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

Performance evaluation of data-driven techniques for the softwarized and agnostic management of an N×N photonic switch

The emerging Software Defined Networking (SDN) paradigm paves the way for flexible and automatized management at each layer. The SDN-enabled optical network requires each network element’s software abstraction to enable complete control by the centralized network controller. Nowadays, silicon photonics due to its low energy consumption, low latency, and small footprint is a promising technology for implementing photonic switching topologies, enabling transparent lightpath routing in re-configurable add-drop multiplexers. To this aim, a model for the complete management of photonic switching systems’ control states is fundamental for network control. Typically, photonics-based switches are structured by exploiting the modern technology of Photonic Integrated Circuit (PIC) that enables complex elementary cell structures to be driven individually. Thus PIC switches’ control states are combinations of a large set of elementary controls, and their definition is a challenging task. In this scenario, we propose the use of several data-driven techniques based on Machine Learning (ML) to model the control states of a PIC N×N photonic switch in a completely blind manner. The proposed ML-based techniques are trained and tested in a completely topological and technological agnostic way, and we envision their application in a real-time control plane. The proposed techniques’ scalability and accuracy are validated by considering three different switching topologies: the Honey-Comb Rearrangeable Optical Switch (HCROS), Spanke-Beneš, and the Beneš network. Excellent results in terms of predicting the control states are achieved for all of the considered topologies

Machine learning Assisted Accurate Estimation of QoT Impairments of Photonics Switching System on 400ZR

We propose a machine learning-based technique that accurately estimates quality-of-transmission (QoT) impairments of an optical switch on 400ZR. The proposed scheme works in an entirely agnostic way reduces inaccuracy in QoT impairments estima­tion by 1.5 dB

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

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

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

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

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

Modular Photonic-Integrated Device for Multi-Band Wavelength-Selective Switching

We propose a Silicon Photonics based WSS for S+C+L bands, independently routing any input channel to the desired output fiber. BER and OSNR for a system with 30 total channels are evaluated with Synopsys Optsim