Flexible Technologies to Increase Optical Network Capacity | IEEE Journals & Magazine | IEEE Xplore

Flexible Technologies to Increase Optical Network Capacity


Abstract:

Increased global traffic puts tough requirements not just on fiber communications links but on the entire network. This manifests itself in multiple ways, including how t...Show More

Abstract:

Increased global traffic puts tough requirements not just on fiber communications links but on the entire network. This manifests itself in multiple ways, including how to optimize wavelength routing around the network, how to maximize the benefits arising from fine-control DSP with increasingly accurate real-time monitoring, and how to best deploy multiband or multiple fiber connectivity. This article will summarize research into all these areas to present a full picture of how future optical networks will play their role in supporting the continuing traffic demands of broadband, 5G, and associated applications.
Published in: Proceedings of the IEEE ( Volume: 110, Issue: 11, November 2022)
Page(s): 1714 - 1724
Date of Publication: 20 July 2022

ISSN Information:

Funding Agency:

References is not available for this document.

I. Introduction

Optical fiber networks are indisputably the technology of choice when it comes to transporting high volumes of data across long distances. Over recent decades, fiber spectrum in optical networks is allocated according to the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) dense wavelength division multiplexing (DWDM) [1] standard, which uses optical signals generated by fixed bit rate (FBR) transponders, over fixed-spectrum 50-GHz channels, operating across the C-band (1530–1565 nm).

