  Beam management (BM) protocols are critical for establishing and maintaining
connectivity between network radio nodes and User Equipments (UEs). In
Distributed Multiple Input Multiple Output systems (D-MIMO), a number of access
points (APs), coordinated by a central processing unit (CPU), serves a number
of UEs. At mmWave frequencies, the problem of finding the best AP and beam to
serve the UEs is challenging due to a large number of beams that need to be
sounded with Downlink (DL) reference signals. The objective of this paper is to
investigate whether the best AP/beam can be reliably inferred from sounding
only a small subset of beams and leveraging AI/ML for inference of best
beam/AP. We use Random Forest (RF), MissForest (MF) and conditional Generative
Adversarial Networks (c-GAN) for demonstrating the performance benefits of
inference.
