  Deep neural networks (DNNs) have become a popular approach for wireless
localization based on channel state information (CSI). A common practice is to
use the raw CSI in the input and allow the network to learn relevant channel
representations for mapping to location information. However, various works
show that raw CSI can be very sensitive to system impairments and small changes
in the environment. On the contrary, hand-designing features may hinder the
limits of channel representation learning of the DNN. In this work, we propose
attention-based CSI for robust feature learning. We evaluate the performance of
attended features in centralized and distributed massive MIMO systems for
ray-tracing channels in two non-stationary railway track environments. By
comparison to a base DNN, our approach provides exceptional performance.
