Optical Transport Networks Research Articles

Optimal Path Calculation for Multi-ring Based Packet–Optical Transport Networks

Multi-domain optical transport networks are inherently non-interoperable and require integrated orchestration and path provisioning mechanisms at the network-wide level. Moreover, ensuring the network’s survivability is a critical issue. While the MPLS-TP (multi-protocol label switching-transport profile) defines various protection and recovery mechanisms as standards, it does not address methods for calculating and selecting protection and recovery paths. Therefore, an algorithm is needed to calculate and set up paths to ensure quick protection and recovery across the entire integrated network, minimizing conflicts in protection and recovery at the packet optical integrated network level. In this paper, we proposed an algorithm that calculates and sets a path that enables rapid protection and recovery in an MPLS-TP network composed of a multi-ring-mesh topology. To this end, this study proposes the concept of a transparent node (T-node) for calculating link-disjoint SPF (shortest path first) in a multi-ring network with dual or more rings. A T-node is a node in a ring with more than a dual ring and indicates that the node has been used once in route calculation. Therefore, during path calculation, a T-node can be used as a source node and an intermediate node but not as a destination node.

(OFC 2024) An Intent-Based Control and Management Framework for Optical Transport Networks supporting 6G Services Empowered by Large Language Models

(OFC 2024) An Intent-Based Control and Management Framework for Optical Transport Networks supporting 6G Services Empowered by Large Language Models

A Novel Framework for Optical Layer Device Board Failure Localization in Optical Transport Network

A Novel Framework for Optical Layer Device Board Failure Localization in Optical Transport Network

Multi-layer resource scheduling architecture and algorithm for a service-oriented optical network based on a fine grain OTN

With the development of service, there are demands for flexible connection scheduling based on customer-side services, fast connection response, and efficient scheduling of line-side resources in an optical transport network (OTN). This article proposes a service-oriented multi-layer resource scheduling architecture and algorithm based on the introduction of OTN fine grain technology. Specifically, we propose a service awareness scheme that enables OTNs to better match service requirements. Based on a centralized distributed collaborative architecture, we extend a path computation element communication protocol (PCEP) to achieve more efficient connection distribution. We further design a resource scheduling strategy and an algorithm based on multi-layer collaboration to perform differentiated scheduling for services with different priorities. Through the mechanisms and algorithms proposed, it is technically feasible to achieve precise matching of customer-side services, flexible scheduling of connections and resources, and efficient service response, and promote the evolution of the OTN service supply from traditional fixed point-to-point connections to flexible service-oriented optical network (SOON) connections.

High-speed optical channels of advanced optical transport networks OTN/DWDM. Part 2: Communication range, spectral efficiency and symbol rate.

The series of articles presents analytical materials on the throughput and evolution of modulation formats of OTN/DWDM optical channels (part 1), on the communication range, spectral efficiency and symbol rate of OTN/DWDM optical channels (part 2).

Coexistence of 11 Tbps (110×100 Gbps) classical optical communication and quantum key distribution based on single-mode fiber.

Integrating quantum key distribution (QKD) with classical optical communication is a deployment-friendly and cost-effective approach to advancing QKD network implementation. However, the noise introduced by intense classical signals into the quantum channel severely impacts the performance of the QKD system, presenting challenges for long-distance coexistence transmission. In this paper, we successfully demonstrate the simultaneous propagation of QKD and optical transport network (OTN) in the C-band over distances exceeding 100 km. We establish a coexistence transmission system based on a commercial OTN equipment and QKD device equipped with the Fabry-Perot filters, demonstrating the feasibility of combining QKD with 11 Tbps (110×100 Gbps) classical data over a 101.86 km fiber. To investigate longer transmission distances, we employ an optimized wavelength allocation method and conduct the co-propagation of QKD and 1 Tbps classical data over a 152.34 km fiber. To the best of our knowledge, these results are the first time that report the integration of QKD and large-capacity classical communication over hundred-kilometer scale fiber. Our works represent a significant advancement in developing OTN networks incorporating QKD systems.

