  Enriching geometric information on radio frequency (RF) signal power
distribution in wireless communication systems, the radiomap has become an
essential tool for resource allocation and network management. Usually, a dense
radiomap is reconstructed from sparse observations collected by deployed
sensors or mobile devices, which makes the radiomap estimation an urgent
challenge. To leverage both physical principles of radio propagation models and
data statistics from sparse observations, this work introduces a novel
task-incentivized generative learning model, namely TiRE-GAN, for radiomap
estimation. Specifically, we first introduce a radio depth map as input to
capture the overall pattern of radio propagation and shadowing effects,
following which a task-driven incentive network is proposed to provide feedback
for radiomap compensation depending on downstream tasks. Our experimental
results demonstrate the power of the radio depth map to capture radio
propagation information, together with the efficiency of the proposed TiRE-GAN
for radiomap estimation.
