October 08, 2017

Near Far Effect on Coded Slotted Aloha

  • Alloum A.
  • Cedric Adjih
  • Ehsan Ebrahimi Khaleghi
  • Paul Muhlethaler

Motivated by scenario requirements for 5G cellular networks, we study one among the protocols candidate to the massive random access: the family of random access methods known as Coded Slotted ALOHA (CSA). Recent body of research has explored aspects of such methods in various contexts, but one aspect has not been fully taken into account: the impact of the path loss, which is a major design constraint in long-range wireless networks. In this article, we explore the behavior of CSA, by focusing on the path loss component correlated to the distance to the base station. Path loss provides opportunities for capture, improving the performance of CSA. We revise methods for estimating CSA behavior, provide bounds of performance, and then, focusing on the achievable throughput, we extensively explore the key parameters, and their associated gain (experimentally). Our results shed light on the open question of the optimal distribution of repetitions in actual wireless networks.

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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
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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 ...