Channel-gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. State-of-the-art on this subject includes tomography-based approaches, where shadowing effects are modeled by the weighted integral of a spatial loss field (SLF) that captures the propagation environment. To learn SLFs exhibiting statistical heterogeneity induced by spatially diverse propagation environments, the present work develops a Bayesian approach comprising a piecewise homogeneous SLF with an underlying hidden Markov random field model. Built on a variational Bayes scheme, the novel approach yields efficient field estimators at affordable complexity. In addition, a data-adaptive sensor selection algorithm is developed to collect informative measurements for effective learning of the SLF. Numerical tests demonstrate the capabilities of the novel approach.
|Original language||English (US)|
|Title of host publication||2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - May 2019|
|Event||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom|
Duration: May 12 2019 → May 17 2019
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019|
|Period||5/12/19 → 5/17/19|
Bibliographical noteFunding Information:
The work in this paper was supported by NSF grants 1442686, 1508993, 1509040.
© 2019 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.
- active learning
- channel-gain cartography
- radio tomography
- variational Bayes