Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 39,
  • Issue 19,
  • pp. 6097-6106
  • (2021)

Effects of Receiver-Side Optical Filtering On Optical Superchannel System Performance

Not Accessible

Your library or personal account may give you access

Abstract

Optical superchannels provide a route to obtaining the highest achievable data rates in optical fiber systems. Here, we investigate the role of optical filtering on system performance and required optical signal-to-noise ratio, when receiving a single sub-band from an optical superchannel, where the dominant noise source changes from optical to transceiver noise. We find that optical filtering can provide improvement up to 3 dB in signal quality and around 1 b/symbol in information rate, in an experiment where transceiver noise is dominant.

PDF Article
More Like This
Building up low-complexity spectrally-efficient Terabit superchannels by receiver-side duobinary shaping

Jianqiang Li, Martin Sjödin, Magnus Karlsson, and Peter A. Andrekson
Opt. Express 20(9) 10271-10282 (2012)

Parameter Selection in Optical Networks With Variable-Code-Rate Superchannels

André L. N. Souza, Eduardo J. Mayoral Ruiz, Jacklyn D. Reis, Luis H. H. Carvalho, Juliano R. F. Oliveira, Dalton S. Arantes, Max H. M. Costa, and Darli A. A. Mello
J. Opt. Commun. Netw. 8(7) A152-A161 (2016)

Nyquist WDM superchannel using offset-16QAM and receiver-side digital spectral shaping

Meng Xiang, Songnian Fu, Ming Tang, Haoyuan Tang, Perry Shum, and Deming Liu
Opt. Express 22(14) 17448-17457 (2014)

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.