for the end user.

Scalability analysis of machine learning QoT estimators for a cloud-native SDN controller on a WDM over SDM network

Maintaining a good quality of transmission (QoT) in optical transport networks is key to maintaining the service level agreement between the user and the service provider. QoT prediction techniques have been used to assure the quality of new lightpaths as well as that of the previously provisioned ones. Traditionally, two different approaches have been used: analytical methods, which take into account most physical impairments that are accurate but complex, and high margin formulas, which require much less computational resources at the cost of high margins. With the recent progress of machine learning (ML) together with software defined networking (SDN), ML has been considered as another option that could be both accurate and that does not consume as many resources as analytical methods. SDN architectures are difficult to scale because they are usually centralized; this is even worse with QoT predictors using ML. In this paper, a solution to this issue is presented using a cloud-native architecture, and its scalability is evaluated using three different ML QoT predictors and experimentally validated in a real wavelength-division multiplexing (WDM) over spatial-division multiplexing (SDM) testbed.

Innovation and demonstration of optical service unit-based metro-optimized OTN technologies

To efficiently support the transport of sub-1-Gbps services, an optical service unit (OSU)-based metro-optimized optical transport network (M-OTN) technology framework is proposed and described. The proposed M-OTN/OSU technologies have been implemented by major OTN equipment vendors and verified in a network operator’s lab for the first time to our knowledge. Moreover, a multivendor interoperability test has been conducted in a real-time field trial. The experimental results show that M-OTN/OSU has clear advantages over existing SDH VC/packet-enhanced OTN technologies in several key aspects, e.g., low latency, massive connections, and agile hitless bandwidth adjustment. M-OTN/OSU will be valuable for network operators providing high-quality leased line services and video/VR services in modern OTNs with unprecedented scalability and performance.

Performance improvement in high speed optical networks with low cost amplifier configuration

In this paper, the application example of 8 × 100 Gbit/s over 463 km (76.9 dB) optical transport network is realized by using commercial bidirectional Raman amplifier and additional auxiliary laser without any remotely pump amplifier. The positions of the auxiliary laser in the commercial Raman amplifier are compared and optimized experimentally. The results show that 7.3 dB and 10.2 dB more power budget is obtained with the auxiliary laser in the backward direction and forward direction respectively comparing to the situation without any auxiliary laser. The results also show that forward second order Raman amplifier is a good choice in the condition without remotely pump amplifier.

Cost-effective strategies to scale the capacity of regional optical transport networks

The different segments of optical transport networks—including metro, regional, and long-haul—will have to scale to accommodate growing capacity requirements. In metro networks, lower capacity (compared to the other two segments) but more stringent power consumption and interoperability requirements will likely favor the adoption of pluggable coherent interfaces, whereas in long-haul, the best-in-class spectral efficiency of embedded proprietary coherent interfaces is paramount. However, it is less clear which will be the interface type of choice in regional transport networks. This paper compares different strategies to increase the capacity of regional transport networks, which not only exploit the usage of pluggable or embedded interfaces but also enhanced line system solutions, comprising the SuperC-band and hybrid Raman/erbium-doped fiber amplification. Simulation results obtained in two reference regional-sized networks highlight that the utilization of embedded coherent interfaces enables a considerably higher traffic load to be supported over the existing fiber infrastructure, even when the usage of pluggable interfaces is complemented with exploiting the SuperC-band and hybrid amplification. Moreover, for the same transported traffic load, the simulation results also show that embedded interfaces can reduce by up to half the number of line interfaces required, when compared to pluggable interfaces.

4G to 6G: disruptions and drivers for optical access [Invited

The exponential growth of diverse smart mobile applications such as smart monitoring, holograms, and autonomous vehicles are driving mobile technologies toward a more intelligent and software-defined communication system. This growing demand has led to the next giant leap in next-generation wireless communication technology, i.e., sixth-generation (6G) mobile technology, which anticipates providing 1 Tbps data rates and ultralow latency over ubiquitous 3D coverage areas. However, the transport network, which connects hundreds of thousands of cell sites and the network core to enable intelligence, virtualization, and other key features of 6G, has not advanced enough to cater the dense cell deployment expected in future 6G networks. Because many aspects of 6G remain undefined, this provides us with the opportunity to take optical transport networks into design consideration to realize the benefits that 6G has to offer. To this end, in this paper, we present a comprehensive view on the role of optical access networks in supporting fourth-generation (4G), fifth-generation (5G), and beyond wireless technologies. In particular, we discuss design and deployment strategies and their challenges when using optical access as a transport solution during the evolution of wireless access technologies. For this purpose, we first identify how an optical access network is used to support high-capacity transport networks in 4G, followed by the challenges that 5G brought into optical transport networks and its diverse solutions. We conclude the paper by providing insights into how an optical transport network can be designed to support 6G.

