Presentation 2012-01-27
Learning-based Cell Selection for Open-access Femtocell Networks
Chaima Dhahri, Tomoaki Ohtsuki,
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Abstract(in English) In an open-access femtocell networks, nearby cellular users (Macro User: MU) may join one of the neighboring femtocells to enhance their capacity through a handover procedure. To avoid undesirable effects such as the ping-pong effect after a handover, the effectiveness of cell selection method must be ensured. Previous work related to such a problem is based on instantaneous measure of single or multiple metrics, e.g. capacity, received signal strength (RSS), load, 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 horizon. In this report, we propose a Reinforcement Learning (RL) Q-learning algorithm as a model-free solution for the cell selection problem in a non-stationary femtocell network. The MU takes advantage of the RL algorithm, during a handover decision, to estimate the efficiency of neighboring femtocells through trial-and-error interaction with its environment. The simulation results show the benefits of using learning in terms of the gained capacity and the number of handovers with respect to different selection methods in the literature (least loaded (LL), random and capacity-based).
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Keyword(in English) Femtocell networks / cell selection / handover / reinforcement learning / Q-learning
Paper # SIP2011-121,RCS2011-310
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Committee RCS
Conference Date 2012/1/19(1days)
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Registration To Radio Communication Systems (RCS)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Learning-based Cell Selection for Open-access Femtocell Networks
Sub Title (in English)
Keyword(1) Femtocell networks
Keyword(2) cell selection
Keyword(3) handover
Keyword(4) reinforcement learning
Keyword(5) Q-learning
1st Author's Name Chaima Dhahri
1st Author's Affiliation Graduate School of Science and Technology, Keio University()
2nd Author's Name Tomoaki Ohtsuki
2nd Author's Affiliation Faculty of Science and Technology, Keio University
Date 2012-01-27
Paper # SIP2011-121,RCS2011-310
Volume (vol) vol.111
Number (no) 404
Page pp.pp.-
#Pages 6
Date of Issue