One-Bit Feedback Exponential Learning for Beam Alignment in Mobile mmWave

Abstract

Efficient beam alignment in wireless networks capable of supporting device mobility is currently one of the major challenges in mmWave communications. In this context, we formulate the beam-alignment problem via the adversarial multi-armed bandit (MAB) framework, which copes with arbitrary network dynamics including non-stationary or adversarial components. Building on the well known exponential weights algorithm (EXP3) and by exploiting the structure and sparsity of the mmWave channel, we propose a modified (MEXP3) policy that requires solely one-bit of feedback information (reducing the amount of exchanged data during the beam-alignment process). Our MEXP3 comes with optimal theoretical guarantees in terms of asymptotic regret. Moreover, for finite horizons, our regret upper-bound is tighter than that of the original EXP3 suggesting better performance in practice. We then introduce an additional modification that accounts for the temporal correlation between successive beams and propose another beam-alignment policy. Our numerical results demonstrate that our beam-alignment policies outperform existing ones with respect to the regret but also to the outage, throughput and delay in typical mobile mmWave settings.