Abstract
The evolution of Internet and communication systems is exponentially increasing the complexity in communication networks. This paved the way for the incorporation of artificial intelligence (AI), machine learning (ML), and recently deep learning (DL), in various aspects so as to improve the intelligence in communication networks. As DL techniques are superior on finding solutions to complex problems, they are been utilized for optical network applications. This paper aims to review the progress of AI in optical communication and the advancements from ML to DL. The paper also presents a review of nine research papers that utilized conventional DL techniques along with their contribution in optical and wavelength division multiplexing (WDM) networks. Toward the end of this paper, the DL algorithm is studied and its performance parameters are compared to evaluate the simulation outputs of its variants. The comparative analysis shows that in future, improvements in the outputs of WDMs can be made by applying DL-based algorithms.
Similar content being viewed by others
References
L. Pakorn, N. Wattanapongsakorn, C. Charnsripinyo, Multi-objective routing wavelength assignment in WDM network using SPEA2 approach, In: 9th International Symposium on Communications and Information Technology, IEEE, (2009)
B. Kavitha, J. Triay, VM. Vokkarane, Dynamic anycast routing and wavelength assignment in WDM networks using ant colony optimization (ACO), In: IEEE International Conference on Communications (ICC), pp. 1–6, (2011)
F. Lezama, C. Gerardo, S. Ana Maria, Routing and wavelength assignment in all optical networks using differential evolution optimization. Photon. Netw. Commun. 26(2), 103–19 (2013)
B. C. Chatterjee, N. Sarma, P. P. Sahu, Review and performance analysis on routing and wavelength assignment approaches for optical networks. IETE Techn. Rev. 30, 12–23 (2013)
R.T. Koganti, D. Sidhu, Analysis of routing and wavelength assignment in large WDM networks. Procedia Comput. Sci. 34, 71–78 (2014)
H. Kaur, M. Rattan, Improved offline multi-objective routing and wavelength assignment in optical networks. Front. Optoelectron. 12, 433–444 (2019)
E. Cuevas, F. Fausto, A. González, The Locust Swarm Optimization Algorithm. New Advancements in Swarm Algorithms: Operators and Applications (Springer, Cham, 2020)
T. Truong-Huu, P. M. Mohan, M. Gurusamy, Virtual network embedding in ring optical data centers using markov chain probability model. IEEE Trans. Netw. Service Manag. 16, 1724–1738 (2019)
Y. Xiao, J. Zhang, Y. Ji, Integrated resource optimization with WDM-based fronthaul for multicast-service beam-forming in massive MIMO-enabled 5G networks. Photon. Netw. Commun. 37, 349–360 (2019)
A. Wason, D. Malik, Performance investigation of hybrid optical amplifiers for high-speed optical networks. J. Opt. 49, 298–304 (2020)
R. Gu, Z. Yang, Y. Ji, Machine learning for intelligent optical networks: a comprehensive survey. J. Netw. Comput. Appl. 157, 102576 (2020)
L. Li, H.J. Li, Performance analysis of novel routing and spectrum allocation algorithm in elastic optical networks. Optik 212, 1646882020 (2020)
D. Wang, M. Zhang, Artificial intelligence in optical communications: from machine learning to deep learning. Front. Commun. Netw. 2, 656786 (2021)
Y. Na, D.K. Ko, Deep learning based high resolution recognition of fractional-spatial-mode encoded data for free-space optical communications. Sci. Reports 11(1), 1–1 (2021)
Y. Zhang, J. Xin, X. Li, S. Huang, Overview on routing and resource allocation based machine learning in optical networks. Opt. Fiber Technol 60, 102355 (2020)
G. L. Santos, P. T. Endo, D. Sadok, J. Kelner, When 5G meets deep learning: a systematic review. Algorithms 13, 208 (2020)
C. Häger, H.D. Pfister, Physics-based deep learning for fiber-optic communication systems. IEEE J. Sel. Areas Commun. 39(1), 280–294 (2020)
Z. Gao, M. Eisen, A. Ribeiro, Optimal WDM power allocation via deep learning for radio on free space optics systems, In: Intel Science and Technology Center for Wireless Autonomous Systems, (2019).
W. Mo, C.L. Gutterman, Y. Li, S. Zhu, G. Zussman, D.C. Kilper, Deep neural network based wavelength selection and switching in ROADM systems. J. Opt. Commun. Netw. 10(10), D1–D11 (2018)
T.A. Eriksson, H. Bülow, A. Leven, Applying Neural Networks in Optical Communication Systems: Possible Pitfalls. IEEE Photo. Technol. Letters 29(23), 2091–2094 (2017)
B. Karanov, M. Chagnon, F. Thouin, T.A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, L. Schmalen, End-to-end deep learning of optical fiber communications. J. Lightwave Technol. 36(20), 4843–4855 (2018)
M. Hamsaveni, Savita Choudhary, A multi-objective optimization algorithm for routing path selection and wavelength allocation for dynamic WDM network using MO-HLO. Int. J. Eng. Adv. Technol. (IJEAT) 10(5), 111–118 (2021)
R.K. Maurya, J. Thangaraj, V. Priye, Dynamic routing and wavelength assignment using cost based heuristics in WDM optical networks. Wireless Pers. Commun. 115(2), 971–992 (2020)
SS. Gonsalis, Triveni CL, A novel approach for static RWA based PLI evaluation of WDM networks in “WDM-NetSoft”, In: 1st International Conference on Advances in Information Technology, pp. 364–370, (2019)
Zeiler MD, Adadelta: an adaptive learning rate method, arXiv preprint arXiv:1212.5701, (2012)
B.C. Chatterjee, N. Sarma, P.P. Sahu, Priority based routing and wavelength assignment with traffic grooming for optical networks. J. Opt. Commun. Netw. 4(6), 480 (2012)
P.P. Sahu, A new shared protection scheme for optical networks. Current Sci J 91(9), 1176–1184 (2006)
B.C. Chatterjee, N. Sarma, P.P. Sahu, Priority based dispersion-reduced wavelength assignment for optical networks. J. Lightwave Technol. 31(2), 257–263 (2013)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rai, S., Garg, A.K. Deep learning—a route to WDM high-speed optical networks. J Opt 53, 737–745 (2024). https://doi.org/10.1007/s12596-022-00907-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12596-022-00907-y