Model-based power spectrum sensing from a few bits

Omar Mehanna, Nicholas D. Sidiropoulos, Efthymios Tsakonas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Wideband power spectrum sensing is fundamental for numerous applications. When side information on the potentially active emitters is available, such as carriers and spectral masks, it should be exploited to improve sensing performance. Here the power spectrum is modeled as a weighted sum of candidate spectral density primitives. The objective is to estimate the unknown weights from a few randomly filtered broadband power measurement bits, taken using a network of low-end sensors. A linear programming formulation that exploits the sparsity in the unknown weights is proposed. A better approach follows, which exploits the approximately Gaussian distribution of the errors in the power measurements prior to quantization, in a maximum likelihood formulation that includes a sparsity-inducing penalty term. Simulations show that the model weights can be accurately estimated from few bits, even when the errors are significant.

Original languageEnglish (US)
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
StatePublished - Jan 1 2013
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: Sep 9 2013Sep 13 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Other

Other2013 21st European Signal Processing Conference, EUSIPCO 2013
CountryMorocco
CityMarrakech
Period9/9/139/13/13

Fingerprint

Power spectrum
Spectral density
Gaussian distribution
Linear programming
Maximum likelihood
Masks
Sensors

Cite this

Mehanna, O., Sidiropoulos, N. D., & Tsakonas, E. (2013). Model-based power spectrum sensing from a few bits. In 2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013 [6811691] (European Signal Processing Conference). European Signal Processing Conference, EUSIPCO.

Model-based power spectrum sensing from a few bits. / Mehanna, Omar; Sidiropoulos, Nicholas D.; Tsakonas, Efthymios.

2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013. European Signal Processing Conference, EUSIPCO, 2013. 6811691 (European Signal Processing Conference).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mehanna, O, Sidiropoulos, ND & Tsakonas, E 2013, Model-based power spectrum sensing from a few bits. in 2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013., 6811691, European Signal Processing Conference, European Signal Processing Conference, EUSIPCO, 2013 21st European Signal Processing Conference, EUSIPCO 2013, Marrakech, Morocco, 9/9/13.
Mehanna O, Sidiropoulos ND, Tsakonas E. Model-based power spectrum sensing from a few bits. In 2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013. European Signal Processing Conference, EUSIPCO. 2013. 6811691. (European Signal Processing Conference).
Mehanna, Omar ; Sidiropoulos, Nicholas D. ; Tsakonas, Efthymios. / Model-based power spectrum sensing from a few bits. 2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013. European Signal Processing Conference, EUSIPCO, 2013. (European Signal Processing Conference).
@inproceedings{f454906adf0943d3b41e3f596d34ad5c,
title = "Model-based power spectrum sensing from a few bits",
abstract = "Wideband power spectrum sensing is fundamental for numerous applications. When side information on the potentially active emitters is available, such as carriers and spectral masks, it should be exploited to improve sensing performance. Here the power spectrum is modeled as a weighted sum of candidate spectral density primitives. The objective is to estimate the unknown weights from a few randomly filtered broadband power measurement bits, taken using a network of low-end sensors. A linear programming formulation that exploits the sparsity in the unknown weights is proposed. A better approach follows, which exploits the approximately Gaussian distribution of the errors in the power measurements prior to quantization, in a maximum likelihood formulation that includes a sparsity-inducing penalty term. Simulations show that the model weights can be accurately estimated from few bits, even when the errors are significant.",
author = "Omar Mehanna and Sidiropoulos, {Nicholas D.} and Efthymios Tsakonas",
year = "2013",
month = "1",
day = "1",
language = "English (US)",
isbn = "9780992862602",
series = "European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
booktitle = "2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013",

}

TY - GEN

T1 - Model-based power spectrum sensing from a few bits

AU - Mehanna, Omar

AU - Sidiropoulos, Nicholas D.

AU - Tsakonas, Efthymios

PY - 2013/1/1

Y1 - 2013/1/1

N2 - Wideband power spectrum sensing is fundamental for numerous applications. When side information on the potentially active emitters is available, such as carriers and spectral masks, it should be exploited to improve sensing performance. Here the power spectrum is modeled as a weighted sum of candidate spectral density primitives. The objective is to estimate the unknown weights from a few randomly filtered broadband power measurement bits, taken using a network of low-end sensors. A linear programming formulation that exploits the sparsity in the unknown weights is proposed. A better approach follows, which exploits the approximately Gaussian distribution of the errors in the power measurements prior to quantization, in a maximum likelihood formulation that includes a sparsity-inducing penalty term. Simulations show that the model weights can be accurately estimated from few bits, even when the errors are significant.

AB - Wideband power spectrum sensing is fundamental for numerous applications. When side information on the potentially active emitters is available, such as carriers and spectral masks, it should be exploited to improve sensing performance. Here the power spectrum is modeled as a weighted sum of candidate spectral density primitives. The objective is to estimate the unknown weights from a few randomly filtered broadband power measurement bits, taken using a network of low-end sensors. A linear programming formulation that exploits the sparsity in the unknown weights is proposed. A better approach follows, which exploits the approximately Gaussian distribution of the errors in the power measurements prior to quantization, in a maximum likelihood formulation that includes a sparsity-inducing penalty term. Simulations show that the model weights can be accurately estimated from few bits, even when the errors are significant.

UR - http://www.scopus.com/inward/record.url?scp=84901358238&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901358238&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9780992862602

T3 - European Signal Processing Conference

BT - 2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013

PB - European Signal Processing Conference, EUSIPCO

ER -