A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions

Yishen Huang; Wiem Samoud; Craig L. Gutterman; Cedric Ware; Mounia Lourdiane; Gil Zussman; Payman Samadi; Keren Bergman

A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions

Yishen Huang
, Wiem Samoud
, Craig L. Gutterman
, Cedric Ware
, Mounia Lourdiane
, Gil Zussman
, Payman Samadi
, Keren Bergman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We demonstrate a machine learning approach to characterize channel dependence of power excursions in multi-span EDFA networks. This technique can determine accurate recommendations for channel add/drop with minimal excursions and is applicable to different network designs.

Original language	English
Title of host publication	ECOC 2016; 42nd European Conference on Optical Communication
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	268-270
Number of pages	3
ISBN (Electronic)	9783800742745
Publication status	Published - 1 Jan 2016
Externally published	Yes
Event	42nd European Conference on Optical Communication, ECOC 2016 - Dusseldorf, Germany Duration: 18 Sept 2016 → 22 Sept 2016

Publication series

Name	European Conference on Optical Communication, ECOC

Conference

Conference	42nd European Conference on Optical Communication, ECOC 2016
Country/Territory	Germany
City	Dusseldorf
Period	18/09/16 → 22/09/16

Cite this

Huang, Y., Samoud, W., Gutterman, C. L., Ware, C., Lourdiane, M., Zussman, G., Samadi, P., & Bergman, K. (2016). A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions. In ECOC 2016; 42nd European Conference on Optical Communication (pp. 268-270). (European Conference on Optical Communication, ECOC). Institute of Electrical and Electronics Engineers Inc..

@inproceedings{f6c8f2a7bb4d4800954357f1e1b4323a,

title = "A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions",

abstract = "We demonstrate a machine learning approach to characterize channel dependence of power excursions in multi-span EDFA networks. This technique can determine accurate recommendations for channel add/drop with minimal excursions and is applicable to different network designs.",

author = "Yishen Huang and Wiem Samoud and Gutterman, \{Craig L.\} and Cedric Ware and Mounia Lourdiane and Gil Zussman and Payman Samadi and Keren Bergman",

note = "Publisher Copyright: {\textcopyright} VDE VERLAG GMBH Berlin Offenbach.; 42nd European Conference on Optical Communication, ECOC 2016 ; Conference date: 18-09-2016 Through 22-09-2016",

year = "2016",

month = jan,

day = "1",

language = "English",

series = "European Conference on Optical Communication, ECOC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "268--270",

booktitle = "ECOC 2016; 42nd European Conference on Optical Communication",

}

Huang, Y, Samoud, W, Gutterman, CL, Ware, C , Lourdiane, M, Zussman, G, Samadi, P & Bergman, K 2016, A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions. in ECOC 2016; 42nd European Conference on Optical Communication. European Conference on Optical Communication, ECOC, Institute of Electrical and Electronics Engineers Inc., pp. 268-270, 42nd European Conference on Optical Communication, ECOC 2016, Dusseldorf, Germany, 18/09/16.

A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions. / Huang, Yishen; Samoud, Wiem; Gutterman, Craig L. et al.
ECOC 2016; 42nd European Conference on Optical Communication. Institute of Electrical and Electronics Engineers Inc., 2016. p. 268-270 (European Conference on Optical Communication, ECOC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions

AU - Huang, Yishen

AU - Samoud, Wiem

AU - Gutterman, Craig L.

AU - Ware, Cedric

AU - Lourdiane, Mounia

AU - Zussman, Gil

AU - Samadi, Payman

AU - Bergman, Keren

PY - 2016/1/1

Y1 - 2016/1/1

N2 - We demonstrate a machine learning approach to characterize channel dependence of power excursions in multi-span EDFA networks. This technique can determine accurate recommendations for channel add/drop with minimal excursions and is applicable to different network designs.

AB - We demonstrate a machine learning approach to characterize channel dependence of power excursions in multi-span EDFA networks. This technique can determine accurate recommendations for channel add/drop with minimal excursions and is applicable to different network designs.

UR - https://www.scopus.com/pages/publications/85019886923

M3 - Conference contribution

AN - SCOPUS:85019886923

T3 - European Conference on Optical Communication, ECOC

SP - 268

EP - 270

BT - ECOC 2016; 42nd European Conference on Optical Communication

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 42nd European Conference on Optical Communication, ECOC 2016

Y2 - 18 September 2016 through 22 September 2016

ER -

Huang Y, Samoud W, Gutterman CL, Ware C , Lourdiane M, Zussman G et al. A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions. In ECOC 2016; 42nd European Conference on Optical Communication. Institute of Electrical and Electronics Engineers Inc. 2016. p. 268-270. (European Conference on Optical Communication, ECOC).

A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions

Abstract

Publication series

Conference

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