Enabling Optical Network Technologies for 5G and Beyond

We review a series of innovative optical network technologies for 5G and beyond mobile networks, enabling high-throughput mobile any-haul (x-haul) via wavelength-division multiplexing, bandwidth-efficient mobile front-haul via hybrid digital-analog radio-over-fiber, Shannon-limit-approaching long-haul transmission for core networks via advanced coding and probabilistic constellation shaping, low-latency 50-Gb/s passive optical network for cost-effective x-haul traffic aggregation, and service-enabling optical transport network capable of bandwidth-guaranteed network slicing with fine granularity. The vision and main application scenarios of the 5^th generation fixed network (F5G) are also discussed. With its capability to support enhanced fixed broadband, guaranteed reliable experience, full fiber connection, energy-efficient broadband communication, real-time broadband communication, and harmonized communication and sensing, F5G is well positioned to not only support mobile networks, but also complement them to jointly meet the ever-increasing communication demands in the era of 5G and 6G.

Potential failure cause identification for optical networks using deep learning with an attention mechanism

With a focus on failure management in optical networks, we propose a potential failure cause identification scheme using an attention mechanism for optical transport network boards, leveraging actual datasets from a network operator. The attention mechanism allows the model to dynamically pay attention to only certain input information that is closely related to the target task (failure prediction), which can be effectively applied to identify the potential cause of the failure. In this paper, two typical attention mechanisms are comparatively studied to obtain the attention weights, which are additive attention and dot-product attention. A bi-directional long short-term memory network is selected as the failure prediction model due to its superior performance in time-series processing cases, which can capture bi-directional input information. Experimental results show that the average accuracy, F1 score, and false negative and false positive rates of the proposed scheme are 98.73%, 97.19%, 2.6%, and 0.91%, respectively. Moreover, based on the attention weight, it is confirmed that the highest-relevance input feature for equipment failure is the average value of input optical power, which may be caused by disconnection of the receiving port of the board or fiber cut of the adjacent link; the next most relevant feature is the minimum value of the environmental temperature, which may be caused by a broken fan or overheated chip. It is proven that the proposed scheme can not only find potential failure causes but also improve the performance of the failure prediction model, which is significant for optical networks realizing failure diagnosis and recovery.

QoS-Aware Multicast for Crowdsourced 360° Live Streaming in SDN Aided NG-EPON

The IEEE 802.3ca Task Force group has recently approved the standardization of the next-generation Ethernet passive optical network (NG-EPON) to satisfy future bandwidth demands. NG-EPON architecture classifies the optical network unit (ONU) transmission to allow simultaneous Wavelength-Agile (WA-PON) transmission, which increases the network capacity (50 Gb/s) and flexibility. The recently launched 5G services and related functional standards, new capabilities, and quality of service (QoS) imposed on the optical transport network. The Peer-to-peer (P2P) based live streaming application is the most popular multimedia application in access networks, i.e., Facebook Live, YouTube Live, Twitch, and other streaming and video conferencing services are increasing, especially in global epidemics which requires a higher QoS. Hence, media-based applications require adequate infrastructure resources to meet customer demands such as high bandwidth, low latency, high quality audiovisual, live streaming, and services. Therefore, this paper proposes an SDN enhanced P2P virtual reality (VR) live video streaming application NG-EPON multicast architecture based on 5G-enabled Tactile Internet (TI) and aims to improve the QoS, QoE and provide flexible services to the end-users. Finally, we proposed an SDN-based TI-dynamic wavelength and bandwidth allocation (SD-TI-DWBA) mechanism. Simulation results show that our proposed architecture can improve the system performance and QoS metrics regarding packet delay, jitter, system throughput, and packet loss.

Optimization of routing and wavelength optimization algorithm for optical transport network based on reinforcement learning

Optimization of routing and wavelength optimization algorithm for optical transport network based on reinforcement learning