Spectral processing techniques for efficient monitoring in optical networks

Fabiano Locatelli; Konstantinos Christodoulopoulos; Michela Svaluto Moreolo; Josep M. Fàbrega; Laia Nadal; Salvatore Spadaro

doi:10.1364/JOCN.418800

Journal of Optical Communications and Networking
Vol. 13,
Issue 7,
pp. 158-168
(2021)
•https://doi.org/10.1364/JOCN.418800

Spectral processing techniques for efficient monitoring in optical networks

Fabiano Locatelli, Konstantinos Christodoulopoulos, Michela Svaluto Moreolo, Josep M. Fàbrega, Laia Nadal, and Salvatore Spadaro

Not Accessible

Your library or personal account may give you access

Get PDF
Email
Share
Get Citation
Copy Citation Text
Fabiano Locatelli, Konstantinos Christodoulopoulos, Michela Svaluto Moreolo, Josep M. Fàbrega, Laia Nadal, and Salvatore Spadaro, "Spectral processing techniques for efficient monitoring in optical networks," J. Opt. Commun. Netw. 13, 158-168 (2021)

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Citation alert
Save article

Abstract

Having ubiquitous optical monitors in dense wavelength-division multiplexing (DWDM) or flex-grid networks allows the estimation in real time of crucial parameters. Such monitoring would be even more important in disaggregated optical networks, to inspect performance issues related to inter-vendor interoperability. Several important parameters can be retrieved using optical spectrum analyzers (OSAs). However, omnipresent OSAs represent an infeasible solution. Nevertheless, the advent of new, relatively cheap, compact and medium-resolution optical channel monitors (OCMs) enable a more intensive deployment of these devices. In this paper, we identify two main scenarios for the placement of such monitors: at the ingress and at the egress of the optical nodes. In the ingress scenario, we can directly estimate the parameters related to the signals, but not those related to the filters. On the contrary, in the egress scenario, the filter-related parameters can be easily detected, but not those related to amplified spontaneous emission. Therefore, we present two methods that, leveraging a curve fitting and a machine learning regression algorithm, allow detection of the missing parameters. We verify the proposed solutions with spectral data acquired in simulation and experimental setups. We obtained good estimation accuracy for both setups and for both studied placement scenarios. It is noteworthy that in the experimental assessment of the ingress scenario, we achieved a maximum absolute error (MAE) lower than 1 GHz in filter bandwidth estimation and a MAE lower than 0.5 GHz in filter frequency shift estimation. In addition, by comparing the relative errors of the considered parameters, we identified the ingress scenario as the more beneficial. In particular, we estimated the filter central frequency shift with 84% and the filter 6 dB bandwidth with 75% higher accuracy, with respect to datasheet/reference values. This translates into a total reduction of the estimated signal-to-noise ratio (SNR) penalty, introduced by a single optical filter, of 0.24 dB.

Full Article | PDF Article

More Like This

Machine learning techniques for quality of transmission estimation in optical networks

Yvan Pointurier
J. Opt. Commun. Netw. 13(4) B60-B71 (2021)

Self-Learning Monitoring On-Demand Strategy for Optical Networks

Fanchao Meng, Alex Mavromatis, Yu Bi, Rui Wang, Shuangyi Yan, Reza Nejabati, and Dimitra Simeonidou
J. Opt. Commun. Netw. 11(2) A144-A154 (2019)

QoT assessment of the optical spectrum as a service in disaggregated network scenarios

Kaida Kaeval, Tobias Fehenberger, Jim Zou, Sander Lars Jansen, Klaus Grobe, Helmut Griesser, Jörg-Peter Elbers, Marko Tikas, and Gert Jervan
J. Opt. Commun. Netw. 13(10) E1-E12 (2021)

