Exploiting Channel Sparsity for Beam Alignment in mmWave Systems via Exponential Learning

Abstract

The large available spectrum in the millimeter wave (mmWave) band represents an attractive alternative for the congested sub-6 GHz spectrum. To overcome the difficult propagation conditions at high frequencies, directional communications via multiple antenna arrays and high-gain beams can be employed. Nevertheless, these beams need to be well aligned to reliably transmit data, which is a challenging task given the user mobility and the unpredictable changes of the wireless environment. In this paper, we propose a new distributed beam-alignment strategy relying on a single bit of feedback, which equals one if the signal-to-interference-plus-noise (SINR) reaches a predefined threshold. The novelty consists in a modified reward function, inspired from the sparse nature of the mmWave channel, coupled with the well-known exponential weights algorithm (EXP3). First, we show that our resulting adaptive policy comes with optimal theoretical guarantees in terms of sub-linear regret. Second, our numerical results demonstrate significant performance gains of our beam-alignment policy compared with the original EXP3 algorithm and other existing policies in a mmWave setting with user mobility.