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A statistical approach to spectrum sensing using bayes factor and p-Values
by
Ravinder, Y.
, Reddy, Deepa N.
in
Antennas
/ Cognitive radio
/ Computer simulation
/ Detection
/ Error detection
/ Error reduction
/ Hypotheses
/ Hypothesis testing
/ Signal to noise ratio
/ Statistical tests
2019
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A statistical approach to spectrum sensing using bayes factor and p-Values
by
Ravinder, Y.
, Reddy, Deepa N.
in
Antennas
/ Cognitive radio
/ Computer simulation
/ Detection
/ Error detection
/ Error reduction
/ Hypotheses
/ Hypothesis testing
/ Signal to noise ratio
/ Statistical tests
2019
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A statistical approach to spectrum sensing using bayes factor and p-Values
Journal Article
A statistical approach to spectrum sensing using bayes factor and p-Values
2019
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Overview
The sensing methods with multiple receive antennas in the Cognitive Radio (CR) device, provide a promising solution for reducing the error rates in the detection of the Primary User (PU) signal. The received Signal to Noise Ratio at the CR receiver is enhanced using the diversity combiners. This paper proposes a statistical approach based on minimum Bayes factors and p-Values as diversity combiners in the spectrum sensing scenario. The effect of these statistical measures in sensing the spectrum in a CR environment is investigated. Through extensive Monte Carlo simulations it is shown that this novel statistical approach based on Bayes factors provides a promising solution to combine the test statistics from multiple receiver antennas and can be used as an alternative to the conventional hypothesis testing methods for spectrum sensing. The Bayesian results provide more accurate results when measuring the strength of the evidence against the hypothesis.
Publisher
IAES Institute of Advanced Engineering and Science
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