  Perfect alignment in chosen beam sectors at both transmit- and receive-nodes
is required for beamforming in mmWave bands. Current 802.11ad WiFi and emerging
5G cellular standards spend up to several milliseconds exploring different
sector combinations to identify the beam pair with the highest SNR. In this
paper, we propose a machine learning (ML) approach with two sequential
convolutional neural networks (CNN) that uses out-of-band information, in the
form of camera images, to (i) rapidly identify the locations of the transmitter
and receiver nodes, and then (ii) return the optimal beam pair. We
experimentally validate this intriguing concept for indoor settings using the
NI 60GHz mmwave transceiver. Our results reveal that our ML approach reduces
beamforming related exploration time by 93% under different ambient lighting
conditions, with an error of less than 1% compared to the time-intensive
deterministic method defined by the current standards.
