  Channel state information (CSI) needs to be estimated for reliable and
efficient communication, however, location information is hidden inside and can
be further exploited. This article presents a detailed description of a Massive
Multi-Input Multi-Output (MaMIMO) testbed and provides a set of experimental
location-labelled CSI data. In this article, we focus on the design of the
hardware and software of a MaMIMO testbed for gathering multiple CSI data sets.
We also show this data can be used for learning-based localization and enhanced
communication research. The data set presented in this work is made fully
available to the research community. We show a CSI-based joint communication
and sensing processing pipeline can be evaluated and designed based on the
collected data set. Specifically, the localization output obtained by a
convolutional neural network (CNN) trained on the data sets is used to schedule
users for improving the spectral efficiency (SE) of the communication system.
Finally, we pose promising directions on further exploiting this data set and
creating future data sets.
