Energy-efficient proactive scheduling in ultra dense networks

Matthieu De Mari, Tony Quek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


In this paper, we investigate the energy efficiency (EE) performance of optimal scheduling strategies in ultradense networks (UDNs). We consider a network deployment of base stations (BSs) based on a homogeneous Poisson Point Process (PPP), and assume users requests, modeled according to a space-time homogeneous Point Process (STPP), are to be served within a given service time. The objective is to define the optimal scheduling strategy, that allows to serve every request during its required service time, while minimizing the energy consumed in the process. The optimization consists of a Dynamic Stochastic Game (DSG), which is hard to solve in the UDNs context, due to the coupling of interference, the large number of elements interacting, as well as uncertainties on the channel dynamics, interference and future requests. Our contribution lies in addressing the inherent complexity issue of the DSG, by transitioning into an equivalent and more tractable Mean Field Game (MFG). By combining the MFG framework with elements of stochastic geometry and queuing theory, the analysis of the optimal scheduling strategies is then conducted. The provided numerical simulations give good insights on notable performance gains, in terms of EE.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Communications, ICC 2017
EditorsMerouane Debbah, David Gesbert, Abdelhamid Mellouk
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467389990
Publication statusPublished - 2017 Jul 28
Event2017 IEEE International Conference on Communications, ICC 2017 - Paris, France
Duration: 2017 May 212017 May 25

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Other2017 IEEE International Conference on Communications, ICC 2017

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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