What if AI Fails: Protection against Failure of AI-Based QoT Prediction

Guo, Ningning; Li, Longfei; Xiang, Lian; Bose, Sanjay K.; Shen, Gangxiang

doi:10.1364/ofc.2020.th2a.31

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…To protect against the failure of the ML-based QoT prediction, we consider the two protection techniques, i.e., 1+1 path protection and SBPP. These two techniques can achieve different availabilities for each lightpath service in case of lightpath QoT failure 1 . Next, we describe how to calculate the lightpath service availability under these two protection techniques.…”

Section: B Estimating Availability Of a Protected Lightpath Servicementioning

confidence: 99%

“…ccurate prediction of quality of transmission (QoT) of a lightpath is essential for optical network design. This can reduce QoT margin required for a lightpath and allocate A part of this paper was presented at OFC 2020 [1]. Ningning Guo, Longfei Li, Biswanath Mukherjee, and Gangxiang Shen (corresponding author: Gangxiang Shen; e-mail: shengx@suda.edu.cn) are with Jiangsu New Optical Fiber Technology and Communication Network Engineering Research Center, Suzhou Key Laboratory of Advanced Optical Communication Network Technology, Institute for Broadband Research & Innovation (IBRI) and School of Electronic and Information Engineering, Soochow University, Jiangsu Province 215006, P. R. China.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extended version of a conference paper [1]. The following key aspects have been enhanced in the current paper.…”

Section: Introductionmentioning

confidence: 99%

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Protection against failure of machine-learning-based QoT prediction

Guo

Mukherjee

et al. 2022

J. Opt. Commun. Netw.

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Machine learning (ML)-based methods are being widely explored to predict the quality of transmission (QoT) of a lightpath. They are expected to reduce the signal-to-noise ratio margin reserved for the lightpath, thus improving the spectrum efficiency of an optical network. However, many studies on this prediction are often based on synthetic datasets or datasets obtained from laboratories. As such, these datasets may not accurately represent the entire state space of a practical optical network, which is exposed in harsh environments. There are risks of failure when using these ML-based QoT prediction models. Thus, it is necessary to develop a mechanism that can guarantee the reliability of lightpath service even if the prediction model fails. For this scenario, we propose to employ the conventional network protection techniques that are usually implemented to protect against network node/link failures to also protect against the failure of QoT prediction. Based on the two representative protection techniques, i.e., 1 + 1 dedicated path protection and shared backup path protection (SBPP), the performance of the proposed protection mechanism is evaluated by reserving different margins for the working and protection lightpaths. For 1 + 1 path protection, we find that the proposed mechanism can achieve a zero design margin (D-margin) for a working lightpath, thereby significantly improving network spectrum efficiency, while not sacrificing the availability of lightpath services. For SBPP, we find that an optimal D-margin should be identified to balance the spectrum efficiency and service availability; the proposed mechanism can save up to 0.5-dB D-margin for a working lightpath, while guaranteeing service availability.

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Section: B Estimating Availability Of a Protected Lightpath Servicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Protection against failure of machine-learning-based QoT prediction

Guo

Mukherjee

et al. 2022

J. Opt. Commun. Netw.

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“…However, due to the uncertainty of the future and the fact that the data reflecting the facts have certain limits, the prediction result. There will still be a certain degree of gap with the truth [8][9][10][11].…”

Section: Prediction Theorymentioning

confidence: 99%

Using Artificial Intelligence in IC Substrate Production Predicting

Liu

2021

Preprint

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Today's technology products are changing with each day, the purpose is to bring more convenience to people, but also the competition among the technology industries is more competitive. In such environment, whether the company's decision-making is correct or not will directly affect the future development of an enterprise. Therefore, how an enterprise can formulate and construct a set of appropriate decision-making systems to accurately predict the future market will be the first important issue for enterprises. This research proposed an artificial intelligence predicting system to estimate manufacturing capacities and client demands, and providing it to manufacturing managers as a reference for inventory arrangements so that inventory can be adjusted appropriately to avoid excessive inventory levels. In recent years, neural networks have been widely and effectively applied to many predicting problems. The main reason is that most of the predicting problems are nonlinear models. And the backward neural network has the ability to construct nonlinear models. In this study, a predicting model combining grey correlation and neural network will be used to establish a high-accuracy predition system for the production predict of IC product. First, grey correlation analysis will be used to screen out the most relevant factors among many factors. And then put these factors into the neural network prediction model for training and prediction. The results show that the training prediction error and the empirical error value are about 14%. This value indicates that the prediction ability is better, so the proposed prediction model can be applied to the prediction of IC substrate production. It provided a predictive reference material and provide decision making with a more accurate, convenient and a fast tool to enhance the company’s competitiveness.

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A hybrid $${Q}_{L}$$ ANN model designed to improve the Quality of Transmission of optical communication network

Almawgani

2023

Opt Quant Electron

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What if AI Fails: Protection against Failure of AI-Based QoT Prediction

Cited by 6 publications

References 1 publication

Protection against failure of machine-learning-based QoT prediction

Protection against failure of machine-learning-based QoT prediction

Using Artificial Intelligence in IC Substrate Production Predicting

A hybrid $${Q}_{L}$$ ANN model designed to improve the Quality of Transmission of optical communication network

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