Massive MIMO Channel Measurement Data Set for Localization and Communication
Achiel Colpaert, Sibren De Bast, Robbert Beerten, Andrea P. Guevara, Zhuangzhuang Cui, Sofie Pollin
Abstract
Channel state information (CSI) needs to be estimated for reliable and efficient communication, however, user 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-labeled CSI data. We first focus on the design of the hardware and software of a MaMIMO testbed for gathering multiple CSI data sets. We then show this data can be used for learning-based localization and enhanced communication research. The presented data set is made fully available to the research community. We illustrate that 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 to improve the spectral efficiency (SE) of the communication system. Finally, we pose promising directions for further exploiting this data set and creating more data sets.