Radio Map Estimation -- An Open Dataset with Directive Transmitter
  Antennas and Initial Experiments
Fabian Jaensch Giuseppe Caire Begüm Demir
Over the last years, several works have explored the application of deep
learning algorithms to determine the large-scale signal fading (also referred
to as ``path loss'') between transmitter and receiver pairs in urban
communication networks. The central idea is to replace costly measurement
campaigns, inaccurate statistical models or computationally expensive
ray-tracing simulations by machine learning models which, once trained, produce
accurate predictions almost instantly. Although the topic has attracted
attention from many researchers, there are few open benchmark datasets and
codebases that would allow everyone to test and compare the developed methods
and algorithms. We take a step towards filling this gap by releasing a publicly
available dataset of simulated path loss radio maps together with realistic
city maps from real-world locations and aerial images from open datasources.
Initial experiments regarding model architectures, input feature design and
estimation of radio maps from aerial images are presented and the code is made
available.