  Power spectral density (PSD) maps providing the distribution of RF power
across space and frequency are constructed using power measurements collected
by a network of low-cost sensors. By introducing linear compression and
quantization to a small number of bits, sensor measurements can be communicated
to the fusion center with minimal bandwidth requirements. 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 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.
