Detection of Gaussian signals in unknown time-varying channels
Daniel Romero, Javier Vía, Roberto López-Valcarce, Ignacio Santamaría
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.
2012 IEEE Statistical Signal Processing Workshop, SSP 2012
https://doi.org/10.1109/SSP.2012.6319858
2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Detection theory
basis expansion model
generalized likelihood ratio
locally most powerful invariant
time-varying channels
10.1109/SSP.2012.6319858
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Detectors Engineering & Materials Science
Probability density function Engineering & Materials Science
Maximum likelihood Engineering & Materials Science
Romero, D., Vía, J., López-Valcarce, R., & Santamaría, I. (2012). Detection of Gaussian signals in unknown time-varying channels. In 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 (pp. 916-919). [6319858] (2012 IEEE Statistical Signal Processing Workshop, SSP 2012). https://doi.org/10.1109/SSP.2012.6319858
Detection of Gaussian signals in unknown time-varying channels. / Romero, Daniel; Vía, Javier; López-Valcarce, Roberto; Santamaría, Ignacio.
2012 IEEE Statistical Signal Processing Workshop, SSP 2012. 2012. p. 916-919 6319858 (2012 IEEE Statistical Signal Processing Workshop, SSP 2012).
Romero, D, Vía, J, López-Valcarce, R & Santamaría, I 2012, Detection of Gaussian signals in unknown time-varying channels. in 2012 IEEE Statistical Signal Processing Workshop, SSP 2012., 6319858, 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, pp. 916-919, 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, Ann Arbor, MI, United States, 8/5/12. https://doi.org/10.1109/SSP.2012.6319858
Romero D, Vía J, López-Valcarce R, Santamaría I. Detection of Gaussian signals in unknown time-varying channels. In 2012 IEEE Statistical Signal Processing Workshop, SSP 2012. 2012. p. 916-919. 6319858. (2012 IEEE Statistical Signal Processing Workshop, SSP 2012). https://doi.org/10.1109/SSP.2012.6319858
Romero, Daniel ; Vía, Javier ; López-Valcarce, Roberto ; Santamaría, Ignacio. / Detection of Gaussian signals in unknown time-varying channels. 2012 IEEE Statistical Signal Processing Workshop, SSP 2012. 2012. pp. 916-919 (2012 IEEE Statistical Signal Processing Workshop, SSP 2012).
@inproceedings{0a18ca2b9d1d4602bfb18376f94c08da,
title = "Detection of Gaussian signals in unknown time-varying channels",
abstract = "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.",
keywords = "Detection theory, basis expansion model, generalized likelihood ratio, locally most powerful invariant, time-varying channels",
author = "Daniel Romero and Javier V{\'i}a and Roberto L{\'o}pez-Valcarce and Ignacio Santamar{\'i}a",
doi = "10.1109/SSP.2012.6319858",
series = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012",
booktitle = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012",
note = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012 ; Conference date: 05-08-2012 Through 08-08-2012",
T1 - Detection of Gaussian signals in unknown time-varying channels
AU - Romero, Daniel
AU - Vía, Javier
AU - López-Valcarce, Roberto
AU - Santamaría, Ignacio
N2 - 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.
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.
KW - Detection theory
KW - basis expansion model
KW - generalized likelihood ratio
KW - locally most powerful invariant
KW - time-varying channels
U2 - 10.1109/SSP.2012.6319858
DO - 10.1109/SSP.2012.6319858
T3 - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012
BT - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012