With millimeter wave wireless communications, the resulting radiation reflects on most visible objects, creating rich multi path environments, namely in urban scenarios. The radiation captured by a listening device is thus shaped by the obstacles encountered, which carry latent information regarding their relative positions. In this paper, a system to convert the received millimeter wave radiation into the device's position is proposed, making use of the aforementioned hidden information. Using deep learning techniques and a pre-established codebook of beamforming patterns transmitted by a base station, the simulations show that average estimation errors below 10 meters are achievable in realistic outdoors scenarios that contain mostly non-line-of-sight positions, paving the way for new positioning systems.
Beamformed Fingerprint Learning for Accurate Millimeter Wave Positioning
01.08.2018
872130 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Optical Beamformed Array Performance
British Library Conference Proceedings | 2006
|Accurate Design of Finline Tapers for Millimeter Wave Applications
British Library Online Contents | 2005
|