November 01, 2017

Massive MIMO with Max-Min Power Control in Line-of-Sight Propagation Environment

  • Marzetta T.
  • Yang H.

A key component for the success of Massive MIMO is the asymptotic orthogonality of channel vectors. With M service antennas, the expected correlation between a pair of channel vectors in line-of-sight (LoS) propagation environment decreases at least as fast as log(M)/M while its counterpart in independent and identically distributed (iid) Rayleigh fading propagation environment decreases much slower at 1/sqrt(M), but the variance is higher in LoS. This signifies that most likely channel vectors are more nearly orthogonal in LoS than in iid Rayleigh, but in the worst case they can be highly correlated in LoS. Massive MIMO with max-min power control performs comparably in LoS and iid Rayleigh when a simple algorithm is applied to drop a few users with high channel vector correlation from service in LoS.

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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 ...