January 01, 2017

An Accurate Field Model Requiring Minimal Map Data for Guiding and Diffusion in Streets and Buildings

Theory of normal mode propagation in a line-ofsight street scenario is extended to include propagation into buildings through coupling to a diffuse indoor field. Signal strength predictions are in close agreement with measurements, producing 2 dB and 3:5 dB root-mean-square model-data difference, in line-of-sight and outdoor-indoor scenarios, respectively. The full 3D field model predicts actual signal directions and antenna correlations as a function of range, important for evaluating performance of directional antennas and spatial diversity. Only minimal description of the environment is needed, i.e. street width and representative building wall properties, without any interior details.

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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 ...