Adaptive Bayesian Channel Gain Cartography

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 p...

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Bibliographic Details
Published in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3554 - 3558
Main Authors Lee, Donghoon, Berberidis, Dimitris, Giannakis, Georgios B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
active learning
Attenuation
Bayes methods
channel gain cartography
Gain measurement
Hidden Markov models
Markov chain Monte Carlo
Markov processes
radio tomography
Shadow mapping
Uncertainty
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8461412