Optimal control of Beneš optical networks assisted by machine learning

Ihtesham Khan; Lorenzo Tunesi; Muhammad Umar Masood; Enrico Ghillino; Paolo Bardella; Andrea Carena; Vittorio Curri

doi:10.1117/12.2608595

3 March 2022 Optimal control of Beneš optical networks assisted by machine learning

Ihtesham Khan, Lorenzo Tunesi, Muhammad Umar Masood, Enrico Ghillino, Paolo Bardella, Andrea Carena, Vittorio Curri

Proceedings Volume 12028, Next-Generation Optical Communication: Components, Sub-Systems, and Systems XI; 120280I (2022) https://doi.org/10.1117/12.2608595
Event: SPIE OPTO, 2022, San Francisco, California, United States

Conference Poster

Abstract

Beneš networks represent an excellent solution for the routing of optical telecom signals in integrated, fully reconfigurable networks because of their limited number of elementary 2x2 crossbar switches and their nonblocking properties. Various solutions have been proposed to determine a proper Control State (CS) providing the required permutation of the input channels; since for a particular permutation, the choice is not unique, the number of cross-points has often been used to estimate the cost of the routing operation. This work presents an advanced version of this approach: we deterministically estimate all (or a reasonably large number of) the CSs corresponding to the permutation requested by the user. After this, the retrieved CSs are exploited by a data-driven framework to predict the Optical Signal to Noise Ratio (OSNR) penalty for each CS at each output port, finally selecting the CS providing minimum OSNR penalty. Moreover, three different data-driven techniques are proposed, and their prediction performance is analyzed and compared.

The proposed approach is demonstrated using 8x8 Beneš architecture with 20 ring resonator-based crossbar switches. The dataset of 1000 OSNRs realizations is generated synthetically for random combinations of the CSs using Synopsys® Optsim™ simulator. The computational cost of the proposed scheme enables its real-time operation in the field.

Conference Presentation

Citation Download Citation

Ihtesham Khan, Lorenzo Tunesi, Muhammad Umar Masood, Enrico Ghillino, Paolo Bardella, Andrea Carena, and Vittorio Curri "Optimal control of Beneš optical networks assisted by machine learning", Proc. SPIE 12028, Next-Generation Optical Communication: Components, Sub-Systems, and Systems XI, 120280I (3 March 2022); https://doi.org/10.1117/12.2608595

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available