End-to-End Data Flows Management in the Decentralized 5G/6G Mobile Networks

The evolution of 5G and the anticipated emergence of 6G networks demand significant enhancements in optical backhaul infrastructure to support higher bandwidth, low latency, and increased reliability. Considering a wide range of feasible applications with different quality requirements, the key challenge for the underlying backhaul and optical transport network is in the high load variance in the optical switching nodes and complicated data flows management. In this paper, a novel approach is proposed for end-to-end data flows management in decentralized 5G/6G mobile networks, which are interconnected by the optical burst switching transport infrastructure. The key idea is to train a deep recurrent neural network over a real network statistic obtain at the different network segments and service slices. Then, predictions made by deep neural network are used for predictive resource allocation in each node of the optical burst switching network to ensure a target quality of service for each end-to-end data flow. The experimental results show that proposed approach provides 90% accuracy of predictions and allows to effectively utilize the resources of optical network.

High-speed optical channels of advanced optical transport networks OTN/DWDM

The series of articles presents analytical materials on the throughput and evolution of modulation formats for OTN/DWDM FOTS optical channels; in terms of communication range, spectral efficiency and symbol rate of OTN/DWDM FOTS optical channels.

Joint Bandwidth and Key on Demand (BKoD) Provisioning for Dynamic Service of Optical Transport Networks in F6G

Joint Bandwidth and Key on Demand (BKoD) Provisioning for Dynamic Service of Optical Transport Networks in F6G

AlarmGPT: an intelligent alarm analyzer for optical networks using a generative pre-trained transformer

The proliferating development of optical networks has broadened the network scope and caused a corresponding rise in equipment deployment. This growth potentially results in a significant number of alarms in the case of equipment malfunctions or broken fiber. Managing these alarms efficiently and accurately has always been a critical concern within the research and industry community. The alarm processing workflow typically includes filtration, analysis, and diagnostic stages. In current optical networks, these procedures are often performed by experienced engineers, utilizing their expert knowledge and extensive experience. This method requires considerable human resources and time, as well as demanding proficiency prerequisites. To address this issue, we propose an intelligent alarm analysis assistant, “AlarmGPT,” for optical networks, utilizing a generative pre-trained transformer (GPT) and LangChain. The proposed AlarmGPT exhibits a high level of semantic comprehension and contextual awareness of alarm data, significantly enhancing the model’s ability of interpreting, classifying, and solving alarm events. Through verification of extensive alarm data collected from real optical transport networks (OTNs), the usability of AlarmGPT has been validated in the tasks of alarm knowledge Q&A, alarm compression, alarm priority analysis, and alarm diagnosis. This method has the potential to significantly reduce the labor and time required for alarm processing, while also lowering the experiential requisites incumbent upon network operators.

Automated fiber switch with path verification enabled by an AI-powered multi-task mobile robot

As the capacity of optical transport networks undergoes significant growth, there is an ongoing discussion on how to effectively leverage both spectral and spatial degrees of freedom to scale future network capacity. This paper presents an artificial intelligence (AI)-powered multi-task robot comprising a collaborative robotic arm and a mobile robotic base designed for optical network automation. The robot demonstrates the capability of direct fiber switching, establishing static fiber links that consume zero power and have minimal insertion loss from fiber connectors. As a precautionary measure before physically switching fiber cables, the robot performs path verification by detecting robot-driven events using real-time coherent receivers, aiming to avoid accidental unplugging. Additionally, the robot showcases its mobility by efficiently navigating between different network racks and rooms while executing various tasks. Implementing the automation of network operations using robots has the potential to reduce both capital and operational expenditures.

Fault Identification in Optical Transport Network Using CNN

The growing diversity Regarding the provision of information and services transmitted Using optical technology transport networks (OTN) has made network survival a crucial issue in current study. Fault detection refers to the process of identifying faults that arise from various issues such as packet loss, disconnection, and others in the OTN. The determination of the fault location in an OTN is highly significant in the analysis of the resilience of optical networks. This study presents a fault diagnosis system that utilises a Convolutional Neural Network (CNN), focusing primarily on distinguishing between hard faults (HF) and soft faults (SF). The CNN is implemented in where the fault is situated domain of OTN to determine the presence or absence of potential fault locations. The notion An F-Measure is implemented in order to quantify the impact of positioning.utilising Location, time, mean squared error (MSE), and F-measure. The scientific investigation demonstrates that the suggested CNN neural network achieves the highest performance. The suggested CNN has a reduced localization time and achieves an F1-score of 0.98 after 85 iterations. This level of accuracy and real-time performance fulfils the requirements for fault identification. Hence, there is significant potential and practical utility in incorporating neural networks in the field of identifying and locating faults in optical transport networks.

