TY - CONF T1 - Detection of Gaussian signals in unknown time-varying channels T2 - IEEE Statistical Signal Processing Workshop (SSP 2012) Y1 - 2012 A1 - Daniel Romero A1 - Javier Vía A1 - R. López-Valcarce A1 - Ignacio Santamaría KW - cognitive radio KW - dynacs KW - spectrum sensing AB -
Detecting the presence of a white Gaussian signal distorted by a noisy time-varying channel is addressed by means of three different detectors. First, the generalized likelihood ratio test (GLRT) is found for the case where the channel has no temporal structure, resulting in the well-known Bartlett’s test. Then it is shown that, under the transformation group given by scaling factors, a locally most powerful invariant test (LMPIT) does not exist. Two alternative approaches are explored in the low signal-to-noise ratio (SNR) regime: the first assigns a prior probability density function (pdf) to the channel (hence modeled as random), whereas the second assumes an underlying basis expansion model (BEM) for the (now deterministic) channel and obtains the maximum likelihood (ML) estimates of the parameters relevant for the detection problem. The performance of these detectors is evaluated via Monte Carlo simulation.
JF - IEEE Statistical Signal Processing Workshop (SSP 2012) PB - IEEE CY - Ann Arbor, MI ER -