  The 3rd Generation Partnership Project (3GPP) is currently studying machine
learning (ML) for the fifth generation (5G)-Advanced New Radio (NR) air
interface, where spatial and temporal-domain beam prediction are important use
cases. With this background, this letter presents a low-complexity ML design
that expedites the spatial-domain beam prediction to reduce the power
consumption and the reference signaling overhead, which are currently
imperative for frequent beam measurements. Complexity analysis and evaluation
results showcase that the proposed model achieves state-of-the-art accuracy
with lower computational complexity, resulting in reduced power consumption and
faster beam prediction. Furthermore, important observations on the
generalization of the proposed model are presented in this letter.
