Diffraction and Scattering Aware Radio Map and Environment
  Reconstruction using Geometry Model-Assisted Deep Learning
Wangqian Chen Junting Chen
Machine learning (ML) facilitates rapid channel modeling for 5G and beyond
wireless communication systems. Many existing ML techniques utilize a city map
to construct the radio map; however, an updated city map may not always be
available. This paper proposes to employ the received signal strength (RSS)
data to jointly construct the radio map and the virtual environment by
exploiting the geometry structure of the environment. In contrast to many
existing ML approaches that lack of an environment model, we develop a virtual
obstacle model and characterize the geometry relation between the propagation
paths and the virtual obstacles. A multi-screen knife-edge model is adopted to
extract the key diffraction features, and these features are fed into a neural
network (NN) for diffraction representation. To describe the scattering, as
oppose to most existing methods that directly input an entire city map, our
model focuses on the geometry structure from the local area surrounding the
TX-RX pair and the spatial invariance of such local geometry structure is
exploited. Numerical experiments demonstrate that, in addition to
reconstructing a 3D virtual environment, the proposed model outperforms the
state-of-the-art methods in radio map construction with 10%-18% accuracy
improvements. It can also reduce 20% data and 50% training epochs when
transferred to a new environment.