Cell discovery based on historical user's location in mmWave 5G

By 2020, the global mobile data traffic will reach 30.6 exabytes per month. Hence, microwave bands will become saturated and insufficient to deliver that increment of data. Millimeter wave (a.k.a. mmWave) is a promising band, from 30 to 300 GHz, to allocate that data. A drawback is the high attenuation in non-line-of-sight scenarios because the millimeter wavelengths are blocked by common obstacles. Opposite to Long Term Evolution (LTE) networks which transmit in an isotropic manner, mmWave small base stations (SBS) have to transmit through directional antennas to achieve a sufficient signal to noise ratio within a radius of up to 200 meters. Moreover, current SBSs do not adapt to the environment to increase their efficiency, since they are configured manually. In this paper, we propose a learning Weight-based Algorithm to decrease the delay of finding UEs by autonomously prioritizing those sectors where more users are expected to be found according to previous experience. Our results show an increment of the number of UEs found in the first scan by over 19% and a delay reduction by over 84%, on average for all SBS-UE distances, with respect to comparable state-of-the-art approaches.