Ms. Naveenta Gautam Profile

Naveenta Gautam

at IIT Delhi

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 1 August 2021 Presentation + Paper

Neural networks for modelling nonlinear pulse propagation

Naveenta Gautam, Amol Choudhary, Brejesh Lall

Proceedings Volume 11843, 118430Q (2021) https://doi.org/10.1117/12.2596460

KEYWORDS: Nonlinear optics, Neural networks, Picosecond phenomena, Optical communications, Modeling, Terahertz radiation, Dispersion, Ultrafast phenomena, Telecommunications, Network architectures

Read Abstract +

The interaction of dispersion and nonlinear effects gives rise to a wide variety of pulse dynamics and proves to be a fundamental bottleneck for high speed communications. Traditionally, time consuming and computationally inefficient algorithms are used for this purpose¹ and therefore, research in nonlinear optics and optical communications is now implementing machine learning based methods.^2–5 We show a comprehensive comparison of different neural network (NN) architectures to learn the nonlinear Schrodinger equation (NLSE). We have used a NN based approach to reconstruct the pulse (temporal and spectral domain) at the transmitter from the pulse received through a highly nonlinear fiber (HNLF) without the prior knowledge of fiber parameters. Additionally, the trained network can also predict the dispersion and nonlinear parameters of an unknown fiber. The proposed NN also mitigates the need of using iterative reconstruction methods which are computationally expensive and slow. A detailed comparison of six different NN based techniques namely fully connected NN (FCNN), cascade NN (CaNN), convolutional NN (CNN), long short term memory networks (LSTM), bidirectional LSTM (BiLSTM) and gated recurrent unit (GRU) is presented. To our knowledge, the literature does not contain a detailed discussion of the NN architecture which is most suitable for learning the transfer function of the fiber. We perform a comprehensive study by including all popular NN architectures which enables the estimation of pulse profile for arbitrary pulse width, chirp, second and third order dispersion, nonlinearity and fiber length which can benefit nonlinear optics experiments and coherent optical communications. The growing popularity of NNs is resulting in increased design and development of hardware that is optimized for processing NN architectures. In light of this flexibility and optimised hardware, popularity of NN in optics is set to increase.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years