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We focus on the performance of the energy detector for cognitive radio applications. Our aim is to incorporate, into the energy detector, low-complexity algorithms that compute the performance of the detector itself. The main parameters of interest are the probability of detection and the required number of samples. Since the exact performance analysis involves complicated functions of two variables,...
We examine the small sample size performance of the energy detector for spectrum sensing in AWGN. By making use of the cube-of-Gaussian approximation of chi-squared random variables, we derive a novel, simple, and accurate analytical expression for the minimum number of samples required to achieve a desired probability of detection and false alarm. This way, the number of samples can be calculated...
We propose a three-stage centralized spectrum sensing for cognitive radios in OFDM systems, where local decisions of cooperative sensors are combined at the fusion center. The fusion center, if necessary, can invoke a third stage for finer sensing. The first two stages employ distributed energy detectors and a centralized voting rule, while the third stage exploits the OFDM signal correlation. The...
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