Ensemble Techniques for Scheduling in Heterogeneous Wireless CommunicationscNetworks

  • Claussen H.
  • David Lynch
  • Kucera S.
  • Michael Fenton
  • Michael O’Neill

Operators can increase the capacity of their wireless networks by installing Small Cells in high traffic regions. However, User Equipments (UEs) at the edges of Small Cells suffer severe interference from neighbouring high-powered Macro Cells. It is desirable to tradeoff the rates of cell-centre UEs, so that cell-edge UEs do not experience dropped calls or unacceptably slow rates. Fairness can be achieved by intelligently scheduling Small Cell attached UEs. Grammar-based Genetic Programming is employed to automatically evolve models which map measurement reports to schedules on a millisecond timescale. The evolved models operate as ensembles. The proposed system significantly outperforms a state of the art benchmark algorithm and is within 7.5% of the estimated optimum.

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 ...