@article {878, title = {Learning Power Spectrum Maps from Quantized Power Measurements}, journal = {IEEE Transactions on Signal Processing}, volume = {65}, year = {2017}, month = {05/2017}, pages = {2547-2560}, abstract = {
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.\ 
}, keywords = {cognitive radio, compass, compressed sensing, spectrum sensing, winter, wsn}, doi = {10.1109/TSP.2017.2666775}, author = {Daniel Romero and Seung-Jun Kim and Georgios Giannakis and R. L{\'o}pez-Valcarce} }