Using power measurements collected by a network of low-cost sensors, power spectral density (PSD) maps are con-

structed to capture the distribution of RF power across space and frequency. Linearly compressed and quantized power measure-

ments enable wideband sensing at affordable implementation complexity using a small number of bits. Strengths of data- and model-

driven approaches are combined to develop estimators capable of incorporating multiple forms of spectral and propagation prior

information while fitting the rapid variations of shadow fading across space. To this end, novel nonparametric and semiparametric

formulations are investigated. It is shown that the desired PSD maps can be obtained using support vector machine-type solvers.

In addition to batch approaches, an online algorithm attuned to real-time operation is developed. Numerical tests assess the performance of the novel algorithms.

VL - 65
IS - 10
ER -
TY - CONF
T1 - Spectrum Cartography using quantized observations
T2 - IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP)
Y1 - 2015
A1 - Daniel Romero
A1 - Seung-Jun Kim
A1 - R. López-Valcarce
A1 - Georgios Giannakis
KW - cognitive radio
KW - compass
KW - compressed sensing
KW - spectrum sensing
AB - This work proposes a spectrum cartography algorithm used for learning the power spectrum distribution over a wide frequency band across a given geographic area. Motivated by low-complexity sensing hardware and stringent communication constraints, compressed and quantized measurements are considered. Setting out from a nonparametric regression framework, it is shown that a sensible approach leads to a support vector machine formulation. The simulated tests verify that accurate spectrum maps can be constructed using a simple sensing architecture with significant savings in the feedback.

JF - IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP)
PB - IEEE
CY - Brisbane, Australia
ER -