The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform

Tadashi Ikeuchi - One of the best experts on this subject based on the ideXlab platform.

  • accurate prediction of quality of transmission based on a dynamically configurable Optical Impairment model
    IEEE\ OSA Journal of Optical Communications and Networking, 2018
    Co-Authors: Marti Ouda, Shoichiro Oda, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Olga Vassilieva, Tadashi Ikeuchi
    Abstract:

    We have proposed a dynamically configurable and fast Optical Impairment model for the abstraction of the Optical physical layer, enabling new capabilities such as indirect estimation of physical operating parameters in multivendor networks based on pre-FEC BER information and machine learning. BER is commonly reported by deployed coherent transponders; therefore, this scheme does not increase hardware cost. The estimated parameters can subsequently be used to predict Optical signal quality at the receiver of not-already-established Optical connections more accurately than possible based on the limited amount of information available at the time of offline system design. The higher accuracy and certainty reduce the required amount of required system margin that must be allocated to guarantee reliable Optical connectivity. The remaining margin can then be applied toward increased transmission capacity, or a reduced number of regenerators in the network. We demonstrate the quality of transmission prediction experimentally in an Optical mesh network with 0.6 dB Q-factor accuracy, and quantify the benefit in terms of network capacity gain in metro networks by Impairment-aware network simulation.

  • accurate prediction of quality of transmission with dynamically configurable Optical Impairment model
    Optical Fiber Communication Conference, 2017
    Co-Authors: Marti Ouda, Shoichiro Oda, Olga Vasilieva, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Tadashi Ikeuchi
    Abstract:

    We propose a dynamically configurable Optical Impairment model for a physical layer abstraction enabling physical parameters learning in multi-vendor networks. We experimentally demonstrate quality of transmission prediction in mesh networks with 0.6 dB Q-factor accuracy.

Marti Ouda - One of the best experts on this subject based on the ideXlab platform.

  • accurate prediction of quality of transmission based on a dynamically configurable Optical Impairment model
    IEEE\ OSA Journal of Optical Communications and Networking, 2018
    Co-Authors: Marti Ouda, Shoichiro Oda, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Olga Vassilieva, Tadashi Ikeuchi
    Abstract:

    We have proposed a dynamically configurable and fast Optical Impairment model for the abstraction of the Optical physical layer, enabling new capabilities such as indirect estimation of physical operating parameters in multivendor networks based on pre-FEC BER information and machine learning. BER is commonly reported by deployed coherent transponders; therefore, this scheme does not increase hardware cost. The estimated parameters can subsequently be used to predict Optical signal quality at the receiver of not-already-established Optical connections more accurately than possible based on the limited amount of information available at the time of offline system design. The higher accuracy and certainty reduce the required amount of required system margin that must be allocated to guarantee reliable Optical connectivity. The remaining margin can then be applied toward increased transmission capacity, or a reduced number of regenerators in the network. We demonstrate the quality of transmission prediction experimentally in an Optical mesh network with 0.6 dB Q-factor accuracy, and quantify the benefit in terms of network capacity gain in metro networks by Impairment-aware network simulation.

  • accurate prediction of quality of transmission with dynamically configurable Optical Impairment model
    Optical Fiber Communication Conference, 2017
    Co-Authors: Marti Ouda, Shoichiro Oda, Olga Vasilieva, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Tadashi Ikeuchi
    Abstract:

    We propose a dynamically configurable Optical Impairment model for a physical layer abstraction enabling physical parameters learning in multi-vendor networks. We experimentally demonstrate quality of transmission prediction in mesh networks with 0.6 dB Q-factor accuracy.

Shoichiro Oda - One of the best experts on this subject based on the ideXlab platform.

  • accurate prediction of quality of transmission based on a dynamically configurable Optical Impairment model
    IEEE\ OSA Journal of Optical Communications and Networking, 2018
    Co-Authors: Marti Ouda, Shoichiro Oda, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Olga Vassilieva, Tadashi Ikeuchi
    Abstract:

    We have proposed a dynamically configurable and fast Optical Impairment model for the abstraction of the Optical physical layer, enabling new capabilities such as indirect estimation of physical operating parameters in multivendor networks based on pre-FEC BER information and machine learning. BER is commonly reported by deployed coherent transponders; therefore, this scheme does not increase hardware cost. The estimated parameters can subsequently be used to predict Optical signal quality at the receiver of not-already-established Optical connections more accurately than possible based on the limited amount of information available at the time of offline system design. The higher accuracy and certainty reduce the required amount of required system margin that must be allocated to guarantee reliable Optical connectivity. The remaining margin can then be applied toward increased transmission capacity, or a reduced number of regenerators in the network. We demonstrate the quality of transmission prediction experimentally in an Optical mesh network with 0.6 dB Q-factor accuracy, and quantify the benefit in terms of network capacity gain in metro networks by Impairment-aware network simulation.

