An active-sensing-based learning algorithm is proposed to solve the
near-field beam alignment problem with the aid of wavenumber-domain transform
matrices (WTMs). Specifically, WTMs can transform the antenna-domain channel
into a sparse representation in the wavenumber domain. The dimensions of WTMs
can be further reduced by exploiting the dominance of line-of-sight (LoS)
links. By employing these lower-dimensional WTMs as mapping functions, the
active-sensing-based algorithm is executed in the wavenumber domain, resulting
in an acceleration of convergence. Compared with the codebook-based beam
alignment methods, the proposed method finds the optimal beam pair in a
ping-pong fashion, thus avoiding high training overheads caused by beam
sweeping. Finally, the numerical results validate the effectiveness of the
proposed method