NDLI: Throughput maximization for opportunistic spectrum access via transmission probability scheduling scheme

Content Provider	IEEE Xplore Digital Library
Author	Yang Cao Daiming Qu Guohui Zhong Tao Jiang
Copyright Year	2010
Description	Author affiliation: Wuhan National Laboratory for Optoelectronics, Department of Electronics and Information Engineering, Huazhong University of Science and Technology, China (Yang Cao; Daiming Qu; Guohui Zhong; Tao Jiang)
Abstract	In this paper, we propose a novel transmission probability scheduling scheme for opportunistic spectrum access in cognitive radio networks. With the proposed scheme, the secondary user's throughput over a single channel is maximized while collision probability perceived by the primary user is constrained under a required threshold since the secondary user optimally schedules its transmission probabilities during the primary user's idle period. Moreover, we deeply study the maximum achievable throughput of the secondary user when the idle period of the primary user is different distribution to analyze the performance of the proposed transmission probability scheduling scheme. In a practical communication systems, where primary user's traffic pattern and parameters are unknown and the secondary user's spectrum sensing is imperfect, a novel predictor, which is based on hidden Markov model, is also proposed to predict the future channel states and to enable the transmission probability scheduling for the secondary user. Conducted simulations show that the proposed transmission probability scheduling scheme and predictor are effective and can offer good system performances.
Starting Page	1
Ending Page	6
File Size	138093
Page Count	6
File Format	PDF
e-ISBN	9780984589333
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	ICST
Subject Keyword	Schedules OFDM Hidden Markov models Interference Markov processes Throughput Sensors
Content Type	Text
Resource Type	Article

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