Beamformed Fingerprint Learning for Accurate Millimeter Wave Positioning

Through 5G related research, the door to the so called millimeter wave (mmWave) frequencies reopened, unlocking a huge chunk of untapped bandwidth [1]. With mmWaves, the propagation changes dramatically: the resulting radiation has severe path loss properties and reflects on most visible obstacles [2]. To counteract the aforementioned characteristics, beamforming (BF) is usually employed in systems containing multiple-input and multiple-output (MIMO) antennas, enabling steerable and focused radiation patterns. With that recent focus on mmWaves, new positioning systems based on these frequencies were proposed [3]. The achievable accuracy in controlled conditions is remarkable, with sub-meter accuracy in indoor [4] and ultra-dense line-ofsight (LOS) outdoor scenarios [5]. Nevertheless, in order to be useful in outdoor scenarios, a mmWave positioning system must also be able to deal with devices in non-line-of-sight (NLOS) locations. The works developed in [6]-[9] attempt to address this concern, being capable of locating devices in both LOS and NLOS situations. The method in [6] applies compressed sensing on information gathered from static listeners, while in [7] multiple access points are used to create a location fingerprint database of received powers and angles-of-arrival (AoA). In [8], the authors use multiple BF transmissions and an iterative algorithm to estimate the position and orientation of the device.