  Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has
been widely recognized as an enabling technology for improving radio spectrum
efficiency. However, fast and reliable radio map prediction can be very
challenging due to the complicated interaction between radio waves and the
environment. In this paper, a novel Transformer based deep learning model
termed as RadioNet is proposed for radio map prediction in urban scenarios. In
addition, a novel Grid Embedding technique is proposed to substitute the
original Position Embedding in Transformer to better anchor the relative
position of the radiation source, destination and environment. The
effectiveness of proposed method is verified on an urban radio wave propagation
dataset. Compared with the SOTA model on RMP task, RadioNet reduces the
validation loss by 27.3\%, improves the prediction reliability from 90.9\% to
98.9\%. The prediction speed is increased by 4 orders of magnitude, when
compared with ray-tracing based method. We believe that the proposed method
will be beneficial to high-efficiency wireless communication, real-time radio
visualization, and even high-speed image rendering.
