A 3-dimensional fast machine learning algorithm for mobile unmanned aerial vehicle base stations

Abstract

The 5G technology is predicted to achieve the unoptimized millimeter Wave (mmWave) of 30 to 300 GHz bands. This unoptimized band is because of the loss of mm-Wave bands, like path attenuation and propagation losses. Nonetheless, because: (i) Directional transmission paving way for beamforming to recompense for the path attenuation; and (ii) Sophisticated placement concreteness of the base stations (BS) is the best alternative for array wireless communications in mmWave bands (that is to say 100 to 150 m). The advance in technology and innovation of unmanned aerial vehicles (UAVs) necessitates many opportunities and uncertainties. UAVs are agile and can fly all complexities of the terrains making ground robots unsuitable. The UAV may be managed either independently aboard computers or through distance controlled by a flight attendant on pulverized wireless communication links in our case 5G. Although a fast algorithm solved the problematic aspect of beam selection for 2-dimensional scenarios. This paper presents 3-dimensional scenarios for UAVs. We modeled beam selection with environmental responsiveness in millimeter Wave UAV to accomplish close optimum assessments on a regular period through learning from the available situation.