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

Demonstration of a 5-Gbit/s optical time-division multiple-access network

Not Accessible

Your library or personal account may give you access

Abstract

Optical microarea networks (μANs) are proposed as a way of providing flexible communications among VLSI processors and eliminating electrical I/O bottlenecks. Time-division multiple access (TDMA) may be more practical to implement in a μAN than other shared-medium multiple access protocols such as wavelength-division, frequency-division, or code-division. The passive-star architecture of the TDMA μAN investigated in this work is illustrated in Fig. 1 and described in detail elsewhere.1 This network uses a 100-MHz repetition-rate mode-locked laser with external modulators to generate the baseband data and operates at a multiplexed data rate of 5 Gbit/s to support up to 50 user channels. Figure 2(a) is an oscilloscope trace of the portion of the 10-ns data frame showing channels 50, 1,2, and 25 transmitting a one bit. The performance of this network relies on two key components. One component is variable-integer delay line which must provide a rapid high-precision wide tuning range for distribution of the system clock.2 The other component is a high-speed optical correlation receiver.

© 1992 Optical Society of America

PDF Article
More Like This
Demonstration on a programmable ultrafast all-optical code-division multiple access network

Wing C. Kwong, Yan-Ming Liu, and Paul R. Prucnal
CMH2 Conference on Lasers and Electro-Optics (CLEO:S&I) 1992

100 Gbit/s Synchronous All-Optical Time-Division Multiplexing Multi-Access Network Testbed

S. A. Hamilton and B. S. Robinson
ThEE3 Optical Fiber Communication Conference (OFC) 2002

Programmable ultrafast all-optical code-division multiple access networks

Wing C. Kwong and Paul R. Prucnal
WH7 Optical Fiber Communication Conference (OFC) 1992

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.