Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Performance improvement of bandwidth-flexible reconfigurable optical add/drop multiplexers with wavelength converters

Not Accessible

Your library or personal account may give you access

Abstract

In this paper, we propose a novel bandwidth-flexible reconfigurable optical add/drop multiplexer (ROADM) architecture based on coherent optical-orthogonal frequency division multiplexing (CO-OFDM) technology. The bandwidth-flexible ROADM architecture enables sub-wavelength, superwavelength, and multiple-rate data traffic accommodation in a highly spectrum-efficient manner, thereby providing a fractional bandwidth service. We simulate and compare the blocking performance of bandwidth-flexible ROADM with and without wavelength converters. It is found that wavelength converter could obviously improve the blocking performance of bandwidth-flexible ROADM with different frequency grid. Moreover, the conversion ratios are calculated for different load and channel spacing. Based on the analysis of conversion ratio, we could make an appropriate configuration of wavelength converters in bandwidth-flexible ROADM.

© 2011 Optical Society of America

PDF Article
More Like This
Reconfigurable Optical Add/Drop Multiplexer Based on Bidirectional Wavelength Selective Switches

Philip N. Ji, Yoshiaki Aono, and Ting Wang
PWB1 Photonics in Switching (PS) 2010

Reconfigurable Optical Add/Drop Multiplexer Realized with Novel Tunable Devices

Lei Zong, Philip Ji, Ting Wang, Hongbo Liu, and Osamu Matsuda
NThK1 National Fiber Optic Engineers Conference (NFOEC) 2005

Low power and compact eight-channel reconfigurable optical add-drop multiplexers based on cascaded microring resonators

Yonghui Tian, Ruiqiang Ji, Lei Zhang, Jianfeng Ding, Hongtao Chen, and Lin Yang
83070O Asia Communications and Photonics Conference and Exhibition (ACP) 2011

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.