Stochastic semiparametric regression for spectrum cartography

Daniel Romero; Seung Jun Kim; Georgios B Giannakis

doi:10.1109/CAMSAP.2015.7383849

Stochastic semiparametric regression for spectrum cartography

Daniel Romero
, Seung Jun Kim
, Georgios B Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Scopus citations

Abstract

An online spectrum cartography algorithm is proposed to reconstruct power spectral density (PSD) maps in space and frequency based on compressed and quantized sensor measurements. The emerging regression task is addressed by decomposing the PSD at every location into a linear combination of the power spectra (due to individual transmitters and background noise) scaled by attenuation functions capturing propagation effects. The attenuation functions are, in turn, postulated to be a sum of two terms: the first is a linear combination of a collection of basis functions whereas the second is an element of a reproducing kernel Hilbert space (RKHS) of vector-valued functions. A novel stochastic gradient descent algorithm is proposed to compute both components in an online fashion. Numerical tests verify the map estimation performance of the proposed technique.

Original language	English (US)
Title of host publication	2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	513-516
Number of pages	4
ISBN (Electronic)	9781479919635
DOIs	https://doi.org/10.1109/CAMSAP.2015.7383849
State	Published - 2015
Event	6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 - Cancun, Mexico Duration: Dec 13 2015 → Dec 16 2015

Publication series

Name	2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015

Other

Other	6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Country/Territory	Mexico
City	Cancun
Period	12/13/15 → 12/16/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Publisher link

10.1109/CAMSAP.2015.7383849

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Romero, D., Kim, S. J., & Giannakis, G. B. (2015). Stochastic semiparametric regression for spectrum cartography. In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 (pp. 513-516). Article 7383849 (2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMSAP.2015.7383849

Stochastic semiparametric regression for spectrum cartography. / Romero, Daniel; Kim, Seung Jun; Giannakis, Georgios B.
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 513-516 7383849 (2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Romero, D, Kim, SJ & Giannakis, GB 2015, Stochastic semiparametric regression for spectrum cartography. in 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015., 7383849, 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015, Institute of Electrical and Electronics Engineers Inc., pp. 513-516, 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015, Cancun, Mexico, 12/13/15. https://doi.org/10.1109/CAMSAP.2015.7383849

Romero D, Kim SJ, Giannakis GB. Stochastic semiparametric regression for spectrum cartography. In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 513-516. 7383849. (2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015). doi: 10.1109/CAMSAP.2015.7383849

Romero, Daniel ; Kim, Seung Jun ; Giannakis, Georgios B. / Stochastic semiparametric regression for spectrum cartography. 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 513-516 (2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015).

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abstract = "An online spectrum cartography algorithm is proposed to reconstruct power spectral density (PSD) maps in space and frequency based on compressed and quantized sensor measurements. The emerging regression task is addressed by decomposing the PSD at every location into a linear combination of the power spectra (due to individual transmitters and background noise) scaled by attenuation functions capturing propagation effects. The attenuation functions are, in turn, postulated to be a sum of two terms: the first is a linear combination of a collection of basis functions whereas the second is an element of a reproducing kernel Hilbert space (RKHS) of vector-valued functions. A novel stochastic gradient descent algorithm is proposed to compute both components in an online fashion. Numerical tests verify the map estimation performance of the proposed technique.",

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AB - An online spectrum cartography algorithm is proposed to reconstruct power spectral density (PSD) maps in space and frequency based on compressed and quantized sensor measurements. The emerging regression task is addressed by decomposing the PSD at every location into a linear combination of the power spectra (due to individual transmitters and background noise) scaled by attenuation functions capturing propagation effects. The attenuation functions are, in turn, postulated to be a sum of two terms: the first is a linear combination of a collection of basis functions whereas the second is an element of a reproducing kernel Hilbert space (RKHS) of vector-valued functions. A novel stochastic gradient descent algorithm is proposed to compute both components in an online fashion. Numerical tests verify the map estimation performance of the proposed technique.

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Stochastic semiparametric regression for spectrum cartography

Abstract

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