60 research outputs found
Optimal control of Beneš optical networks assisted by machine learning
Optimal control of Beneˇs optical networks
assisted by machine learning
Ihtesham Khana, Lorenzo Tunesia, Muhammad Umar Masooda, Enrico Ghillinob,
Paolo Bardellaa, Andrea Carenaa, and Vittorio Curria
aPolitecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy
bSynopsys Inc., Executive Blvd 101, Ossining, New York, USA
ABSTRACT
Beneˇs 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 non-
blocking 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 Beneˇs 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
A Data-Driven Approach to Autonomous Management of Photonic Switching System
We propose a data-driven approach based on Machine Learning (ML) to predict control signals of a photonic switching system. The proposed ML agent is trained and tested in a completely topological and technological agnostic way and we envision its application in real-time control-planes
Softwarized and Autonomous Management of Photonic Switching Systems Using Machine Learning
We propose a machine learning-based approach for the management of photonic switching systems in a software-defined network context. This work aims to describe a soft-warized system that is both topological and technological agnostic and can be employed in real-time
Performance Analysis of Novel Multi-band Photonic-integrated WSS Operated on 400ZR
We present a detailed performance analysis of a novel photonic integrated wide-band wavelength selective switch operating in S+c+L bands. The results demonstrate that the proposed device offers low loss and frequency flat behavior for the considered band in a single or cascade implementation
Two-step machine learning assisted extraction of VCSEL parameters
We propose a Machine Learning (ML) assisted procedure to extract Vertical Cavity Surface Emitting Lasers (VCSELs) parameters from Light-Current (L-I) and S21 curves using a two-step algorithm to ensure high accuracy of the prediction. In the first step, temperature effects are not included and a Deep Neural Network (DNN) is trained on a dataset of 10000 mean-field VCSEL simulations, obtained changing nine temperature-independent parameters. The agent is used to retrieve those parameters from experimental results at a fixed temperature. Secondly, additional nine temperature-dependent parameters are analyzed while keeping as constant the extracted ones and changing the operation temperature. In this way a second dataset of 10000 simulations is created and a new agent in trained to extract those parameters from temperature-dependent L-I and S21 curves
Machine Learning-based Model for Defining Circuit-level Parameters of VCSEL
Recently, many computationally efficient models have been introduced to accurately define the static and dynamic Vertical Cavity Surface Emitting Laser (VCSEL) behaviors. However, in these models, many physical parameters must be appropriately set to reproduce existing laser sources' behavior accurately. The extraction of these unknown physical parameters from experimental curves is generally time-consuming and relies mainly on trial and error approaches or regression analysis, requiring extra effort. In this scenario, we propose a machine learning-based solution to the problem, which can effectively extract the required VCSEL parameters from experimental data in real-time. The proposed approach predicts the parameters exploiting the light-current curve and small-signal modulation responses with two steps at constant and variable temperature, respectively. Promising results are achieved in terms of relative prediction error
Novel Design and Operation of Photonic- integrated WSS for Ultra-wideband Applications
Photonic integrated solutions for switching applications can yield large bandwidth and high reconfigurability while requiring low power and footprint. We propose a modular, scalable photonic integrated multi-band wavelength selective switch, able to independently route the input fiber channels to an arbitrary number of output ports
Modular and scalable photonic integrated multi-band wavelength-selective switch
Today’s optical transmission landscape is seeing a rapid increase in resource demand, due to bandwidth-intensive
applications, emerging standards, such as 5G, as well as the expansion of the Internet-of-Things (IoT) paradigm. This
requires an expansion of the current optical network infrastructure and capability, accommodating the increasing
demand [1]. From the network operator standpoint, two main solutions are available: new infrastructure can be
deployed, which represents the expensive solution, or the residual capacity of the existing network can be exploited
through multi-band paradigms, which represents the more cost-effective solution [2].
To achieve the full utilization of the remaining available fiber spectrum, new technologies such as Band-Division
Multiplexing (BDM) must be enabled on top of the already existing Wavelength-Division Multiplexing (WDM) based
network. This requires switching and filtering elements suited for an ultra-wide bandwidth of operation, allowing
consistent performances in the whole needed spectrum. For this purpose, photonic integrated circuits (PICs)
represent an ideal solution, as they provide a large bandwidth of operation while maintaining low footprint, cost,
and power consumption. To this end, we propose a fully integrated modular wavelength-selective switch (WSS),
able to independently route each of the input signal channels towards the desired output port, operating on the
S+C+L optical transmission windows
Autonomous Data-driven Model for Extraction of VCSEL Circuit-level Parameters
In recent years, a number of computationally efficient models have been developed that adequately describe the static and dynamic behavior of the Vertical Cavity Surface Emitting Laser (VCSEL). In order to correctly recreate the behavior of existing laser sources, a large number of physical parameters must be specified. Finding these unknown physical characteristics in experimental curves may be time-consuming, and mainly requires trial and error processes or regression analysis. Instead of manually analyzing experimental data to find the best VCSEL parameters, we propose a Machine Learning (ML) based solution to automate the process. The proposed approach exploits the parametric dataset obtained from Light-current and Small-signal modulation responses to extract the required model parameters. Excellent results are obtained in terms of relative prediction error
Machine Learning Assisted Extraction of Vertical Cavity Surface Emitting Lasers Parameters
We propose a machine learning-based framework to extract circuit-level VCSEL model parameters. The proposed approach predicts the parameters exploiting the light-current curve and small-signal modulation responses with two steps at constant and variable temperature, respectively. Promising results are achieved in terms of relative prediction error
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