  This paper proposes exploiting the spatial correlation of wireless channel
statistics beyond the conventional received signal strength maps by
constructing statistical radio maps to predict any relevant channel statistics
to assist communications. Specifically, from stored channel samples acquired by
previous users in the network, we use Gaussian processes (GPs) to estimate
quantiles of the channel distribution at a new position using a non-parametric
model. This prior information is then used to select the transmission rate for
some target level of reliability. The approach is tested with synthetic data,
simulated from urban micro-cell environments, highlighting how the proposed
solution helps to reduce the training estimation phase, which is especially
attractive for the tight latency constraints inherent to ultra-reliable
low-latency (URLLC) deployments.

Signal Processing