  This paper presents a robust beam alignment technique for millimeter-wave
communications in low signal-to-noise ratio (SNR) environments. The core
strategy of our technique is to repeatedly transmit the most probable beam
candidates to reduce beam misalignment probability induced by noise.
Specifically, for a given beam training overhead, both the selection of
candidates and the number of repetitions for each beam candidate are optimized
based on channel prior information. To achieve this, a deep neural network is
employed to learn the prior probability of the optimal beam at each location.
The beam misalignment probability is then analyzed based on the channel prior,
forming the basis for an optimization problem aimed at minimizing the analyzed
beam misalignment probability. A closed-form solution is derived for a special
case with two beam candidates, and an efficient algorithm is developed for
general cases with multiple beam candidates. Simulation results using the
DeepMIMO dataset demonstrate the superior performance of our technique in
dynamic low-SNR communication environments when compared to existing beam
alignment techniques.
