Learning-based cell selection method for femtocell networks

Chaima Dhahri, Tomoaki Ohtsuki

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

24 Citations (Scopus)


In open-access non-stationary femtocell networks, cellular users (also known as macro users or MU) may join, through a handover procedure, one of the neighboring femtocells so as to enhance their communications/increase their respective channel capacities. To avoid frequent communication disruptions owing to effects such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell selection method. Traditionally, such selection method is usually a measured channel/cell quality metric such as the channel capacity, the load of the candidate cell, the received signal strength (RSS), etc. However, one problem with such approaches is that present measured performance does not necessarily reflect the future performance, thus the need for novel cell selection that can predict the \textit{horizon}. Subsequently, we present in this paper a reinforcement learning (RL), i.e, Q- learning algorithm, as a generic solution for the cell selection problem in a non-stationary femtocell network. After comparing our solution for cell selection with different methods in the literature (least loaded (LL), random and capacity-based), simulation results demonstrate the benefits of using learning in terms of the gained capacity and the number of handovers.

Original languageEnglish
Title of host publicationIEEE 75th Vehicular Technology Conference, VTC Spring 2012 - Proceedings
Publication statusPublished - 2012 Aug 20
EventIEEE 75th Vehicular Technology Conference, VTC Spring 2012 - Yokohama, Japan
Duration: 2012 May 62012 Jun 9

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


OtherIEEE 75th Vehicular Technology Conference, VTC Spring 2012

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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