Deep Learning-Based mmWave Beam Alignment with Only Pilot Channel Measurements

Taekyun Lee, Hyeji Kim, Jeffrey G. Andrews

Abstract: For millimeter wave (mmWave) communication, fast and accurate beam alignment is essential but challenging. Site-specific beam adaptation using deep learning is a very promising paradigm for beam alignment, but such methods typically require a lot of clean channel measurements for training, which can be difficult or even impossible to achieve in practice. This paper introduces a novel method to learn beam alignment policies using only uplink (UL) pilot measurements. The proposed method integrates a generative adversarial network (GAN)-based channel estimation (CE) model with an unsupervised deep learning model beam alignment engine (BAE). We introduce an efficient form of dataset amplification for improved training that leverages the randomness of the deep generative model (DGM) and an early stopping mechanism. Our experiments show that the GAN-BAE method achieves a better signal-to-noise ratio (SNR) by nearly 3 dB compared to compressed sensing (CS) methods such as orthogonal matching pursuit (OMP) and EM-GM-AMP (an Approximate Message Passing algorithm), especially when there are limited pilot measurements from each mobile user.