  • accurate prediction of quality of transmission with dynamically configurable Optical Impairment model
    Optical Fiber Communication Conference, 2017
    Co-Authors: Marti Ouda, Shoichiro Oda, Olga Vasilieva, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Tadashi Ikeuchi
    Abstract:

    We propose a dynamically configurable Optical Impairment model for a physical layer abstraction enabling physical parameters learning in multi-vendor networks. We experimentally demonstrate quality of transmission prediction in mesh networks with 0.6 dB Q-factor accuracy.

Masatake Miyabe - One of the best experts on this subject based on the ideXlab platform.

  • accurate prediction of quality of transmission based on a dynamically configurable Optical Impairment model
    IEEE\ OSA Journal of Optical Communications and Networking, 2018
    Co-Authors: Marti Ouda, Shoichiro Oda, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Olga Vassilieva, Tadashi Ikeuchi
    Abstract:

    We have proposed a dynamically configurable and fast Optical Impairment model for the abstraction of the Optical physical layer, enabling new capabilities such as indirect estimation of physical operating parameters in multivendor networks based on pre-FEC BER information and machine learning. BER is commonly reported by deployed coherent transponders; therefore, this scheme does not increase hardware cost. The estimated parameters can subsequently be used to predict Optical signal quality at the receiver of not-already-established Optical connections more accurately than possible based on the limited amount of information available at the time of offline system design. The higher accuracy and certainty reduce the required amount of required system margin that must be allocated to guarantee reliable Optical connectivity. The remaining margin can then be applied toward increased transmission capacity, or a reduced number of regenerators in the network. We demonstrate the quality of transmission prediction experimentally in an Optical mesh network with 0.6 dB Q-factor accuracy, and quantify the benefit in terms of network capacity gain in metro networks by Impairment-aware network simulation.

  • accurate prediction of quality of transmission with dynamically configurable Optical Impairment model
    Optical Fiber Communication Conference, 2017
    Co-Authors: Marti Ouda, Shoichiro Oda, Olga Vasilieva, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Tadashi Ikeuchi
    Abstract:

    We propose a dynamically configurable Optical Impairment model for a physical layer abstraction enabling physical parameters learning in multi-vendor networks. We experimentally demonstrate quality of transmission prediction in mesh networks with 0.6 dB Q-factor accuracy.

Setsuo Yoshida - One of the best experts on this subject based on the ideXlab platform.

  • accurate prediction of quality of transmission based on a dynamically configurable Optical Impairment model
    IEEE\ OSA Journal of Optical Communications and Networking, 2018
    Co-Authors: Marti Ouda, Shoichiro Oda, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Olga Vassilieva, Tadashi Ikeuchi
    Abstract:

    We have proposed a dynamically configurable and fast Optical Impairment model for the abstraction of the Optical physical layer, enabling new capabilities such as indirect estimation of physical operating parameters in multivendor networks based on pre-FEC BER information and machine learning. BER is commonly reported by deployed coherent transponders; therefore, this scheme does not increase hardware cost. The estimated parameters can subsequently be used to predict Optical signal quality at the receiver of not-already-established Optical connections more accurately than possible based on the limited amount of information available at the time of offline system design. The higher accuracy and certainty reduce the required amount of required system margin that must be allocated to guarantee reliable Optical connectivity. The remaining margin can then be applied toward increased transmission capacity, or a reduced number of regenerators in the network. We demonstrate the quality of transmission prediction experimentally in an Optical mesh network with 0.6 dB Q-factor accuracy, and quantify the benefit in terms of network capacity gain in metro networks by Impairment-aware network simulation.

  • accurate prediction of quality of transmission with dynamically configurable Optical Impairment model
    Optical Fiber Communication Conference, 2017
    Co-Authors: Marti Ouda, Shoichiro Oda, Olga Vasilieva, Masatake Miyabe, Setsuo Yoshida, Toru Katagiri, Yasuhiko Aoki, Takeshi Hoshida, Tadashi Ikeuchi
    Abstract:

    We propose a dynamically configurable Optical Impairment model for a physical layer abstraction enabling physical parameters learning in multi-vendor networks. We experimentally demonstrate quality of transmission prediction in mesh networks with 0.6 dB Q-factor accuracy.