  While initial beam alignment (BA) in millimeter-wave networks has been
thoroughly investigated, most research assumes a simplified terminal model
based on uniform linear/planar arrays with isotropic antennas. Devices with
non-isotropic antenna elements need multiple panels to provide good spherical
coverage, and exhaustive search over all beams of all the panels leads to
unacceptable overhead. This paper proposes a location- and orientation-aware
solution that manages the initial BA for multi-panel devices. We present three
different neural network structures that provide efficient BA with a wide range
of training dataset sizes, complexity, and feedback message sizes. Our proposed
methods outperform the generalized inverse fingerprinting and hierarchical
panel-beam selection methods for two considered edge and edge-face antenna
placement designs.
