A Learning Living Network With Open ROADMs

Oda, Shunri; Miyabe, Masataka; Yoshida, Setsuo; Katagiri, Takahiro; Aoki, Yuriko; Hoshida, Takeshi; Rasmussen, Jens C.; Birk, Martin; Tse, Kathy

doi:10.1109/jlt.2017.2660540

Cited by 50 publications

(21 citation statements)

References 8 publications

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“…Linear regression [38]: uses BER information monitoring and a learning process (based on linear regression) in order to estimate the BER of each new service request. Support vector machines [59]: proposes a fast and accurate lightpath QoT estimator based on SVM to decide whether a lightpath fulfils QoT requrements or not.…”

Section: Qot Estimationmentioning

confidence: 99%

“…Another proposal for QoT estimation is that of Barletta et al [37], who apply a machine learning-based classifier, specifically a random forest, to predict the probability that the BER of a candidate lightpath will not exceed a given threshold. Finally, Oda et al [38] present the concept of "living network", an optical network which keeps records of its path-level performance, which takes advantage of BER information monitoring and of a learning process (based on linear regression) in order to estimate the BER of each new service request.…”

Section: Quality Of Transmission (Qot) Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

Miguel

Durán

et al. 2018

Optical Switching and Networking

337

174

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Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

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Section: Qot Estimationmentioning

confidence: 99%

Section: Quality Of Transmission (Qot) Estimationmentioning

confidence: 99%

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

Miguel

Durán

et al. 2018

Optical Switching and Networking

337

174

View full text Add to dashboard Cite

show abstract

“…II-B. 3 If PS is employed, the BER after PS decoding, if that is placed after the FEC decoding, is more relevant [30]- [32].…”

Section: A Performance In Systems With Binary Modulationmentioning

confidence: 99%

Performance Monitoring for Live Systems with Soft FEC and Multilevel Modulation

Yoshida

Mazur

Schröder

et al. 2020

J. Lightwave Technol.

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Performance monitoring is an essential function for margin measurements in live systems. Historically, system budgets have been described by the Q-factor converted from the bit error rate (BER) under binary modulation and direct detection. The introduction of hard forward error correction (FEC) did not change this. In recent years, technologies have changed significantly to comprise coherent detection, multilevel modulation and soft FEC. In such advanced systems, different metrics such as (nomalized) generalized mutual information (GMI/NGMI) and asymmetric information (ASI) are regarded as being more reliable. On the other hand, Q budgets are still useful because pre-FEC BER monitoring is established in industry for live system monitoring.The pre-FEC BER is easily estimated from available information of the number of flipped bits in the FEC decoding, which does not require knowledge of the transmitted bits that are unknown in live systems. Therefore, the use of metrics like GMI/NGMI/ASI for performance monitoring has not been possible in live systems. However, in this work we propose a blind soft-performance estimation method. Based on a histogram of log-likelihood-values without the knowledge of the transmitted bits, we show how the ASI can be estimated.We examine the proposed method experimentally for 16-and 64-ary quadrature amplitude modulation (QAM) and probabilistically shaped 16-, 64-, and 256-QAM in recirculating loop experiments. We see a relative error of 2.4%, which corresponds to around 0.5 dB signal-to-noise ratio difference for binary modulation, in the regime where the ASI is larger than the assumed FEC threshold. For this proposed method, the digital signal processing circuitry requires only the minimal additional function of storing the L-value histograms before the soft FEC decoder.

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“…Di Giglio explored monitoring information for cross-layer optimization and active-control functions in optical networks [15]. With BER monitoring, a network can learn from its path-level performance for small-margin network operation [16]. Optical performance monitoring techniques could also support cognitive optical networking [17], and benefit cognitive SDN orchestration [18] and on-demand control plane functions [19].…”

Section: Review Of Related Researchmentioning

confidence: 99%

Multilayer Network Analytics With SDN-Based Monitoring Framework

Yan¹,

Aguado²,

Ou³

et al. 2017

J. Opt. Commun. Netw.

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This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at https://www.osapublishing.org/jocn/abstract.cfm?uri=jocn-9-2-A271. Please refer to any applicable terms of use of the publisher. University of Bristol -Explore Bristol Research General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1  Abstract-Optical transport networks with expanding variety and volume of network data services challenge network provider's ability to provide high-quality service assurance and network managements. Software-defined networks (SDN) decouple the data plane and control plane and enable network programmability in optical networks with a centralized network controller. The optical network would become more dynamic in network architecture and require frequent network reconfigurations. The dynamic optical networks require all kinds of visibility into application data types, traffic flows and end-to-end connections. Thus, we propose an SDN-based monitoring framework that expands network analytics to a converged packet and optical network. With a designated monitoring hub, monitoring information from multiple layers are collected and processed in a centralized server. Several monitoring technologies are provided as network services with the architecture-on-demand optical node architecture. The developed network applications on top of SDN controller process all the collected monitoring information and enables multilayer network analytics based the SDN-based monitoring framework. We demonstrate the proposed multi-layer network analytics successfully in several use cases. In the demonstrations, the multi-layer network analytics enables QoS recovery to avoid network disruption, optical power equalization at any combining device and network debugging and restoration in optical networks. The developed multilayer network analytics provides powerful tools for network re-planning and optimization dynamically.

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A Learning Living Network With Open ROADMs

Cited by 50 publications

References 8 publications

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Performance Monitoring for Live Systems with Soft FEC and Multilevel Modulation

Multilayer Network Analytics With SDN-Based Monitoring Framework

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