Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97009
Title: Channel status prediction for cognitive radio networks
Authors: Tumuluru, Vamsi Krishna
Wang, Ping
Niyato, Dusit
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2010
Source: Tumuluru, V. K., Wang, P., & Niyato, D. (2012). Channel status prediction for cognitive radio networks. Wireless Communications and Mobile Computing, 12(10), 862-874.
Series/Report no.: Wireless communications and mobile computing
Abstract: The cognitive radio (CR) technology appears as an attractive solution to effectively allocate the radio spectrum among the licensed and unlicensed users. With the CR technology the unlicensed users take the responsibility of dynamically sensing and accessing any unused channels (frequency bands) in the spectrum allocated to the licensed users. As spectrum sensing consumes considerable energy, predictive methods for inferring the availability of spectrum holes can reduce energy consumption of the unlicensed users to only sense those channels which are predicted to be idle. Prediction-based channel sensing also helps to improve the spectrum utilization (SU) for the unlicensed users. In this paper, we demonstrate the advantages of channel status prediction to the spectrum sensing operation in terms of improving the SU and saving the sensing energy. We design the channel status predictor using two different adaptive schemes, i.e., a neural network based on multilayer perceptron (MLP) and the hidden Markov model (HMM). The advantage of the proposed channel status prediction schemes is that these schemes do not require a priori knowledge of the statistics of channel usage. Performance analysis of the two channel status prediction schemes is performed and the accuracy of the two prediction schemes is investigated.
URI: https://hdl.handle.net/10356/97009
http://hdl.handle.net/10220/11732
ISSN: 1530-8677
DOI: 10.1002/wcm.1017
Rights: © 2010 John Wiley & Sons, Ltd.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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