Machine Learning on Camera Images for Fast mmWave Beamforming

TL;DR

This paper introduces a machine learning method using camera images and CNNs to significantly speed up mmWave beamforming, reducing exploration time by 93% in indoor environments.

Contribution

It presents a novel ML approach with CNNs that leverages camera images for rapid beam alignment in mmWave systems, outperforming traditional methods.

Findings

Reduces beamforming exploration time by 93%

Achieves less than 1% error compared to deterministic methods

Works effectively under different lighting conditions

Abstract

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.