Machine Learning-Based Vision-Aided Beam Selection for mmWave Multiuser MISO System

In this letter, we propose a machine learning-based vision-aided beam selection (ML-VBS) for millimeter-wave indoor multi-user communications. The proposed scheme is aimed at addressing the beam selection overhead with narrow beams in a multi-user scenario. The proposed scheme relies on a base station (BS) equipped with a single camera to observe the scene and estimates the angles to the multiple users. Given the estimated angle information and the limited number of radio frequency chains at the BS, two serial deep neural network structures are employed for joint user and beam selection subject to a minimum rate constraint. The numerical evaluation shows that the proposed ML-VBS scheme achieves a good performance in terms of the multi-user angle estimation, achievable sum rate and low computational complexity compared to conventional beam selection techniques.

Automated summary

This paper proposes a machine learning-based vision-aided beam selection scheme for millimeter-wave indoor multi-user communications. By utilizing a camera to observe the scene and estimate user angles, and employing deep neural networks for joint user and beam selection with limited radio frequency chains, the proposed scheme achieves good performance.