NDLI: A Proximity-Based Q-Learning Reward Function for Femtocell Networks

Content Provider	IEEE Xplore Digital Library
Author	Tefft, J.R. Kirsch, N.J.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of New Hampshire, Durham, NH, USA (Tefft, J.R.; Kirsch, N.J.)
Abstract	Q-learning has become a prominent tool for resource allocation and management in femtocell networks, as it can decrease the time the network takes to determine an allocation of resources by sharing information. The choice of reward function in Q-learning can greatly affect the performance of the Q-learning algorithm in the resource allocation process. In this work, we present a novel reward function for Q-learning that takes into consideration the femtocell proximity to mobile users. Components of the reward function are emphasized and de-emphasized as a function of distance, based on the influence of the agent on each component. In comparison with other methods, the proposed method ensures a certain level of service for the primary user and increases the sum capacity of the network.
Starting Page	1
Ending Page	5
File Size	139418
Page Count	5
File Format	PDF
ISBN	9781467361873
ISSN	10903038
DOI	10.1109/VTCFall.2013.6692057
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radio frequency Downlink Femtocell networks Resource management Interference Signal to noise ratio
Content Type	Text
Resource Type	Article

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