In this paper we present a unified comparison of the performance of four\r\ndetection techniques for centralized data-fusion cooperative spectrum sensing in cognitive\r\nradio networks under impulsive noise, namely, the eigenvalue-based generalized likelihood\r\nratio test (GLRT), the maximum-minimum eigenvalue detection (MMED), the maximum\r\neigenvalue detection (MED), and the energy detection (ED). We consider two system\r\nmodels: an implementation-oriented model that includes the most relevant signal processing\r\ntasks realized by a real cognitive radio receiver, and the theoretical model conventionally\r\nadopted in the literature. We show that under the implementation-oriented model, GLRT\r\nand MMED are quite robust under impulsive noise, whereas the performance of MED and\r\nED is drastically degraded. We also show that performance under the conventional model\r\ncan be too pessimistic if impulsive noise is present, whereas it can be too optimistic in the\r\nabsence of this impairment. We also discuss the fact that impulsive noise is not such a severe\r\nproblem when we take into account the more realistic implementation-oriented model.
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