Select All
1.
Spectral Grids for WDM Applications: DWDM Frequency Grid, document ITU-T G.694.1, Oct. 2020. [Online]. Available: https://www.itu.int/rec/T-REC-G.694.1/en
2.
S. Frisken, G. Baxter, D. Abakoumov, H. Zhou, I. Clarke, and S. Poole, “Flexible and grid-less wavelength selective switch using LCOS technology,” in Proc. Opt. Fiber Commun. Conf. Expo. Nat. Fiber Opt. Eng. Conf., Mar. 2011, pp. 1–3.
3.
M. Jinno, “Distance-adaptive spectrum resource allocation in spectrum-sliced elastic optical path network [topics in optical communications],” IEEE Commun. Mag., vol. 48, no. 8, pp. 138–145, Aug. 2010.
4.
N. Sambo, “Sliceable transponder architecture including multiwavelength source,” J. Opt. Commun. Netw., vol. 6, no. 7, pp. 590–600, Jul. 2014.
5.
B. C. Chatterjee, S. Ba, and E. Oki, “Fragmentation problems and management approaches in elastic optical networks: A survey,” IEEE Commun. Surveys Tuts., vol. 20, no. 1, pp. 183–210, 1st Quart., 2018.
6.
R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network Flows: Theory, Algorithms, and Applications. Upper Saddle River, NJ, USA : Prentice-Hall, 1993.
7.
T. Ahmed, A. Mitra, S. Rahman, M. Tornatore, A. Lord, and B. Mukherjee, “C+L-band upgrade strategies to sustain traffic growth in optical backbone networks,” J. Opt. Commun. Netw., vol. 13, no. 7, pp. 193–203, Jul. 2021.
8.
A. Mitra, A. Lord, S. Kar, and P. Wright, “Effect of link margin and frequency granularity on the performance of a flexgrid optical network,” Opt. Exp., vol. 22, no. 1, p. 41, Jan. 2014.
9.
A. Mitra, A. Lord, S. Kar, and P. Wright, “Effect of link margins and frequency granularity on the performance and modulation format sweet spot of multiple flexgrid optical networks,” in Proc. OFC, 2014, pp. 1–3.
10.
J. Pesic, T. Zami, P. Ramantanis, and S. Bigo, “Faster return of investment in WDM networks when elastic transponders dynamically fit ageing of link margins,” in Proc. Opt. Fiber Commun. Conf. Exhib. (OFC), 2016, pp. 1–3.
11.
Y. Pointurier, “Design of low-margin optical networks,” J. Opt. Commun. Netw., vol. 9, no. 1, pp. A9–A17, 2017.
12.
A. Napoli, “Next generation elastic optical networks: The vision of the European research project IDEALIST,” IEEE Commun. Mag., vol. 53, no. 2, pp. 152–162, Feb. 2015.
13.
Z. Zhu, W. Lu, L. Zhang, and N. Ansari, “Dynamic service provisioning in elastic optical networks with hybrid single-/multi-path routing,” J. Lightw. Technol., vol. 31, no. 1, pp. 15–22, Jan. 1, 2013.
14.
B. Mukherjee, Optical WDM Networks. New York, NY, USA : Springer, 2006.
15.
M. Salani, C. Rottondi, and M. Tornatore, “Routing and spectrum assignment integrating machine-learning-based QoT estimation in elastic optical networks,” in Proc. IEEE Conf. Comput. Commun. (IEEE INFOCOM), Apr. 2019, pp. 1738–1746.
16.
G. Gamrath and M. E. Lübbecke, “Experiments with a generic Dantzig–Wolfe decomposition for integer programs,” in Experimental Algorithms, vol. 6049, D. Hutchison, Eds. Berlin, Germany : Springer, 2010, pp. 239–252.
17.
M. Tornatore, C. Rottondi, R. Goscien, K. Walkowiak, G. Rizzelli, and A. Morea, “On the complexity of routing and spectrum assignment in flexible-grid ring networks [invited],” J. Opt. Commun. Netw., vol. 7, no. 2, pp. A256–A267, Feb. 2015.
18.
O. Karandin, F. Musumeci, O. Ayoub, A. Ferrari, Y. Pointurier, and M. Tornatore, “Quantifying resource savings from low-margin design in optical networks with probabilistic constellation shaping,” in Proc. Eur. Conf. Opt. Commun. (ECOC), Sep. 2021, pp. 1–4.
19.
N. Shahriar, “Disruption minimized bandwidth scaling in EON-enabled transport network slices,” IEEE J. Sel. Areas Commun., vol. 39, no. 9, pp. 2734–2747, Sep. 2021.
20.
M. Aibin and K. Walkowiak, “Simulated annealing algorithm for optimization of elastic optical networks with unicast and anycast traffic,” in Proc. 16th Int. Conf. Transparent Opt. Netw. (ICTON), Jul. 2014, pp. 1–4.
21.
M. Klinkowski, “A genetic algorithm for solving rsa problem in elastic optical networks with dedicated path protection,” in Proc. Int. Joint Conf. CISIS’12-ICEUTE’12-SOCO’12 Special Sessions, vol. 189, Á. Herrero, Eds. Berlin, Germany : Springer, 2013, pp. 167–176.
22.
Y. Bengio, A. Lodi, and A. Prouvost, “Machine learning for combinatorial optimization: A methodological tour d’horizon,” Eur. J. Oper. Res., vol. 290, no. 2, pp. 405–421, Apr. 2021.
23.
X. Chen, B. Li, R. Proietti, H. Lu, Z. Zhu, and S. J. B. Yoo, “DeepRMSA: A deep reinforcement learning framework for routing, modulation and spectrum assignment in elastic optical networks,” J. Lightw. Technol., vol. 37, no. 16, pp. 4155–4163, Aug. 15, 2019.
24.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” 2017, arXiv:1710.10903.
25.
K. Zhu, H. Zhu, and B. Mukherjee, “Traffic engineering in multigranularity heterogeneous optical WDM mesh networks through dynamic traffic grooming,” IEEE Netw., vol. 17, no. 2, pp. 8–15, Mar./Apr. 2003.
26.
W. Feijen and G. Schäfer, “Using machine learning predictions to speed-up Dijkstra’s shortest path algorithm,” CoRR, vol. abs/2112.11927, pp. 1–28, Dec. 2021.
27.
I. Martín, J. A. Hernández, S. Troia, F. Musumeci, G. Maier, and O. G. de Dios, “Is machine learning suitable for solving RWA problems in optical networks?,” in Proc. Eur. Conf. Opt. Commun. (ECOC), Sep. 2018, pp. 1–3.
28.
N. D. Cicco, “On deep reinforcement learning for static routing and wavelength assignment,” IEEE J. Sel. Topics Quantum Electron., vol. 28, no. 4, pp. 1–12, Jul. 2022.
29.
D. Rafique and L. Velasco, “Machine learning for network automation: Overview, architecture, and applications [invited tutorial],” J. Opt. Commun. Netw., vol. 10, no. 10, pp. D126–D143, 2018.
30.
F. Musumeci, “An overview on application of machine learning techniques in optical networks,” IEEE Commun. Surveys Tuts., vol. 21, no. 2, pp. 1383–1408, 2nd Quart., 2019.

Contact IEEE to Subscribe

References

References is not available for this document.