Localization-Free Power Cartography

Abstract

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using measurements of spatially distributed sensors. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radio to name a few. Existing spectrum cartography methods necessitate knowledge of sensor locations, but such locations cannot be accurately determined from pilot positioning signals (such as those in LTE or GPS) in indoor or dense urban scenarios due to multipath. To circumvent this limitation, this paper proposes localization-free cartography, where spectral maps are directly constructed from features of these positioning signals rather than from location estimates. The proposed algorithm capitalizes on the framework of kernel-based learning and offers improved prediction performance relative to existing alternatives, as demonstrated by a simulation study in a street canyon.