  Millimeter wave beam alignment (BA) is a challenging problem especially for
large number of antennas. Compressed sensing (CS) tools have been exploited due
to the sparse nature of such channels. This paper presents a novel
deterministic CS approach for BA. Our proposed sensing matrix which has a
Kronecker-based structure is sparse, which means it is computationally
efficient. We show that our proposed sensing matrix satisfies the restricted
isometry property (RIP) condition, which guarantees the reconstruction of the
sparse vector. Our approach outperforms existing random beamforming techniques
in practical low signal to noise ratio (SNR) scenarios.