Modelling of Timestamp Processing within Transport Network Equipment

The modern telecommunication networks are forming on the basis of different transmission and data processing technologies. High load on the transport layer nodes demands quality synchronization of equipment that works on it. The transport network is an important element which delivers the time scale to slave nodes. Using the time-division multiplexing for the high-speed channel processing with the use of wavelength division multiplexing (OTN, Optical Transport Network) and classic Ethernet networks as a transport layer systems needs the precision time synchronization which can be achieved with the use of Precision Time Protocol (PTP). The modeling apparatus of PTP messages exchanging within OTN multiplexers and IP-network based on the routers are suggested. The packet transferring through the network are given as sequence of simple processing steps. The impact of a network load on PTP information processing capacity on the routers and efficiency of the Forward Error Correction algorithm for OTN equipment are analyzed. The experimental distribution of variable delays on that network elements are given.

Long-term upgrade strategies in multiband and multifiber optical transport networks

Flexible-grid optical transport networks (OTNs) are currently undergoing a massive scale upgrade to bandwidth variable transceivers (BVTs) to cater to the exponential Internet traffic growth. Network operators seeking to plan future capacity in a scalable manner can also add additional bands of operations or additional fibers in the OTN. We propose two strategies for planning multiband upgrades in OTNs, which use an integer linear programming (ILP) solution and a heuristic solution with a reinforcement learning (RL)-based extension. Network simulation studies on three differently sized OTNs show that the heuristic and its RL-based extension perform extremely close to the optimal ILP-based solution while reducing execution times drastically. Finally, a techno-economic study based on what we believe to be a novel cost model highlights the cost-per-bit-per-second savings through carefully planned upgrades.

Investigating optical transport network performance: A recurrence plot approach

Investigating optical transport network performance: A recurrence plot approach

Cost minimization of multi-class demands groomed over multi-rate OTN interfaces considering WDM-layer constraints

This paper introduces a practical comprehensive approach to optimize the cost of installed Optical Transport Network (OTN) interfaces, as well as the required number of wavelengths in the substrate Wavelength Division Multiplexing (WDM) optical network. The approach assumes grooming variable-bit-rate traffic demands on multi-rate OTN interfaces. We propose an Integer Linear Programming (ILP) formulation to address the combined problem of OTN interface deployment, virtual-network formation, traffic grooming on the established distinct-capacity virtual paths, and virtual network embedding on the WDM physical substrate. The presented model focuses on mitigating the capital expenditure required to deploy OTN interfaces, usually the most costly element in the network, as well as minimizing the required number of wavelengths in the Routing and Wavelength Assignment (RWA) problem, contributing to the overall reduction in network costs and ensuring open capacity for future expansions. As the RWA problem is NP-hard and involves significant computational costs, we also propose a multi-step heuristic approach to tackle larger network instances more efficiently. In this approach, the optimization steps for the OTN design and the RWA solutions are conducted independently. For the OTN-layer planning, the optimum ILP formulation (OTN-ILP) is maintained, so that the optimum solution for the cost of installed interfaces on the OTN layer is guaranteed. The RWA problem is proposed through either an ILP formulation (RWA-ILP) or a heuristic approach. The RWA-ILP model can find the same or slightly superior number of wavelengths compared to the optimum solution of the Integrated ILP formulation, but it can provide results in reasonable times only for small networks. On the other hand, the heuristic RWA is able to run any network size, but it requires more wavelengths compared to the RWA-ILP model.

