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Beamforming is the key enabler for wireless communications in the mmWavebands. 802.11ad and WiGig are wireless technologies that currently use the 60GHz unlicensed mmWave spectrum via beamforming techniques. It is likely that 5Gsystems will be considered for 60GHz unlicensed spectrum (apart from otherunlicensed bands) deployments and hence must co-exist with 802.11ad and WiGig.3GPP is taking steps towards achieving the same and the standardization forthis is underway. The first step to achieve this co-existence is to find theinterference-free directions, in other words identify the directions in whichthe nodes using these incumbent technologies are communicating and eliminatethose directions from further communications. Such a mechanism can help toexploit the spatial holes rather than avoid communications even when only a fewspatial directions are used by incumbents. Such a mechanism trivially increasesthe throughput of the proposed 5G systems. However, since the incumbenttechnologies may be unknown to the 5G mmWave nodes and their behavior may alsobe unknown apriori (for instance, parameters such as duty cycle, power levels,CSMA parameter used by 802.11ad are unknown to the 5G nodes), this spatialdirection finding must be performed in a blind manner. In this paper, we usemulti-armed bandits-based algorithms, a variant of machine learning algorithms,to blindly detect the beam directions (both along azimuth and elevation i.e.,3D-beamforming) used for communication by the incumbents. This work paves theway for combining the powerful of machine learning algorithms into 5Gunlicensed mmWave systems. Numerical results show the superior performance ofthese algorithms over techniques that are commonly employed in such blindsettings.

Beam Learning -- Using Machine Learning for Finding Beam Directions