Reinforcement learning based routing in all-optical networks with physical impairments

Pointurier, Yvan; Heidari, Fariba

doi:10.1109/broadnets.2007.4550535

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Many researchers used machine learning techniques in optical communication for routing and resource allocation. The authors in Pointurier and Heidari, 33 Garcia et al, 34 and Kiran et al 35 used Q learning for routing and resource allocation. The training set requirement was removed by Pointurier and Heidari 33 for routing and wavelength allocation.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in Pointurier and Heidari, 33 Garcia et al, 34 and Kiran et al 35 used Q learning for routing and resource allocation. The training set requirement was removed by Pointurier and Heidari 33 for routing and wavelength allocation. Garcia et al 34 reduced the blocking probability for Multicast Routing and Wavelength Assignment (MRWA).…”

Section: Related Workmentioning

confidence: 99%

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Machine learning‐based regression models for predicting signal quality of dense wavelength division multiplexing (DWDM) optical communication network

Masih

Kaur

2023

Int J Communication

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Summary Over the years, optical communication systems have been a significant source of fast and secure communication. However, factors like noise and mitigation error can degrade the bit error rate (BER) and quality factor (Q factor) of optical communication systems. Predicting the optimal threshold, Q factor, and BER is usually a difficult task. Therefore, in this paper, machine learning‐based linear regression, least absolute shrinkage and selection operator (LASSO) regression, and Ridge regression have been used for a dense wavelength division multiplexing (DWDM)‐based optical communication network to predict the signal quality. These techniques have been used to predict the desired BER, Q factor, threshold, and eye height of the system. To demonstrate this research concept, a DWDM‐based optical communication network of 50 km length is designed and simulated using Optisystem‐14.0. After data preparation, regression models have been developed and validated through diagnostic plots. Results show that mean square error (MSE) has a significant decline with an increase in the number of epochs for all four models. LASSO and Ridge regression have effectively resolved the issue of overfitting, which occurred in the linear regression case. Furthermore, the mean MSE plot proved the significant reduction of mean MSE in the case of LASSO regression. Results show that min BER for LASSO regression came out to be −173,627.14, providing a robust and cost‐efficient process.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Machine learning‐based regression models for predicting signal quality of dense wavelength division multiplexing (DWDM) optical communication network

Masih

Kaur

2023

Int J Communication

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“…[19] has used a reinforcement learning for selecting the route of the burst transmission, and [20] has proposed a deflection routing method based on reinforcement learning. For the lightpath establishment, [21] and [22] have proposed a reinforcement learning algorithm for the routing and wavelength allocation (RWA). Especially, in [22], physical impairment has been considered in the reinforcement learning algorithm.…”

Section: Network Management Based On Reinforcement Learningmentioning

confidence: 99%

“…For the lightpath establishment, [21] and [22] have proposed a reinforcement learning algorithm for the routing and wavelength allocation (RWA). Especially, in [22], physical impairment has been considered in the reinforcement learning algorithm.…”

Section: Network Management Based On Reinforcement Learningmentioning

confidence: 99%

A Reinforcement Learning-Based Lightpath Establishment for Service Differentiation in All-Optical WDM Networks

Koyanagi

Tachibana

Sugimoto

2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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In this paper, we propose a lightpath establishment method based on reinforcement learning for providing the service differentiation in all-optical WDM networks. In our proposed method, the optimal policy for the lightpath establishment is derived with Q-learning. With the derived policy, each node decides whether a lightpath establishment request of each class should be accepted or not. This method can be available even if the number of wavelengths is large and there is no assumption about the lightpath establishment. We also discuss how the proposed method is utilized with Generalized Multi-Protocol Label Switching (GMPLS). In numerical examples, we investigate the impacts of learning parameters on the performance of the proposed method. Then, we show that our proposed method can provide the service differentiation for the lightpath blocking probability, while utilizing wavelengths effectively.

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“…In order to do this, they need to consistently acquire the quality of signals along the various paths. In EON's, once signal quality has been obtained through Optical Performance Monitoring (OPM), it can then be used in impairment-aware Routing, Modulation and Spectrum Assignment (RMSA) for example Chen et al (2018) applied a Quality of Transmission (QoT) estimator which acquired the modulation format and physical layer performance using machine learning and then used it as input to a broker that made intelligent routing and spectrum assignment decisions, while Pointurier and Heidari (2007) and Yang and Kuipers (2012)have also shown routing algorithms that are impairment-aware and achieved better blocking performance than similar ones that were unaware of the physical impairments.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

2022

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Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

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Reinforcement learning based routing in all-optical networks with physical impairments

Abstract: Abstract-We present and evaluate a reinforcement learningbased RWA algorithm for all-optical networks subject to physical impairments. The technique is suitable for decentralized networks and is compared with other techniques with similar computational complexity.

Cited by 5 publications

References 6 publications

Machine learning‐based regression models for predicting signal quality of dense wavelength division multiplexing (DWDM) optical communication network

Machine learning‐based regression models for predicting signal quality of dense wavelength division multiplexing (DWDM) optical communication network

A Reinforcement Learning-Based Lightpath Establishment for Service Differentiation in All-Optical WDM Networks

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

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