Semi-supervised learning model synergistically utilizing labeled and unlabeled data for failure detection in optical networks

In optical networks, reliable failure detection is essential for maintaining quality of service. The methodology has evolved from traditional performance threshold-driven approaches to contemporary data-driven AI algorithms, predominantly employing supervised and unsupervised learning. However, with the advent of second-level telemetry, optical transport networks have amassed a wealth of unlabeled performance data, while labeled data remains limited due to the intensive effort required for annotation. In this scenario, to address the challenges of scarce labeled data in supervised learning and the accuracy issues in unsupervised methods, we propose an OpenFE-VIME semi-supervised model. This model synergizes the robustness of supervised approaches with the flexibility of unsupervised approaches. It not only leverages the abundant reservoir of unlabeled data but also addresses the challenges posed by the limited availability of labeled data, enabling reliable and efficient failure detection. Upon evaluation using performance data from OTN node devices in the operator’s optical backbone network, the OpenFE-VIME model demonstrates remarkable performance, achieving an F1-score of 0.947 and accuracy of 0.946, while significantly reducing false negative and false positive rates to 0.073 and 0.035, respectively. Moreover, our research explores the model’s capabilities in utilizing both labeled and unlabeled data and investigates the threshold for training convergence across various data ratios. Additionally, the model’s internal mechanisms and decision-making processes are interpreted using t-SNE visualization, offering enhanced insights into its operational efficacy.

Upgrading strategies for long-haul quantum key distribution networks

Quantum key distribution (QKD) has become a promising option for transmitting sensitive data due to the increased maturity of QKD devices and the threat scalable quantum computers imposes on asymmetric public-key cryptosystems. By utilizing existing infrastructure, e.g., amplifier huts and dark fibers, the cost and complexity of deploying QKD networks (QKDNs) can be reduced. In this study, we develop a topology optimization algorithm that minimizes the cost of the QKDN deployment and maximizes the key capacity between any pair of nodes. We present three deployment upgrade strategies for facilitating the encryption of increasing throughput of QKD-secured optical transport networks and enabling a pay-as-you-grow approach. Comparing different strategies in a capacity-planning study allows operators to assess the scalability of deployments and upgrades. Depending on the availability requirements, our results compare the cost of protection measures. We confirm that adding trusted nodes is the most cost-efficient upgrading strategy based on our analysis.

Feature of the forward error correction constructions for optical transport networks

Due to the rapid increase of network traffic in the last few years, many telecommunication operators have started transitions to 100G optical networks and beyond. However, high speed optical networks need more efficient Forward Error Correction (FEC) codes to deal with the optical impairments. Forward error correction is widely used in optical communications to improve error correction and extend optical transmission distance. It is also used to reduce optical transmitter power and system costs. In response to the rapid growth of optical communications, the ITU-T has started researching FEC coding and has recommended ITU-T G.709 and G.975.1. As optical transmission systems evolve towards longer transmission distance, greater capacity, and speeds of 100G and beyond a variety of unwanted effects are seriously affecting smooth evolution. In optical transport network, FEC is getting more and more important as spectral efficiency is increasing. As 100G system is commercialized, there are some trends in the evolution of FEC, for example overhead is increased from 7% to 20% or even more. High-performance FEC codes are therefore being developed so that higher net coding gain (NCG) and better error correction can be achieved. The Optical Internetworking Forum (OIF) suggests that soft-decision forward-error correction (SD-FEC) with a redundancy of 20% and beyond be used in a 100G DWDM coherent system. The use of advanced modulation formats is a promising solution for attaining such high data rates in optical networks. However, as a signal constellation grows in size, so does the optical signal to signal-to-noise ratio it requires to achieve a certain bit error rate (BER). This will degrade the error correction capability. Decoding is moving from hard to soft. Vendors are developing their own advanced codes instead of using codes recommended by ITU-T. Because different application systems have different decoding performance, latency, power and cost requirements, FEC systems with various transmission overhead, implementation complexity, burst-error correction ability and error floor are available in today’s market. Soft-decision FEC often uses turbo product codes and low density parity check (LDPC) codes. In research area, new codes such as LDPC codes and their different varieties are introduced into optical communication. Optimized codeword structure and decoding algorithm that provides low BER. LDPC codes can be very powerful, but their practical implementation for optical communications links at ultra-high data rates remains a challenge since the decoding of the code requires soft-decision bits about the received bits. A family of structured codes known as quasi-cyclic LDPC codes, whose common structural properties can be exploited by unified encoding and decoding architectures to reduce the complexity in implementation.

Deployment Route Length Estimation in Optical Transport Networks

Deployment Route Length Estimation in Optical Transport Networks