@conference {654,
	title = {Detection of Gaussian signals in unknown time-varying channels},
	booktitle = {IEEE Statistical Signal Processing Workshop (SSP 2012)},
	year = {2012},
	month = {08/2012},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Ann Arbor, MI},
	abstract = {<div>Detecting the presence of a white Gaussian signal distorted by a\&nbsp;noisy time-varying channel is addressed by means of three different\&nbsp;detectors. First, the generalized likelihood ratio test (GLRT) is\&nbsp;found for the case where the channel has no temporal structure, resulting\&nbsp;in the well-known Bartlett{\textquoteright}s test. Then it is shown that, under\&nbsp;the transformation group given by scaling factors, a locally most\&nbsp;powerful invariant test (LMPIT) does not exist. Two alternative approaches\&nbsp;are explored in the low signal-to-noise ratio (SNR) regime:\&nbsp;the first assigns a prior probability density function (pdf) to the channel\&nbsp;(hence modeled as random), whereas the second assumes an underlying\&nbsp;basis expansion model (BEM) for the (now deterministic)\&nbsp;channel and obtains the maximum likelihood (ML) estimates of the\&nbsp;parameters relevant for the detection problem. The performance of\&nbsp;these detectors is evaluated via Monte Carlo simulation.</div>},
	keywords = {cognitive radio, dynacs, spectrum sensing},
	doi = {10.1109/SSP.2012.6319858},
	author = {Daniel Romero and Javier V{\'\i}a and R. L{\'o}pez-Valcarce and Ignacio Santamar{\'\i}a}
}
