NDLI: Exploiting spatial diversity in multiagent reinforcement learning based spectrum sensing

Content Provider	IEEE Xplore Digital Library
Author	Lunden, J. Koivunen, V. Kulkarni, S.R. Poor, H.V.
Copyright Year	2011
Description	Author affiliation: Princeton University, Department of Electrical Engineering, NJ, USA (Kulkarni, S.R.; Poor, H.V.) \|\| Aalto University, SMARAD CoE, Department of Signal Processing and Acoustics, Finland (Lunden, J.; Koivunen, V.)
Abstract	In this paper a multiband, multiagent reinforcement learning based distributed sensing policy for cognitive radio networks is proposed. In the proposed sensing policy the secondary users (SUs) collaborate with neighboring users by exchanging information locally. The objective is to maximize the amount of free spectrum found for secondary use while guaranteeing a certain probability of detection. The SUs employ spatial diversity through collaborative sensing to control the false alarm rate and thus the probability of finding available spectrum opportunities. The SUs in the cognitive radio network make local decisions based on their own and their neighbors' local test statistics to identify unused spectrum locally. Simulation results show that the proposed sensing policy provides a straightforward approach for obtaining a good tradeoff between sensing more spectrum and the reliability of the sensing results.
Starting Page	325
Ending Page	328
File Size	349271
Page Count	4
File Format	PDF
ISBN	9781457721045
e-ISBN	9781457721052
DOI	10.1109/CAMSAP.2011.6136016
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Collaboration Probability Time frequency analysis Vectors Sensors Cognitive radio
Content Type	Text
Resource Type	Article

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