Adaptive Bayesian Channel Gain Cartography

Donghoon Lee; Dimitris Berberidis; Georgios B Giannakis

doi:10.1109/ICASSP.2018.8461412

Adaptive Bayesian Channel Gain Cartography

Donghoon Lee, Dimitris Berberidis, Georgios B Giannakis

Electrical and Computer Engineering

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

5 Scopus citations

Abstract

Channel gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) depending on the propagation environment. Currently, the SLF is learned via regularization methods tailored to the propagation environment. However, the effectiveness of existing approaches remains unclear especially when the propagation environment involves heterogeneous characteristics. To cope with this, the present work considers a piecewise homogeneous SLF with a hidden Markov random field (MRF) model under the Bayesian framework. Efficient field estimators are obtained by using samples from Markov chain Monte Carlo (MCMC). Furthermore, an uncertainty sampling algorithm is developed to adaptively collect measurements. Real data tests demonstrate the capabilities of the novel approach.

Original language	English (US)
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3554-3558
Number of pages	5
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/ICASSP.2018.8461412
State	Published - Sep 10 2018
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: Apr 15 2018 → Apr 20 2018

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2018-April
ISSN (Print)	1520-6149

Other

Other	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	4/15/18 → 4/20/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Active learning
Channel gain cartography
Markov chain Monte Carlo
Radio tomography

Publisher link

10.1109/ICASSP.2018.8461412

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Lee, D., Berberidis, D., & Giannakis, G. B. (2018). Adaptive Bayesian Channel Gain Cartography. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 3554-3558). Article 8461412 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8461412

Adaptive Bayesian Channel Gain Cartography. / Lee, Donghoon; Berberidis, Dimitris; Giannakis, Georgios B.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3554-3558 8461412 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April).

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

Lee, D, Berberidis, D & Giannakis, GB 2018, Adaptive Bayesian Channel Gain Cartography. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8461412, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, Institute of Electrical and Electronics Engineers Inc., pp. 3554-3558, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 4/15/18. https://doi.org/10.1109/ICASSP.2018.8461412

Lee D, Berberidis D, Giannakis GB. Adaptive Bayesian Channel Gain Cartography. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3554-3558. 8461412. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2018.8461412

Lee, Donghoon ; Berberidis, Dimitris ; Giannakis, Georgios B. / Adaptive Bayesian Channel Gain Cartography. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3554-3558 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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abstract = "Channel gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) depending on the propagation environment. Currently, the SLF is learned via regularization methods tailored to the propagation environment. However, the effectiveness of existing approaches remains unclear especially when the propagation environment involves heterogeneous characteristics. To cope with this, the present work considers a piecewise homogeneous SLF with a hidden Markov random field (MRF) model under the Bayesian framework. Efficient field estimators are obtained by using samples from Markov chain Monte Carlo (MCMC). Furthermore, an uncertainty sampling algorithm is developed to adaptively collect measurements. Real data tests demonstrate the capabilities of the novel approach.",

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AB - Channel gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) depending on the propagation environment. Currently, the SLF is learned via regularization methods tailored to the propagation environment. However, the effectiveness of existing approaches remains unclear especially when the propagation environment involves heterogeneous characteristics. To cope with this, the present work considers a piecewise homogeneous SLF with a hidden Markov random field (MRF) model under the Bayesian framework. Efficient field estimators are obtained by using samples from Markov chain Monte Carlo (MCMC). Furthermore, an uncertainty sampling algorithm is developed to adaptively collect measurements. Real data tests demonstrate the capabilities of the novel approach.

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Adaptive Bayesian Channel Gain Cartography

Abstract

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