Dr. Benjamin A. Small Profile

Dr. Benjamin A. Small

at Columbia Univ

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Proceedings Article | 18 June 2004 Paper

Modeling, simulating, and characterizing performance in optical switching networks

J. Nathan Kutz, B. Small, W. Lu, K. Bergman

Proceedings Volume 5349, (2004) https://doi.org/10.1117/12.528067

KEYWORDS: Network architectures, Switching, Performance modeling, Wavelength division multiplexing, Data modeling, Optical networks, Electronics, Optical switching, Logic, Device simulation

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Photonic packet switching for all-optical networks is a rapidly developing technology since it circumvents many of the traditional bottlenecks created by the use of electronics. All-optical networking has application to both long-haul communications systems and high-performance computing systems. In each case, all-optical technologies are responsible for the routing, switching and logic decisions of the network. Characterizing the performance of a network includes calculating the latency and scalability of a given architecture assuming ideal behavior of its physical components. However, the physical layer ultimately determines the feasibility of data transmission. Thus accurately calculating the accumulated bit-error-rate (BER) is fundamental to evaluating the optical network as a whole, regardless of the network architecture. A new simulation technique, which is based upon experimental findings, is introduced which characterizes the physical layer performance of a given network architecture known as the Data Vortex. Experiments show that almost all the physical layer penalty is generated by the nodes which are used for switching and routing. Specifically, at each node data packets are amplified by a semiconductor optical amplifier so that coupling and routing losses are compensated. In this process, the data packets receive a noise penalty which results primarily from amplified spontaneous emission and in small part from spectral broadening. By using a phenomenological approach to modeling the noise penalties, the performance of the network nodes can be characterized. The modeling allows for a comprehensive understanding of the network and is a highly efficient computational tool for evaluating performance when compared to conventional time-domain techniques.

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