February 20, 2014

Spatial Multiplexing in Fiber Optics: The 10X Scaling of Metro/Core Capacities

  • Winzer P.

The amount of information created and replicated annually--not only by individuals, but increasingly by machines through distributed computing, sensor networks, and cloud-based services--has long surpassed the Zettabyte mark. Growth rates range between 40% and 50% per year, and microprocessing capabilities are growing at rates approaching 80% annually, triggering proportional growth in computer interface rates. Consequently, bandwidth demands across all network segments--from local-area and access networks to inter- and intra-datacenter networks and metro and core networks--are growing at comparable rates as well. This paper will examine technology options that will allow optical interface rates and network capacities to scale to the requirements of the decade ahead.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...