Current management of optical communication systems is conservative, manual-based, and time-consuming. To improve this situation, building an intelligent closed-loop control system is becoming an active topic of the industry. One of the key techniques to achieve such a management system is physical layer impairment telemetry, with the help of which the controller can make proper instructions. However, it is challenging to implement an accurate telemetry module due to the complex mechanisms of various impairments. To overcome that, many studies have been done. In this paper, those recent studies are reviewed, and the design of telemetry is discussed systematically. We analyze metrics for evaluating system performance and mechanisms of various impairments comprehensively, which are the theoretical foundations for designing telemetry modules. We then summarize a unified workflow for designing telemetry modules based on the review of previous works. Its effectiveness is then verified by concrete use cases of our previous studies. Finally, we discuss the challenges of deploying machine-learning-based telemetry techniques in optical communication systems.

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For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred. Considering a large number of optical components in an optical network and many digital signal processing modules in each optical transceiver, massive real-time data can be collected. However, for a traditional monitoring structure, collecting, storing and processing a large size of data are challenging tasks. Moreover, strong correlations and similarities between data from different sources and regions are not properly considered, which may limit function extension and accuracy improvement. To address abovementioned issues, a data-fusion-assisted telemetry layer between the physical layer and control layer is proposed in this paper. The data fusion methodologies are elaborated on three different levels: Source Level, Space Level and Model Level. For each level, various data fusion algorithms are introduced and relevant works are reviewed. In addition, proof-of-concept use cases for each level are provided through simulations, where the benefits of the data-fusion-assisted telemetry layer are shown.

To support the development of intelligent optical networks, accurate modeling of the physical layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time data, provides a new paradigm to build a virtual replica of the physical layer with a significant improvement in accuracy and reliability. In addition, DT models will be able to forecast future change by analyzing historical data. In this tutorial, we introduce and discuss three key technologies, including modeling, telemetry, and self-learning, to build a DT for optical networks. The principles and progress of these technologies on major impairments that affect the quality of transmission are presented, and a discussion on the remaining challenges and future research directions is provided.

Background The benefits of breastfeeding especially exclusive breastfeeding have been well recognized. Aim To investigate the prevalence and influencing factors of exclusive breastfeeding intentions for the first six months among pregnant women. Methods A self-designed questionnaire was adopted to collect information on maternal intention on exclusive breastfeeding and other related social characteristics. The primary outcome was intention of mother on exclusively breastfeeding which derived by a response to the question “would you be will to breastfeed exclusively for the first 6 months?” Adjusted odds ratio (OR) with 95% confidence intervals were obtained by multiple logistic regression after adjusted by maternal age. Findings: A total of 2,479 pregnant women in the third trimester were interviewed. 60.8% of them planned to exclusively breastfeed during the first 6 months. After adjusted by maternal age, intentions to exclusively breastfeed was lower in mothers with a graduate degree (OR,0.70; 0.525–0.987). Mothers who received supports of exclusive breastfeeding from spouses and parents were more likely to intent to exclusively breastfeed than those not. The intention of exclusively breastfeed was higher among mothers (OR = 1.436, 1.199–1.719) who participated in breastfeeding courses during pregnancy were pregnant women independent influencing factors of breastfeeding intention in late pregnant women. Conclusion Over half of the pregnancy women showed an intention to exclusive breastfeeding for the first 6 months. Antenatal breastfeeding courses and supportive practices from family need to be enforced to improve mothers' and family members' perception about exclusive breastfeeding, in order to increase the rate and duration of exclusive breastfeeding.