TY - GEN
T1 - Blind channel gain cartography
AU - Romero, Daniel
AU - Lee, Donghoon
AU - Giannakis, Georgios B.
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/4/19
Y1 - 2017/4/19
N2 - Channel gain cartography relies on sensor measurements to construct maps providing the attenuation between arbitrary transmitter-receiver locations. A number of applications involving interference control, such as wireless network planning or cognitive radio, can benefit from channel gain maps. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) that depends on the propagation environment. Currently, the SLF is learned from sensor measurements whereas functions weighting the SLF are heuristically selected, but the effectiveness of the latter remains unclear. This paper leverages the framework of nonparametric regression in reproducing kernel Hilbert spaces to propose an algorithm that relies on the same sensor measurements as existing approaches to learn not only the SLF but also the associated weight function. Such an algorithm therefore constitutes a universal tool for channel gain cartography while revealing the nature of the propagation medium. An optimization method is proposed to minimize the pertinent criterion with closed-form updates. Simulation tests demonstrate the capabilities of the proposed algorithm.
AB - Channel gain cartography relies on sensor measurements to construct maps providing the attenuation between arbitrary transmitter-receiver locations. A number of applications involving interference control, such as wireless network planning or cognitive radio, can benefit from channel gain maps. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) that depends on the propagation environment. Currently, the SLF is learned from sensor measurements whereas functions weighting the SLF are heuristically selected, but the effectiveness of the latter remains unclear. This paper leverages the framework of nonparametric regression in reproducing kernel Hilbert spaces to propose an algorithm that relies on the same sensor measurements as existing approaches to learn not only the SLF but also the associated weight function. Such an algorithm therefore constitutes a universal tool for channel gain cartography while revealing the nature of the propagation medium. An optimization method is proposed to minimize the pertinent criterion with closed-form updates. Simulation tests demonstrate the capabilities of the proposed algorithm.
KW - Channel gain cartography
KW - Cognitive radio
KW - Kernel-based learning
KW - RF tomography
UR - http://www.scopus.com/inward/record.url?scp=85019184091&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019184091&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2016.7906014
DO - 10.1109/GlobalSIP.2016.7906014
M3 - Conference contribution
AN - SCOPUS:85019184091
T3 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
SP - 1110
EP - 1115
BT - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Y2 - 7 December 2016 through 9 December 2016
ER -