Blind channel gain cartography

Daniel Romero, Donghoon Lee, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1110-1115
Number of pages6
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

Keywords

  • Channel gain cartography
  • Cognitive radio
  • Kernel-based learning
  • RF tomography

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