Outdoor radio map estimation is an important tool for network planning and
resource management in modern Internet of Things (IoT) and cellular systems.
Radio map describes spatial signal strength distribution and provides network
coverage information. A practical goal is to estimate fine-resolution radio
maps from sparse radio strength measurements. However, non-uniformly positioned
measurements and access obstacles can make it difficult for accurate radio map
estimation (RME) and spectrum planning in many outdoor environments. In this
work, we develop a two-phase learning framework for radio map estimation by
integrating radio propagation model and designing a conditional generative
adversarial network (cGAN). We first explore global information to extract the
radio propagation patterns. We then focus on the local features to estimate the
effect of shadowing on radio maps in order to train and optimize the cGAN. Our
experimental results demonstrate the efficacy of the proposed framework for
radio map estimation based on generative models from sparse observations in
outdoor scenarios