June 01, 2017

How learning processes allows reducing uncertainties on network parameters and design margins

  • Delezoide C.
  • Pesic J.
  • Pointurier Y.
  • Seve E.

We show that, using a set of monitored physical parameters (received SNR, powers), the network planning tool can learn to reduce the uncertainties on system margins thanks to a more accurate estimation of the input parameters used in the Quality of Transmission (QoT) predictor tool.

Recent Publications

June 04, 2017

A New PRACH Transmission Scheme in Unlicensed Spectrum

  • Luo Z.
  • Meng Y.
  • Tao T.

For the unlicensed spectrum, the occupied bandwidth requirement is demanded by some regulations. The legacy scheme of Physical Random Access Channel (PRACH) for Long Term Evolution (LTE) cannot satisfy it. In this paper, we propose a novel PRACH transmission scheme to satisfy the requirement of unlicensed spectrum based on preamble ...

June 01, 2017

Mutual service processes in Euclidean spaces: existence and ergodicity

  • Baccelli F.
  • Mathieu F.
  • Norros I.

Consider a set of objects, abstracted to points of a spatially stationary point process in R-d, that deliver to each other a service at a rate depending on their distance. Assume that the points arrive as a Poisson process and leave when their service requirements have been fulfilled. We show ...

June 01, 2017

Incentivizing social media users for mobile crowdsourcing

  • Aiello L.
  • Karaliopoulos M.
  • Koutsopoulos I.
  • Micholia P.
  • Morales G.
  • Quercia D.

We focus on the problem of contributor-task matching in mobile crowd-sourcing. The idea is to identify existing social media users who possess domain expertise (e.g., photography) and incentivize them to perform some tasks (e.g., take quality pictures). To this end, we propose a framework that extracts the potential contributors' expertise ...