Site-Specific Beam Alignment in 6G via Deep Learning
CoRR(2024)
Abstract
Beam alignment (BA) in modern millimeter wave standards such as 5G NR and
WiGig (802.11ay) is based on exhaustive and/or hierarchical beam searches over
pre-defined codebooks of wide and narrow beams. This approach is slow and
bandwidth/power-intensive, and is a considerable hindrance to the wide
deployment of millimeter wave bands. A new approach is needed as we move
towards 6G. BA is a promising use case for deep learning (DL) in the 6G air
interface, offering the possibility of automated custom tuning of the BA
procedure for each cell based on its unique propagation environment and user
equipment (UE) location patterns. We overview and advocate for such an approach
in this paper, which we term site-specific beam alignment (SSBA). SSBA largely
eliminates wasteful searches and allows UEs to be found much more quickly and
reliably, without many of the drawbacks of other machine learning-aided
approaches. We first overview and demonstrate new results on SSBA, then
identify the key open challenges facing SSBA.
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