Previous Article Next Article

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (11)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (5)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (2)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Links (# of Spans)				Filters (6 dB Bandwidth [GHz] $+$ Central Freq. Shift [GHz])
0–1	1–2	2–3	3–4	1	2	3
				$37.5 + 0$	$37.5 + 2$	$37.5 + 1$
$\binom{}{5}$	$\binom{}{2}$	$\binom{}{2}$	$\binom{}{3}$	$37.5 + 1$	$37.5 + 2$	$37.5 + 2$
				$37.5 + 2$	$37.5 + 1$	$37.5 + 0$
				$37.5 + 2$	$37 . 5 - 1$	$37.5 + 2$
				$37.5 + 0$	$37.5 + 2$	$37.5 + 1$
$\binom{}{2}$	$\binom{}{5}$	$\binom{}{2}$	$\binom{}{3}$	$37.5 + 1$	$37.5 + 2$	$37.5 + 2$
				$37.5 + 2$	$37.5 + 1$	$37.5 + 0$
				$37.5 + 2$	$37.5 - 1$	$37.5 + 2$
				$36.5 + 0$	$36.5 + 2$	$36.5 + 1$
$\binom{}{2}$	$\binom{}{5}$	$\binom{}{2}$	$\binom{}{3}$	$36.5 + 1$	$36.5 + 2$	$36.5 + 2$
				$36.5 + 2$	$36.5 + 1$	$36.5 + 0$
				$36.5 + 2$	$36.5 - 1$	$36.5 + 2$
				$38.5 + 0$	$38.5 + 2$	$38.5 + 1$
$\binom{}{2}$	$\binom{}{5}$	$\binom{}{2}$	$\binom{}{3}$	$38.5 + 1$	$38.5 + 2$	$38.5 + 2$
				$38.5 + 2$	$38.5 + 1$	$38.5 + 0$
				$38.5 + 2$	$38.5 - 1$	$38.5 + 2$

Node	Estimated Feature	MSE	σ [GHz]	MIN [GHz]	MAX [GHZ]
1	Centr. freq. shift	0.0019	0.0334	−0.0391	0.0655
	6 dB BW	0.0183	0.0807	−0.0249	0.1937
2	Centr. freq. shift	0.0008	0.0178	−0.0147	0.0454
	6 dB BW	0.0024	0.0479	−0.1057	0.0672
3	Centr. freq. shift	0.0026	0.0482	−0.0702	0.0997
	6 dB BW	0.0163	0.1247	−0.1470	0.2962

	MSE	MIN [dB]	MAX [dB]
Simulation	0.0018	−0.3134	0.6013
Experiment	0.0136	−0.3911	0.3866

	Filter 1 6 dB BW (shift) [GHz]	Link (1,2) ${V O A}_{1}$ [dB]	Filter 2 6 dB BW (shift) [GHz]	Link (2,3) ${V O A}_{2}$ [dB]
1	74 (−2)	0	73, 75, 77 (−1, 0, $+ 1$ )	10
2	74 (−2)	0	73, 75, 77 (−1, 0, $+ 1$ )	20
3	74 (−2)	10	73, 75, 77 (−1, 0, $+ 1$ )	10
4	69 (−2)	10	73, 75, 77 (−1, 0, $+ 1$ )	10
5	74 (−2)	5	73, 75, 77 (−1, 0, $+ 1$ )	10
6	74 (−2)	2.5	73, 75, 77 (−1, 0, $+ 1$ )	15
7	69 (−2)	7.5	73, 75, 77 (−1, 0, $+ 1$ )	20

Setup	Scenario	Parameter	Roll-Off Factor	Parameter Error Range	Reference Error Range	Relative Error
	$\binom{}{_{Ingress}}$	Filter central freq. shift	0.1	0.17 GHz	4 GHz	4.3%
Simulation		Filter 6 dB bandwidth	0.1	0.44 GHz	4 GHz	11%
	Egress	ASE noise	0.1	0.91 dB	1 dB	91%
		$\binom{}{_{Filter central freq. shift}}$	0.1	0.63 GHz	4 GHz	15.8%
	$\binom{}{_{Ingress}}$		0.2	0.5 GHz	4 GHz	12.5%
Experimental		$\binom{}{_{Filter 6 dB bandwidth}}$	0.1	1 GHz	4 GHz	25%
			0.2	0.93 GHz	4 GHz	23.3%
	Egress	ASE noise	0.1	0.78 dB	1 dB	78%

Fabiano Locatelli	https://orcid.org/0000-0002-2971-1303
Michela Svaluto Moreolo	https://orcid.org/0000-0001-6859-2573
Josep M. Fàbrega	https://orcid.org/0000-0001-9285-9768
Laia Nadal	https://orcid.org/0000-0003-0798-6370

Abstract

Cited By

Figures (11)

Tables (5)

Equations (2)

Journal of Optical Communications and Networking