Beam Learning -- Using Machine Learning for Finding Beam Directions
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Beamforming is the key enabler for wireless communications in the mmWave bands. 802.11ad and WiGig are wireless technologies that currently use the 60 GHz unlicensed mmWave spectrum via beamforming techniques. It is likely that 5G systems will be considered for 60GHz unlicensed spectrum (apart from other unlicensed bands) deployments and hence must co-exist with 802.11ad and WiGig. 3GPP is taking steps towards achieving the same and the standardization for this is underway. The first step to achieve this co-existence is to find the interference-free directions, in other words identify the directions in which the nodes using these incumbent technologies are communicating and eliminate those directions from further communications. Such a mechanism can help to exploit the spatial holes rather than avoid communications even when only a few spatial directions are used by incumbents. Such a mechanism trivially increases the throughput of the proposed 5G systems. However, since the incumbent technologies may be unknown to the 5G mmWave nodes and their behavior may also be unknown apriori (for instance, parameters such as duty cycle, power levels, CSMA parameter used by 802.11ad are unknown to the 5G nodes), this spatial direction finding must be performed in a blind manner. In this paper, we use multi-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 the way for combining the powerful of machine learning algorithms into 5G unlicensed mmWave systems. Numerical results show the superior performance of these algorithms over techniques that are commonly employed in such blind settings.
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