NDLI: Spectrum sensing under distribution uncertainty in cognitive radio networks

Content Provider	IEEE Xplore Digital Library
Author	Shimin Gong Ping Wang Wei Liu
Copyright Year	2012
Description	Author affiliation: Nanyang Technological University, Singapore (Shimin Gong; Ping Wang) \|\| Huazhong University of Science and Technology, China (Wei Liu)
Abstract	The successful coexistence of cognitive radio systems with licensed system requires the secondary users the capability of interference-awareness, i.e., knowing which spectrum bands are occupied by primary users, i.e., the legacy users. Spectrum sensing thus is a key enabling module, which usually models the sensing process as a binary hypothesis testing assuming known signal distribution. However, an unrealistic assumption regarding the signal distribution easily leads to unreliable detection probability. In this paper, we study the sensing performance considering the distribution uncertainty in hypothesis testing, i.e., the actual distribution function of the received signal strength is not known. According to different signal characteristics, we define appropriate uncertainty sets respectively for different hypotheses. Then we present an approximate approach to determine the robust decision threshold, and investigate the performance bounds for the detection probability under distribution uncertainty. Moreover, we provide an analytical expression for the lower bound of detection probability. Numerical results are given to validate our conclusions.
Starting Page	1512
Ending Page	1516
File Size	330692
Page Count	5
File Format	PDF
ISBN	9781457720529
ISSN	15503607
e-ISBN	9781457720536
e-ISBN	9781457720512
DOI	10.1109/ICC.2012.6363671
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Robustness Sensors Noise Gaussian distribution Upper bound Cognitive radio distribution uncertainty spectrum sensing
Content Type	Text
Resource Type	Article

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