  Signal maps are essential for the planning and operation of cellular
networks. However, the measurements needed to create such maps are expensive,
often biased, not always reflecting the metrics of interest, and posing privacy
risks. In this paper, we develop a unified framework for predicting cellular
signal maps from limited measurements. Our framework builds on a
state-of-the-art random-forest predictor, or any other base predictor. We
propose and combine three mechanisms that deal with the fact that not all
measurements are equally important for a particular prediction task. First, we
design quality-of-service functions ($Q$), including signal strength (RSRP) but
also other metrics of interest to operators, i.e., coverage and call drop
probability. By implicitly altering the loss function employed in learning,
quality functions can also improve prediction for RSRP itself where it matters
(e.g., MSE reduction up to 27% in the low signal strength regime, where errors
are critical). Second, we introduce weight functions ($W$) to specify the
relative importance of prediction at different locations and other parts of the
feature space. We propose re-weighting based on importance sampling to obtain
unbiased estimators when the sampling and target distributions are different.
This yields improvements up to 20% for targets based on spatially uniform loss
or losses based on user population density. Third, we apply the Data Shapley
framework for the first time in this context: to assign values ($\phi$) to
individual measurement points, which capture the importance of their
contribution to the prediction task. This improves prediction (e.g., from 64%
to 94% in recall for coverage loss) by removing points with negative values,
and can also enable data minimization. We evaluate our methods and demonstrate
significant improvement in prediction performance, using several real-world
datasets.
