Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 32,
  • Issue 5,
  • pp. 978-985
  • (2014)

Analytical Prediction of the Main Oscillation Power and Spurious Levels in Optoelectronic Oscillators

Not Accessible

Your library or personal account may give you access

Abstract

We use a nonlinear analytic approach to predict the main oscillation mode power and spurious levels in ultrapure microwave optoelectronic oscillators (OEOs). This approach takes into account N simultaneously mode falling inside the RF filter bandwidth and calculates all the dominant InterModulation Products (IMPs) that fall in the fundamental zone. We show that nonlinear microwave photonic links exhibit the capture effect. By considering this effect, we derive analytical expressions that govern the behavior of the OEOs in the steady state. We find that when the small-signal open-loop gain is increased beyond a critical value, OEOs start a multimode operation from which the spurious levels grow rapidly. Our analytical predictions are verified by numerical simulations and experimental data.

© 2013 IEEE

PDF Article
More Like This
Prediction of the noise spectrum in optoelectronic oscillators: an analytical conversion matrix approach

Sajad Jahanbakht, S. Esmail Hosseini, and Ali Banai
J. Opt. Soc. Am. B 31(8) 1915-1925 (2014)

Spurious mode reduction in dual injection-locked optoelectronic oscillators

O. Okusaga, E. J. Adles, E. C. Levy, W. Zhou, G. M. Carter, C. R. Menyuk, and M. Horowitz
Opt. Express 19(7) 5839-5854 (2011)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.