Nil Garcia

Location-aided mm-wave channel estimation for vehicular communication

Millimeter-wave (mm-wave) communication is a promising technology for next-generation wireless systems. One challenging application lies in the vehicular domain, where mm-wave should support ultra-fast and high-rate data exchanges among vehicles and between vehicles and infrastructure. To achieve ultra-fast initial access between nodes, we propose a location-aided beamforming strategy and analyze the resulting performance in terms of antenna gain and latency. We find that location information can significantly speed up initial access.

Direct Localization for Massive MIMO

Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization, thanks to their high angular resolution. A difficult problem arising indoors and outdoors is localizing users over multipath channels. Localization based on angle of arrival (AOA) generally involves a two-step procedure, where signals are first processed to obtain a users AOA at different base stations, followed by triangulation to determine the users position. In the presence of multipath, the performance of these methods is greatly degraded due to the inability to correctly detect and/or estimate the AOA of the line-of-sight (LOS) paths. To counter the limitations of this two-step procedure which is inherently suboptimal, we propose a direct localization approach in which the position of a user is localized by jointly processing the observations obtained at distributed massive MIMO base stations. Our approach is based on a novel compressed sensing framework that exploits channel properties to distinguish LOS from non-LOS signal paths, and leads to improved performance results compared to previous existing methods.